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4 Conversational AI Software Tools for CX and eCommerce in 2025—and How GenAI is Making Them Better

Posted on Mark Kowal

This comprehensive guide explores the four most impactful conversational AI software tools shaping the future of customer experience and eCommerce, helping businesses deliver exceptional experiences while optimizing operational efficiency.

What is conversational AI software?

Conversational AI software represents a technology stack that enables machines to understand, process, and respond to human language naturally and contextually. At its core, it combines several key components:

  • Machine Learning: Enables systems to learn from interactions and improve over time
  • Natural Language Processing (NLP): Helps computers understand human language
  • Natural Language Understanding (NLU): Interprets intent and context behind user inputs
  • Natural Language Generation (NLG): Produces human-like responses

Clearing up the confusion between conversational AI vs. GenAI vs. agentic AI: Why are we using the former term?

Before diving into specific tools, it’s essential to understand the distinction between conversational AI, agentic AI, and generative AI (GenAI). There’s a lot of confusion between these three terms, in part because of how fast AI is developing.

Conversational AI enables natural language interactions between users and systems. In contrast, agentic AI autonomously makes decisions and executes tasks. In fact, Agentic AI can independently decide what actions to take, persist in completing tasks, and adapt its approach based on outcomes—similar to how a human employee would work through a problem.

Generative AI, on the other hand, creates new content based on existing data patterns.

Think about it like this: Conversational AI is like a back-and-forth conversation between two friends, like you are used to experiencing with chatbots. You say something like “Hi, how are you?” I reply, ‘Fine, thanks, how are you?’ and we go on and on until the conversation stops.

But with GenAI, it’s similar to a ‘speak when spoken to’ situation: It’s up to you to ask me questions you want responses to. Generative experiences are typically not programmed to ask clarifying questions. And agentic AI, well, it’s a workhorse!

Nowadays, the term “conversational AI” tends to describe previous-generation technologies, while “agentic AI”, which Quiq is the leader in, is currently next-generation. We are using “conversational AI” here for a couple reasons: 1) Many people use “conversational AI” to describe AI software, even if they technically refer to GenAI or agentic AI; 2) The tools described here are not agentic, or autonomous decision-making, by default. But we’ve highlighted how generative or agentic elements make it more effective.

I want to be clear about all this because many people use these terms interchangeably, but they’re quite different, which is important. Forrester released a trends report called The State of Conversational AI, too. I’d encourage you to download and read to dive deeper into how the technology has changed and is projected to continue changing.

Okay, with that out of the way, let’s dive in.

Comparison table: Conversational AI vs. traditional chatbots

Even though conversational AI is not the latest and greatest AI out there, it’s still miles ahead of the basic chatbots of yesteryear, and the technology can still do a lot. Let’s look at a side-by-side comparison of where conversational AI elevated the previous tech.

Feature Conversational AI Traditional Chatbots
Learning Capability Continuous learning from interactions Static, rule-based responses
Language Processing Advanced natural language understanding Basic keyword matching
Contextual Understanding Maintains context across conversations Limited or no context retention
Personalization Adaptive and personalized responses Generic, pre-programmed responses
Complexity of Tasks Can handle complex queries and tasks Limited to simple, predefined tasks

Benefits of conversational AI software

By combining natural language processing with machine learning capabilities, conversational AI can provide intelligent, automated solutions that enhance both the customer experience and eCommerce business operations. Here’s a detailed look at the key benefits across different areas:

Benefits for CX

The customer experience landscape has been dramatically enhanced through conversational AI implementation. Here are the biggest benefits for CX:

  • Personalized experiences: Uses historical data and context to provide tailored recommendations and solutions.
  • Quick issue resolution: Handles common queries immediately, reducing resolution time and customer frustration.
  • Scalable support: Manages multiple conversations simultaneously, without compromising service quality.
  • Language support: Communicates in multiple languages, making services accessible to a global audience.
  • 24/7 availability: Provides instant support to customers around the clock, eliminating wait times and improving satisfaction.
  • Consistent interactions: Delivers uniform responses and maintains brand voice across all customer touch points.

Benefits for eCommerce

In the eCommerce sector, conversational AI has become a crucial tool for driving business growth and efficiency. Here are the primary benefits of conversational AI software for eCommerce:

  • Increased conversion rates: Guides customers through the purchase journey, addressing concerns in real-time to boost sales.
  • Reduced cart abandonment: Proactively engages with customers, shows exit intent, and resolves checkout issues. This proactive approach extends to reducing cart abandonment, as the AI can engage with customers showing exit intent and swiftly resolve any checkout issues that might arise.
  • Product discovery: Helps customers find relevant products through intelligent recommendations and natural conversation
  • Upselling opportunities: Suggests complementary products and premium options based on customer preferences, directly impacting revenue growth.
  • Cost efficiency: Reduces operational costs by automating routine customer interactions.
  • Data collection: Gathers valuable customer insights and shopping behavior patterns for business optimization.
  • Inventory management: Inventory management becomes more streamlined, with the AI providing real-time stock information and automated customer notifications about product availability.
  • Streamlined returns: Simplifies the returns process by guiding customers through procedures and policies.

These automated solutions continue to evolve, offering increasingly sophisticated capabilities that benefit both businesses and their customers. By implementing conversational AI, organizations can significantly improve their customer service operations, while driving sales and efficiency in their eCommerce platforms.

The 4 best conversational AI tools

Now that we’ve properly defined conversational AI and outlined the main benefits for CX and eCommerce, here are the four best conversational AI services and tools across both sectors.

Tool #1: Conversational eCommerce assistants

A conversational eCommerce assistant is a virtual tool designed to enhance the customer shopping experience by providing real-time support directly on your website through web chat or other business messaging channels. These assistants can help facilitate sales and improve customer engagement by offering a range of valuable features. Even last-generation conversational AI eCommerce assistants are equipped with capabilities such as:

  • Personalized product recommendations: Tailored suggestions based on a customer’s browsing history, preferences, or past purchases, helping them find exactly what they need.
  • Intelligent cart abandonment prevention: Proactively engaging with customers to remind them about items left in their cart and encouraging them to complete their purchase.
  • Real-time inventory updates: Ensuring customers have accurate information about product availability, reducing the chance of disappointment or frustration.
  • Seamless payment processing integration: Simplifying the checkout process with smooth and secure payment options, minimizing barriers to purchase.

However, while traditional conversational AI is effective, GenAI-powered tools take these capabilities to the next level. GenAI excels at contextualizing conversations, understanding customer needs in greater detail, and delivering offerings that feel more natural and personalized.

This enhanced ability to adapt and respond to individual shoppers makes GenAI an even more powerful tool for driving sales and creating positive customer experiences. (By the way: Check out how we’re harnessing both GenAI and agentic AI via next-generation AI agents).

Tool #2: Voice-activated AI bots

If you’re leading eCommerce, you’ve likely explored Voice Commerce, with tools like Amazon Alexa and Google Assistant leading the charge. Lots of people love the shopping experiences these robust bots offer, enhanced with AI.

Key features include:

  • Hands-free shopping experience
  • Natural language order processing
  • Voice-based product search and comparison
  • Integration with smart home devices

On the customer experience side, multimodal voice AI harnesses the latest tech in speech recognition with LLM-powered AI to create incredible, modern voice experiences with major cost reduction benefits for businesses. Not conversational AI, but impressive and worth checking out:

Tool #3: Multilingual AI chat solutions

For global businesses handling a high volume of support inquiries across multiple markets, AI-powered translation has become an invaluable tool. These solutions allow companies to break down language barriers while maintaining efficiency and quality in customer interactions. With the help of conversational AI tools, businesses can enjoy features such as:

  • Real-time translation in over 100 languages, enabling seamless communication with customers worldwide.
  • Context preservation, ensuring that the nuances and intent of conversations remain accurate across languages.
  • Automatic language detection, eliminating the need for customers to select their preferred language manually.
  • Consistent brand voice across languages, aligning your messaging and tone, no matter where your customers are located.

Thanks to advances in GenAI, these tools have evolved into far more powerful solutions, offering faster, smarter, and more accurate translations. For businesses aiming to expand globally, multilingual AI chat solutions are critical for delivering exceptional customer experiences while reducing operational challenges.

Tool #4: AI-powered training assistants

AI-powered training assistants transform the way employees learn and grow within organizations. While rule-based tools may work in certain applications, such as HR benefit matching, understanding-based tools take training to the next level by leveraging advanced AI capabilities.

These tools revolutionize employee training by offering:

  • Personalized learning paths: Tailored to each employee’s strengths, weaknesses, and learning pace, ensuring more effective skill development.
  • Real-time feedback and assessment: Providing instant insights to help employees understand their progress and areas for improvement.
  • Interactive scenario-based training: Simulating real-world situations to equip employees with practical skills and better decision-making abilities.
  • Progress tracking and reporting: Monitoring individual and team performance over time, allowing managers to identify trends and adjust strategies as needed.

According to Forrester’s report on The State of Conversational AI: “Conversational AI can reduce new-hire onboarding time from days to hours.” By combining AI technology with interactive and personalized learning, these tools enhance employee engagement and make training more impactful across various industries.

Interested in learning more? Check out how Quiq’s employee-facing AI assistants work—and discover how our technology helped one National Furniture Retailer Reduce Escalations to Human Agents by 33%.

Final thoughts on conversational AI software

Conversational AI might be a last-gen term, but conversational AI platforms can still be valuable for businesses aiming to deliver exceptional customer experiences while maintaining operational efficiency.

To stay competitive and future-proof your operations, consider strategically implementing these tools or any of their next-gen successors, starting with areas where they can provide the most immediate impact. Remember, the key to success lies in selecting the right tool, proper implementation, and continuous optimization.

If you need help with conversational AI, let us know. Or we’d be happy to get you up to speed with GenAI, and then agentic, depending on your organization’s needs.

Interested in exploring the next-generation of AI? Learn about AI Studio and agentic AI.

From Contact Center to AI Leader: Embracing AI to Upgrade CX (Webinar Recap)

Posted on Mark Kowal

The evolution of contact centers and customer experience (CX) has reached a pivotal moment. While traditional setups face mounting challenges, such as high agent turnover rates, system complexities, and skyrocketing customer expectations, forward-thinking businesses are turning to AI to flip the script. But how can companies integrate AI effectively, while ensuring it enhances both customer satisfaction and business outcomes, and delivers ROI?

This was the focus of a recent webinar I led with my colleague, Quiq VP of EMEA Chris Humphris, as we explored the future of customer service using agentic AI: From Contact Center to Agentic AI Leader: Embracing AI to Upgrade CX

Below, I break down the key insights and takeaways from the session to help you stay ahead in the age of AI-powered CX.

The case for AI in modern contact centers

Traditional Contact Centers Need an AI Revolution

Current challenges within contact centers

Contact centers today face several pain points that require immediate attention:

  • Integration and tech stack complexities

Legacy systems, disparate platforms, and inconsistent omnichannel experiences hinder operational efficiency. Two-thirds of contact centers report difficulties with technology integration and orchestration.

  • The productivity crisis

Spiraling agent turnover rates, averaging 30–45%, coupled with increasing complexity in customer interactions and high training costs, are pushing teams to their limits.

