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Tag: AI Assistant

Engineering Excellence: How to Build Your Own AI Assistant – Part 2

Posted on John Andersen

In Part One of this guide, we explored the foundational architecture needed to build production-ready AI agents – from cognitive design principles to data preparation strategies. Now, we’ll move from theory to implementation, diving deep into the technical components that bring these architectural principles to life when you attempt to build your own AI assistant or agent.

Building on those foundations, we’ll examine the practical challenges of natural language understanding, response generation, and knowledge integration. We’ll also explore the critical role of observability and testing in maintaining reliable AI systems, before concluding with advanced agent behaviors that separate exceptional implementations from basic chatbots.

Whether you’re implementing your first AI assistant or optimizing existing systems, these practical insights will help you create more sophisticated, reliable, and maintainable AI agents.

Section 1: Natural Language Understanding Implementation

With well-prepared data in place, we can focus on one of the most challenging aspects of AI agent development: understanding user intent. While LLMs have impressive language capabilities, translating user input into actionable understanding requires careful implementation of several key components.

While we use terms like ‘natural language understanding’ and ‘intent classification,’ it’s important to note that in the context of LLM-based AI agents, these concepts operate at a much more sophisticated level than in traditional rule-based or pattern-matching systems. Modern LLMs understand language and intent through deep semantic processing, rather than predetermined pathways or simple keyword matching.

Vector Embeddings and Semantic Processing

User intent often lies beneath the surface of their words. Someone asking “Where’s my stuff?” might be inquiring about order status, delivery timeline, or inventory availability. Vector embeddings help bridge this gap by capturing semantic meaning behind queries.

Vector embeddings create a map of meaning rather than matching keywords. This enables your agent to understand that “I need help with my order” and “There’s a problem with my purchase” request the same type of assistance, despite sharing no common keywords.

Disambiguation Strategies

Users often communicate vaguely or assume unspoken context. An effective AI agent needs strategies for handling this ambiguity – sometimes asking clarifying questions, other times making informed assumptions based on available context.

Consider a user asking about “the blue one.” Your agent must assess whether previous conversation provides clear reference, or if multiple blue items require clarification. The key is knowing when to ask questions versus when to proceed with available context. This balance between efficiency and accuracy maintains natural, productive conversations.

Input Processing and Validation

Before formulating responses, your agent must ensure that input is safe and processable. This extends beyond security checks and content filtering to create a foundation for understanding. Your agent needs to recognize entities, identify key phrases, and understand patterns that indicate specific user needs.

Think of this as your agent’s first line of defense and comprehension. Just as a human customer service representative might ask someone to slow down or clarify when they’re speaking too quickly or unclearly, your agent needs mechanisms to ensure it’s working with quality input, which it can properly process.

Intent Classification Architectures

Reliable intent classification requires a sophisticated approach beyond simple categorization. Your architecture must consider both explicit statements and implicit meanings. Context is crucial – the same phrase might indicate different intents depending on its place in conversation or what preceded it.

Multi-intent queries present a particular challenge. Users often bundle multiple requests or questions together, and your architecture needs to recognize and handle these appropriately. The goal isn’t just to identify these separate intents but to process them in a way that maintains a natural conversation flow.

Section 2: Response Generation and Control

Once we’ve properly understood user intent, the next challenge is generating appropriate responses. This is where many AI agents either shine or fall short. While LLMs excel at producing human-like text, ensuring that those responses are accurate, appropriate, and aligned with your business needs requires careful control and validation mechanisms.

Output Quality Control Systems

Creating high-quality responses isn’t just about getting the facts right – it’s about delivering information in a way that’s helpful and appropriate for your users. Think of your quality control system as a series of checkpoints, each ensuring that different aspects of the response meet your standards.

A response can be factually correct, yet fail by not aligning with your brand voice or straying from approved messaging scope. Quality control must evaluate both content and delivery – considering tone, brand alignment, and completeness in addressing user needs.

Hallucination Prevention Strategies

One of the more challenging aspects of working with LLMs is managing their tendency to generate plausible-sounding but incorrect information. Preventing hallucinations requires a multi-faceted approach that starts with proper prompt design and extends through response validation.

Responses must be grounded in verifiable information. This involves linking to source documentation, using retrieval-augmented generation for fact inclusion, or implementing verification steps against reliable sources.

Input and Output Filtering

Filtering acts as your agent’s immune system, protecting both the system and users. Input filtering identifies and handles malicious prompts and sensitive information, while output filtering ensures responses meet security and compliance requirements while maintaining business boundaries.

Implementation of Guardrails

Guardrails aren’t just about preventing problems – they’re about creating a space where your AI agent can operate effectively and confidently. This means establishing clear boundaries for:

  • What types of questions your agent should and shouldn’t answer
  • How to handle requests for sensitive information
  • When to escalate to human agents

Effective guardrails balance flexibility with control, ensuring your agent remains both capable and reliable.

Response Validation Methods

Validation isn’t a single step but a process that runs throughout response generation. We need to verify not just factual accuracy, but also consistency with previous responses, alignment with business rules, and appropriateness for the current context. This often means implementing multiple validation layers that work together to ensure quality responses, all built upon a foundation of reliable information.

Section 3: Knowledge Integration

A truly effective AI agent requires seamlessly integrating your organization’s specific knowledge, layering that on top of the communication capabilities of language models.This integration should be reliable and maintainable, ensuring access to the right information at the right time. While you want to use the LLM for contextualizing responses and natural language interaction, you don’t want to rely on it for domain-specific knowledge – that should come from your verified sources.

Retrieval-Augmented Generation (RAG)

RAG fundamentally changes how AI agents interact with organizational knowledge by enabling dynamic information retrieval. Like a human agent consulting reference materials, your AI can “look up” information in real-time.

The power of RAG lies in its flexibility. As your knowledge base updates, your agent automatically has access to the new information without requiring retraining. This means your agent can stay current with product changes, policy updates, and new procedures simply by updating the underlying knowledge base.

Dynamic Knowledge Updates

Knowledge isn’t static, and your AI agent’s access to information shouldn’t be either. Your knowledge integration pipeline needs to handle continuous updates, ensuring your agent always works with current information.

This might include:

  • Customer profiles (orders, subscription status)
  • Product catalogs (pricing, features, availability)
  • New products, support articles, and seasonal information

Managing these updates requires strong synchronization mechanisms and clear protocols to maintain data consistency without disrupting operations.

Context Window Management

Managing the context window effectively is crucial for maintaining coherent conversations while making efficient use of your knowledge resources. While working memory handles active processing, the context window determines what knowledge base and conversation history information is available to the LLM. Not all information is equally relevant at every moment, and trying to include too much context can be as problematic as having too little.

Success depends on determining relevant context for each interaction. Some queries need recent conversation history, while others benefit from specific product documentation or user history. Proper management ensures your agent accesses the right information at the right time.

Knowledge Attribution and Verification

When your agent provides information, it should be clear where that information came from. This isn’t just about transparency – it’s about building trust and making it easier to maintain and update your knowledge base. Attribution helps track which sources are being used effectively and which might need improvement.

Verification becomes particularly important when dealing with dynamic information. As an AI engineer, you need to ensure that responses are grounded in current, verified sources, giving you confidence in the accuracy of every interaction.

Section 4: Observability and Testing

With the core components of understanding, response generation, and knowledge integration in place, we need to ensure our AI agent performs reliably over time. This requires comprehensive observability and testing capabilities that go beyond traditional software testing approaches.

Building an AI agent isn’t a one-time deployment – it’s an iterative process that requires continuous monitoring and refinement. The probabilistic nature of LLM responses means traditional testing approaches aren’t sufficient. You need comprehensive observability into how your agent is performing, and robust testing mechanisms to ensure reliability.

Regression Testing Implementation

AI agent testing requires a more nuanced approach than traditional regression testing. Instead of exact matches, we must evaluate semantic correctness, tone, and adherence to business rules.

Creating effective regression tests means building a suite of interactions that cover your core use cases while accounting for common variations. These tests should verify not just the final response, but also the entire chain of reasoning and decision-making that led to that response.

Debug-Replay Capabilities

When issues arise – and they will – you need the ability to understand exactly what happened. Debug-replay functions like a flight recorder for AI interactions, logging every decision point, context, and data transformation. This visibility lets you trace paths from input to output, simplifying issue identification and resolution. This level of visibility allows you to trace the exact path from input to output, making it much easier to identify where adjustments are needed and how to implement them effectively.

Performance Monitoring Systems

Monitoring an AI agent requires tracking multiple dimensions of performance. Start with the fundamentals:

  • Response accuracy and appropriateness
  • Processing time and resource usage
  • Business-defined KPIs

Your monitoring system should provide clear visibility into these metrics, allowing you to set baselines, track deviations, and measure the impact of any changes you make to your agent. This data-driven approach focuses optimization efforts on metrics that matter most to business objectives.

Iterative Development Methods

Improving your AI agent is an ongoing process. Each interaction provides valuable data about what’s working and what’s not. You want to establish systematic methods for:

  • Collecting and analyzing interaction data
  • Identifying areas for improvement
  • Testing and validating changes
  • Rolling out updates safely

Success comes from creating tight feedback loops between observation, analysis, and improvement, always guided by real-world performance data.

Section 5: Advanced Agent Behaviors

While basic query-response patterns form the foundation of AI agent interactions, implementing advanced behaviors sets exceptional agents apart. These sophisticated capabilities allow your agent to handle complex scenarios, maintain goal-oriented conversations, and effectively manage uncertainty.

Task Decomposition Strategies

Complex user requests often require breaking down larger tasks into manageable components. Rather than attempting to handle everything in a single step, effective agents need to recognize when to decompose tasks and how to manage their execution.

Consider a user asking to “change my flight and update my hotel reservation.” The agent must handle this as two distinct but related tasks, each with different information needs, systems, and constraints – all while maintaining coherent conversation flow.

Goal-oriented Planning

Outstanding AI agents don’t just respond to queries – they actively work toward completing user objectives. This means maintaining awareness of both immediate tasks and broader goals throughout the conversation.

The agent should track progress, identify potential obstacles, and adjust its approach based on new information or changing circumstances. This might mean proactively asking for additional information when needed or suggesting alternative approaches when the original path isn’t viable.

Multi-step Reasoning Implementation

Some queries require multiple steps of logical reasoning to reach a proper conclusion. Your agent needs to be able to:

  • Break down complex problems into logical steps
  • Maintain reasoning consistency across these steps
  • Draw appropriate conclusions based on available information

Uncertainty Handling

Building on the flexible frameworks established in your initial design, advanced AI agents need sophisticated strategies for managing uncertainty in real-time interactions. This goes beyond simply recognizing unclear requests – it’s about maintaining productive conversations even when perfect answers aren’t possible.

Effective uncertainty handling involves:

  • Confidence assessment: Understanding and communicating the reliability of available information
  • Partial solutions: Providing useful responses even when complete answers aren’t available
  • Strategic escalation: Knowing when and how to involve human operators

The goal isn’t eliminating uncertainty, but to make it manageable and transparent. When definitive answers aren’t possible, agents should communicate limitations while moving conversations forward constructively.

Building Outstanding AI Agents: Bringing It All Together

Creating exceptional AI agents requires careful orchestration of multiple components, from initial planning through advanced behaviors. Success comes from understanding how each component works in concert to create reliable, effective interactions.

Start with clear purpose and scope. Rather than trying to build an agent that does everything, focus on specific objectives and define clear success criteria. This focused approach allows you to build appropriate guardrails and implement effective measurement systems.

Knowledge integration forms the backbone of your agent’s capabilities. While Large Language Models provide powerful communication abilities, your agent’s real value comes from how well it leverages your organization’s specific knowledge through effective retrieval and verification systems.

Building an outstanding AI agent is an iterative process, with comprehensive observability and testing capabilities serving as essential tools for continuous improvement. Remember that your goal isn’t to replace human interaction entirely, but to create an agent that handles appropriate tasks efficiently, while knowing when to escalate to human agents. By focusing on these fundamental principles and implementing them thoughtfully, you can create AI agents that provide real value to your users while maintaining reliability and trust.

Ready to put these principles into practice? Do it with AI Studio, Quiq’s enterprise platform for building sophisticated AI agents.

AI Assistant Builder: An Engineering Guide to Production-Ready Systems – Part 1

Posted on John Andersen

Modern AI agents, powered by Large Language Models (LLMs), are transforming how businesses engage with users through natural, context-aware interactions. This marks a decisive shift away from traditional chatbot building platforms with their rigid decision trees and limited understanding. For AI assistant builders, engineers and conversation designers, this evolution brings both opportunity and challenge. While LLMs have dramatically expanded what’s possible, they’ve also introduced new complexities in development, testing, and deployment.

In Part One of this technical guide, we’ll focus on the foundational principles and architecture needed to build production-ready AI agents. We’ll explore purpose definition, cognitive architecture, model selection, and data preparation. Drawing from real-world experience, we’ll examine key concepts like atomic prompting, disambiguation strategies, and the critical role of observability in managing the inherently probabilistic nature of LLM-based systems.

Rather than treating LLMs as black boxes, we’ll dive deep into the structural elements that make AI agents exceptional – from cognitive architecture design to sophisticated response generation. Our approach balances practical implementation with technical rigor, emphasizing methods that scale effectively and produce consistent results.

Then, in Part Two, we’ll explore implementation details, observability patterns, and advanced features that take your AI agents from functional to exceptional.

Whether you’re looking to build AI assistants for customer service, internal tools, or specialized applications, these principles will help you create more capable, reliable, and maintainable systems. Ready? Let’s get started.

Section 1: Understanding the Purpose and Scope

When you set out to design an AI agent, the first and most crucial step is establishing a clear understanding of its purpose and scope. The probabilistic nature of Large Language Models means we need to be particularly thoughtful about how we define success and measure progress. An agent that works perfectly in testing might struggle with real-world interactions if we haven’t properly defined its boundaries and capabilities.

Defining Clear Objectives

The key to successful AI agent development lies in specificity. Vague objectives like “provide customer support” or “help users find information” leave too much room for interpretation and make it difficult to measure success. Instead, focus on concrete, measurable goals that acknowledge both the capabilities and limitations of your AI agent.

For example, rather than aiming to “answer all customer questions,” a better objective might be to “resolve specific categories of customer inquiries without human intervention.” This provides clear development guidance while establishing appropriate guardrails.

Requirements Analysis and Success Metrics

Success in AI agent development requires careful consideration of both quantitative and qualitative metrics. Response quality encompasses not just accuracy, but also relevance and consistency. An agent might provide factually correct information that fails to address the user’s actual need, or deliver inconsistent responses to similar queries.

Tracking both completion rates and solution paths helps us understand how our agent handles complex interactions. Knowledge attribution is critical – responses must be traceable to verified sources to maintain system trust and accountability.

Designing for Reality

Real-world interactions rarely follow ideal paths. Users are often vague, change topics mid-conversation, or ask questions that fall outside the agent’s scope. Successful AI agents need effective strategies for handling these situations gracefully.

Rather than trying to account for every possible scenario, focus on building flexible response frameworks. Your agent should be able to:

  • Identify requests that need clarification
  • Maintain conversation flow during topic changes
  • Identify and appropriately handle out-of-scope requests
  • Operate within defined security and compliance boundaries

Anticipating these real-world challenges during planning helps build the necessary foundations for handling uncertainty throughout development.