  • ROI barriers to AI adoption

While 84% of organizations plan to invest in AI, only 16% have successfully implemented it (Source). Confusion over ROI metrics, coupled with fears of disruption and limited expertise, often stalls projects.

The AI opportunity

AI presents a way out of these obstacles, offering tools that streamline workflows, enhance customer satisfaction, and deliver measurable ROI. Businesses that incorporate AI into their CX can:

  • Extend their reach with scalable self-service capabilities.
  • Inject intelligence into customer interactions, ensuring personalized and proactive engagements.
  • Gain insights from real-time analytics for better decision-making.

Agentic AI stands out as the next big step, promising improvement to automate complex tasks, to adapt, learn, and make contextual decisions akin to human intelligence, and support human agents on their job to improve performance and maintain consistency within the contact center.

Audience poll #1: Have you adopted AI already?

After outlining how AI stands to offer a major helping hand, I asked the audience if they’ve already adopted AI.

Poll Question #1

I was surprised to see that most respondents say they have adopted AI. But then I wanted to know from those who said they have not yet, what the timeline looks like.

Audience poll #2: What’s your current timeline for implementing AI in the contact center?

Poll Question #2

Unsurprisingly, those who have not yet implemented AI in their contact centers are looking to do so this year and start harnessing the many benefits we’ve highlighted.

Understanding agentic AI and its transformative potential

What is agentic AI?

Unlike static AI systems, agentic AI dynamically plans, executes, and adapts strategies to achieve outcomes, much like a skilled human employee. It:

  1. Autonomously executes multi-step, complex tasks.
  2. Changes tactics when initial approaches fail.
  3. Maintains contextual understanding and continuously learns from outcomes.

Types of agentic AI

Quiq’s platform facilitates agentic AI in three versatile forms:

  • AI Agents: Fully autonomous systems capable of managing customer queries and completing tasks, freeing up human agents for complex responsibilities.
  • AI Assistants: These support human agents by automating repetitive tasks, suggesting real-time responses, and enhancing service quality.
  • Agentic AI Services: Seamlessly integrate with existing workflows via APIs, allowing enterprises to add advanced AI capabilities to the other tools they already use without overhauling legacy systems.

These innovations allow businesses to tackle rising customer demands while maintaining operational efficiency.

Using AI to revolutionize CX across key areas

1. Customer service excellence

Through AI-powered solutions, businesses can enhance customer service by providing:

  • Seamless multi-channel experiences: AI integrates across platforms like WhatsApp, website live chats, and social media to ensure consistent support.
  • Intelligent escalations: When AI can’t resolve an issue, it transfers the case to a human agent with full contextual information, enabling smoother transitions.
  • Proactive outreach and updates: AI proactively sends reminders and notifications like, “Your subscription is renewing next week. Tap here to update payment info,” increasing engagement and retention.

2. Elevating agent productivity

AI doesn’t just improve customer-facing operations—it also empowers human agents by:

  • Automating mundane back-office tasks.
  • Providing real-time recommendations to handle inquiries more effectively.
  • Offering predictive insights on customer intent.

The result? Faster response times, improved accuracy, and greater job satisfaction among agents.

3. Driving data-driven personalization

AI’s ability to process large datasets in real-time ensures interactions are tailored to individual customers. By analyzing order history, browsing behavior, and past inquiries, AI can craft hyper-personalized responses. The result? Stronger customer relationships.

Case study highlight:

Customer Success Story

A leading flooring retailer in the UK leveraged AI on WhatsApp to redefine its customer journeys. Features included:

  • Proactive order updates and stock alerts.
  • AI-driven personalization based on preferences and past purchases.
  • Integration with multiple channels for consistent communication.

These efforts led to higher CSAT scores, faster resolutions, and increased revenue.

Overcoming barriers to AI adoption

Debunking ROI fears

For hesitant decision-makers, I’ll re-emphasize one critical insight: AI’s ROI becomes evident when implemented with clear objectives and measurable KPIs. Quiq’s quick-to-value solutions ensure businesses start seeing operational gains almost instantly.

Phasing implementation for success

Adopting AI doesn’t require an all-at-once approach. Start small, focusing on areas like customer service or self-service automation, where AI can deliver immediate wins. Once comfortable, scale adoption across more complex workflows.

Ensuring seamless human-AI collaboration

One common pitfall is neglecting how AI and human teams collaborate. Businesses must prioritize:

  • Smooth handoffs between AI and live agents.
  • Continuous learning opportunities where AI adapts based on human agent interactions.
  • Comprehensive training to ensure agents are equipped to leverage AI.

By bridging these gaps, organizations can future-proof their operations while setting themselves up as AI leaders.

Next steps for becoming an AI-powered CX leader

The road from a traditional contact center to an AI-powered CX leader has its hurdles, but the rewards far outweigh the challenges. Companies must stay focused on:

  1. Breaking down technical barriers through innovative platforms like Quiq.
  2. Investing in agentic AI to redefine operational efficiency.
  3. Starting with small, strategic AI interactions before scaling solutions to achieve omnichannel excellence.

Quiq’s agentic AI platform streamlines implementation, ensuring businesses can unlock the full value of AI without overhauling their existing systems. Businesses across industries—from eCommerce to retail—are already seeing the benefits of intelligent automation, proactive engagement, and personalized service at scale.

If you’re ready to move to the next generation of contact center and transform your CX, start your AI-powered transformations today. Visit Quiq’s AI Studio to explore how we can integrate scalable AI into your workflows.

Heads up! Your AI Agent Will Probably Trip Over at Least One of These Five Pitfalls

Posted on Max Fortis

Who could forget the beloved search engine butler, Jeeves? Launched in 1997, Ask Jeeves was considered cutting edge and different from other search engines because it allowed users to ask questions and receive answers in natural, conversational language — much like the goal of today’s AI agents.

Unfortunately, Jeeves just couldn’t keep up with rapidly evolving search technology and the likes of Google and Yahoo. While the site is still in operation, it’s no longer a major player in the search engine market. Instead, it functions primarily as a question-and-answer site and web portal, combining search results from other engines with its own Q&A content.

Like Jeeves, which once boasted upwards of a million daily searches in 1999, AI agents have become very popular, very fast. So much so that 80% of companies worldwide now feature AI-powered chat on their websites. And unless companies want their AI agents to experience the same fate as poor Jeeves, it’s critical they take proactive measures to avoid the obstacles and shortcomings that arise as AI continues to advance and customer expectations evolve.

In this blog post, we’ll cover:

  • How AI agents differ from traditional chatbots
  • Five challenges your AI agent is likely to face
  • Tips to help you navigate these roadblocks
  • Resources so you can dive in and learn more

What Is an AI Agent?

Before we dig into our five AI agent pitfalls, it’s critical to understand what an AI agent is and how it’s different from the first-generation AI chatbots we’re all familiar with.

To put it simply and in the context of CX, an AI agent combines the reasoning and communication power of Large Language Models, or LLMs, to understand the meaning and context of a user’s inquiry, as well as generate an accurate, personalized, and on-brand response. They can also interact with customers in a variety of other ways (more on that in a minute).

In contrast, an AI chatbot is rules-based and uses Natural Language Processing, or NLP, to try to match the intent behind a user’s inquiry to a single question and a specific, predefined answer. While some AI chatbots may use an LLM to generate a response from a knowledge base, these answers are often insufficient or irrelevant, because they still rely on the same outdated, intent-based process to determine the user’s request in the first place.

In other words, your customer experience is already behind the times if your company uses an AI chatbot rather than an AI agent! For more information about this distinction and how AI chatbots negatively impact your customer journey, check out this article.

Chatbot vs. AI Agent

AI Agent vs. AI Assistant

Another term you’re likely familiar with is “AI assistant.” AI agents offer information and services directly to customers to improve their experiences, and are also used to educate employees to elevate customer service. Meanwhile, AI assistants augment authentic or human agent intelligence to eliminate busy work and accelerate response times. A tool that automatically corrects a human agent’s grammar and spelling before they reply back to a customer is an example of an AI assistant.

AI Agent Pitfall #1: It Doesn’t Leverage Agentic AI

Because AI is advancing so rapidly, it’s easy to get confused by the latest terms and capabilities, especially when the vision and goal of each generation has been largely the same. But with the rise of agentic AI, it appears the technology is finally delivering on its ultimate promise.

While “AI agent” and “agentic AI” sound similar, they are not the same and cannot be used interchangeably. As we discussed in the previous section of this post, AI agents harness the latest and greatest AI advancements — or LLMs, GenAI, and agentic AI — to do a specific job, like interacting with customers across voice, email, and digital messaging channels.

LLMs offer language understanding and generation functionality, which GenAI models can use to craft contextually-relevant, human-like content or responses. Agentic AI can use both LLMs and GenAI to reason, make decisions, and take actions to proactively achieve specific goals. It helps to think of these three types of AI as matryoshka or nesting dolls, with LLMs being the smallest doll and agentic AI being the largest.

Understanding Agentic AI

Here at Quiq, we define agentic AI as a type of AI designed to exhibit autonomous reasoning, goal-directed behavior, and a sense of self or agency, rather than simply following pre-programmed instructions or reacting to external stimuli. Agentic AI systems may interact with humans in a way that is similar to human-human interaction, such as through natural language processing or other forms of communication.

An example of agentic AI might be an advanced personal assistant that not only responds to requests, but also proactively manages your schedule. Imagine an AI agent that notices you’re running low on groceries, checks your calendar for free time, considers your dietary preferences and budget, creates a shopping list, and schedules a delivery — all without explicit instructions.

It might even adjust these plans if it notices you’ve been ordering healthier foods lately or if your schedule suddenly changes. This kind of autonomous, goal-oriented behavior with genuine understanding and adaptation is what sets agentic AI apart from other AI systems.

Listen in as four industry luminaries discuss what agentic AI is, how it works, and what sets it apart from other AI systems.

[Watch the Webinar]

AI Agent Pitfall #2: It’s Siloed from Your CX Stack

Imagine if a sales team couldn’t see customers’ purchase history — how much harder would it be for them to up-sell? Or if the only shipping detail customer service could access was the estimated delivery date — how would they help customers track their orders? Well, just like a human agent, an AI agent is only as effective as the data it has access to.

From CRM to marketing automation platforms to help desk software, customer-facing teams use many technologies to manage client engagements and information. Today, most of these tools can pass information back and forth to help humans avoid these issues and provide exceptional customer experiences. However, many AI agents remain separate from the rest of the technology stack.

This renders them unable to provide customers with anything other than general information and basic company policies that can be found on the company’s website or knowledge base. Customers enter AI agent conversations expecting personalized assistance and to get things done, so receiving the same general information already available via helpdesk articles adds little value and leaves them disappointed and frustrated. These interactions must be passed to human agents, defeating the purpose of employing an AI agent in the first place.

Shatter This AI Agent Silo

Bridging this gap and ensuring your AI agent can provide the level of personalization modern consumers expect requires connecting it to the tools already in your tech stack. Even if they provide robust out-of-the-box integrations, your AI for CX vendor should still offer the customizations you need to ensure your AI agent fits seamlessly into your existing ecosystem and has access to the same information sources as your human agents.

It’s also important that these integrations are bi-directional, or that the AI agent can also pass any actions taken, newly collected data, or updated customer information back to the appropriate system(s). This helps prevent the creation of any new silos, especially between pre- and post-sales teams.

Last but not least, bake these integrations into the business logic and conversational architecture that guides your AI agents’ interactions. This gives them the power to automatically inform customer interactions with additional, personal attributes accessed from other CX systems, such as a person’s member status or most recent order, without having to explicitly ask, driving efficiencies and accelerating resolutions.

Learn more about this and three other major silos hurting your customers, agents,and business, plus tips for how to shatter them with agentic AI.

[Get the Guide]

AI Agent Pitfall #3: It Doesn’t Work Across Channels

Just over 70% of customers prefer to interact with companies over multiple channels, with consumers using an average of eight channels and business buyers using an average of ten. These include email, voice, web chat, mobile app, WhatsApp, Apple Messaging for Business, Facebook, and more.

This alone presents a major hurdle for IT teams that want to build versus buy AI agents. But modern consumers want more than just the ability to interact with companies using their channel of choice. They also want to engage using more than one of these channels in a single conversation — maybe even simultaneously — or over the course of multiple interactions, without having to reestablish context or repeat themselves.

Unfortunately, many AI for CX vendors still fail to support these types of multimodal and omnichannel interactions. What’s more, the capabilities they support for each channel are also limited. For example, while a chatbot may work on Instagram Direct Messaging for Business, it may not support rich messaging functionality like buttons, carousel cards, or videos. This prevents companies from meeting customers where they are and offering them the best experiences possible, even one channel at a time.

Types of channels

How Top Brands Avoid This Roadblock

A leading US-based airline saw a large percentage of its customers call to reschedule their flights versus using other channels. While cancelling their current flight was fairly straightforward, the company’s existing IVR system made it cumbersome to select a new one. Customers had to navigate through multiple menus and listen to long lists of flight options, often multiple times.

The airline decided to shift to a next-generation agentic AI solution that enabled them to easily build and manage AI agents across channels using a single platform. Their new Voice AI Agent can now automatically understand when a customer is trying to reschedule a flight, and offer them the ability to review their options and select a new flight via text. This multimodal approach provides customers with a much more seamless experience.

See how Quiq delivers seamless customer journeys across voice, email, and messaging channels

[Watch the Video]

AI Agent Pitfall #4: It Hallucinates

AI hallucinations are often classified as outlandish or incorrect answers, but there’s a lesser known yet more common type of hallucination that happens when an AI agent provides accurate information that doesn’t effectively answer the user’s question. These misleading statements can be more problematic than obvious errors, because they are trickier to identify — and prevent.

For example, imagine a customer asks an AI agent for help with their TV. The agent might provide perfectly valid troubleshooting steps, but for a completely different TV model than the one the customer owns. So while the information itself is technically correct, it’s irrelevant to the customer’s specific situation because the AI failed to understand the proper context.

The most thorough and reliable way to define a hallucination is as a breach of the Cooperative Principle of Conversation. Philosopher H. Paul Grice introduced this principle in 1975, along with four maxims that he believed must be followed to have a meaningful and productive conversation. Anytime an AI agent’s response fails to observe any of these four maxims, it can be classified as a hallucination:

  1. Quality: Say no more and no less than is necessary or required.
  2. Quantity: Don’t make any assumptions or unsupported claims.
  3. Manner: Communicate clearly, be brief, and stay organized.
  4. Relevance: Keep comments closely related to the topic at hand.

Protect Your Brand From Hallucinations

Preventing these hallucinations is more than a technical task. It requires sophisticated business logic that guides the flow of the conversation, much like how human agents are trained to follow specific questioning protocols.

After a user asks a question, a series of “pre-generation checks” happen in the background, requiring the LLM to answer “questions about the question.” For example, is the user asking about a particular product or service? Is their question inappropriate or sensitive in nature?

From there, a process known as Retrieval Augmented Generation (RAG) ensures that the LLM can only generate a response using information from pre-approved, trusted sources — not its general training data. Last but not least, before sending the response to the customer, the LLM runs it through a series of “post-generation checks,” or “questions about the answer,” to verify that it’s in context, on brand, accurate, etc.

 

Learn about the three types of AI hallucinations, how they manifest themselves in your customer experience, and the best ways to help your AI agent avoid them.

[Watch the Full Video]

AI Agent Pitfall #5: It Doesn’t Measure the Right Things

Nearly 70% of customers say they would refuse to use a company’s AI-powered chat again after a single bad experience. This makes identifying and remedying knowledge gaps and points of friction critical for maximizing a brand’s self-service investment, reputation, and customer loyalty.

Yet gaining insight into anything outside of containment and agent escalations is tedious and imprecise, even for something as high-level as contact drivers. At worst, it requires CX leaders to manually parse through and tag each individual conversation transcript. At best, they must make sense of out-of-the-box reports or .csv exports that feature hundreds of closely related, pre-defined intents.

What’s more, certain events like hotel bookings or abandoned shopping carts are tracked and managed in other systems. Getting an end-to-end view of path to conversion or resolution often requires combining data from these other tools and your AI for CX platform, which is typically impossible without robust, bi-directional integrations or data scientist intervention.

What To Do About It

Since next-generation AI agents harness LLMs for more than just answer generation, they can also leverage their reasoning power for reporting purposes. Remember the pre- and post-generation checks these LLMs run in the background to determine whether users’ questions are in scope, and sufficient evidence backs their answers? These same prompts can be used to understand conversation topics and identify top contact drivers, which are then seamlessly rolled up into reports.

It’s also possible to build funnels, or series of actions, intended to lead to a specific outcome — even if some of these events happen in other tools. For example, picture the steps that should ideally occur when an AI agent recommends a product to a customer. A product recommendation funnel would enable the team to see what percentage of customers provide the AI agent with their budget and other relevant information, click on the agent’s recommendation, and ultimately check out.

The ability to easily see where customers are dropping off or escalating to a human agent gives CX teams actionable insight into which areas of the customer journey need to be fine-tuned. From there, they can click into individual conversation transcripts at each stage for further detail. For example, do customers have questions during the checkout process? Is there insufficient knowledge regarding returns or exchanges? Custom, bi-directional integrations with other CX tools also make it possible to pass the steps happening in the AI for CX platform back to a CRM or web analytics platform, for example, for additional analysis.

Uncover all the ways your chatbot may be killing your customer journey — and the steps you can take to put a stop to it.

[Read the Guide]

Ensure Your AI Agent Is Always Cutting Edge

If you’re still thinking about Jeeves and wondering what happened to him (and we know you are), he was officially “retired” in 2006, just one year after the company re-branded to Ask.com. Staying on the forefront of technology and ensuring your company consistently offers customers a cutting-edge experience isn’t easy — especially in the rapidly evolving world of AI.

That’s why leading companies rely on Quiq’s advanced agentic AI platform to guarantee their AI agents are always ahead of the curve. It offers technical teams the flexibility, visibility, and control they crave to build secure, custom experiences that satisfy business needs, as well as their desire to create and manage AI agents. At the same time, it saves time, money, and resources to handle the maintenance, scalability, and ecosystem required for CX leaders to deliver impactful AI-powered customer interactions.

We would love to show you Quiq in action! Schedule a free, personalized demo today.

Enterprise AI Chatbot Solutions are Failing Businesses: Why Agentic AI is the Path Forward

Posted on Mark Kowal

The integration of AI into enterprise operations is no longer a futuristic concept—it’s the present. From customer service to supply chain management, enterprises are adopting AI at an unprecedented rate, transforming workflows and outcomes. The global AI chatbot market is expected to be worth $455 million by the end of 2027, underscoring its growing importance. But while conventional AI chatbots have proven beneficial, they are no longer enough in an era demanding higher adaptability, smarter decision-making, and process integration.

Enter agentic AI, the next leap in enterprise technology. Evolving beyond chatbots, agentic AI agents offer enterprises proactive and autonomous solutions designed to optimize operations across departments.

This blog will explore the limitations of traditional enterprise AI chatbot solutions, introduce agentic AI as a transformational catalyst, and highlight how enterprise leaders can leverage it for sustained competitive advantage.

What is an enterprise AI chatbot solution?

Enterprise AI chatbot solutions are software platforms driven by artificial intelligence, natural language processing (NLP), and machine learning (ML) to automate customer interactions and internal processes. With natural language understanding (NLU), these chatbots can interpret customer intent, offer personalized responses, and escalate complex issues to human agents.

Legacy conversational AI in enterprise AI chatbot solutions

Conversational AI in enterprise solutions represents technology that enables natural, human-like interactions between businesses and their customers through intelligent chatbots and virtual assistants. These systems combine the technologies described above—Natural Language Processing (NLP), Machine Learning, and Deep Learning—to understand, process, and respond to human language in context.

But as we will see, agentic AI takes over from conversational AI to handle complex dialogues, remember conversation history, understand user intent, and provide personalized responses across multiple channels and languages in a way that prior-gen AI could not. These solutions can integrate with existing business systems (like CRM, ERP, and knowledge bases) to automate customer service, sales support, and internal operations, while continuously learning from interactions to improve accuracy and effectiveness.

Core features of enterprise AI chatbot solutions

1. Handling high volumes of requests

Enterprise chatbots aim to manage thousands of simultaneous interactions, offering round-the-clock availability without human intervention.

2. Escalation to human agents

When complex issues arise, chatbots transfer customers to live agents without losing conversation context for continuity and smooth interactions.

3. Integration with other enterprise tools

Integrating AI chatbots with existing tech stacks improves efficiency and customer experience. By connecting with tools like CRMs, ERPs, HR systems, and helpdesk software, chatbots access data to deliver personalized, accurate responses. For instance, they can check inventory, update orders, or enable targeted upselling, streamlining operations and enhancing service quality for customers and employees.

4. Support for internal processes

Beyond customer service, chatbots help employees with onboarding, training, and data collection, making them indispensable for growing enterprises.

Benefits of enterprise AI chatbot solutions

Cost savings

Automating repetitive tasks reduces reliance on human agents, leading to savings in labor costs.

Enhanced operations

Chatbots streamline workflows, reduce wait times, and improve customer satisfaction scores.

Scalable and consistent service

Whether answering FAQs or managing complex queries, these bots offer consistent service quality at scale.

However, despite their utility, traditional enterprise AI chatbots remain reactive—responding to instructions, but unable to anticipate problems or dynamics. This is where agentic AI paves the way forward.

From chatbots to agentic AI for enterprises

Agentic AI represents an evolution in enterprise artificial intelligence. While chatbots are reactive tools limited to predefined interactions, agentic AI agents are capable of proactive decision-making and autonomous action. With capabilities rooted in real-time adaptation, agentic AI has redefined AI’s role in the enterprise landscape.

Chatbots vs. agentic AI agents

Reactive vs. proactive

Chatbots react to user queries; agentic AI anticipates needs before they are expressed. For example, instead of merely answering a customer’s inquiry about delayed shipments, agentic AI could autonomously identify delays, notify affected customers, and initiate remediation.