Section 2: Cognitive Architecture Fundamentals

The cognitive architecture of an AI agent defines how it processes information, makes decisions, and maintains state. This fundamental aspect of agent design in AI must handle the complexities of natural language interaction while maintaining consistent, reliable behavior across conversations.

Knowledge Representation Systems

An AI agent needs clear access to its knowledge sources to provide accurate, reliable responses. This means understanding what information is available and how to access it effectively. Your agent should seamlessly navigate reference materials and documentation while accessing real-time data through APIs when needed. The knowledge system must maintain conversation context while operating within defined business rules and constraints.

Memory Management

AI agents require sophisticated memory management to handle both immediate interactions and longer-term context. Working memory serves as the agent’s active workspace, tracking conversation state, immediate goals, and temporary task variables. Think of it like a customer service representative’s notepad during a call – holding important details for the current interaction without getting overwhelmed by unnecessary information.

Beyond immediate conversation needs, agents must also efficiently handle longer-term context through API interactions. This could mean pulling customer data, retrieving order information, or accessing account details. The key is maintaining just enough state to inform current decisions, while keeping the working memory focused and efficient.

Decision-Making Frameworks

Decision making in AI agents should be both systematic and transparent. An effective framework begins with careful input analysis to understand the true intent behind user queries. This understanding combines with context evaluation – assessing both current state and relevant history – to determine the most appropriate action.

Execution monitoring is crucial as decisions are made. Every action should be traceable and adjustable, allowing for continuous improvement based on real-world performance. This transparency enables both debugging when issues arise and systematic enhancement of the agent’s capabilities over time.

Atomic Prompting Architecture

Atomic prompting is fundamental to building reliable AI agents. Rather than creating complex, multi-task prompts, we break down operations into their smallest meaningful units. This approach significantly improves reliability and predictability – single-purpose prompts are more likely to produce consistent results and are easier to validate.

A key advantage of atomic prompting is efficient parallel processing. Instead of sequential task handling, independent prompts can run simultaneously, reducing overall response time. While one prompt classifies an inquiry type, another can extract relevant entities, and a third can assess user emotion. These parallel operations improve efficiency while providing multiple perspectives for better decision-making.

The atomic nature of these prompts makes parallel processing more reliable. Each prompt’s single, well-defined responsibility allows multiple operations without context contamination or conflicting outputs. This approach simplifies testing and validation, providing clear success criteria for each prompt and making it easier to identify and fix issues when they arise.

For example, handling a customer order inquiry might involve separate prompts to:

  • Classify the inquiry type
  • Extract relevant identifiers
  • Determine needed information
  • Format the response appropriately

Each step has a clear, single responsibility, making the system more maintainable and reliable.

When issues do occur, atomic prompting enables precise identification of where things went wrong and provides clear paths for recovery. This granular approach allows graceful degradation when needed, maintaining an optimal user experience even when perfect execution isn’t possible.

Section 3: Model Selection and Optimization

Choosing the right language models for your AI agent is a critical architectural decision that impacts everything from response quality to operational costs. Rather than defaulting to the most powerful (and expensive) model for all tasks, consider a strategic approach to model selection.

Different components of your agent’s cognitive pipeline may require different models. While using the latest, most sophisticated model for everything might seem appealing, it’s rarely the most efficient approach. Balance response quality with resource usage – inference speed and cost per token significantly impact your agent’s practicality and scalability.

Task-specific optimization means matching different models to different pipeline components based on task complexity. This strategic selection creates a more efficient and cost-effective system while maintaining high-quality interactions.

Language models evolve rapidly, with new versions and capabilities frequently emerging. Design your architecture with this evolution in mind, enabling model version flexibility and clear testing protocols for updates. This approach ensures your agent can leverage improvements in the field while maintaining reliable performance.

Model selection is crucial, but models are only as good as their input data. Let’s examine how to prepare and organize your data to maximize your agent’s effectiveness.

Section 4: Data Collection and Preparation

Success with AI agents depends heavily on data quality and organization. While LLMs provide powerful baseline capabilities, your agent’s effectiveness relies on well-structured organizational knowledge. Data organization, though typically one of the most challenging and time-consuming aspects of AI development, can be streamlined with the right tools and approach. This allows you to focus on building exceptional AI experiences rather than getting bogged down in manual processes.

Dataset Curation Best Practices

When preparing data for your AI agent, prioritize quality over quantity. Start by identifying content that directly supports your agent’s objectives – product documentation, support articles, FAQs, and procedural guides. Focus on materials that address common user queries, explain key processes, and outline important policies or limitations.

Data Cleaning and Preprocessing

Raw documentation rarely comes in a format that’s immediately useful for an AI agent. Think of this stage as translation work – you’re taking content written for human consumption and preparing it for effective AI use. Long documents must be chunked while maintaining context, key information extracted from dense text, and formatting standardized.

Information should be presented in direct, unambiguous terms, which could mean rewriting complex technical explanations or breaking down complicated processes into clearer steps. Consistent terminology becomes crucial throughout your knowledge base. During this process, watch for:

  • Outdated information that needs updating
  • Contradictions between different sources
  • Technical details that need validation
  • Coverage gaps in key areas

Automated Data Transformation and Enrichment

Manual data preparation quickly becomes unsustainable as your knowledge base grows. The challenge isn’t just handling large volumes of content – it’s maintaining quality and consistency while keeping information current. This is where automated transformation and enrichment processes become essential.

Effective automation starts with smart content processing. Tools that understand semantic structure can automatically segment documents while preserving context and relationships, eliminating the need for manual chunking decisions.

Enrichment goes beyond basic processing. Modern tools can identify connections between information, generate additional context, and add appropriate classifications. This creates a richer, more interconnected knowledge base for your AI agent.

Perhaps most importantly, automated processes streamline ongoing maintenance. When new content arrives – whether product information, policy changes, or updated procedures – your transformation pipeline processes these updates consistently. This ensures your AI agent works with current, accurate information without constant manual intervention.

Establishing these automated processes early lets your team focus on improving agent behavior and user experience rather than data management. The key is balancing automation with oversight to ensure both efficiency and reliability.

What’s Next?

The foundational elements we’ve covered – from cognitive architecture to knowledge management – are essential building blocks for production-ready AI agents. But understanding architecture is just the beginning.

In Part Two, we’ll move from principles to practice, exploring implementation patterns, observability systems, and advanced features that separate exceptional AI agents from basic chatbots. Whether you’re building customer service assistants, internal tools, or specialized AI applications, these practical insights will help you create more capable, reliable, and sophisticated systems.

Read the next installment of this guide: Engineering Excellence: How to Build Your Own AI Assistant – Part 2

How a Leading Office Supply Retailer Answered 35% More Store Associate Questions with Generative AI

Posted on Mike Zinne

In an era where artificial intelligence is rapidly transforming various industries, the retail sector is no exception. One leading national office supply retailer has taken a bold step forward, harnessing the power of generative AI to revolutionize their in-store experience and empower their associates.

This innovative approach has not only enhanced customer satisfaction but has also led to remarkable improvements in employee efficiency. In fact, the company has experienced a 35% increase in containment rates (with a 6-month average containment rate of 65%) vs. its legacy solution.

We’re excited to share the details of this groundbreaking initiative. Keep reading as we examine the company’s vision, their strategic approach to implementation, and the key objectives that drove their AI adoption. We’ll also discuss their GenAI assistant’s primary capabilities and how it’s improving both customer experiences and employee satisfaction. By the end, you’ll see how much potential lies in applying this use case to additional employees—not just in-store associates—as well as customers. There’s so much to unlock. Ready? Let’s dive in.

The Vision: Empowering Associates with GenAI

This company is dedicated to helping businesses of all sizes become more productive, connected, and inspired. Their team recognized the immense potential of GenAI early on. The vision? To create a GenAI-powered assistant that could enhance the capabilities of their store associates, leading to improved customer service, increased productivity, and higher job satisfaction.

Key objectives of the GenAI initiative:

  • Simplify store associate experience
  • Streamline access to information for associates
  • Improve customer service efficiency
  • Boost associate confidence and job satisfaction
  • Increase overall store associate productivity

Charting the Course to Building a GenAI-Powered Assistant

By partnering with Quiq, the national office supply retailer launched its employee-facing GenAI assistant in just 6 weeks. Here’s what the launch process looked like in 9 primary steps:

  1. Discovery of AI enhancements
  2. Pulling content from current systems
  3. Run a Proof of Concept with Quiq team
  4. Run testing through all categories of content
  5. Approval to Pilot with Top Associate Group
  6. Refine content based on associate feedback for chain rollout
  7. Run additional testing through all categories
  8. Starting chain deployment to larger district of stores
  9. Maintain content accuracy and refine based on updates

Examining the Office Supplier’s Phased Approach to Adoption

Pre-launch, the teams worked together to ensure all content was updated and accurate. Then they launched a phased testing approach, going through several rounds of iterative testing. After that, the retailer shared the GenAI assistant with a top internal associate team to test and try and break it. Finally, the internal team utilized a top associate group to share excitement before launch.

At launch, the office supplier created a standalone page dedicated to the assistant and launched a SharePoint site to share updates for the internal team. They also facilitated internal learning sessions and quickly adapted to low feedback numbers. Last but not least, the team made it fun by branding the assistant with a fun, on-brand name and personality.

Post-launch, the retailer includes the AI assistant in all communications to associates, with tips on what to search for in the assistant. They also leverage the assistant’s proactive messaging capabilities to build excitement for new launches, promotions, and best practices.

Primary Capabilities and Focus

Launching the GenAI assistant has been transformative because it is trained on all things related to the office supply retailer, which has simplified and accelerated access to information. That means associates can help customers faster, answering questions accurately the first time and every time, regardless of tenure. Ultimately, AI is empowering associates to do even better work—including enhanced cross and upselling with proactive messages.

Proactive messaging to associates helps keep rotating sales goals top of mind so they can weave additional revenue opportunities into customer interactions. For example, if the design services team has unexpected bandwidth, the AI assistant can send a message letting associates know, inspiring them to highlight design and print services to customers who may be interested. It also provides a fun countdown to important launches, like back-to-school season, and “fun facts” that help build up useful knowledge over time. It’s like bite-size bits of training.

GenAI Transforms the In-Store Experience in 4 Critical Ways

Implementing the GenAI assistant has had a profound impact on in-store operations. By providing associates with instant access to accurate information, it has:

  1. Enhanced Customer Service: Associates can now provide faster, more accurate responses to customer questions.
  2. Increased Efficiency: The time it takes to find information has been significantly reduced, allowing associates to serve more customers.
  3. Boosted Confidence: With a reliable AI assistant at their fingertips, associates feel more empowered in their roles. Plus, new associates can be as effective as experienced ones with the assistant by their side.
  4. Improved Job Satisfaction: The reduced stress of information retrieval has led to higher job satisfaction among associates. Not to mention, the GenAI assistant is there to converse and empathize with employees who experience stressful situations with customers.

Results + What’s Next?

As a result of launching its GenAI assistant with Quiq, our national office supply retailer customer has realized a:

  • 68% self service rate resolution rate, allowing associates to get immediate answers to questions 2 out of 3 times
  • Associate satisfaction with AI 4.82 out of 5

And as for next steps, the team is excited to:

  • Launch a selling assisted path
  • Expand to additional departments within stores
  • Add more devices in store for easier accessibility
  • Integrate with internal systems to be able to answer even more types of questions with real-time access to orders and other information

The Lesson: Humans and AI Can Work Together to Play Their Strongest Roles

The office supply retailer’s successful implementation of GenAI serves as a powerful example of how the technology can transform retail operations by helping human employees work more efficiently. By focusing on empowering associates with AI, the company has not only improved customer service but also enhanced employee satisfaction and productivity.

Interested in Diving Deeper into GenAI?

Download Two Truths and a Lie: Breaking Down the Major GenAI Misconceptions Holding CX Leaders Back. This comprehensive guide illuminates the path through the intricate landscape of generative AI in CX. We cut through the fog of misconceptions, offering crystal-clear, practical advice to empower your decision-making.

The Truth About APIs for AI: What You Need to Know

Posted on The Quiq Team

Large language models hold a lot of power to improve your customer experience and make your agents more effective, but they won’t do you much good if you don’t have a way to actually access them.

This is where application programming interfaces (APIs) come into play. If you want to leverage LLMs, you’ll either have to build one in-house, use an AI API deployment to interact with an external model, or go with a customer-centric AI for CX platform. The latter choice is most ideal because it offers a guided building environment that removes complexity while providing the tools you need for scalability, observability, hallucination prevention, and more.

From a cost and ease-of-use perspective this third option is almost always best, but there are many misconceptions that could potentially stand in the way of AI API adoption.

In fact, a stronger claim is warranted: to maximize AI API effectiveness, you need a platform to orchestrate between AI, your business logic, and the rest of your CX stack.

Otherwise, it’s useless.

This article aims to bridge the gap between what CX leaders might think is required to integrate a platform, and what’s actually involved. By the end, you’ll understand what APIs are, their role in personalization and scalability, and why they work best in the context of a customer-centric AI for CX platform.

How APIs Facilitate Access to AI Capabilities

Let’s start by defining an API. As the name suggests, APIs are essentially structured protocols that allow two systems (“applications”) to communicate with one another (“interface”). For instance, if you’re using a third-party CRM to track your contacts, you’ll probably update it through an API.

All the well-known foundation model providers (e.g., OpenAI, Anthropic, etc.) have a real-world AI API implementation that allows you to use their service. For an AI API practical example, let’s look at OpenAI’s documentation:

(Let’s take a second to understand what we’re looking at. Don’t worry – we’ll break it down for you. Understanding the basics will give you a sense for what your engineers will be doing.)

The top line points us to a URL where we can access OpenAI’s models, and the next three lines require us to pass in an API key (which is kind of like a password giving access to the platform), our organization ID (a unique designator for our particular company, not unlike a username), and a project ID (a way to refer to this specific project, useful if you’re working on a few different projects at once).

This is only one example, but you can reasonably assume that most protocols built according to AI API best practices will have a similar structure.

This alone isn’t enough to support most AI API use cases, but it illustrates the key takeaway of this section: APIs are attractive because they make it easy to access the capabilities of LLMs without needing to manage them on your own infrastructure, though they’re still best when used as part of a move to a customer-centric AI orchestration platform.

How Do APIs Facilitate Customer Support AI Assistants?