Static decision-making vs. dynamic learning

Where chatbots rely on static rules, agentic AI improves continuously by learning from interactions, refining its predictive capabilities.

Siloed functionality vs. cross-departmental efficiency

Traditional chatbots typically serve a single function (e.g., customer service). Agentic AI spans departments, breaking silos by automating workflows in HR, supply chains, marketing, and more.

Cost vs. ROI

Agentic AI is already providing faster time-to-value than last-gen enterprise AI chatbot solutions. While implementing agentic AI requires an initial investment in technology and training, the returns justify the expenditure. Organizations typically see ROI through reduced operational costs, increased efficiency in process completion, higher customer satisfaction scores, and improved employee productivity.

When evaluating costs, consider not just platform pricing, but also integration expenses, training requirements, and maintenance—then weigh these against projected gains in automation, reduced error rates, faster resolution times, and the ability to scale operations without proportional increases in headcount.

Practical applications of agentic AI in enterprises

1. Customer service

Agentic AI can revolutionize customer service by going beyond simply answering customer queries. Imagine an AI that not only resolves issues efficiently, but also analyzes customer sentiment, behavior, and usage patterns to predict potential churn well in advance.

By identifying dissatisfied customers, it can automatically trigger personalized retention efforts, such as offering discounts, tailored recommendations, or proactive solutions, ensuring a more seamless and satisfying customer experience while boosting loyalty.

2. Human resources

Agentic AI can significantly streamline and enhance human resources operations. For example, it can handle the initial stages of hiring by screening resumes and applications for relevant skills and experience, thereby reducing the workload of HR teams.

It can also manage interview scheduling, ensuring candidates and hiring managers are aligned with minimal manual intervention. Once employees are onboarded, agentic AI can be used to monitor engagement and morale through sentiment analysis of internal communications or surveys, helping HR teams identify potential issues, such as burnout or dissatisfaction, before they escalate. This proactive approach fosters a healthier and more motivated workforce.

3. Supply chain management

In the realm of supply chain management, agentic AI can help businesses maintain agility and cost-efficiency. By analyzing historical data, market trends, and real-time inputs, it can accurately anticipate demand surges and adjust inventory levels dynamically to prevent shortages or overstocking. This is particularly valuable during peak seasons or unforeseen disruptions.

Moreover, agentic AI can optimize logistics by suggesting the most efficient routes and delivery schedules, reducing costs and improving supply chain performance. By automating these complex processes, businesses can react faster to changes in demand and ensure smoother operations.

These examples illustrate why enterprises can no longer rely solely on static chatbot solutions. Agentic AI offers dynamic, intelligent, and proactive capabilities that go beyond traditional automation, driving better outcomes across various business functions. Investing in these advanced AI solutions is becoming essential for staying competitive.

9 key features that set agentic AI apart in enterprise applications

To fully grasp agentic AI’s potential, it’s essential to understand the distinct features that differentiate it from its chatbot predecessors.

1. Contextual understanding

Agentic AI excels at maintaining context across multi-turn conversations, enabling more natural, human-like interactions compared to general chatbots, which often reset or lose track of context.

2. Proactive adaptability

Agentic AI evolves dynamically by analyzing patterns, allowing it to predict user needs and act without user prompting. For example, an agentic AI might automatically notify customers of service disruptions and provide alternatives.

3. Enhanced decision-making

Agentic AI provides real-time data-driven insights, enabling businesses to act swiftly and effectively. By analyzing patterns, it identifies opportunities and offers strategic recommendations.

4. Scalability without compromise

Despite handling vast interactions, agentic AI maintains the precision and personalization that differentiate high-quality customer experiences from generic ones.

5. Dynamic integrations

With the ability to integrate into multiple systems—be it CRMs or ERPs—agentic AI streamlines sophisticated workflows and data sharing, and facilitates cross-departmental communication effortlessly.

6. Multilingual capabilities

Designed for global enterprises, agentic AI can carry out region-specific conversations in multiple languages, ensuring effective communication across borders.

7. Security and compliance

Given the growing scrutiny of AI technologies, agentic AI comes with built-in safeguards to ensure user data is protected and compliance thresholds are met.

8. Human handoff recognition

Unlike basic chatbots that can create frustrating experiences when escalating to human agents, agentic AI possesses sophisticated recognition capabilities to identify when human intervention is necessary.

The system can intelligently determine the complexity and emotional nuance of interactions, seamlessly transferring conversations to human agents at the optimal moment, while providing them with full context and relevant customer data to ensure a smooth transition.

9. Learning and adaptation

Agentic AI continuously learns and adapts from interactions, improving over time and delivering increasingly accurate and efficient responses.

How to get started with agentic AI

Transitioning from conventional chatbot solutions to agentic AI may seem daunting, but it can be achieved with a structured approach.

Step 1: Conduct a needs assessment

Evaluate your enterprise’s current processes and identify areas where greater autonomy and efficiency are required.

Step 2: Choose the right agentic AI solution

You’ll need to decide whether to build or buy your AI, or buy-to-build. Prioritize solutions like Quiq’s AI Studio, which focus on AI practices like scalability, integration, and observability.

Step 3: Plan for phased implementation

Adopt a phased strategy to minimize operational disruptions during the transition from traditional tools to agentic AI systems.

Step 4: Train your teams

Equip employees with the resources and skills needed to leverage agentic AI effectively within their workflows.

Step 5: Monitor and optimize

Continuously analyze the impact of agentic AI on KPIs like cost savings, customer satisfaction, and employee productivity. Use this data to refine operations.

Agentic AI is the strategic advantage of tomorrow

The transition from basic enterprise AI chatbot solutions to the cutting-edge potential of agentic AI has begun. Enterprises that adopt this new technology will unlock operational efficiencies, improve customer experiences, and gain competitive advantages that were once unimaginable.
Agentic AI is not just a tool—it’s a strategy for building future-ready enterprises prepared for the demands of a dynamic business ecosystem.

Apple Business Updates – A New Way To Proactively Engage Customers on Apple Messages for Business

Posted on Max Fortis

In mid-September 2024, Apple announced an exciting improvement to Messages for Business called Business Updates that will allow your business to proactively contact your customers in specific use cases—improving the customer experience, security, and making it easier for you to connect with your customers.

What is Apple Messages for Business?

As you’re no doubt aware, one of the most used apps on an iPhone is the Messages app. Apple Messages for Business is the technology that makes it possible for businesses to interact with their customers using Apple Messages. For customers, their conversations with businesses live alongside their other messages with other Apple devices (blue blurbs) and SMS messages (green blurbs) and work just like any other conversation.

This is a powerful way for contact centers like yours to reach the more than 1 billion people worldwide who use iPhones for daily communication, so it’s worth paying careful attention to.

Apple’s Big Announcement – Business Updates

Business Updates will allow you to reach out to your customers proactively, in a private and secure way, utilizing just their phone number and Apple’s pre-approved templates.

This will have positive impacts on both customer experience and your business. As you can see, there’s no downside here—which is why we’re so excited about it!

1. How will Business Updates Improve Customer Experiences?

Previously, Apple provided a world-class branded experience for businesses to message with their customers right inside the Messages app everyone is used to. In order to prevent spam and protect privacy, Apple previously required that customers send the first message. This limited the use cases for Apple Messages for Business to inbound customer support questions. As compared to SMS, this eliminated the ability to send proactive notifications, such as order updates.

Apple Business Updates lets businesses send proactive messages to customers—and it protects against spam by only allowing this option for approved use cases. This is great for business, too: a little over 60% of customers have stated they want businesses to be more proactive in reaching out, and (in a charming coincidence), offering text messaging can reduce per-customer support costs by about 60%. It’s nice when things work out like that!

Let’s say a little bit more about those use cases. Initially, Apple is focused on sending proactive, business-initiated messages related to orders, but a “Connect Using Messages” notification is also supported, which businesses can use to switch phone calls to Apple Messages.

The data indicates that IVR is a sensible self-service option, but this ability to switch will give customers the choice to switch channels from a call to text messaging, meaning you can better meet them where they are and meet their preferences.

This is all done using templates. The full list can be found in Apple’s documentation, but here are a few samples. The first two are examples of “Connect Using Messages” which could be used to offer a customer the option to switch a phone call to messages:

Apple Messages for Business

2. How will the Apple Messages for Business Update Improve Your Business?

Now, let’s turn to the other side of the equation, the impact of the announcement on your business operations.

Apple has released Business Updates in iOS 18, the newest iPhone operating system that was announced in September 2024, allowing businesses that work with an Apple Messaging Service Provider (MSP), like Quiq, to initiate a conversation with a customer from their branded experiences. Order updates and converting calls to messaging (discussed above) are two obvious early use cases.

Consistent with Apple’s commitment to security, Apple does not read messages or store conversations. In a world more and more besieged by data breaches, hacks, and invasions of privacy, your users need not fear that Apple is using the messages inappropriately.

A final note: Android devices, or devices that do support Apple Messages for Business, will automatically fall back to SMS when messages are sent in Quiq. This means that you can configure your business processes to send notifications to all customers and Quiq will make sure they are delivered on the best possible channel.

The Future of Apple Messages for Business

Contact centers and CX teams are always looking for new ways to better meet customer needs, and this announcement opens up some exciting possibilities. You can now reach out in more ways and integrate more robustly with the rest of the Apple ecosystem, leading to a reduction in distraction and search fatigue for your users—and a reduction in expenses for you.

If you want to learn more about how Quiq enables Apple Messages for Business, you can do that here.

Reinventing Customer Support: How Contact Center AI Delivers Efficiency Like Never Before

Posted on Michael Hartsog

Contact centers face unprecedented pressure managing sometimes hundreds of thousands of daily customer interactions across multiple channels. Traditional approaches, with their rigid legacy systems and manual processes, often buckle under these demands, leading to frustrated customers and overwhelmed agents. This was certainly the case during the past few years, when many platforms and processes collapsed under the weight of astronomical volumes due to natural disasters and other unplanned events.

So we set out to build a solution to tackle these pressures.

Our solution? Contact center AI – an agentic AI-based solution that transforms how businesses handle customer support.

In this article, I will give you a lay of the contact center AI land. I’ll explain what it is and how it’s best used, as well as ways to start implementing it.

What is contact center AI?

Contact center AI represents a sophisticated fusion of artificial intelligence and machine learning technologies designed to optimize every aspect of customer service operations. It’s more than just basic automation—it’s about creating smarter, more efficient systems that enhance both customer and agent experiences.

This advanced technology incorporates tools like Large Language Models (LLMs), which allow it to understand and respond to customer queries in a conversational and human-like manner. It also leverages real-time transcription, allowing customer interactions to be recorded and analyzed instantly, providing actionable insights for improving service quality. Additionally, intelligent task automation streamlines repetitive tasks, freeing agents to focus on more complex customer needs.

By understanding customer intent, analyzing context, and processing natural language queries, contact center AI can even make rapid, data-driven decisions to determine the best way to handle every interaction.

Whether routing a customer to the right department or providing instant answers through AI agents, this technology ensures a more dynamic, responsive, and efficient customer service environment. It’s a game-changer for businesses looking to improve operational efficiency and deliver exceptional customer experiences.

AI-powered solutions for contact center challenges

1. Managing high volumes efficiently

During peak periods, managing high customer interaction volumes can be a significant challenge for contact centers. This is where contact center AI steps in, offering intelligent automation and advanced routing capabilities to streamline operations.

AI-powered systems can automatically deflect routine inquiries, such as negative value, redundant conversations—like ‘where’s my order?’ or account updates—to AI agents that provide quick and accurate responses. This ensures that customers get instant answers for simpler questions without wait.

Meanwhile, human agents are free to focus on more complex or sensitive cases that require their expertise. This smart delegation not only reduces wait times, but also helps maintain high customer satisfaction levels by ensuring every interaction is handled appropriately. Each human agent has all the information gathered in the interaction at the start of the conversation, eliminating repetition and frustration.

2. Real-time AI to empower your agents

Injecting generative AI into your contact center empowers human agents by significantly enhancing their efficiency and effectiveness in managing customer interactions. These AI systems provide real-time assistance during conversations, suggesting responses for agents to send, as well as taking action on their behalf when appropriate—like adding a checked bag to a customer’s flight.
This gives agents the time to focus on issues that require human judgment, reducing the effort and time needed to resolve customer concerns. The seamless collaboration between AI and human agents elevates the quality of customer service, boosts agent productivity, and enhances customer satisfaction.

3. Improving complex case routing

Advanced AI solutions now integrate into various systems to streamline customer service operations. These systems analyze multiple factors, including customer history, intent, preferences, and the unique expertise of available agents, to match each case to the most suitable representative. Then, AI can analyze call data in real time, continuously optimizing routing processes to further enhance efficiency during high-demand periods.

By ensuring the right agent handles the right query from the start, these AI-driven systems significantly enhance first-call resolution rates, reduce wait times, and improve customer satisfaction. This not only boosts operational efficiency, but also fosters stronger customer loyalty and trust in the long term.

4. Enabling 24/7 customer support

Modern consumers expect round-the-clock support, but maintaining a full staff 24/7 can be both costly and impractical for many businesses—especially if they require multilingual global support. AI-powered virtual agents step in to bridge this gap, offering reliable and consistent assistance at any time of day or night.

These tools are designed to handle a wide range of customer inquiries, all while adapting to different languages and maintaining a high standard of service. Additionally, they can manage high volumes of inquiries simultaneously, ensuring no customer is left waiting. By leveraging AI, businesses can not only meet customer expectations, but also enhance efficiency and reduce operational costs.

4 key benefits of contact center AI

Now that we’ve touched on what contact center AI is and how it can help businesses most, let’s go into the top benefits of implementing AI in your contact center.

Contact Center AI-4-AI-benefits

1. Enhanced customer experience

AI is revolutionizing the customer experience through multiple transformative capabilities. By providing instant response times through always-on AI agents, customers no longer face frustrating queues or delayed support. These agentic AI agents deliver personalized interactions by analyzing customer history and preferences, offering tailored recommendations and maintaining context from previous conversations. And all this context is available to human agents, should the issues be escalated to them.

Problem resolution becomes more efficient through predictive analytics and intelligent routing, ensuring customers connect with the most qualified agents for faster first-call resolution.

The technology also maintains consistent service quality across all channels, offering standardized responses and multilingual support without additional staffing, even during peak times. AI takes customer service from reactive to proactive by identifying potential issues before they escalate, sending automated reminders, and suggesting relevant products based on customer behavior.

Perhaps most importantly, AI enables a seamless customer experience across all channels, maintaining conversation context across multiple touch points and facilitating smooth transitions between automated systems and human agents. This unified approach creates a more efficient, personalized, and satisfying customer experience that balances automated convenience with human expertise when needed.

2. Boosted agent productivity

AI automation significantly enhances agent productivity by taking over time-consuming routine tasks, such as call summarization, data entry, and follow-up scheduling.

By automating these repetitive processes, agents can save significant time, giving them more freedom to engage with customers on a deeper level. This shift allows agents to prioritize building meaningful relationships, addressing complex customer needs, and delivering a more personalized service experience, ultimately driving better outcomes for both the business and its customers.

3. Cost savings

Organizations can significantly cut operational expenses by leveraging automated interactions and improving agent processes. Automation allows businesses to handle much higher volumes of customer inquiries without the need to hire additional staff, reducing labor costs.

Optimized processes ensure that agents are deployed effectively, minimizing downtime and maximizing productivity. Together, these strategies help organizations save money while maintaining high levels of service quality.

4. Increased (and improved) data insights

Analytics into AI performance offers businesses a deeper understanding of their operations by delivering actionable insights into customer interactions, agent performance, and operational efficiency.

These data-driven insights help identify trends, pinpoint areas for improvement, and make informed decisions that enhance both service quality and customer satisfaction. With continuous monitoring and analysis, businesses can adapt quickly to changing demands and maintain a competitive edge.

Implementation tips to start your contact center AI

If you want to add AI to your contact center, there are a handful of important decisions you need to make first that’ll determine your approach. Here are the most important ones to get you started.

1. Define your business objectives

Begin by assessing specific challenges and objectives, so that you can identify areas where automation could have the most significant impact later on—such as streamlining processes, reducing costs, or improving customer experiences.

Consider how AI can address these pain points and align with your long-term goals, but remember to start small. You just need one use case to get going. This allows you to test the solution in a controlled environment, gather valuable insights, and identify potential challenges.

2. Identify the best touch points in your customer journey

After you define your business objectives, you’ll want to identify the touch points within your customer journey that are best for AI. Within those touch points are End User Stories that will help you determine the data sources, escalation and automation paths, and success metrics that will lead you to significant outcomes. Our expert team of AI Engineers and Program Managers will help you map out the correct path.

3. Decide how you’ll acquire your AI: build, buy, or buy-to-build?

When choosing AI solutions, ensure they align with your organization’s size, industry, and specific requirements. Look at factors such as scalability to accommodate future growth, integration capabilities with your existing systems, and the level of vendor support offered.

It’s important to consider the solution’s ease of use, cost-effectiveness, and potential for customization to meet your unique needs. Another critical factor is observability, so you can avoid “black box AI” that’s nearly impossible to manage and improve.

You’ll also need to evaluate whether it’s best to buy an off-the-shelf solution, build a custom AI system tailored to your needs, or opt for a buy-to-build approach, which combines pre-built technology with customization options for greater flexibility.

4. Prep for human agent training at the outset

Invest in robust training programs to equip agents with the knowledge and skills needed to work effectively alongside AI tools. This includes developing expertise in areas where human input is crucial, such as managing complex emotional situations, problem-solving, and building rapport with customers.

5. Plan for integration and compatibility

Remember: Your AI will only be as good as the data and systems it can access. Verify compatibility with your existing systems, like CRM, ticketing platforms, or live chat tools. Integration to these systems is critical to the success of your contact center AI solution.
You also want to plan how AI will seamlessly integrate into human agents’ daily tasks without disrupting their workflows, and include all data within your project scope.

6. Establish monitoring and feedback loops

Before making any changes to your contact center, benchmark KPIs like first-call resolution, average handling time, and customer sentiment. Then regularly update and retrain the AI based on human agent and customer feedback to experiment and make the most critical changes for your business.

7. Plan for scalability

Implement AI solutions in phases, beginning with just one or two specific use cases. Look for solutions designed to help your business scale by accommodating different communication channels and adapting to evolving technologies.
By focusing on skills that complement AI capabilities, agents can provide a seamless, empathetic, and personalized experience that enhances customer satisfaction.

Final thoughts on contact center AI

Contact center AI represents a true organizational transformation opportunity in customer support, offering unprecedented ways to improve efficiency while enhancing customer experiences. Rather than replacing human agents, it empowers them to work more effectively, focusing on high-value interactions that require emotional intelligence and complex problem-solving skills.

The future of customer support lies in finding the right balance between automated efficiency and human touch. Organizations that successfully move from a conversational AI contact center to fully generative AI experiences will see significant lifts in key metrics and will be well-positioned to meet evolving customer expectations.

Highlights from My Build vs. Buy Discussion with TTEC: How to Make the Right Strategic Choice for Your Organization

Posted on Mike Myer

As the founder of Quiq and a veteran in the CX technology space, I recently had the pleasure of joining TTEC Digital‘s Experience Exchange Series over on LinkedIn Live to discuss one of the most pressing questions facing enterprises today:

Should organizations build their own AI solutions or buy existing ones?

In my conversation with Tom Lewis, SVP of Consulting at TTEC Digital, we explored this complex decision-making process and its implications for customer experience success. Here’s an overview of our discussion —and the highlights of our conversation if you missed it.

My key takeaways:

  1. Assess your organization’s capabilities and resources honestly before deciding to build or buy
  2. Ensure strong collaboration between CX and IT teams
  3. Prioritize knowledge and data quality and governance for both building and buying
  4. Consider a hybrid build and buy approach when appropriate
  5. Maintain focus on risk management and compliance
  6. Stay adaptable as technology evolves and keep your eye on prize AKA CX outcomes

Understanding the AI build vs. buy dilemma.

The rapid advancement of AI technology has created both opportunities and challenges for enterprises. While the promise of AI to transform the customer experience is clear, the path to implementation isn’t always straightforward. Organizations must carefully evaluate their resources, capabilities, and objectives when deciding between building custom AI solutions or purchasing existing platforms.

When considering the build approach, organizations gain complete control over their AI solution and can tailor it precisely to their needs. However, this comes with significant investments in time, talent, and resources. During our discussion, I emphasized that building in-house requires not just initial development capabilities, but ongoing maintenance and governance of the system.

On the buy side, organizations can benefit from immediate deployment, proven solutions, and regular updates from vendors who specialize in AI technology. The trade-off here might be less customization and potential dependency on third-party providers.

Bridging the gap with IT.

One crucial aspect we explored was the importance of alignment between CX leaders and IT departments. Success in AI implementation requires a collaborative approach where both teams understand:

  • Technical requirements and limitations
  • Integration capabilities with existing systems
  • Data security protocols
  • Scalability needs

I shared that the most successful implementations often occur when CX and IT teams establish clear communication channels and shared objectives early in the process.

Data and knowledge are the foundations of AI success.

Regardless of the build or buy decision, data preparation and having the right knowledge in your knowledge base to train the AI is crucial. Organizations need to:

  • Audit existing data quality and accessibility
  • Establish data governance frameworks
  • Ensure compliance with privacy regulations
  • Create clear data management protocols

During our conversation, I stressed that the quality of AI outputs directly correlates with the quality of input data. We recently released a guide on 3 Simple Steps to Get Your CX Data Ready for Quiq — I highly recommend you check that out for more actionable tips on data readiness.

Don’t let perfect be the enemy of good.

Many CIOs are concerned that it’ll take years to prepare their knowledge and data for AI. To that, my advice is: ‘Don’t let perfect be the enemy of good.’ You’ve got to start somewhere, and there is sure to be a crawl-walk-run framework you can devise with the data available to you now. It’s all about identifying and isolating a first use case.

My other piece of advice to CIOs who may be inundated with AI data concerns is to get your hands dirty and start using AI. Get started with an implementation that you don’t expect to last a whole five years, but are rather expecting to learn, iterate, and fail forward from. Now is the time to lean in, not sit back—even if things are not perfect to start.