It’s good to understand what APIs are used for in AI assistants. It’s pretty straightforward—here’s the bulk of it:

  • Personalizing customer communications: One of the most exciting real-world benefits of AI is that it enables personalization at scale because you can integrate an LLM with trusted systems containing customer profiles, transaction data, etc., which can be incorporated into a model’s reply. So, for example, when a customer asks for shipping information, you’re not limited to generic responses like “your item will be shipped within 3 days of your order date.” Instead, you can take a more customer-centric approach and offer specific details, such as, “The order for your new couch was placed on Monday, and will be sent out on Wednesday. According to your location, we expect that it’ll arrive by Friday. Would you like to select a delivery window or upgrade to white glove service?”
  • Improving response quality: Generative AI is plagued by a tendency to fabricate information. With an AI API, work can be decomposed into smaller, concrete tasks before being passed to an LLM, which improves performance. You can also do other things to get better outputs, such as create bespoke modifications of the prompt that change the model’s tone, the length of its reply, etc.
  • Scalability and flexibility in deployment: A good customer-centric, AI-for-CX platform will offer volume-based pricing, meaning you can scale up or down as needed. If customer issues are coming in thick and fast (such as might occur during a new product release, or over a holiday), just keep passing them to the API while paying a bit more for the increased load; if things are quiet because it’s 2 a.m., the API just sits there, waiting to spring into action when required and costing you very little.
  • Analyzing customer feedback and sentiment: Incredible insights are waiting within your spreadsheets and databases, if you only know how to find them. This, too, is something APIs help with. If, for example, you need to unify measurements across your organization to send them to a VOC (voice of customer) platform, you can do that with an API.

Looking Beyond an API for AI Assistants

For all this, it’s worth pointing out that there’s still many real-world AI API challenges. By far the quickest way to begin building an AI assistant for CX is to pair with a customer-centric AI platform that removes as much of the difficulty as possible.

The best such platforms not only allow you to utilize a bevy of underlying LLM models, they also facilitate gathering and analyzing data, monitoring and supporting your agents, and automating substantial parts of your workflow.

Crucially, almost all of those critical tasks are facilitated through APIs, but they can be united in a good platform.

3 Common Misconceptions about Customer-Centric AI for CX Platforms.

Now, let’s address some of the biggest myths surrounding the use of AI orchestration platforms.

Myth 1: Working with a customer-centric AI for CX Platform Will be a Hassle

Some CX leaders may worry that working with a platform will be too difficult. There are challenges, to be sure, but a well-designed platform with an intuitive user interface is easy to slip into a broader engineering project.

Such platforms are designed to support easy integration with existing systems, and they generally have ample documentation available to make this task as straightforward as possible.

Myth 2: AI Platforms Cost Too Much

Another concern CX leaders have is the cost of using an AI orchestration platform. Platform costs can add up over time, but this pales in comparison to the cost of building in-house solutions. Not to mention the potential costs associated with the risks that come with building AI in an environment that doesn’t protect you from things like hallucinations.

When you weigh all the factors impacting your decision to use AI in your contact center, the long-run return on using an AI orchestration platform is almost always better.

Myth 3: Customer-Centric AI Platforms are Just Too Insecure

The smart CX leader always has one eye on the overall security of their enterprise, so they may be worried about vulnerabilities introduced by using an AI platform.

This is a perfectly reasonable concern. If you’re trying to choose between a few different providers, it’s worth investigating the security measures they’ve implemented. Specifically, you want to figure out what data encryption and protection protocols they use, and how they think about compliance with industry standards and regulations.

At a minimum, the provider should be taking basic steps to make sure data transmitted to the platform isn’t exposed.

Is an AI Platform Right for Me?

With a platform focused on optimizing CX outcomes, you can quickly bring the awesome power and flexibility of generative AI into your contact center – without ever spinning up a server or fretting over what “backpropagation” means. To the best of our knowledge, this is the cheapest and fastest way to demo this API technology in your workflow to determine whether it warrants a deeper investment.

To parse out more generative AI facts from fiction, download our e-book on AI misconceptions and how to overcome them. If you’re concerned about hallucinations, data privacy, and similar issues, you won’t find a better one-stop read!

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Going Beyond the GenAI Hype — Your Questions, Answered

Posted on J.R. Rettenmeyer

We recently hosted a webinar all about how CX leaders can go beyond the hype surrounding GenAI, sift out the misinformation, and start driving real business value with AI Assistants. During the session, our speakers shared specific steps CX leaders can take to get their knowledge ready for AI, eliminate harmful hallucinations, and solve the build vs. buy dilemma.

We were overwhelmed with the number of folks who tuned in to learn more and hear real-life challenges, best practices, and success stories from Quiq’s own AI Assistant experts and customers. At the end of the webinar, we received so many amazing audience questions that we ran out of time to answer them all!

So, we asked speaker and Quiq Product Manager Max Fortis, to respond to a few of our favorites. Check out his answers in the clips below, and be sure to watch the full 35-minute webinar on-demand.

Ensuring Assistant Access to Personal and Account Information

 

 

Using a Knowledge Base Written for Internal Agents

 

 

Teaching a Voice Assistant vs. a Chat Assistant

 

 

Monitoring and Improving Assistant Performance Over Time

 

 

Watch the Full Webinar to Dive Deeper

Whether you were unable to tune in live or want to watch the rerun, this webinar is available on-demand. Give it a listen to hear Max and his Quiq colleagues offer more answers and advice around how to assess and fill critical knowledge gaps, avoid common yet lesser-known hallucination types, and partner with technical teams to get the AI tools you need.

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Does GenAI Leak Your Sensitive Data? Exposing Common AI Misconceptions (Part Three)

Posted on The Quiq Team

This is the final post in a three-part series clarifying the biggest misconceptions holding CX leaders like you back from integrating GenAI into their CX strategies. Our goal? To assuage your fears and help you start getting real about adding an AI Assistant to your contact center — all in a fun “two truths and a lie” format.

There are few faux pas as damaging and embarrassing for brands as sensitive data getting into the wrong hands. So it makes sense that data security concerns are a major deterrent for CX leaders thinking about getting started with GenAI.

In the first post of our AI Misconceptions series, we discussed why your data is definitely good enough to make GenAI work for your business. Next, we explored the different types of hallucinations that CX leaders should be aware of, and how they are 100% preventable with the right guardrails in place.

Now, let’s wrap up our series by exposing the truth about GenAI potentially leaking your company or customer data.

Misconception #3: “GenAI inadvertently leaks sensitive data.”

As we discussed in part one, AI needs training data to work. One way to collect that data is from the questions users ask. For example, if a large language model (LLM) is asked to summarize a paragraph of text, that text could be stored and used to train future models.

Unfortunately, there have been some famous examples of companies’ sensitive information becoming part of datasets used to train LLMs — take Samsung, for instance. Because of this, CX leaders often fear that using GenAI will result in their company’s proprietary data being disclosed when users interact with these models.

Truth #1: Public GenAI tools use conversation data to train their models.

Tools like OpenAI’s ChatGPT and Google Gemini (formerly Bard) are public-facing and often free — and that’s because their purpose is to collect training data. This means that any information that users enter while using these tools is free game to be used for training future models.

This is precisely how the Samsung data leak happened. The company’s semiconductor division allowed its engineers to use ChatGPT to check their source code. Not only did multiple employees copy/paste confidential code into ChatGPT, but one team member even used the tool to transcribe a recording of an internal-only meeting!

Truth #2: Properly licensed GenAI is safe.

People often confuse ChatGPT, the application or web portal, with the LLM behind it. While the free version of ChatGPT collects conversation data, OpenAI offers an enterprise LLM that does not. Other LLM providers offer similar enterprise licenses that specify that all interactions with the LLM and any data provided will not be stored or used for training purposes.

When used through an enterprise license, LLMs are also Service Organization Control Type 2, or SOC 2, compliant. This means they have to undergo regular audits from third parties to prove that they have the processes and procedures in place to protect companies’ proprietary data and customers’ personally identifiable information (PII).

The Lie: Enterprises must use internally-developed models only to protect their data.

Given these concerns over data leaks and hallucinations, some organizations believe that the only safe way to use GenAI is to build their own AI models. Case in point: Samsung is now “considering building its own internal AI chatbot to prevent future embarrassing mishaps.”

However, it’s simply not feasible for companies whose core business is not AI to build AI that is as powerful as commercially available LLMs — even if the company is as big and successful as Samsung. Not to mention the opportunity cost and risk of having your technical resources tied up in AI instead of continuing to innovate on your core business.

It’s estimated that training the LLM behind ChatGPT cost upwards of $4 million. It also required specialized supercomputers and access to a data set equivalent to nearly the entire Internet. And don’t forget about maintenance: AI startup Hugging Face recently revealed that retraining its Bloom LLM cost around $10 million.

GenAI Misconceptions

Using a commercially available LLM provides enterprises with the most powerful AI available without breaking the bank— and it’s perfectly safe when properly licensed. However, it’s also important to remember that building a successful AI Assistant requires much more than developing basic question/answer functionality.

Finding a Conversational CX Platform that harnesses an enterprise-licensed LLM, empowers teams to build complex conversation flows, and makes it easy to monitor and measure Assistant performance is a CX leader’s safest bet. Not to mention, your engineering team will thank you for giving them optionality for the control and visibility they want—without the risk and overhead of building it themselves!

Feel Secure About GenAI Data Security

Companies that use free, public-facing GenAI tools should be aware that any information employees enter can (and most likely will) be used for future model-training purposes.

However, properly-licensed GenAI will not collect or use your data to train the model. Building your own GenAI tools for security purposes is completely unnecessary — and very expensive!

Want to read more or revisit the first two misconceptions in our series? Check out our full guide, Two Truths and a Lie: Breaking Down the Major GenAI Misconceptions Holding CX Leaders Back.

Will GenAI Hallucinate and Hurt Your Brand? Exposing Common AI Misconceptions (Part Two)

Posted on The Quiq Team

This is the second post in a three-part series clarifying the biggest misconceptions holding CX leaders like you back from integrating GenAI into their CX strategies. Our goal? To assuage your fears and help you start getting real about adding an AI Assistant to your contact center — all in a fun “two truths and a lie” format.

Did you know that the Golden Gate Bridge was transported for the second time across Egypt in October of 2016?

Or that the world record for crossing the English Channel entirely on foot is held by Christof Wandratsch of Germany, who completed the crossing in 14 hours and 51 minutes on August 14, 2020?

Probably not, because GenAI made these “facts” up. They’re called hallucinations, and AI hallucination misconceptions are holding a lot of CX leaders back from getting started with GenAI.

In the first post of our AI Misconceptions series, we discussed why your data is definitely good enough to make GenAI work for your business. In fact, you actually need a lot less data to get started with an AI Assistant than you probably think.

Now, we’re debunking AI hallucination myths and separating some of the biggest AI hallucination facts from fiction. Could adding an AI Assistant to your contact center put your brand at risk? Let’s find out.

Misconception #2: “GenAI will hallucinate and hurt my brand.”

While the example hallucinations provided above are harmless and even a little funny, this isn’t always the case. Unfortunately, there are many examples of times chatbots have cussed out customers or made racist or sexist remarks. This causes a lot of concern among CX leaders looking to use an AI Assistant to represent their brand.

Truth #1: Hallucinations are real (no pun intended).

Understanding AI hallucinations hinges on realizing that GenAI wants to provide answers — whether or not it has the right data. Hallucinations like those in the examples above occur for two common reasons.

AI-Induced Hallucinations Explained:

  1. The large language model (LLM) simply does not have the correct information it needs to answer a given question. This is what causes GenAI to get overly creative and start making up stories that it presents as truth.
  2. The LLM has been given an overly broad and/or contradictory dataset. In other words, the model gets confused and begins to draw conclusions that are not directly supported in the data, much like a human would do if they were inundated with irrelevant and conflicting information on a particular topic.

Truth #2: There’s more than one type of hallucination.

Contrary to popular belief, hallucinations aren’t just incorrect answers: They can also be classified as correct answers to the wrong questions. And these types of hallucinations are actually more common and more difficult to control.

For example, imagine a company’s AI Assistant is asked to help troubleshoot a problem that a customer is having with their TV. The Assistant could give the customer correct troubleshooting instructions — but for the wrong television model. In this case, GenAI isn’t wrong, it just didn’t fully understand the context of the question.

GenAI Misconceptions

The Lie: There’s no way to prevent your AI Assistant from hallucinating.

Many GenAI “bot” vendors attempt to fine-tune an LLM, connect clients’ knowledge bases, and then trust it to generate responses to their customers’ questions. This approach will always result in hallucinations. A common workaround is to pre-program “canned” responses to specific questions. However, this leads to unhelpful and unnatural-sounding answers even to basic questions, which then wind up being escalated to live agents.

In contrast, true AI Assistants powered by the latest Conversational CX Platforms leverage LLMs as a tool to understand and generate language — but there’s a lot more going on under the hood.

First of all, preventing hallucinations is not just a technical task. It requires a layer of business logic that controls the flow of the conversation by providing a framework for how the Assistant should respond to users’ questions.

This framework guides a user down a specific path that enables the Assistant to gather the information the LLM needs to give the right answer to the right question. This is very similar to how you would train a human agent to ask a specific series of questions before diagnosing an issue and offering a solution. Meanwhile, in addition to understanding what the intent of the customer’s question is, the LLM can be used to extract additional information from the question.

Referred to as “pre-generation checks,” these filters are used to determine attributes such as whether the question was from an existing customer or prospect, which of the company’s products or services the question is about, and more. These checks happen in the background in mere seconds and can be used to select the right information to answer the question. Only once the Assistant understands the context of the client’s question and knows that it’s within scope of what it’s allowed to talk about does it ask the LLM to craft a response.

But the checks and balances don’t end there: The LLM is only allowed to generate responses using information from specific, trusted sources that have been pre-approved, and not from the dataset it was trained on.

In other words, humans are responsible for providing the LLM with a source of truth that it must “ground” its response in. In technical terms, this is called Retrieval Augmented Generation, or RAG — and if you want to get nerdy, you can read all about it here!

Last but not least, once a response has been crafted, a series of “post- generation checks” happens in the background before returning it to the user. You can check out the end-to-end process in the diagram below:

RAG

Give Hallucination Concerns the Heave-Ho

To sum it up: Yes, hallucinations happen. In fact, there’s more than one type of hallucination that CX leaders should be aware of.

However, now that you understand the reality of AI hallucination, you know that it’s totally preventable. All you need are the proper checks, balances, and guardrails in place, both from a technical and a business logic standpoint.

Now that you’ve had your biggest misconceptions about AI hallucination debunked, keep an eye out for the next blog in our series, all about GenAI data leaks. Or, learn the truth about all three of CX leaders’ biggest GenAI misconceptions now when you download our guide, Two Truths and a Lie: Breaking Down the Major GenAI Misconceptions Holding CX Leaders Back.

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Is Your CX Data Good Enough for GenAI? Exposing Common AI Misconceptions (Part One)

Posted on The Quiq Team

If you’re feeling unprepared for the impact of generative artificial intelligence (GenAI), you’re not alone. In fact, nearly 85% of CX leaders feel the same way. But the truth is that the transformative nature of this technology simply can’t be ignored — and neither can your boss, who asked you to look into it.

We’ve all heard horror stories of racist chatbots and massive data leaks ruining brands’ reputations. But we’ve also seen statistics around the massive time and cost savings brands can achieve by offloading customers’ frequently asked questions to AI Assistants. So which is it?

This is the first post in a three-part series clarifying the biggest misconceptions holding CX leaders like you back from integrating GenAI into their CX strategies. Our goal? To assuage your fears and help you start getting real about adding an AI Assistant to your contact center — all in a fun “two truths and a lie” format. Prepare to have your most common AI misconceptions debunked!

Misconception #1: “My data isn’t good enough for GenAI.”

Answering customer inquiries usually requires two types of data:

  1. Knowledge (e.g. an order return policy) and
  2. Information from internal systems (e.g. the specific details of an order).

It’s easy to get caught up in overthinking the impact of data quality on AI performance and wondering whether or not your knowledge is even good enough to make an AI Assistant useful for your customers.

Updating hundreds of help desk articles is no small task, let alone building an entire knowledge base from scratch. Many CX leaders are worried about the amount of work it will require to clean up their data and whether their team has enough resources to support a GenAI initiative. In order for GenAI to be as effective as a human agent, it needs the same level of access to internal systems as human agents.

Truth #1: You have to have some amount of data.

Data is necessary to make AI work — there’s no way around it. You must provide some data for the model to access in order to generate answers. This is one of the most basic AI performance factors.

But we have good news: You need a lot less data than you think.

One of the most common myths about AI and data in CX is that it’s necessary to answer every possible customer question. Instead, focus on ensuring you have the knowledge necessary to answer your most frequently asked questions. This small step forward will have a major impact for your team without requiring a ton of time and resources to get started

Truth #2: Quality matters more than quantity.

Given the importance of relevant data in AI, a few succinct paragraphs of accurate information is better than volumes of outdated or conflicting documentation. But even then, don’t sweat the small stuff.

For example, did a product name change fail to make its way through half of your help desk articles? Are there unnecessary hyperlinks scattered throughout? Was it written for live agents versus customers?

No problem — the right Conversational CX Platform can easily address these AI data dependency concerns without requiring additional support from your team.

The Lie: Your data has to be perfectly unified and specifically formatted to train an AI Assistant.

Don’t worry if your data isn’t well-organized or perfectly formatted. The reality is that most companies have services and support materials scattered across websites, knowledge bases, PDFs, .csvs, and dozens of other places — and that’s okay!

Today, the tools and technology exist to make aggregating this fragmented data a breeze. They’re then able to cleanse and format it in a way that makes sense for a large language model (LLM) to use.

For example if you have an agent training manual in Google Docs and a product manual in PDF, this information can be disassembled, reformatted, and rewritten by an AI-powered transformation that makes it subsequently usable.

What’s more, the data used by your AI Assistant should be consistent with the data you use to train your human agents. This means that not only is it not required to build a special repository of information for your AI Assistant to learn from, but it’s not recommended. The very best AI platforms take on the work of maintaining this continuity by automatically processing and formatting new information for your Assistant as it’s published, as well as removing any information that’s been deleted.

Put Those Data Doubts to Bed

Now you know that your data is definitely good enough for GenAI to work for your business. Yes, quality matters more than quantity, but it doesn’t have to be perfect.

The technology exists to unify and format your data so that it’s usable by an LLM. And providing knowledge around even a handful of frequently asked questions can give your team a major lift right out the gate.

Keep an eye out for the next blog in our series, all about GenAI hallucinations. Or, learn the truth about all three of CX leaders’ biggest GenAI misconceptions now when you download our guide, Two Truths and a Lie: Breaking Down the Major GenAI Misconceptions Holding CX Leaders Back.

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Everything You Need To Know About The Role Of Vector Databases In AI for CX

Posted on The Quiq Team

All businesses are influenced by the emergence of new technologies, and contact centers are no different. In the constant battle to provide a better experience for agents and customers, contact center managers and their technical partners are always on the lookout for new tools that will make everyone’s lives easier.

We’ve talked a lot about this subject, and today we’re going to continue this streak by diving into the fundamentals of vector databases. If you’re researching the potential of generative AI for your CX teams, vector databases and their role in AI for customer experience is a key strategic component to understand.

Why You Should Care About Vector Databases

Vector databases matter because, amongst many other things, they help you understand how your AI experience is working and where you can improve. If you pick a vendor that has an integrated vector database, you’ll want to make sure that the toolkit gives you visibility into how your data is stored.

AI is impacting use cases across the enterprise. Organizations are therefore identifying which use cases are core to their differentiation and where they have unique data.

Most enterprises choose to buy CX solutions since the industry is so well-developed and mature. With this next generation of AI, vector databases are a critical part of the stack — and we will explain why in this article.

We’ll also touch on why you should choose an AI software vendor with an integrated vector database offering (Pro tip: This is how you get all the benefits with none of the risks).

Why Are Vector Databases Useful in Building an AI Assistant for CX?

As you may know, databases are essentially like warehouses where various kinds of information can be stored, and a vector database is just a warehouse whose function is to store vectors.

A vector is essentially a high-dimensional mathematical representation of something like an image or a word. There are many ways of generating vectors, but at the end of the process, what you’ll have is an array of floating-point (i.e. non-integer) numbers that look like this:

[.8, 1.1, -0.4, 21.3,….,17.8]

A vector embedding for a word might contain thousands of these floating-point numbers, and a corpus of text might contain thousands of words that need to be embedded. This is far too much information to store in a spreadsheet or .txt file, so vector databases were invented to hold these data structures and make them easy to access. In addition, a dedicated vector database will have all sorts of special functions that allow you to calculate the similarity of different vectors, search over them with a query, and do myriad other things people do with data.

The reason this impacts building AI assistants for CX use cases is that much of the power of these tools comes from the underlying vectors. If you build an application that’s able to dynamically answer user questions based on your internal documentation, then it will almost certainly be working with vector embeddings of those documents.

You might wonder why traditional relational databases or NoSQL databases couldn’t be used for this purpose. It’s possible that they could, but different kinds of databases are optimized for different use cases. Relational databases, for example, are excellent at storing structured data, such as customer IDs, purchase histories, etc.

How Does a Vector Database Work for AI Assistants?

There are really only a few things happening inside a vector database when we focus on the main concepts.

First, you have your content, which is whatever you want to vectorize. This content is passed into an embedding model, and that model generates the embeddings we discussed above. Those embeddings are stored in the vector database where an AI assistant can use them, and there’s always some pointer tying each vector to the content that was used to generate it.

When your AI assistant needs to use these embeddings, it does so with a query. This query is vectorized using the same embedding model that generated the vectors in the database, and any vectors that are similar to the query can, therefore, be located quickly and efficiently. Because each vector remains tied to its originating content, that content can be returned to the application.

To concretize this, suppose you had a vector database containing a lot of content related to retail, and your AI assistant submits a query like: “My new jacket arrived in a medium. Can I exchange it for a small?” The database will be able to locate articles containing relevant information based on the similarity between the vectors for the query and the vectors in the database.

Importantly, this is not a simple keyword search. The vector database will return useful results even if there are no strict word matches at all. So, if the retail content says “coat” instead of jacket and “return” instead of exchange, it’ll still match the content to the query and give you something worthwhile.

How Vector Databases Supercharge AI Assistants

What would you be able to do if you took all of your FAQs, product catalogs, documentation, past conversations, etc., and created embeddings from them?

Well, suppose a customer shows up and asks a fairly basic question about your product. You could vectorize their question and match it against your database, returning relevant material even if the query is phrased in different words (or even an entirely different language).

Or suppose an agent wants to see if the thorny issue they’re dealing with relates to anything other agents have had to tackle in the past. As in the previous example, the agent can submit their conversation to the vector database and turn up similar interactions that have taken place, even if the language is different.

Advantages of AI Vector Databases

Vector databases have many compelling properties that make them popular for working with diverse data types.

First, this data tends to be “high-dimensional,” which is a more precise way of saying “big and complicated.” The way vector databases store and index high-dimensional data means that they operate with a speed and efficiency that would be hard to achieve if you stored the same data in a traditional database.

Then, it turns out that a lot of data can be vectorized. We already mentioned words and images, but you can also turn audio, connected graphs (such as those used to represent social networks), and many other kinds of data into embeddings. Even better, it’s often possible to create “multi-modal embeddings” to simultaneously represent a video’s audio, images, and text. This means you could use simple, textual queries to search over hundreds of hours of audio conversations with customers and textual transcripts, for example.

Finally, vector databases offer support for many complex analytics and machine-learning tasks. They can be used to build recommendation systems, perform sentiment analysis, or power generative AI applications.

As impressive as all this is, you probably don’t want to spend too much time thinking about the intricacies of a specialized database.

Managing a vector database is heavy on resources and can be complicated. So, one option we offer at Quiq is a straightforward GUI (Graphical User Interface) called AI Studio that allows you to load your data in a vector database that’s integrated directly into our platform.

Challenges and Considerations of Vector Databases

For all this, vector databases do, of course, have their drawbacks.

To begin with, vector databases are very specialized tools. While they are wonderful for working with the high-dimensional data that will power AI assistants in a contact center, they are not well-suited to storing tabular data. This means you’ll probably need to accommodate a traditional database and its vector-optimized counterpart – unless you work with a conversational AI vendor that has one built in.

There’s also a lot to think about regarding how it integrates with your existing data infrastructure. These days, most vector database companies consider this problem carefully and try to design their systems so that they’re easy to integrate with the rest of your stack.

But, as with everything else, actually going through the steps will require time and energy from your engineers. That said, there are many options to getting the job done. For example, if you partner with Quiq, we enable teams to build out AI assistants in an environment created specifically for this purpose: AI Studio.

Why does any of this matter when you’re exploring the options of introducing generative AI?

In a nutshell: vector databases are critical to safely and effectively using an AI assistant for your organization. But working with such a specialized technology is far from trivial, which is why so many are choosing instead to partner with a team that can handle vector management, or provide you with a tool to make it easier for you to handle it on your own.

If you have already decided to move forward with a vector database and don’t have multiple engineers to throw at the problem, this is what you should be looking for. Get in touch if you want to talk over your options.

Future Trends and Developments for Vector Databases

In this penultimate section, we’ll speculate a bit about where vector databases are heading.

Let’s begin with an easy prediction: vector databases will become more widely used and important. As generative AI continues to rise, there will be more places to utilize vectors, and as such, more companies will turn to them to store embeddings of their datasets.

But, we also think that many of these companies will then have to take a sober look at their cost structure. Vectors are flexible data structures that are uniquely able to power applications like search based on retrieval augmented generation (RAG), but they’re not equally applicable to every problem.

Finally, the trends indicate the vector databases of the future will have a wider range of capabilities. As things stand, they’re mostly built around doing various kinds of search based on the similarity of the underlying vectors. But there’s no reason they couldn’t handle exact matches, too. Together, these would allow you to get a broad, contextual overview and a precise, targeted result.

In the same vein, vector databases will eventually support other vector-based tasks, like classifying vectors or creating vector clusters. This would make it easier to do anomaly detection and similar kinds of unsupervised learning work.

Final Thoughts on Vector Databases

Vector databases are a remarkable technology that is especially important in the age of generative AI, and their rise is part of a bigger shift toward leveraging AI for many tasks.

That said, for contact center teams that are thinking about building a homegrown AI solution for CX, it’s critical to be realistic about the role that vector databases play in building a solution. It’s equally as important to plan ahead to mitigate the risks by bringing on support to help make the project successful.

Quiq’s AI offering features an integrated vector database, and partnering with us means one less thing to worry about. Reach out if you’d like to learn more.

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4 Reasons Why Every Hotel Needs an AI Assistant

Posted on The Quiq Team

Artificial intelligence (AI) has been all the rage for the past year, owing to its remarkable abilities to generate convincing text (and video!), automate major parts of different jobs, and boost the productivity of everyone using it.

Naturally, this has sparked the interest of professionals in the hospitality sector, which will be our focus today. We’ll talk about how AI assistants can be used in hotels, the size of the relevant market, and some potential issues you should look out for.

It’s an exciting topic, so let’s dive right in!

What is an AI Assistant for a Hotel?

Leaving aside a bit of nuance, the phrase “AI assistant” broadly covers using algorithmic technologies such as large language models to “assist” in various aspects of your work. A very basic example is the bundle of spell checkers, suggested edits, and autocomplete that is all but ubiquitous in text editors, email clients, and blogging platforms; a more involved example would be carefully crafting a prompt to generate convincing copy to sell a product or service.

If you’re interested in digging in further, check out some of our earlier posts for more details.

What is the Importance of Artificial Intelligence in the Hotel Industry?

In the next section, we cover the nuts and bolts of what AI assistants can do to streamline your operations, reduce the burden on your (human) staff, and improve the experience of guests staying at your hotel.

But in this one, we’re just going to talk dollars and cents. And to be clear, there are a lot of dollars and cents on the table. Experts who’ve studied the potential market for AI assistants in hospitality believe that it was worth something like $90 million in 2022, and this figure is expected to climb to an eye-watering $8 billion over the next decade.

“Hang on,” you’re thinking to yourself. “That’s great for the investors who fund these companies and the early employees that work in them, but the fact that a market is worth a lot of money doesn’t mean it’s actually going to have much impact on day-to-day hospitality.”

We admire your skeptical mind, and this is indeed a worthwhile concern. AI, after all, is renowned for its ups and downs; there’ll be years of frenzied excitement and near-delirious predictions that entire segments of the economy are poised for complete automation, followed by “AI winters” so deep even Ned Stark can’t get warm behind the walls of Winterfell.

Making the case that AI in hospitality will, in fact, be a trend worth thinking about is our next task.

The 4 Reasons Every Hotel Should be Using an AI Assistant

As promised, we’ll now cover all the reasons why you should seriously investigate the potential of AI assistants in your hotel. To paraphrase a famous saying, “Fortune favors the innovative,” and you can’t afford to ignore such a transformative technology.

#1 AI Assistants Can Help Drive Bookings and Sales

There are many ways in which AI will change the hotel booking process because it can act as a dynamic tool for enhancing guest interactions and driving sales directly through your hotel’s website. To start, AI assistants can significantly reduce the likelihood of potential guests abandoning their bookings midway by providing real-time answers to their questions, alleviating doubts about the details of a stay, and offering instant booking confirmations. Not only do such seamless experiences simplify the booking experience, they also contribute to an increase in direct bookings – a crucial advantage for hotels, as it eliminates the need for commission payouts and boosts profitability.

But that’s not all. These assistants are increasingly being integrated into social media and instant messaging platforms, enabling guests to start the booking process through their preferred channel or, failing that, redirecting them to the main hotel booking system. Throughout, they can proactively gather information about the guests’ preferences and budget, making tailored recommendations that increase the likelihood of conversion.

As you’re no doubt aware, a hotel doesn’t just make its money from bookings – there are also many opportunities for upselling and cross-selling hotel services. This, too, is a place where AI assistants can help. While interacting with a potential customer, they can suggest additional breakfast options, spa appointments, room upgrades, etc., based on the customer’s current selection and previous interactions with you.

Moreover, an AI assistant can modernize hotel marketing strategies, which have traditionally relied on relatively static methods like email campaigns. Properly tuned language models are capable of engaging in personalized, two-way conversations via social media or on your website, allowing them to deliver more effective promotional messages and alerts about special events or loyalty programs. All of this makes your messaging more likely to resonate with guests, ultimately boosting the all-important bottom line.

#2 AI Assistants Can Help Reduce Burnout and Turnover

About a year ago, we covered a landmark study from economists Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond that examined how generative AI was changing contact centers. Though there were (and are) many concerns about automation taking jobs, the study concluded that this new technology was helping newer agents onboard more quickly, was making mid-tier agents perform better, and was overall reducing burnout and turnover by lessening each agent’s burden.

Most of these factors also apply to your hospitality staff. Let’s see how.