Managing risk and ensuring compliance.

One thing I highlighted to Tom was that AI is not super valuable to your business all by itself. What makes it so is combining it with your company data. And that means risk management is paramount. Key considerations include:

  • Data privacy and security
  • Regulatory compliance
  • Transparency in AI decision-making

When planning for risk management and compliance, organizations can build trust by:

  1. Implementing robust security measures
  2. Maintaining clear communication about AI use
  3. Regular auditing and monitoring of AI systems
  4. Establishing clear governance frameworks

What happens when AI creates delightful experiences that customers want to interact with even more?

Tom’s theory is that if you make communicating with a brand effortless, consumers will interact with that brand more, not less. I not only agree, but I think it’s a goal brands should strive for.

Customers are more likely to self-service via AI-powered conversations than on the phone or in person, especially when it comes to the minutia of decision-making. For example, a customer is more likely to ask “What’s the sofa frame made out of?” when evaluating a furniture purchase over chat or their digital messaging channel of their choice. These types of questions are not usually the ones people pick up the phone or march into a physical store to ask, but they are the kind of conversations that lead to more purchases.

Similarly to how retail clerks are ever-present for customers to ask questions, AI that understands and responds to natural language can create even more delightful experiences that build relationships and brand loyalty while driving more revenue.

Final thoughts and looking forward.

The path to AI implementation in CX isn’t one-size-fits-all. Success lies in making informed decisions based on your organization’s unique needs, capabilities, and objectives. Whether building or buying, the focus should remain on delivering value to customers while managing risks and resources effectively.

That said, this technology is exciting, moving fast, and stands to deliver on its promises when done correctly. In fact, I think in the next five years, there’s going to be a shift in customer perception that AI provides even better service than human agents.

Want to listen to my whole conversation with Tom? Check out the replay here.

These are the Most Essential Customer Service Chat Platform Types

Posted on Mark Kowal

Customer service has come a long way since the first contact centers. These days, most complaints are dealt with through a customer service platform, and those platforms are often focused on using chat to communicate.

In this piece, we’ll discuss customer service chat platforms, comparing various features you should be looking out for and how they’ll help your business. Let’s get going!

Key takeaways:

  • Customer service remains incredibly important, and there’s a ton of data to back that up.
  • More and more companies are turning to customer service platforms because they help you fit better into busy customers’ schedules, leverage advanced tools like generative AI, and provide a world-class experience.
  • When evaluating a potential platform, there are a few things you should keep in mind. It should support many different channels (from email and voice to Facebook Messenger and Wechat), it should allow you to have humans do what they’re best at and AI systems do what they’re best at, and you should be able to build a CRM, helpdesk, knowledge base, or agent workspace on top of it without too much difficulty.

Why You Should Be Using a Customer Service Chat Platform

As professionals in the customer service arena, you probably already understand why it’s important to have a dedicated customer service platform–especially when it enables chat.

Still, it’s worth pointing out how compelling the data supporting this conclusion is. High-quality customer service is a huge selling point, as 4 out of 10 customers indicate they will split with a business over bad service (this figure has been considerably higher in prior years). All told, it’s been estimated that U.S. businesses have lost as much as $1.6 trillion when customers switch after having a bad encounter with customer service.

On the other hand, the data also paints a clear picture of how valuable your reputation can be, as 68% of survey respondents have said they will select your company if you are well-known for being helpful and courteous.

How Can a Chat-Based Customer Service Platform Help?

Having said that, here are some of the high-level benefits that accrue to companies that invest in good customer service chat platforms:

  • Meeting your customers where they’re at: A good platform will give you many ways of talking to your customers, which means they can choose the option that makes the most sense given their schedules and preferences. Consult the next section for more information on customer service chat channels.
  • You can integrate with generative AI: The big story of the past few years has been the rise of large language models, with concomitant changes in how contact centers run. You’ll want to vet your platforms to make sure they’ll allow you to leverage this technology–as well as related abilities like advanced natural language processing and sentiment analysis–as it’s rapidly becoming tablestakes.
  • Personalization: One of the other significant applications of modern machine learning is tailoring content to each person’s tastes (think of how Netflix or Spotify learn what shows and music you like and recommend more of it). Using something like retrieval-augmented generation (RAG), you can draw on a customer’s prior interactions or purchases to answer their questions in a way that speaks to their unique circumstances.
  • You can gather better data for KPIs: It’s hard to know whether your strategies are working unless you have the right data. Quality customer service platforms make gathering and analyzing customer data a breeze.
  • Your agents can be more productive: Between generative AI, automation features, self-serve, and live chat, a customer service chat platform can make your agents far more effective. This acts to boost customer satisfaction and agent satisfaction at the same time.

The Main Customer Service Chat Channels–And How to Use Them

Okay, now let’s discuss some available options. There’s a lot to cover here, so we’ve chosen to break things up like this:

  • Customer service channels are the actual applications you use to talk to your customers, like voice chat, email, SMS, Apple Messages for Business, or WhatsApp.
  • Customer service modes are what we call the ways you use your channels, with the three big categories being “speaking to a human agent,” “speaking with an AI agent,” and “speaking with a mix of both.”
  • Customer service platforms are what we call products like Quiq, Zendesk, etc., that serve these capabilities through a user interface.

In the sections below, we’ll discuss all three.

The Major Customer Service Channels

  • Web chat has become a standard-issue channel for many websites. Everyone from energy companies to car dealerships has a portal on their web pages that allows you to speak with either a human or AI agent, and this is something you should offer as well.
  • SMS and rich chat are both crucial channels, given how prevalent phones have become. Many customers find they prefer the convenience of dealing with agents through text messages, as this enables asynchronous conversations that fit better into busy lives. Through multimedia messaging service (MMS), SMS can send more than text–it can handle pictures, videos, emojis, audio, buttons, carousel cards, quick replies, and more. Rich messaging supports this natively, so you have multiple options for creating engaging interactions with your customers.
  • Voice (over the phone) was once one of the dominant ways of handling customer service issues. Though it has lost a lot of ground to texting, email, and chat, it’s still worth having a phone number people can call if they need to.
  • Email is, of course, thoroughly embedded into modern life, and is a common way of communicating. You’re almost certainly already supporting email, and if you’re not, you should be.
  • WhatsApp for Business–a messaging, voice, and video-calling application managed by Meta–boasts over two billion users worldwide. Given its widespread use for texting, it has become a favored platform for businesses looking to utilize instant messaging for commercial purposes.
  • Facebook Messenger is another service Meta provides that customer-centric businesses should consider, especially due to its large user base. Furthermore, companies implementing effective automation solutions on Facebook Messenger can respond to 80% of incoming customer inquiries and attain customer satisfaction rates up to 95%.
  • Instagram Messenger is important because many members of your audience are already engaging with businesses on the widely-used Instagram platform. Additionally, its appealing API facilitates the setup of automation, enhancing the efficiency of your business.
  • Apple Messages for Business greatly enhances your customers’ access to your business by providing a “message” icon on Maps, Siri, Safari, Spotlight, or your company website, along with other contact options such as QR codes. Furthermore, the widespread popularity of Apple and its product ecosystem underscores why you should be investing in its instant messaging capabilities.
  • WeChat is an extremely popular platform in China, boasting over a billion users. Similar to the other platforms we’ve discussed, WeChat offers a Business version that enables you to interact with customers, market your services, and process payments. However, setting up a WeChat Business account requires verification of your business license. If you have a substantial customer base in China, this could be the optimal choice.
Convenient  Asynchronous Scalable AI integrations
Web chat Limited Limited Yes Yes
SMS, MMS, and Rich Messaging Yes Yes Yes Yes
Voice Limited No No (but will become more scalable as AI voice models improve, see next column) Kind of (voice models are getting good, and the best vendors already have multimodal voice support.)
Email Limited Yes Yes Yes
Messaging Application (Facebook, WhatsApp, etc.) Yes Yes Yes Yes

The Major Customer Service Modes

Now, let’s discuss the basic ways of using a customer service communication channel.

Human agents
Obviously, the original way of solving issues was to have a human manually walk through all the steps. The customer service industry remains large in part because there are many aspects of the “human touch” that cannot be automated or replaced.

This is one huge advantage of using human agents, and it’s why we’ll still need humans for a long time. The downside, of course, is that people need breaks, time off, and to sleep, or else they’ll burn out.

AI agents
Until just a few years ago, the best we could do with AI systems was build brittle, rule-based chatbots that were of limited use. That’s no longer the case, and today’s language models can handle many queries directly, or else route to the most qualified human being.

The advantages of AI agents are that they can scale almost infinitely, can work all day and night, and can almost instantly handle complex tasks like translating between different natural languages. The disadvantage of AI agents is that there are still many issues that they simply can’t handle.

A mix of both
But the real power lies in what are commonly called “centaurs,” i.e., systems combining human ingenuity and flexibility with the scalability of AI.

This will look different for different situations, but a common centaur setup is to have routine issues solved by an AI agent, and everything else sent to a human based on its content and priority. Even then, however, AI can be of enormous use. Quiq’s Human-Agent Assistants, for example, use AI to suggest replies to humans, making both the AI component and the human component of the system more effective.

This means you can allow humans and machines to play to their respective strengths, but it can take time and effort to get everything configured and running smoothly.

Human agents AI agents Centaurs
Adaptable Yes Limited Yes
Infinitely scalable No Yes No
Needs breaks Yes No Yes (the human part)

How This Figures Into Picking the Right Customer Service Chat Platform

Now, we’ll close the section by discussing customer service chat platforms like Quiq.

Today, most of these platforms offer functionality like data tracking, CRMs, various communication channels, and native support for generative AI. If you choose a good one, you’ll be able to create reports about the effectiveness of different agents, how happy your customers are on average, how long it takes you to resolve issues, etc. They should also make it easy for you to incorporate your own data sources–a knowledge base, a product catalog, an API–so that you can personalize replies across channels.

Another thing to look out for is how easy a given platform makes it to set up a helpdesk or knowledge base (or to incorporate an external one). The former allows you to track tickets and manage your agent workflows (another thing that AI agents can help with), while the latter offers resources customers can use to resolve their own problems. Both are important.

CRM  Help Desk Knowledge Base Agent workspace
Supports customer segmentation and bespoke communication Yes Yes No Yes
Gives granular data insights into customers Yes No No Yes (but only if the vendor has robust data integrations)
Facilitates multi-channel communication Yes No No Yes
Supports templates Yes Yes Yes Yes
Enables automation Yes Yes Yes Yes
Can integrate with other tools (including AI) Yes Yes Yes Yes

Getting Customer-Centric Chat Right

We’ve offered a lot of context about how to think about customer service chat platforms, but one thing we haven’t discussed is the difference between rolling your own solution vs. going with a third-party platform.

It’s a big topic, involving non-trivial tradeoffs on either side. To get up to speed as quickly as possible, check out our whitepaper!

Transforming Business with a Voice AI Platform

Posted on Mark Kowal

The CX Leaders’ Guide to Crush the Competition

As customer expectations continue to rise, businesses are under increasing pressure to provide fast, efficient, and personalized service around the clock. For CX leaders, keeping up with these demands can be challenging—especially when scaling operations while maintaining high-quality support. This is where a Voice AI platform can give you the edge you need to transform your customer service strategy.