Algorithms offer the distinct advantage of providing continuous service, and operating around the clock without needing breaks or sleep. This ensures that guests receive immediate assistance whenever needed, which will go a long way to cementing their perception of your commitment to exceptional service.

Furthermore, these assistants contribute to the efficiency of face-to-face customer interactions, particularly during routine processes like check-ins and check-outs. This dynamic becomes even more powerful when you integrate conversational AI into mobile apps, guests can complete these procedures directly from their smartphones, bypassing the front desk and avoiding any wait.

Hospitality teams often face high workloads, managing in-person guest interactions, responding to digital communications across multiple platforms, and analyzing feedback from customer surveys. A good AI assistant can substantially reduce this burden by handling routine inquiries and requests. Your human staff can then be left to focus on more complex issues, thereby preventing burnout and improving their capacity to deliver quality service via the fabled “human touch.”

#3 AI Assistants Can Help Improve the Guest Experience

Let’s drill a little bit more into how AI assistants can improve your guest’s stay at your hotel.

We’ve already mentioned some of this. If a customer’s booking goes smoothly, changes are handled promptly, their 2-a.m. questions have been answered, and their stay is replete with little personalized touches, they’re probably going to reflect on it fondly.
But this is hardly everything that can be said about how AI assistants will improve the hotel experience. Consider the fact that today’s language models are almost unbelievably good at translating between languages – especially when those are “high resource” languages, such as Mandarin, Russian, and Spanish.

If you’re a monolingual native English speaker, it can be easy to forget how much cognitive effort is involved in speaking a language in which you’re not fluent. But imagine for a moment that you’re a foreign traveler whose flight was delayed and whose kids never once stopped crying. Wouldn’t you appreciate being greeted with a friendly “欢迎” or “Добро пожаловать”, rather than needing to immediately fumble around in English?

Another subject that is slightly off-topic but is nevertheless worth discussing in this context is trust. People have long known that the internet is hardly a shining example of forthrightness and rectitude, but with the rise of generative AI, it has become even harder to believe what you read online.

We’ve discussed how much AI assistants can do for your hotel, but it’s important to use them judiciously, with appropriate guardrails in place, to reap the most benefit. If one of your language models offers up bad information or harasses a guest, that will reflect negatively on you. This is too big a topic for us to cover in this article, but you can check out earlier posts for more information.

A related issue is the collection of data. Upselling customers or personalizing their room can only be done by gathering data about their preferences. This, too, is something people are gradually becoming more aware of (and worried about), so it’s worth proactively crafting a data collection policy that’s available if anyone asks for it.

#4 AI Assistants Can Help Keep Your Operations Running Smoothly

Finally, we’ll finish by considering how AI can be used to streamline your hotel’s basic operations – making sure everything is in stock, that items make it to the right room, etc.

One significant benefit (which is becoming a more important distinguishing feature) is improving energy efficiency. You’re probably already familiar with smart room technologies, such as thermostats that reduce energy consumption by automatically adjusting themselves based on occupancy. But consider how implementing AI to manage HVAC systems for an entire building could not only optimize energy use and save significant costs, but also make guests more comfortable throughout their stay.

Similarly, AI can revolutionize waste management by employing systems that detect when trash receptacles need servicing. This would reduce the time staff spend checking and clearing bins, allowing them to focus on more valuable tasks.

Beyond these sustainability-focused applications, AI’s role in automating routine hospitality operations is vast. A fun example comes from Silicon Valley, where the Crowne Plaza hotel employs a robotic system named “Dash” to deliver snacks and towels directly to guests.

Even if you’re not particularly interested in having robots wandering your halls, it should hopefully be clear that many parts of running a hotel can be outsourced to machines, freeing you and your staff up to focus on more pressing matters.

Riding the AI Wave with Quiq

After decades of false starts and false promises, it looks like AI is finally having a measurable impact on the hospitality sector.

If you want to leverage this remarkable technology to the fullest but aren’t sure where to start, set up a time to talk with us. Quiq is an industry-leading conversational AI platform that makes deploying and monitoring AI systems for hotels much easier. Let’s explore opportunities to work together!

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6 Amazing Examples of how AI is Changing Hospitality

Posted on The Quiq Team

Recent advances in AI are poised to bring many changes. Though we’re still in the early days of seeing how all this plays out, there’s already clear evidence that generative AI is having a measurable impact in places like contact centers. Looking into the future a bit, multiple reports indicate that AI could add trillions of dollars to the economy before the close of the 2020s, and lead to as much as a doubling in rates of yearly economic growth over the next decade.

The hospitality industry has always been forward-looking, eager to adopt new best practices and technologies. If you’re working in hospitality now, therefore, you might be wondering what AI will mean for you, and what the benefits of AI will be.

That’s exactly what we’re setting out to answer in this article! Below, we’ve collected several of our favorite use cases of AI assistants in both hospitality and travel. Throughout, we’ve tried to anchor the discussion to real-world examples. We hope that, by the end, you’ll feel much better equipped to evaluate whether and how to use AI assistants in your own operations.

Let’s get going!

What is AI in Hospitality and Travel?

The term “artificial intelligence” covers a huge number of topics, approaches, and subdomains, most of which we won’t be able to cover here. But broadly, you can think of AI as being any attempt to train a machine to do useful work.

Two of the more popular methods for accomplishing this task are machine learning and generative AI, the latter of which has become famous due to the recent spectacular successes of large language models.

These are also the methods we’ll be focused on because they’re the ones most commonly used in hospitality. Machine learning, for example, will pop up in examples of dynamic pricing and demand forecasting, while generative AI is a key engine driving advances in automated concierge services.

6 Ways AI Assistants are Transforming Hospitality and Travel

Below, we’ve collected some of the most compelling use cases of AI assistants in the hospitality and travel industry. We’ll begin with their use in educating the rising generation of hospitality professionals, then move on to HR, operations, revenue, and all the other things that go into keeping guests happy!

Use Case #1 – Educating Future Hospitality Professionals

From personalized lesson plans to software-based tutors, applying artificial intelligence to education has long been a dream. This is no different for hospitality, where rising students are using the latest and greatest tools to accelerate their learning.

Students have to figure out how to comport themselves in a variety of challenging circumstances, from interactions at the front desk to ensuring the room service makes it to the right guest. When augmented with artificial intelligence, simulations can help students gain exposure to many of the issues they’ll face in their day-to-day work.

Generative AI, for example, can be used to practice and internalize strategies for dealing with guests who are distraught or downright rude. It can also be used as a general learning tool, helping to break down complex concepts, structure study routines, and more.

Use Case #2 – Hiring and Staffing

Like all businesses, hotels, resorts, and other hospitality staples have to deal with hiring. Talent acquisition is a major unsolved challenge; it can take a long time to find a good hire for a position, and mistakes can cost a lot in terms of time, energy, and money.

This, too, is a place where machine learning can help. A prominent example is Hilton, which has begun using bespoke algorithms to fill its positions. These algorithms can ingest a huge amount of information on the skills and experiences of a set of potential candidates, build profiles for them, and then measure this against the profiles of employees who have been successful in the past. This allows Hilton to better gauge how well these candidates will ultimately be able to live up to the rigors of different roles.

With this approach, Hilton has been able to fill empty positions in as little as a week, all while cutting its turnover in half. Not only does this save a great deal of time for hiring managers and recruiters, it also reduces delays and helps to build a more robust company culture.

This last point warrants a little elaboration. When employees stay with a company for a long time, they gain a very intuitive grasp of its internal workings. When they leave, they take this knowledge with them, and it can take a long time to rebuild. If AI is able to more efficiently find and place candidates, it means that an organization will function better in a thousand little ways, leading to an improved guest experience and more success in the long term.

Use Case #3 – Hotel Operations Management

Hotels have many moving parts. Keeping all the proverbial plates spinning is known as “operations,” and can involve anything from changing a reservation to fielding questions to making sure all the thermostats are functional.

Though much of this still requires the human touch, artificial intelligence can do a lot to lighten the load by automating routine parts of the job. Take booking, for example. It can be complicated, but in many cases, today’s AI assistants are more than capable of helping.

What might that look like? Consider an example of a potential guest who has questions about your amenities. They might want to know whether you have any special programs for kids, whether you have pool-side food service, etc. These are all things that a question-answering AI assistant could help with.

If we assume the guest has decided to book with you, they may later want to change their reservation by a few days. Or, after their stay, they may run into billing issues that need to be reconciled. These are both tasks that are often within the capacity of today’s systems.

This is appealing because it’ll save you time, yes, but there are more opportunities here than may be apparent at first. The Maison Mere hotel in Paris, for example, made the decision to use a contactless check-in service that allowed them to collect little details about their guests before they arrived. Afterward, they used that information to create custom touches in those guest’s rooms, such as personalized greetings and flowers. What’s more, it gave Maison Mere a chance to take advantage of targeted upselling opportunities; guests traveling with pets were offered pet kits, and promotions through the platform led to a boost in reservations at the hotel’s attached restaurant, to name but a few.

Returning to amenities, if you’ve worked in hospitality before, you’ve probably dealt with snack requests, towel deliveries, etc. In Silicon Valley, Crowne Plaza has begun rolling out a robotic system called “Dash” to outsource exactly these kinds of low-level tasks. Dash uses Wi-Fi to move around the hotel, locate guests, and deliver the requested items. It’s even able to check its own battery supply and recharge when it starts running low.

Use Case #4 – Hotel Revenue Management

Like all businesses, hotels exist to make money, and they therefore tend to keep a pretty close eye on their revenue. This might be one of the responsibilities you assume as a hospitality specialist, so it’s worth understanding how AI assistants will impact hotel revenue management.

Some of these developments have been in motion for a while. One tried-and-true technique for maximizing revenue is to better forecast future demand. Unfortunately, most hotels are not booked solid year round, there’ll be periods of extremely high activity and periods of relatively low activity. But these fluctuations aren’t random, and with the right machine learning algorithms, past historical data can be mined to arrive at a pretty accurate picture of when you’re going to be full. This allows you to better plan your inventory, for example, and have all the staff required to ensure everyone enjoys their stay.

For the same reason, many hotels choose to vary their prices based on demand. Premium suites might go for $500 a night in the busy season while commanding a much more affordable $200 a night when no one is visiting.

There exist many AI tools to help with this work, and they’re getting good results. In Thailand, the Narai Hospitality Group utilized a pricing and forecasting platform to grow their average daily rate by more than a quarter, even tripling the rates charged on some rooms during peak traffic months. Grand America Hotels & Resorts was similarly able to keep their revenue management lean and effective as they navigated the post-COVID travel boom using automation-powered software.

Use Case #5 – Marketing and Sales

Another thing the hospitality industry has in common with other industries is that it has to market its services—after all, no one can stay in a hotel they haven’t heard of. Using AI assistants for marketing purposes is hardly new, but there are some exciting developments where hospitality is concerned.

By using an AI-powered marketing intelligence service that dynamically personalizes offerings with real-time data, the U.K.’s Cheval Collection achieved an 82% revenue growth in 2023, compared to just three years prior.

Use Case #6 – Hotel Guest Experience in the AI Age

Above, we’ve discussed operations, revenue, hiring, and all the myriad aspects of running a successful hospitality enterprise. But perhaps the most important part of this process is the one we’ve saved for last: how much people enjoy actually staying with you.

This is generally known as “guest experience,” and it, too, is likely to be disrupted by the widespread use of AI assistants. Consider the example of “Rose,” an AI concierge used by Las Vegas’s Cosmopolitan hotel. When a guest checks in to the Cosmopolitan, they are given a number where they can contact Rose. They can text her if they have requests or call and talk to her if they prefer a voice interface.

Of course, it’s not hard to forecast some of the other ways AI could power an enhanced guest experience. Continuing with the concierge example, imagine smart AI assistants in each guest’s room, offering up recommendations for local restaurants or fun excursions. Since AI has made great strides in personalization, these assistants would be far from generic; they’d be able to utilize information about a guest’s preferences, prior experiences, online profiles or reviews, etc., to offer nuanced, highly-tailored advice.

If you have such a system operational in your hotel, it’s unlikely to be a thing your guests will forget.

Exploring AI in Hospitality: Industry Examples Unveiled

From large language models to machine learning to agentic systems, we’re living in something of a turning point for artificial intelligence. Today’s systems are far from perfect, but they’re clearly capable of doing economically useful work, in the hospitality industry and elsewhere.

But there remain many challenges, not least of which is working with an AI assistant platform you can trust. Quiq is a leader in the conversational AI space, and can help you integrate this cutting-edge technology into your business. Get in touch today to schedule a demo and see how we can help!

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WhatsApp Business: A Guide for Contact Center Managers

Posted on The Quiq Team

In today’s digital era, businesses continually seek innovative ways to connect with their customers, striving to enhance communication and foster deeper relationships. Enter WhatsApp Business – a game-changer in the realm of digital communication. This powerful tool is not just a messaging app; it’s a bridge between businesses and their customers, offering a plethora of features designed to streamline communication, improve customer service, and boost engagement. Whether you’re a small business owner or part of a global enterprise, understanding the potential of WhatsApp Business could redefine your approach to customer communication.

What is Whatsapp Business?

WhatsApp is an application that supports text messaging, voice messaging, and video calling for over two billion global users. Because it leverages a simple internet connection to send and receive data, WhatsApp users can avoid the fees that once made communication so expensive.

Since WhatsApp already has such a large base of enthusiastic users, many international brands have begun leveraging it to communicate with their own audiences. It also has a number of built-in features that make it an attractive option for businesses wanting to establish a more personal connection with their customers, and we’ll cover those in the next section.

What Features Does WhatsApp Business Have?

In addition to its reach and the fact that it reduces the budget needed for communication, WhatsApp Business has additional functionality that makes it ideal for any business trying to interact with its customers.

When integrated with a tool like the Quiq conversational AI platform, WhatsApp Business can automatically transcribe voice-based messages. Even better, WhatsApp Business allows you to export these conversations later if you want to analyze them with a tool like natural language processing.

If your contact center agents and the customers they’re communicating with have both set a “preferred language,” WhatsApp can dynamically translate between these languages to make communication easier. So, if a user sends a voice message in Russian and the agent wants to communicate in English, they’ll have no trouble understanding one another.

What are the Differences Between WhatsApp and WhatsApp Business?

Before we move on, it’s worth pointing out that WhatsApp and WhatsApp Business are two different services. On its own, WhatsApp is the most widely used messaging application in the world. Businesses can use WhatsApp to talk to their customers, but with a WhatsApp Business account, they get a few extra perks.

Mostly, these perks revolve around building brand awareness. Unlike a basic WhatsApp account, a WhatsApp Business account allows you to include a lot of additional information about your company and its services. It also provides a labeling system so that you can organize the conversations you have with customers, and a variety of other tools so you can respond quickly and efficiently to any issues that come up.

The Advantages of WhatsApp Messaging for Businesses

Now, let’s spend some time going over the myriad advantages offered by a WhatsApp outreach strategy. Why, in other words, would you choose to use WhatsApp over its many competitors?

Global Reach and Popularity

First, we’ve already mentioned the fact that WhatsApp has achieved worldwide popularity, and in this section, we’ll drill down into more specifics.

When WhatsApp was acquired by Meta in 2014, it already boasted 450 million active users per month. Today, this figure has climbed to a remarkable 2.7 billion, but it’s believed it will reach a dizzying 3.14 billion as early as 2025.

With over 535 million users, India is the country where WhatsApp has gained the most traction by far. Brazil is second with 148 million users, and Indonesia is third with 112 million users.

The gender divide among WhatsApp users is pretty even – men account for just shy of 54% of WhatsApp users, so they have only a slight majority.

The app itself has over 5 billion downloads from the Google Play store alone, and it’s used to send 140 billion messages each day.

These data indicate that WhatsApp could be a very valuable channel to cultivate, regardless of the market you’re looking to serve or where your customers are located.

Personalized Customer Interactions

Firstly, platforms like WhatsApp enable businesses to customize communication with a level of scale and sophistication previously unavailable.

This customization is powered by machine learning, a technology that has consistently led the charge in the realm of automated content personalization. For example, Spotify’s ability to analyze your listening patterns and suggest music or podcasts that match your interests is powered by machine learning. Now, thanks to advancements in generative AI, similar technology is being applied to text messaging.

Past language models often fell short in providing personalized customer interactions. They tended to be more “rule-based” and, therefore, came off as “mechanical” and “unnatural.” However, contemporary models greatly improve agents’ capacity to adapt their messages to a particular situation.

While none of this suggests generative AI is going to entirely take the place of the distinctive human mode of expression, for a contact center manager aiming to improve customer experience, this marks a considerable step forward.

Below, we have a section talking a little bit more about integrating AI into WhatsApp Business.

End-to-End Encryption

One thing that has always been a selling point for WhatsApp is that it takes security and privacy seriously. This is manifested most obviously in the fact that it encrypts all messages end-to-end.

What does this mean? From the moment you start typing a message to another user all the way through when they read it, the message is protected. Even if another party were to somehow intercept your message, they’d still have to crack the encryption to read it. What’s more, all of this is enabled by default – you don’t have to spend any time messing around with security settings.

This might be more important than you realize. We live in a world increasingly beset by data breaches and ransomware attacks, and more people are waking up to the importance of data security and privacy. This means that a company that takes these aspects of its platform very seriously could have a leg up where building trust is concerned. Your users want to know that their information is safe with you, and using a messaging service like WhatsApp will help to set you apart.

Scalability

Finally, WhatsApp’s Business API is a sophisticated programmatic interface designed to scale your business’s outreach capabilities. By leveraging this tool, companies can connect with a broader audience, extending their reach to prospects and customers across various locations. This expansion is not just about increasing numbers; it’s about strategically enhancing your business’s presence in the digital world, ensuring that you’re accessible whenever your customers need to reach out to you.

By understanding the value WhatsApp’s Business API brings in reaching and engaging with more people effectively, you can make an informed decision about whether it represents the right technological solution for your business’s expansion and customer engagement strategies.

Enhancing Contact Center Performance with WhatsApp Messaging

Now, let’s turn our attention to some of the concrete ways in which WhatsApp can improve your company’s chances of success!

Improving Response and Resolution Metrics Times

Integrating technologies like WhatsApp Business into your agent workflow can drastically improve efficiency, simultaneously reducing response times and boosting customer satisfaction. Agents often have to manage several conversations at once, and it can be challenging to keep all those plates spinning.

However, a quality messaging platform like WhatsApp means they’re better equipped to handle these conversations, especially when utilizing tools like Quiq Compose.

Additionally, less friction in resolving routine tasks means agents can dedicate their focus to issues that necessitate their expertise. This not only leads to more effective problem-solving, it means that fewer customer inquiries are overlooked or terminated prematurely.

Integrating Artificial Intelligence

According to WhatsApp’s own documentation, there’s an ongoing effort to expand the API to allow for the integration of chatbots, AI assistants, and generative AI more broadly.

Today, these technologies possess a surprisingly sophisticated ability to conduct basic interactions, answer straightforward questions, and address a wide range of issues, all of which play a significant role in boosting customer satisfaction and making agents more productive.

We can’t say for certain when WhatsApp will roll out the red carpet for AI vendors like Quiq, but if our research over the past year is any indication, it will make it dramatically easier to keep customers happy!

Gathering Customer Feedback

Lastly, an additional advantage to WhatsApp messaging is the degree to which it facilitates collecting customer feedback. To adapt quickly and improve your services, you have to know what your customers are thinking. And more specifically, you have to know the details about what they like and dislike about your product or service.

In the Olde Days (i.e. 20 years ago year, or so), the only real way to do this was by conducting focus groups, sending out surveys – sometimes through the actual mail, if you can believe it – or doing something similarly labor-intensive.

Today, however, your customers are almost certainly walking around with a smartphone that supports text messaging. And, since it’s pretty easy for them to answer a few questions or dash off a few quick lines describing their experience with your service, odds are that you can gather a great deal of feedback from them.

Now, we hasten to add that you must exercise a certain degree of caution in interpreting this kind of feedback, as getting an accurate gauge of customer sentiment is far from trivial. To name just one example, the feedback might be exaggerated in both the positive and negative direction because the people most likely to send feedback via text messaging are the ones who really liked or really didn’t like you.

That said, so long as you’re taking care to contextualize the information coming from customers, supplementing it with additional data wherever appropriate, it’s valuable to have.

Wrapping Up

From its global reach and popularity to the personalized customer interactions it facilitates, WhatsApp Business stands out as a powerful solution for businesses aiming to enhance their digital presence and customer engagement strategies. By leveraging the advanced features of WhatsApp Business, companies can avail themselves of end-to-end encryption, enjoy scalability, and improve contact center performance, thereby positioning themselves at the forefront of the contact center game.

And speaking of being at the forefront, the Quiq conversational CX platform offers a staggering variety of different tools, from AI assistants powered by language models to advanced analytics on agent performance. Check us out or schedule a demo to see what we can do for your contact center!

Your CX Strategy Should Include Apple Messages for Business. Here’s Why.

Posted on Max Fortis

A common piece of marketing advice says you should “Meet your customers where they’re at.” These days, there are something like 23 billion text messages sent daily across the world, so your customers are probably on their phones.

Twenty years ago, you could be forgiven for thinking that text messaging was a method of communication reserved for teenagers sending each other inscrutable strings of hieroglyphic emojis, but more and more business is being done this way. It’s now relatively common for contact centers to offer customer support over chat, which means text messaging has emerged as a vital customer service channel.

In this piece, we will focus specifically on one text messaging service, Apple Messages, and how it can be leveraged to create personalized and efficient customer interactions. Along the way, we’ll talk about some of the exciting work being done to leverage AI assistants through text messaging so you can stay one step ahead of the competition.

The Advantages of Apple Messages in Customer Service

Here, we’re going to discuss the myriad advantages conferred by using Apple Messages. But before we do that, it’s worth making sure we’re all on the same page by discussing what Apple Messages is in the first place.

You probably already know that Apple’s line of iPhones supports text messaging, like all mobile phones. But Apple Messages is a distinct product designed to allow businesses like yours to interact with customers.

It makes it easy to set up a variety of touchpoints, like QR codes, an app, or an email message, through which customers can make appointments, raise (and resolve) problems, or pay for your services.

There are many ways in which utilizing Apple Messages can help you, which we’ll discuss now.

Personalization at Scale

First, tools like Apple Messages allow businesses to personalize communication at a scale and sophistication never seen before.

This personalization is achieved with machine learning, which has consistently been at the forefront of automated content customization. For instance, Netflix is well-known for identifying trends in your viewing habits and using algorithms to recommend shows that align with your preferences. Now, thanks to generative AI, this technology is making its way into text messaging.

Yesterday’s language models often lacked the flexibility for personalized customer interactions, sounding “robotic” and “artificial.” Modern models significantly bolster agents’ ability to tailor their conversations to the specific context. Though they do not completely replace the unique human element, for a contact center manager focused on enhancing customer experience, this represents a significant advancement.

Speed and Convenience

Another place where text messaging shines strategically is its speed and convenience. Texting became popular in the first place because it streamlined the communication process. But, unlike with a phone call, this communication could be done privately, without disturbing others.

Customers needing to troubleshoot an issue while they’re on the bus or somewhere public will likely want to do so with a chat interface. This provides the opportunity to

High Engagement Rates

One aspect of a customer communication strategy you’ll have to consider is what the likely engagement with it will be. Text messaging, particularly through platforms like Apple Messages, boasts higher open and response rates than other channels.

The statistics backing this up are compelling – 98% of text messages sent to customers are opened and eventually read, with fully 90% of them being read just three minutes after being received. Even better, nearly half (48%) of text messages sent to customers get responses.

On its own, this indicates the enormous potential for text-messaging strategies to get your customers talking to you, but when you consider the fact that only around a quarter of emails are opened and read, it’s hard to escape the conclusion that you should be investing seriously in this channel.

Leveraging AI in Apple Messages

Artificial intelligence, especially large language models, are all the rage these days, and they’re being deployed in text messages as well. Since Apple Messages allows you to use your own bots and virtual agents, it’s worth spending a few minutes talking about how generative AI can help.

There are a few different ways in which an AI customer service agent can streamline your customer service operations.

The simplest is by directly resolving issues—or helping customers to directly resolve their own issues—with little need for intervention by human contact center agents. There are many problems that are too involved for this to work, of course, but if all a customer needs to do is reset a password it could well be sufficient.

(Note, however, that Apple Messages requires you to include an option allowing a customer to escalate to a human agent. As things stand today, that part is non-negotiable).

Even when a human agent needs to get involved, however, generative AI can help. The Quiq conversational CX platform has a tool called “Quiq Compose”, for example, which can help format replies. An agent can input a potential reply with grammatical mistakes, misspellings, and a lack of warmth, and Quiq Compose will work its magic to turn the reply into something polished and empathic.

Improving Contact Center Performance with Apple Messages

Assuming that you’ve set up Apple Messages and supercharged it with the latest and greatest AI customer service agent, what can you expect to happen? That’s the question we’ll address in these sections.

Reducing Response Times

When combined with AI assistants and related technologies, Apple Messages can significantly reduce response times and increase customer satisfaction. It’s well known that contact center agents are often juggling multiple conversations at a time, and it can be hard to keep it all straight. But when they’re backed up by chatbots, Quiq Compose, etc., they can handle this volume in less time than ever before.

Generative AI is now good enough to carry on relatively lightweight interactions, answer basic questions, and help solve myriad issues; this, by itself, will almost certainly reduce response times. But it also means that agents can pivot to focusing on the thorniest, highest-priority tasks, which will further drive response times down.

Increasing Resolution Rates

For all the reasons just mentioned, AI assistants can increase resolution rates. Part of this will stem from the fact that fewer customers will fall through the cracks or end their calls early. But it will also come from agents being less rushed and more able to work on those tickets that really require their attention.

This is easy to see with an example. Imagine two people, each with daunting lists of chores they’re not sure they can finish. One of them is all on their own, while the other can outsource the most banal 30% of their tasks to robots.

Who would you bet on to have the highest chore resolution rate?

Implementing Apple Messages in Your Contact Center

The basic steps for getting started with Apple Messages are easy to follow.

First, you have to register your account. We’ve been using the name “Apple Messages” throughout this piece, but its full name is “Apple Messages for Business,” and your account must be tied to an actual business to be eligible.

Then, you have to create an account where your branding assets will live and where you’ll select the Messaging Service Provider (MSP) that you’d like to use. Apple will then review your submission, and, after a few days, will tell you whether you’ve been approved. As you’re planning your text messaging efforts, make sure that you’re factoring in the approval process.

With that done, you’ll have to start thinking in detail about your customer’s journey by filling out a Use Case template. You need to outline what you hope to achieve with text messaging, then decide on the entry points you want to offer your customers.

Next up, you’ll work out the user experience. This involves creating the automated messages you want to use, configuring Apple Pay if relevant, and designing customer satisfaction surveys.

Afterward, you need to set up metrics to figure out how your text messages are landing and whether there are things you can do to improve. If you’ve read our past articles on leveraging customer insights, you know how important data is to your ultimate success.

Last of all, Apple will spend a week or two reviewing everything you’ve accomplished in these steps and deciding whether anything else needs to be tweaked. Assuming you pass, you’re ready to go with Apple Messages!

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Final Thoughts on Why Your Business Should Use Apple Messages

Contact centers are increasingly coming to resemble technology companies, and the rise of Apple Messages is a great illustration of that. Apple Messages makes it easy to deploy AI assistants to interact with your customers, thereby reaping the enormous benefits of automation.

And speaking of the benefits of automation, check out the Quiq platform while you’re at it. We’ve worked hard to suss out the best ways of applying artificial intelligence to contact centers, and have built a product around our findings. We’ve helped many others, and we can help you too!

Getting the Most Out of Your Customer Insights with AI

Posted on The Quiq Team

The phrase “Knowledge is power” is usually believed to have originated with 16th- and 17th-century English philosopher Francis Bacon, in his Meditationes Sacræ. Because many people recognize something profoundly right about this sentiment, it has become received wisdom in the centuries since.

Now, data isn’t exactly the same thing as knowledge, but it is tremendously powerful. Armed with enough of the right kind of data, contact center managers can make better decisions about how to deploy resources, resolve customer issues, and run their business.

As is usually the case, the data contact center managers are looking for will be unique to their field. This article will discuss these data, why they matter, and how AI can transform how you gather, analyze, and act on data.

Let’s get going!

What are Customer Insights in Contact Centers?

As a contact center, your primary focus is on helping people work through issues related to a software product or something similar. But you might find yourself wondering who these people are, what parts of the customer experience they’re stumbling over, which issues are being escalated to human agents and which are resolved by bots, etc.

If you knew these things, you would be able to notice patterns and start proactively fixing problems before they even arise. This is what customer insights is all about, and it can allow you to finetune your procedures, write clearer technical documentation, figure out the best place to use generative AI in your contact center, and much more.

What are the Major Types of Customer Insights?

Before we turn to a discussion of the specifics of customer insights, we’ll deal with the major kinds of customer insights there are. This will provide you with an overarching framework for thinking about this topic and where different approaches might fit in.

Speech and Text Data

Customer service and customer experience both tend to be language-heavy fields. When an agent works with a customer over the phone or via chat, a lot of natural language is generated, and that language can be analyzed. You might use a technique like sentiment analysis, for example, to gauge how frustrated customers are when they contact an agent. This will allow you to form a fuller picture of the people you’re helping, and discover ways of doing so more effectively.

Data on Customer Satisfaction

Contact centers exist to make customers happy as they try to use a product, and for this reason, it’s common practice to send out surveys when a customer interaction is done. When done correctly, the information contained in these surveys is incredibly valuable, and can let you know whether or not you’re improving over time, whether a specific approach to training or a new large language model is helping or hurting customer satisfaction, and more.

Predictive Analytics

Predictive analytics is a huge field, but it mostly boils down to using machine learning or something similar to predict the future based on what’s happened in the past. You might try to forecast average handle time (AHT) based on the time of the year, on the premise that when an issue arises has something to do with how long it will take to get it resolved.

To do this effectively you would need a fair amount of AHT data, along with the corresponding data about when the complaints were raised, and then you could fit a linear regression model on these two data streams. If you find that AHT reliably climbs during certain periods, you can have more agents on hand when required.