Voice AI is a powerful technology that rapidly changes how businesses interact with their customers. From automating routine tasks to delivering personalized experiences at scale, Voice AI is truly changing the game in customer service.

This article explores how Voice AI works, why it’s more effective than traditional human agents in many areas, and the numerous benefits it offers businesses today. Whether you’re just beginning to explore Voice AI or looking to enhance your existing systems, this guide will provide valuable insights into how this technology can revolutionize your approach to customer service and help you stay ahead of the competition.

What is Voice AI and how does it work?

Voice AI refers to technology that uses artificial intelligence to understand, process, and respond to human speech. It enables machines to engage in natural, spoken conversations with users, typically using tools like automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS) to convert spoken language into text, interpret intent, and generate verbal responses. Voice AI is commonly used in applications like virtual assistants, customer service, and AI agents.

A Voice AI platform is a comprehensive system that enables businesses to build, deploy, and manage voice-based interactions powered by artificial intelligence. It integrates all the technologies mentioned above into a single solution to provide tools to design conversational flows, customize voice AI agents, handle customer queries, and gather analytics to improve performance. Voice AI platforms are now being used to create virtual assistants, customer service, and contact centers to improve the customer experience and change the way customers interact with businesses.

Voice AI enables machines to understand, interpret, and respond to human speech in a natural, conversational way. Unlike the early days of chatbots and voice systems, today’s Voice AI agents are powered by advanced technologies like Large Language Models (LLMs), such as GPT and many others, which allow them to comprehend and respond to human language with impressive accuracy.

LLMs are trained on massive amounts of data, allowing them to recognize patterns in speech, understand different languages and dialects, and predict what a user asks—even if the question is phrased unexpectedly. This is a major leap from traditional, rules-based systems, which required rigid, predefined scripts to function. Now, Voice AI agents can understand intent and context, making conversations feel much more natural and intuitive.

When a customer speaks to a Voice AI agent, the process begins with Automatic Speech Recognition (ASR), which converts spoken language into text. Once the words are captured, Natural Language Processing (NLP) kicks in to interpret the meaning behind the words, analyzing context and intent to determine the best response. From there, the system generates a response using the LLM’s understanding of language, and finally, the text is converted back into speech with Text-to-Speech (TTS) technology. All this happens in real time through a conversational voice AI platform, allowing the customer to interact with the AI as if they were speaking to a human agent.

For CX leaders, this changes everything for your customers. Voice AI agents don’t just hear words—they understand them. This allows you to handle a wide range of customer inquiries, from simple requests like checking order status to more complex tasks such as troubleshooting issues or answering product questions. And because these AI agents constantly learn from each interaction, they continue to improve over time, becoming more efficient and effective with each use.

How Voice AI compares to human agents

One of the most common questions CX leaders ask is how Voice AI compares to human agents. While it’s true that AI agents can’t fully replace the human touch in all interactions, they offer several key advantages that are transforming customer service for the better.

1. Consistency and speed

Unlike human agents, who may vary in their responses or make mistakes, Voice AI agents provide consistent answers every time—as long as they have appropriate guardrails in place to prevent hallucinations. Without these safeguards, AI can generate misinformation and fail to meet customer needs. Properly trained AI agents, equipped with the necessary guardrails, can instantly access vast amounts of information and handle inquiries quickly, making them ideal for routine or frequently asked questions. This ensures that customers receive fast, accurate responses without the need to wait on hold or navigate through multiple layers of human interaction.

2. 24/7 availability

One of the biggest benefits of Voice AI is that it’s available around the clock. Whether it’s the middle of the night or during peak business hours, AI agents are always on, ready to assist customers whenever they need help. This is especially useful for global businesses that operate in different time zones, ensuring customers can access support without delay, no matter where they are.

3. Scalability

Another advantage of Voice AI agents is their ability to scale. Unlike human agents, who can only handle one conversation at a time, AI agents can manage thousands of interactions simultaneously. This makes them particularly valuable during busy periods, such as holidays or product launches, when call volumes can surge. Instead of overwhelming your team, Voice AI ensures that all customers receive the same high level of service, even during high-demand times.

Where human agents excel

Of course, there are still areas where human agents outperform AI. Empathy and emotional intelligence are crucial in customer service, especially when dealing with complex or sensitive issues.

While AI agents can empathize with a user who expresses emotion, they are limited in their ability to interpret those emotions, and lack the personal touch that human agents excel at. Similarly, when faced with complex problem solving that requires out-of-the-box thinking, a human agent’s creativity and judgment are often more effective.

For these reasons, many businesses find that a hybrid approach works best—using Voice AI to handle routine or straightforward tasks, while allowing human agents to focus on more complex or emotionally charged interactions. This not only ensures that customers receive the right level of support, but also frees up human agents to do what they do best: solve problems and connect with customers on a personal level.

5 big business impacts of Voice AI

Now that we’ve covered how Voice AI works and compares to human agents, let’s review the specific benefits it offers businesses. From cost savings to enhanced customer experiences, Voice AI is transforming the way companies approach customer service.

1. Increased efficiency and reduced costs

One of the most significant advantages of using Voice AI is the ability to automate routine tasks. Instead of relying on human agents to handle simple inquiries—such as checking an order status, answering frequently asked questions, or processing payments—AI agents can manage these tasks automatically. This reduces the workload for human agents, allowing them to focus on more strategic or complex issues.

As a result, businesses can operate with smaller customer service teams, reducing labor costs and maintaining high levels of service. This not only saves money, but also improves efficiency, as AI agents can handle repetitive tasks faster and more accurately than humans.

2. Scalability and availability

As mentioned earlier, Voice AI offers unmatched scalability. Whether you’re dealing with a sudden spike in customer inquiries or operating across multiple time zones, Voice AI agents ensure no customer is left waiting. They can manage unlimited interactions simultaneously, providing the same level of service to every customer, regardless of demand.

In addition, because Voice AI operates 24/7, businesses no longer need to worry about staffing for off-hours or paying overtime for extended support. This around-the-clock availability ensures that customers can access help whenever they need it, improving overall satisfaction and reducing wait times.

3.  Enhanced customer experience

One of the most important factors in customer service is the customer experience, and Voice AI has the potential to dramatically improve it. By offering quick, consistent responses, Voice AI agents eliminate the frustration that comes with long wait times and inconsistent answers from human agents.

Voice AI can also deliver personalized interactions by integrating with CRM systems and accessing customer data. This allows the AI to address customers by name, understand their preferences, and provide tailored solutions based on their history with the company. The result is a more engaging and satisfying customer experience that leaves customers feeling valued and understood.

4. Multilingual support

For businesses with different cultural customers, Voice AI offers another important advantage: multilingual support. Many Voice AI systems are equipped to handle multiple languages, making it easier for companies to support customers around the world. Voice AI can detect if your customer is speaking another language and translate their response instantaneously. This extraordinarily personalized service can transform your customer’s interactions and prevent many of the issues that come with language barriers. This not only improves accessibility, but also enhances the overall customer experience, as customers can interact with the company in their preferred language.

5. Improved data collection and insights

In addition to its customer-facing benefits, Voice AI also provides businesses with valuable insights into customer behavior. Because AI agents record and analyze every interaction, companies can use this data to identify trends, spot recurring issues, and improve their products or services based on customer feedback.

By understanding what customers are asking for and where they’re encountering problems, businesses can refine their customer support strategy, improve AI performance, and even identify areas where additional human intervention may be needed. This data-driven approach allows CX leaders to make more informed decisions and continuously improve their customer service operations.

Voice AI for CX is just getting started

Looking ahead, the future of Voice AI is full of exciting possibilities. As technology continues to advance, the gap between human and AI interactions will continue to narrow, with Voice AI agents becoming even more capable of handling complex and nuanced conversations.

Future developments in emotional intelligence and predictive capabilities will allow AI agents to not only understand what customers say, but also anticipate what they need before they even ask.

For CX leaders, staying ahead of these developments is crucial. Businesses that invest in a Voice AI platform will be well-positioned to reap the benefits of these advancements as they continue to evolve. By embracing Voice AI as part of a hybrid approach, companies can create a customer service model that combines the efficiency and scalability of AI with the empathy and creativity of human agents.

Voice AI is a game-changer for CX leaders

The rise of Voice AI revolutionizes how businesses approach customer service, offering scalable, cost-effective, and personalized solutions that were once only possible with large teams of human agents. From automating routine tasks to providing real-time, tailored experiences, Voice AI is reshaping customer interactions in ways that improve efficiency and satisfaction.

For CX leaders, the time to embrace Voice AI is now. As this technology continues to evolve, it will become an even more powerful tool for delivering superior customer experiences, optimizing operational processes, and driving long-term success. By integrating Voice AI agents into your customer service strategy, you can balance the speed and consistency of AI with the empathy and creativity of human agents—building a support system that meets the ever-growing demands of today’s customers.

Voice AI is not just a trend, it’s the future of customer service. Staying ahead of the curve now will ensure your business remains competitive in a world where customer expectations continue to rise, and the need for seamless, personalized interactions becomes even more critical.

Why Even the Best Conversational AI Chatbot Will Fail Your CX

Posted on Mark Kowal

As author, speaker, and customer experience expert Dan Gingiss wrote in his book The Experience Maker, “Most companies must realize that they are no longer competing against the guy down the street or the brand that sells similar products. Instead, they’re competing with every other experience a customer has.”

That’s why so many CX leaders were (cautiously!) optimistic when Generative AI (GenAI) hit the scene, promising to provide instant, round-the-clock responses and faster issue resolutions, automate personalization at scale, and free agents to focus on more complex issues. So much so that a whopping 80% of companies worldwide now have chatbots on their websites.

Yet despite all the hype and good intentions, a recent survey showed that consumers give their chatbot experiences an average rating of 6.4/10 — which isn’t a passing grade in school, and certainly won’t cut it in business.

So why have chatbots fallen so short of company and consumer expectations? The short answer is because they’re not AI agents. Chatbots rely on rigid, rule-based systems. They struggle to understand context and adapt to complex or nuanced questions. Even the best conversational AI chatbot doesn’t have what it takes to enable CX leaders to create seamless customer journeys. This is why they so often fail at driving outcomes like revenue and CSAT.

Let’s look at the most impactful differences between these two AI for CX solutions, including why even the best conversational AI chatbots are failing CX teams and their customers — and how AI agents are changing the game.

Chatbots: First-generation AI and Intent-based Responses

AI is advancing at lightning speed, so it should come as no surprise that many vendors are having trouble keeping up. The truth is that most AI for CX tools still offer chatbots built on first-generation AI, rather than AI agents that are powered by the latest and greatest Large Language Models (LLMs).

This first-generation AI is rule-based and uses Natural Language Processing (NLP) to attempt to match users’ questions to specific, pre-defined queries and responses. In other words, CX teams must create lists of different ways users might pose the same question or request, or “intents.” AI does its best to determine which “intent” a user’s message aligns with, and then sends what has been labeled the “correct” corresponding response.