Data on Agent Performance

Like employees in any other kind of business, agents perform at different levels. Junior agents will likely take much longer to work through a thorny customer issue than more senior ones, of course, and the same could be said for agents with an extensive technical background versus those without the knowledge this background confers. Or, the same agent might excel at certain kinds of tasks but perform much worse on others.

Regardless, by gathering these data on how agents are performing you, as the manager, can figure out where weaknesses lie across all your teams. With this information, you’ll be able to strategize about how to address those weaknesses with coaching, additional education, a refresh of the standard operating procedures, or what have you.

Channel Analytics

These days, there are usually multiple ways for a customer to get in touch with your contact center, and they all have different dynamics. Sending a long email isn’t the same thing as talking on the phone, and both are distinct from reaching out on social media or talking to a bot. If you have analytics on specific channels, how customers use them, and what their experience was like, you can make decisions about what channels to prioritize.

What’s more, customers will often have interacted with your brand in the past through one or more of these channels. If you’ve been tracking those interactions, you can incorporate this context to personalize responses when they reach out to resolve an issue in the future, which can help boost customer satisfaction.

What Specific Metrics are Tracked for Customer Insights?

Now that we have a handle on what kind of customer insights there are, let’s talk about specific metrics that come up in contact centers!

First Contact Resolution (FCR)

The first contact resolution is the fraction of issues a contact center is able to resolve on the first try, i.e. the first time the customer reaches out. It’s sometimes also known as Right First Time (RFT), for this reason. Note that first contact resolution can apply to any channel, whereas first call resolution applies only when the customer contacts you over the phone. They have the same acronym but refer to two different metrics.

Average Handle Time (AHT)

The average handle time is one of the more important metrics contact centers track, and it refers to the mean length of time an agent spends on a task. This is not the same thing as how long the agent spends talking to a customer, and instead encompasses any work that goes on afterward as well.

Customer Satisfaction (CSAT)

The customer satisfaction score attempts to gauge how customers feel about your product and service. It’s common practice, to collect this information from many customers, then averaging them to get a broader picture of how your customers feel. The CSAT can give you a sense of whether customers are getting happier over time, whether certain products, issues, or agents make them happier than others, etc.

Call Abandon Rate (CAR)

The call abandon rate is the fraction of customers who end a call with an agent before their question has been answered. It can be affected by many things, including how long the customers have to wait on hold, whether they like the “hold” music you play, and similar sorts of factors. You should be aware that CAR doesn’t account for missed calls, lost calls, or dropped calls.

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Data-driven contact centers track a lot of metrics, and these are just a sample. Nevertheless, they should convey a sense of what kinds of numbers a manager might want to examine.

How Can AI Help with Customer Insights?

And now, we come to the “main” event, a discussion of how artificial intelligence can help contact center managers gather and better utilize customer insights.

Natural Language Processing and Sentiment Analysis

An obvious place to begin is with natural language processing (NLP), which refers to a subfield in machine learning that uses various algorithms to parse (or generate) language.

There are many ways in which NLP can aid in finding customer insights. We’ve already mentioned sentiment analysis, which detects the overall emotional tenor of a piece of language. If you track sentiment over time, you’ll be able to see if you’re delivering more or less customer satisfaction.

You could even get slightly more sophisticated and pair sentiment analysis with something like named entity recognition, which extracts information about entities from language. This would allow you to e.g. know that a given customer is upset, and also that the name of a particular product kept coming up.

Classifying Different Kinds of Communication

For various reasons, contact centers keep transcripts and recordings of all the interactions they have with a customer. This means that they have access to a vast amount of textual information, but since it’s unstructured and messy it’s hard to know what to do with it.

Using any of several different ML-based classification techniques, a contact center manager could begin to tame this complexity. Suppose, for example, she wanted to have a high-level overview of why people are reaching out for support. With a good classification pipeline, she could start automating the processing of sorting communications into different categories, like “help logging in” or “canceling a subscription”.

With enough of this kind of information, she could start to spot trends and make decisions on that basis.

Statistical Analysis and A/B Testing

Finally, we’ll turn to statistical analysis. Above, we talked a lot about natural language processing and similar endeavors, but more than likely when people say “customer insights” they mean something like “statistical analysis”.

This is a huge field, so we’re going to illustrate its importance with an example focusing on churn. If you have a subscription-based business, you’ll have some customers who eventually leave, and this is known as “churn”. Churn analysis has sprung up to apply data science to understanding these customer decisions, in the hopes that you can resolve any underlying issues and positively impact the bottom line.

What kinds of questions would be addressed by churn analysis? Things like what kinds of customers are canceling (i.e. are they young or old, do they belong to a particular demographic, etc.), figuring out their reasons for doing so, using that to predict which similar questions might be in danger of churning soon, and thinking analytically about how to reduce churn.

And how does AI help? There now exist any number of AI tools that substantially automate the process of gathering and cleaning the relevant data, applying standard tests, making simple charts, and making your job of extracting customer insights much easier.

What AI Tools Can Be Used for Customer Insights?

By now you’re probably eager to try using AI for customer insights, but before you do that, let’s spend some talking about what you’d look for in a customer insights tool.

Performant and Reliable

Ideally, you want something that you can depend upon and that won’t drive you crazy with performance issues. A good customer insights tool will have many optimizations under the hood that make crunching numbers easy, and shouldn’t require you to have a computer science degree to set up.

Straightforward Integration Process

Modern contact centers work across a wide variety of channels, including emails, chat, social media, phone calls, and even more. Whatever AI-powered customer insights platform you go with should be able to seamlessly integrate with all of them.

Simple to Use

Finally, your preferred solution should be relatively easy to use. Quiq Insights, for example, makes it a breeze to create customizable funnels, do advanced filtering, see the surrounding context for different conversations, and much more.

Getting the Most Out of AI-Powered Customer Insights

Data is extremely important to the success or failure of modern businesses, and it’s getting more important all the time. Contact centers have long been forward-looking and eager to adopt new technologies, and the same must be true in our brave new data-powered world.

If you’d like a demo of Quiq Insights, reach out to see how we can help you streamline your operation while boosting customer satisfaction!

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Security and Compliance in Next-Gen Contact Centers

Posted on Michael Hartsog

Along with almost everyone else, we’ve been singing the praises of large language models like ChatGPT for a while now. We’ve noted how they can be used in retail, how they’re already supercharging contact center agents, and have even put out some content on how researchers are pushing the frontiers of what this technology is capable of.

But none of this is to say that generative AI doesn’t come with serious concerns for security and compliance. In this article, we’ll do a deep dive into these issues. We’ll first provide some context on how advanced AI is being deployed in contact centers, before turning our attention to subjects like data leaks, lack of transparency, and overreliance. Finally, we’ll close with a treatment of the best practices contact center managers can use to alleviate these problems.

What is a “Next-Gen” Contact Center?

First, what are some ways in which a next-generation contact center might actually be using AI? Understanding this will be valuable background for the rest of the discussion about security and compliance, because knowing what generative AI is doing is a crucial first step in protecting ourselves from its potential downsides.

Businesses like contact centers tend to engage in a lot of textual communication, such as when resolving customer issues or responding to inquiries. Due to their proficiency in understanding and generating natural language, LLMs are an obvious tool to reach for when trying to automate or streamline these tasks; for this reason, they have become increasingly popular in enhancing productivity within contact centers.

To give specific examples, there are several key areas where contact center managers can effectively utilize LLMs:

Responding to Customer Queries – High-quality documentation is crucial, yet there will always be customers needing assistance with specific problems. While LLMs like ChatGPT may not have all the answers, they can address many common inquiries, particularly when they’ve been fine-tuned on your company’s documentation.

Facilitating New Employee Training – Similarly, a language model can significantly streamline the onboarding process for new staff members. As they familiarize themselves with your technology and procedures, they may encounter confusion, where AI can provide quick and relevant information.

Condensing Information – While it may be possible to keep abreast of everyone’s activities on a small team, this becomes much more challenging as the team grows. Generative AI can assist by summarizing emails, articles, support tickets, or Slack threads, allowing team members to stay informed without spending every moment of the day reading.

Sorting and Prioritizing Issues – Not all customer inquiries or issues carry the same level of urgency or importance. Efficiently categorizing and prioritizing these for contact center agents is another area where a language model can be highly beneficial. This is especially so when it’s integrated into a broader machine-learning framework, such as one that’s designed to adroitly handle classification tasks.

Language Translation – If your business has a global reach, you’re eventually going to encounter non-English-speaking users. While tools like Google Translate are effective, a well-trained language model such as ChatGPT can often provide superior translation services, enhancing communication with a diverse customer base.

What are the Security and Compliance Concerns for AI?

The preceding section provided valuable context on the ways generative AI is powering the future of contact centers. With that in mind, let’s turn to a specific treatment of the security and compliance concerns this technology brings with it.

Data Leaks and PII

First, it’s no secret that language models are trained on truly enormous amounts of data. And with that, there’s a growing worry about potentially exposing “Personally Identifiable Information” (PII) to generative AI models. PII encompasses details like your actual name, residential address, and also encompasses sensitive information like health records. It’s important to note that even if these records don’t directly mention your name, they could still be used to deduce your identity.

While our understanding of the exact data seen by language models during their training remains incomplete, it’s reasonable to assume they’ve encountered some sensitive data, considering how much of that kind of data exists on the internet. What’s more, even if a specific piece of PII hasn’t been directly shown to an LLM, there are numerous ways it might still come across such data. Someone might input customer data into an LLM to generate customized content, for instance, not recognizing that the model often permanently integrates this information into its framework.

Currently, there’s no effective method to extract data from a language model, and no finetuning technique that ensures it never reveals that data again has yet been found.

Over-Reliance on Models

Are you familiar with the term “ultracrepidarianism”? It’s a fancy SAT word that refers to a person who consistently gives advice or expresses opinions on things that they simply have no expertise in.

A similar sort of situation can arise when people rely too much on language models, or use them for tasks that they’re not well-suited for. These models, for example, are well known to hallucinate (i.e. completely invent plausible-sounding information that is false). If you were to ask ChatGPT for a list of 10 scientific publications related to a particular scientific discipline, you could well end up with nine real papers and one that’s fabricated outright.
From a compliance and security perspective, this matters because you should have qualified humans fact-checking a model’s output – especially if it’s technical or scientific.

To concretize this a bit, imagine you’ve finetuned a model on your technical documentation and used it to produce a series of steps that a customer can use to debug your software. This is precisely the sort of thing that should be fact-checked by one of your agents before being sent.

Not Enough Transparency

Large language models are essentially gigantic statistical artifacts that result from feeding an algorithm huge amounts of textual data and having it learn to predict how sentences will end based on the words that came before.

The good news is that this works much better than most of us thought it would. The bad news is that the resulting structure is almost completely inscrutable. While a machine learning engineer might be able to give you a high-level explanation of how the training process works or how a language model generates an output, no one in the world really has a good handle on the details of what these models are doing on the inside. That’s why there’s so much effort being poured into various approaches to interpretability and explainability.

As AI has become more ubiquitous, numerous industries have drawn fire for their reliance on technologies they simply don’t understand. It’s not a good look if a bank loan officer can only shrug and say “The machine told me to” when asked why one loan applicant was approved and another wasn’t.

Depending on exactly how you’re using generative AI, this may not be a huge concern for you. But it’s worth knowing that if you are using language models to make recommendations or as part of a decision process, someone, somewhere may eventually ask you to explain what’s going on. And it’d be wise for you to have an answer ready beforehand.

Compliance Standards Contact Center Managers Should be Familiar With

To wrap this section up, we’ll briefly cover some of the more common compliance standards that might impact how you run your contact center. This material is only a sketch, and should not be taken to be any kind of comprehensive breakdown.

The General Data Protection Regulation (GDPR) – The famous GDPR is a set of regulations put out by the European Union that establishes guidelines around how data must be handled. This applies to any business that interacts with data from a citizen of the EU, not just to companies physically located on the European continent.

The California Consumer Protection Act (CCPA) – In a bid to give individuals more sovereignty over what happens to their personal data, California created the CCPA. It stipulates that companies have to be clearer about how they gather data, that they have to include privacy disclosures, and that Californians must be given the choice to opt out of data collection.

Soc II – Soc II is a set of standards created by the American Institute of Certified Public Accounts that stresses confidentiality, privacy, and security with respect to how consumer data is handled and processed.

Consumer Duty – Contact centers operating in the U.K. should know about The Financial Conduct Authority’s new “Consumer Duty” regulations. The regulations’ key themes are that firms must act in good faith when dealing with customers, prevent any foreseeable harm to them, and do whatever they can to further the customer’s pursuit of their own financial goals. Lawmakers are still figuring out how generative AI will fit into this framework, but it’s something affected parties need to monitor.

Best Practices for Security and Compliance when Using AI

Now that we’ve discussed the myriad security and compliance concerns facing contact centers that use generative AI, we’ll close by offering advice on how you can deploy this amazing technology without running afoul of rules and regulations.

Have Consistent Policies Around Using AI

First, you should have a clear and robust framework that addresses who can use generative AI, under what circumstances, and for what purposes. This way, your agents know the rules, and your contact center managers know what they need to monitor and look out for.

As part of crafting this framework, you must carefully study the rules and regulations that apply to you, and you have to ensure that this is reflected in your procedures.

Train Your Employees to Use AI Responsibly

Generative AI might seem like magic, but it’s not. It doesn’t function on its own, it has to be steered by a human being. But since it’s so new, you can’t treat it like something everyone will already know how to use, like a keyboard or Microsoft Word. Your employees should understand the policy that you’ve created around AI’s use, should understand which situations require human fact-checking, and should be aware of the basic failure modes, such as hallucination.

Be Sure to Encrypt Your Data

If you’re worried about PII or data leakages, a simple solution is to encrypt your data before you even roll out a generative AI tool. If you anonymize data correctly, there’s little concern that a model will accidentally disclose something it’s not supposed to down the line.

Roll Your Own Model (Or Use a Vendor You Trust)

The best way to ensure that you have total control over the model pipeline – including the data it’s trained on and how it’s finetuned – is to simply build your own. That being said, many teams will simply not be able to afford to hire the kinds of engineers who are equal to this task. In such case, you should utilize a model built by a third party with a sterling reputation and many examples of prior success, like the Quiq platform.

Engage in Regular Auditing

As we mentioned earlier, AI isn’t magic – it can sometimes perform in unexpected ways, and its performance can also simply degrade over time. You need to establish a practice of regularly auditing any models you have in production to make sure they’re still behaving appropriately. If they’re not, you may need to do another training run, examine the data they’re being fed, or try to finetune them.

Futureproofing Your Contact Center Security

The next generation of contact centers is almost certainly going to be one that makes heavy use of generative AI. There are just too many advantages, from lower average handling time to reduced burnout and turnover, to forego it.

But doing this correctly is a major task, and if you want to skip the engineering hassle and headache, give the Quiq conversational AI platform a try! We have the expertise required to help you integrate a robust, powerful generative AI tool into your contact center, without the need to write a hundred thousand lines of code.

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LLM-Powered AI Assistants for Hotels – Ultimate Guide

Posted on The Quiq Team

New technologies have always been disruptive, supercharging those firms that embrace it and requiring the others to adapt or be left behind.

With the rise of new approaches to AI, such as large language models, we can see this dynamic playing out again. One place where AI assistants could have a major impact is in the hotel industry.

In this piece, we’ll explore the various ways AI assistants can be used in hotels, and what that means for the hoteliers that keep these establishments running.

Let’s get going!

What is an AI Assistant?

The term “AI assistant” refers to any use of an algorithmic or machine-learning system to automate a part of your workflow. A relatively simple example would be the autocomplete found in almost all text-editing software today, while a much more complex example might involve stringing together multiple chain-of-thought prompts into an agent capable of managing your calendar.

There are a few major types of AI assistants. Near and dear to our hearts, of course, are chatbots that function in places like contact centers. These can be agent-facing or customer-facing, and can help with answering common questions, helping draft replies to customer inquiries, and automatically translating between many different natural languages.

Chatbots (and large language models more generally) can also be augmented to produce speech, giving rise to so-called “voice assistants”. These tend to work like other kinds of chatbots but have the added ability to actually vocalize their text, creating a much more authentic customer experience.

In a famous 2018 demo, Google Duplex was able to complete a phone call to a local hair salon to make a reservation. One remarkable thing about the AI assistant was how human it sounded – its speech even included “uh”s and “mm-hmm”s that made it almost indistinguishable from an actual person, at least over the phone and for short interactions.

Then, there are 3D avatars. These digital entities are crafted to look as human as possible, and are perfect for basic presentations, websites, games, and similar applications. Graphics technology has gotten astonishingly good over the past few decades and, in conjunction with the emergence of technologies like virtual reality and the metaverse, means that 3D avatars could play a major role in the contact centers of the future.

One thing to think about if you’re considering using AI assistants in a hotel or hospitality service is how specialized you want them to be. Although there is a significant effort underway to build general-purpose assistants that are able to do most of what a human assistant does, it remains true that your agents will do better if they’re fine-tuned on a particular domain. For the time being, you may want to focus on building an AI assistant that is targeted at providing excellent email replies, for example, or answering detailed questions about your product or service.

That being said, we recommend you check the Quiq blog often for updates on AI assistants; when there’s a breakthrough, we’ll deliver actionable news as soon as possible.

How Will AI Assistants Change Hotels?

Though the audience we speak to is largely comprised of people working in or managing contact centers, the truth is that there are many overlaps with those in the hospitality space. Since these are both customer-service and customer-oriented domains, insights around AI assistants almost always transfer over.

With that in mind, let’s dive in now to talk about how AI is poised to transform the way hotels function!

AI for Hotel Operations

Like most jobs, operating a hotel involves many tasks that require innovative thinking and improvisation, and many others that are repetitive, rote, and quotidian. Booking a guest, checking them in, making small changes to their itinerary, and so forth are in the latter category, and are precisely the sorts of things that AI assistants can help with.

In an earlier example, we saw that chatbots were already able to handle appointment booking five years ago, so it requires no great leap in imagination to see how slightly more powerful systems would be able to do this on a grander scale. If it soon becomes possible to offload much of the day-to-day of getting guests into their rooms to the machines, that will free up a great deal of human time and attention to go towards more valuable work.

It’s possible, of course, that this will lead to a dramatic reduction in the workforce needed to keep hotels running, but so far, the evidence points the other way; when large language models have been used in contact centers, the result has been more productivity (especially among junior agents), less burnout, and reduced turnover. We can’t say definitively that this will apply in hotel operations, but we also don’t see any reason to think that it wouldn’t.

AI for Managing Hotel Revenues

Another place that AI assistants can change hotels is in forecasting and boosting revenues. We think this will function mainly by making it possible to do far more fine-grained analyses of consumption patterns, inventory needs, etc.

Everyone knows that there are particular times of the year when vacation bookings surge, and others in which there are a relatively small number of bookings. But with the power of big data and sophisticated AI assistants, analysts will be able to do a much better job of predicting surges and declines. This means prices for rooms or other accommodations will be more fluid and dynamic, changing in near real-time in response to changes in demand and the personal preferences of guests. The ultimate effect will be an increase in revenue for hotels.

AI in Marketing and Customer Service

A similar line of argument holds for using AI assistants in marketing and customer service. Just as both hotels and guests are better served when we can build models that allow for predicting future bookings, everyone is better served when it becomes possible to create more bespoke, targeted marketing.

By utilizing data sources like past vacations, Google searches, and browser history, AI assistants will be able to meet potential clients where they’re at, offering them packages tailored to exactly what they want and need. This will not only mean increased revenue for the hotel, but far more satisfaction for the customers (who, after all, might have gotten an offer that they themselves didn’t realize they were looking for.)

If we were trying to find a common theme between this section and the last one, we might settle on “flexibility”. AI assistants will make it possible to flexibly adjust prices (raising them during peak demand and lowering them when bookings level off), flexibly tailor advertising to serve different kinds of customers, and flexibly respond to complaints, changes, etc.

Smart Buildings in Hotels

One particularly exciting area of research in AI centers around so-called “smart buildings”. By now, most of us have seen relatively “smart” thermostats that are able to learn your daily patterns and do things like turn the temperature up when you leave to save on the cooling bill while turning it down to your preferred setting as you’re heading home from work.

These are certainly worthwhile, but they barely even scratch the surface of what will be possible in the future. Imagine a room where every device is part of an internet-of-things, all wired up over a network to communicate with each other and gather data about how to serve your needs.

Your refrigerator would know when you’re running low on a few essentials and automatically place an order, a smart stove might be able to take verbal commands (“cook this chicken to 180 degrees, then power down and wait”) to make sure dinner is ready on time, a smoothie machine might be able to take in data about your blood glucose levels and make you a pre-workout drink specifically tailored to your requirements on that day, and so on.

Pretty much all of this would carry over to the hotel industry as well. As is usually the case there are real privacy concerns here, but assuming those challenges can be met, hotel guests may one day enjoy a level of service that is simply not possible with a staff comprised only of human beings.

Virtual Tours and Guest Experience

Earlier, we mentioned virtual reality in the context of 3D avatars that will enhance customer experience, but it can also be used to provide virtual tours. We’re already seeing applications of this technology in places like real estate, but there’s no reason at all that it couldn’t also be used to entice potential guests to visit different vacation spots.

When combined with flexible and intelligent AI assistants, this too could boost hotel revenues and better meet customer needs.

Using AI Assistants in Hotels

As part of the service industry, hoteliers work constantly to best meet their customers’ needs and, for this reason, they would do well to keep an eye on emerging technologies. Though many advances will have little to do with their core mission, others, like those related to AI assistants, will absolutely help them forecast future demands, provide personalized service, and automate routine parts of their daily jobs.

If all of this sounds fascinating to you, consider checking out the Quiq conversational CX platform. Our sophisticated offering utilizes large language models to help with tasks like question answering, following up with customers, and perfecting your marketing.

Schedule a demo with us to see how we can bring your hotel into the future!

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What is an AI Assistant for Retail?

Posted on Mark Kowal

Over the past few months, we’ve had a lot to say about artificial intelligence, its new frontiers, and the ways in which it is changing the customer service industry.

A natural extension of this analysis is looking at the use of AI in retail. That is our mission today. We’ll look at how techniques like natural language processing and computer vision will impact retail, along with some of the benefits and challenges of this approach.

Let’s get going!

How is AI Used in Retail?

AI is poised to change retail, as it is changing many other industries. In the sections that follow, we’ll talk through three primary AI technologies that are driving these changes, namely natural language processing, computer vision, and machine learning more broadly.

Natural Language Processing

Natural language processing (NLP) refers to a branch of machine learning that attempts to work with spoken or written language algorithmically. Together with computer vision, it is one of the best-researched and most successful attempts to advance AI since the field was founded some seven decades ago.

Of course, these days the main NLP applications everyone has heard of are large language models like ChatGPT. This is not the only way AI assistants will change retail, but it is a big one, so that’s where we’ll start.

An obvious place to use LLMs in retail is with chatbots. There’s a lot of customer interaction that involves very specific questions that need to be handled by a human customer service agent, but a lot of it is fairly banal, consisting of things like “How do I return this item” or “Can you help me unlock my account.” For these sorts of issues, today’s chatbots are already powerful enough to help in most situations.

A related use case for AI in retail is asking questions about specific items. A customer might want to know what fabric an article of clothing is made out of or how it should be cleaned, for example. An out-of-the-box model like ChatGPT won’t be able to help much. but if you’ve used a service like Quiq’s conversational CX platform, it’s possible to finetune an LLM on your specific documentation. Such a model will be able to help customers find the answers they need.

These use cases are all centered around text-based interactions, but algorithms are getting better and better at both speech recognition and speech synthesis. You’ve no doubt had the distinct (dis)pleasure of interacting with an automated system that sounded very artificial and that lacked the flexibility actually to help you very much; but someday soon, you may not be able to tell from a short conversation whether you were talking to a human or a machine.

This may cause a certain amount of concern over technological unemployment. If chatbots and similar AI assistants are doing all this, what will be left for flesh-and-blood human workers? Frankly, it’s too early to say, but the evidence so far suggests that not only is AI not making us obsolete, it’s actually making workers more productive and less prone to burnout.

Computer Vision

Computer vision is the other major triumph of machine learning. CV algorithms have been created that can recognize faces, recognize thousands of different types of objects, and even help with steering autonomous vehicles.

How does any of this help with retail?

We already hinted at one use case in the previous paragraph, i.e. automatically identifying different items. This has major implications for inventory management, but when paired with technologies like virtual reality and augmented reality, it could completely transform the ways in which people shop.

Many platforms already offer the ability to see furniture and similar items in a customer’s actual living space, and there are efforts underway to build tools for automatically sizing them so they know exactly which clothes to try on.

CV is also making it easier to gather and analyze different metrics crucial to a retail enterprise’s success. Algorithms can watch customer foot traffic to identify potential hotspots, meaning that these businesses can figure out which items to offer more of and which to cut altogether.

Machine Learning

As we stated earlier, both natural language processing and computer vision are types of machine learning. We gave them their own sections because they’re so big and important, but they’re not the only ways in which machine learning will impact retail.

Another way is with increasingly personalized recommendations. If you’ve ever taken the advice of Netflix or Spotify as to what entertainment you should consume next then you’ve already made contact with a recommendation engine. But with more data and smarter algorithms, personalization will become much more, well, personalized.

In concrete terms, this means it will become easier and easier to analyze a customer’s past buying history to offer them tailor-made solutions to their problems. Retail is all about consumer satisfaction, so this is poised to be a major development.

Machine learning has long been used for inventory management, demand forecasting, etc., and the role it plays in these efforts will only grow with time. Having more data will mean being able to make more fine-grained predictions. You’ll be able to start printing Taylor Swift t-shirts and setting up targeted ads as soon as people in your area begin buying tickets to her show next month, for example.

Where are AI Assistants Used in Retail?

So far, we’ve spoken in broad terms about the ways in which AI assistants will be used in retail. In these sections, we’ll get more specific and discuss some of the particular locations where these assistants can be deployed.

In Kiosks

Many retail establishments already have kiosks in place that let you swap change for dollars or skip the trip to the DMV. With AI, these will become far more adaptable and useful, able to help customers with a greater variety of transactions.

In Retail Apps

Mobile applications are an obvious place to use recommendations or LLM-based chatbots to help make a sale or get customers what they need.

In Smart Speakers

You’ve probably heard of Alexa, a smart speaker able to play music for you or automate certain household tasks. Well, it isn’t hard to imagine their use in retail, especially as they get better. They’ll be able to help customers choose clothing, handle returns, or do any of a number of related tasks.

In Smart Mirrors

For more or less the same reason, AI-powered smart mirrors could have a major impact on retail. As computer vision improves it’ll be better able to suggest clothing that looks good on different heights and builds, for example.

What are the Benefits of Using AI in Retail?

The main reason that AI is being used more frequently in retail is that there are so many advantages to this approach. In the next few sections, we’ll talk about some of the specific benefits retail establishments can expect to enjoy from their use of AI.

Better Customer Experience and Engagement

These days, there are tons of ways to get access to the goods and services you need. What tends to separate one retail establishment from another is customer experience and customer engagement. AI can help with both.

We’ve already mentioned how much more personalized AI can make the customer experience, but you might also consider the impact of round-the-clock availability that AI makes possible.

Customer service agents will need to eat and sleep sometimes, but AI never will, which means that it’ll always be available to help a customer solve their problems.

More Selling Opportunities

Cross-selling and upselling are both terms that are probably familiar to you, and they represent substantial opportunities for retail outfits to boost their revenue.

With personalized recommendations, sentiment analysis, and similar machine-learning techniques, it will become much faster and easier to identify additional items that a customer might be interested in.

If a customer has already bought Taylor Swift tickets and a t-shirt, for example, perhaps they’d also like a fetching hat that goes along with their outfit. And if you’ve installed the smart mirrors we talked about earlier, AI will even be able to help them find the right size.

Leaner, More Efficient Operations

Inventory management is a never-ending concern in retail. It’s also one place where algorithmic solutions have been used for a long time. We think this trend will only continue, with operations becoming leaner and more responsive to changing market conditions.

All of this ultimately hinges on the use of AI. Better algorithms and more comprehensive data will make it possible to predict what people will want and when, meaning you don’t have to sit on inventory you don’t need and are less likely to run out of anything that’s selling well.

What are the Challenges of Using AI in Retail?

That being said, there are many challenges to using Artificial Intelligence in retail. We’ll cover a few of these now so you can decide how much effort you want to put into using AI.

AI Can Still Be Difficult to Use

To be sure, firing up ChatGPT and asking it to recommend an outfit for a concert doesn’t take very long. But this is a far cry from implementing a full-bore AI solution into your website or mobile applications. Serious technical expertise is required to train, finetune, deploy, and monitor advanced AI, whether that’s an LLM, a computer-vision system, or anything else, and you’ll need to decide whether you think you’ll get enough return to justify the investment.

Expense

And speaking of investment, it remains pretty expensive to utilize AI at any non-trivial scale. If you decide you want to hire an in-house engineering team to build a bespoke model, you’ll have to have a substantial budget to pay for the training and the engineer’s salaries. These salaries are still something you’ll have to account for even if you choose to build on top of an existing solution, because finetuning a model is far from easy.

One solution is to utilize an offering like Quiq. We have already created the custom infrastructure required to utilize AI in a retail setting, meaning you wouldn’t need a serious engineering force to get going with AI.

Bias, Abuse, and Toxicity

A perennial concern with using AI is that a model will generate output that is insulting, harmful, or biased in some way. For obvious reasons this is bad for retail establishments, so you’ll want to make sure that you both carefully finetune this behavior out of your models and continually monitor them in case their behavior changes in the future. Quiq also eliminates this risk.

AI and the Future of Retail

Artificial intelligence has long been expected to change many aspects of our lives, and in the past few years, it has begun delivering on that promise. From ultra-precise recommendations to full-fledged chatbots that help resolve complex issues, retail stands to benefit greatly from this ongoing revolution.

If you want to get in on the action but don’t know where to start, set up a time to check out the Quiq platform. We make it easy to utilize both customer-facing and agent-facing solutions, so you can build an AI-positive business without worrying about the engineering.

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