Best Conversational AI Chatbot

This approach can cause many problems that ultimately add friction to the customer journey and create frustrating brand experiences, including:

  • Intent limitations: If a user asks a multi-part question (e.g. “Can I unsubscribe from your newsletter and have sales contact me?”), the bot will recognize and answer only one intent and ignore the other, which is insufficient.
  • Ridged paths: If a user asks a question that the bot knows requires additional information, it will start the user down a rigid, predefined path to collect that information. If the user provides additional relevant details (e.g. “I would still like to receive customer-only emails”), the bot will continue to push them down this specific path before providing an answer.
    On the other hand, if the user asks an unrelated follow-up question, the bot will zero in on this new “intent” and start the user down a new path, abandoning the previous flow without resolving their original inquiry.
  • Confusing intents: There are countless ways to phrase the same request, so the likelihood of a user’s inquiry not matching a predefined intent is high (e.g. “I want you to delete my contact info!”). In this case, the bot doesn’t know what to do and must escalate to a live agent — or worse, it misunderstands the user’s intent and sends the wrong response.
  • Conflicting intents: Because similar words and phrases can appear across unrelated issues, there is often contention across predefined intents (e.g. “I accidentally unsubscribed from your newsletter.”). Even the best conversational AI chatbot is likely to match the user’s intent with the wrong response and deliver an unrelated and seemingly nonsensical answer — an issue similar to hallucinations.

Some AI for CX vendors claim their chatbots use the most advanced GenAI. However, they are really using only a fraction of an LLM’s power to generate a response from a knowledge base, rather than crafting personalized answers to specific questions. But because they still use the same outdated, intent-based process to determine the user’s request, the LLM will still struggle to generate a sufficient, appropriate response — if the issue isn’t escalated to a live agent first, that is.

AI Agents: Cutting-edge Models with Reasoning Capabilities

Top AI for CX vendors use the latest and greatest LLMs to power every step of the customer interaction, not just at the end to generate a response. This results in a much more accurate, personalized, and empathetic experience, enabling them to provide clients with AI agents — not chatbots.

Best Conversational AI Chatbot

Rather than relying on rigid intent classification, AI agents use LLMs to comprehend language and genuinely understand a user’s request, much like humans do. They can also contextualize the question and append the conversation with additional attributes accessed from other CX systems, such as a person’s location or whether they are an existing customer (more on that in this guide).

This level of reasoning is achieved through business logic, which guides the conversation flow through a series of “pre-generation checks” that happen in the background in mere seconds. These require the LLM to first answer “questions about the question” before generating a response, including if the request is in scope, sensitive in nature, about a specific product or service, or requires additional information to answer effectively.

Sidenote! 

The best AI for CX vendors never use client data to train LLMs to “invent” answers to questions about their products or services. Instead, the LLMs must generate responses using information from specific, trusted knowledge sources that the client has pre-approved. 

This means AI agents harness the language and communication capabilities of GenAI only, greatly reducing the need for CX leaders to worry about data security or hallucinations. You can go here to learn more.

 

Best Conversational AI Chatbot

The same process happens after the LLM has generated a response (“post-generation checks”), where the LLM must answer “questions about the answer” to ensure that it’s accurate, in context, on brand, etc. Leveraging the reasoning power of LLMs coupled with this conversational framework enables the AI agent to outperform even the best conversational AI chatbots in many key areas.

Providing sufficient answers to multi-part questions

Unlike a chatbot, the agent is not trying to map a specific question to a single, canned answer. Instead, it’s able to interpret the entirety of the user’s question, identify all relevant knowledge, and combine it to generate a comprehensive response that directly answers the user’s inquiry.

Dynamically answering unrelated questions and factoring in new information

AI agents will prompt users to provide additional information as needed to effectively respond to their requests. However, if the user volunteers additional information, the agent will factor this into the context of the larger conversation, rather than continuing to force them down a step-by-step path like a chatbot does. This effectively bypasses the need for many disambiguating questions.

Similarly, if a user asks an unrelated follow-up question, the agent will respond to the question without losing sight of the original inquiry, providing answers and maintaining the flow of the conversation while still collecting the information it needs to solve the original issue.

Understanding nuances

Unlike chatbots, next-gen AI agents excel at comprehending human language and picking up on nuances in user questions. Rather than having to identify a user’s intent and match it with the correct, predefined response, they can recognize that similar requests can be phrased differently, and that dissimilar questions may contain many of the same words. This allows them to flexibly understand users’ questions and identify the right knowledge to generate an accurate response without requiring an exact match.

Best Conversational AI Chatbot

It’s also worth noting that first-generation AI vendors often force clients to build a new chatbot for every channel: voice, SMS, Facebook Messenger, etc. Not only does this mean a lot of duplicate work for internal teams on the back end, but it can also lead to disjointed brand experiences on the front end. In contrast, next-generation AI for CX vendors allows clients to build a single agent and run it across multiple channels for a more seamless customer journey.

Is Your “Best-in-Class” AI Chatbot Killing Your Customer Journey?

Some 80% of customers say the experience a company provides is equally as important as its products and services. However, according to Gartner, more than half of large organizations have failed to unify customer engagement channels and provide a streamlined experience across them.

As you now know, even the best conversational AI chatbot will exacerbate rather than improve this issue. Our latest guide deep dives into more ways your chatbot is harming CX, from offering multi-channel-only support to measuring the wrong things, as well as the steps you can take to provide consumers with a more seamless journey. You can give it a read here!

Evolving the Voice AI Chatbot: From Bots to Voice AI Agents & Their Impact on CX Leaders

Posted on Mark Kowal

Voice AI has come a long way from its humble beginnings, evolving into a powerful tool that’s reshaping customer service. In this blog, we’ll explore how Voice AI has grown to address its early limitations, delivering impactful changes that CX leaders can no longer ignore. Learn how these advancements create better customer experiences, and why staying informed is essential to staying competitive.

The Voice AI Journey

Customer expectations have evolved rapidly, demanding faster and more personalized service. Over the years, voice interactions have transformed from rigid, rules-based AI chatbot with voice systems to today’s sophisticated AI-driven solutions. For CX leaders, Voice AI has emerged as a crucial tool for driving service quality, streamlining operations, and meeting customer needs more effectively.

Key Concepts

Before diving into this topic, readers, especially CX leaders, should be familiar with the following key terms to better understand the technology and its impact. The following is not a comprehensive list, but should provide the background to clarify terminology and identify the key aspects that have contributed to this evolution.

Speech-enabled systems vs. chatbots vs. AI agents

  • Speech-enabled systems: Speech-enabled systems are basic tools that convert spoken language into text, but do not include advanced features like contextual understanding or decision-making capabilities.
  • Chatbots: Chatbots are systems that interact with users through text, answering questions, and completing tasks using either set rules or AI to understand user inputs.
  • AI agents: AI agents are smart conversational systems that help with complex tasks, learn from interactions, and adjust their responses to offer more personalized and relevant assistance over time.

Rules-based (previous generation) vs. Large Language Models or LLMs (next generation)

  • Previous gen: Lacks adaptability, struggles with natural language nuances, and fails to offer a personalized experience.
  • Next-gen (LLM-based): Uses LLMs to understand intent, generate responses, and evolve based on context, improving accuracy and depth of interaction.

Agent Escalation: A process in which the Voice AI system hands off the conversation to a human agent, often seamlessly.

AI Agent: A software program that autonomously performs tasks, makes decisions, and interacts with users or systems using artificial intelligence. It can learn and adapt over time to improve its performance, commonly used in customer service, automation, and data analysis.

Depending on their purpose, AI agents can be customer-facing or assist human agents by providing intelligent support during interactions. They function based on algorithms, machine learning, and natural language processing to analyze inputs, predict outcomes, and respond in real-time.

Automated Speech Recognition (ASR): The technology that enables machines to understand and process human speech. It’s a core component of Voice AI systems, helping them identify spoken words accurately.

Context Awareness: Voice AI’s ability to remember previous interactions or conversations, allowing it to maintain a flow of dialogue and provide relevant, contextually appropriate responses.

Conversational AI: Conversational AI refers to technologies that allow machines to interact naturally with users through text or speech, using tools like LLMs, NLU, speech recognition, and context awareness.

Conversation Flow: The logical structure of a conversation, including how the Voice AI chatbot guides interactions, asks follow-up questions, and handles different branches of user input.

Generative AI: A type of artificial intelligence that creates new content, such as text, images, audio, or video, by learning patterns from existing data. It uses advanced models, like LLMs, to generate outputs that resemble human-made content. Generative AI is commonly used in creative fields, automation, and problem-solving, producing original results based on the data it has been trained on.

Intent Recognition: The process by which a Voice AI system identifies the user’s goal or purpose behind their speech input. Understanding intent is critical to delivering appropriate and relevant responses.

LLMs: LLMs are sophisticated machine learning systems trained on extensive text data, enabling them to understand context, generate nuanced responses, and adapt to the conversational flow dynamically.

Machine Learning (ML): A type of AI that allows systems to automatically learn and improve from experience without being explicitly programmed. ML helps voice AI chatbots adapt and improve their responses based on user interactions.

Multimodal: The ability of a system or platform to support multiple modes of communication, allowing customers and agents to interact seamlessly across various channels.

Multi-Turn Conversations: This refers to the ability of Voice AI systems to engage in extended dialogues with users across multiple steps. Unlike simple one-question, one-response setups, multi-turn conversations handle complex interactions.

Natural Language Processing (NLP): Consists of a branch of AI that helps computers understand and interpret human language. It is the key technology behind voice and text-based AI interactions.

Omnichannel Experience: A seamless customer experience that integrates multiple channels (such as voice, text, and chat) into one unified system, allowing customers to seamlessly transition between them.

Rules-based approach: This approach uses predefined scripts and decision trees to respond to user inputs. These systems are rigid, with limited conversational abilities, and struggle to handle complex or unexpected interactions, leading to a less flexible and often frustrating user experience.

Sentiment Analysis: A feature of AI that interprets the emotional tone of a user’s input. Sentiment analysis helps Voice AI determine the customer’s mood (e.g., frustrated or satisfied) and tailor responses accordingly.

Speech Recognition / Speech-to-Text (STT): Speech Recognition, or Speech-to-Text (STT), converts spoken language into text, allowing the system to process it. It’s a key step in making voice-based AI interactions possible.

Text-to-Speech (TTS): The opposite of STT, TTS refers to the process of converting text data into spoken language, allowing digital solutions to “speak” responses back to users in natural language.

Voice AI: Voice AI is a technology that uses artificial intelligence to understand and respond to spoken language, allowing machines to have more natural and intuitive conversations with people.

Voice User Interface (VUI): Voice User Interface (VUI) is the system that enables voice-based interactions between users and machines, determining how naturally and effectively users can communicate with Voice AI systems.

The humble beginnings of rules-based voice systems

Voice AI has been nearly 20 years in the making, starting with basic rules-based systems that followed predefined scripts. These early systems could automate simple tasks, but if customers asked anything outside the programmed flow, the system fell short. It couldn’t handle natural language or adapt to the unexpected, leading to frustration for both customers and CX teams.

For CX leaders, these systems posed more challenges than solutions. Robotic interactions often required human intervention, negating the efficiency benefits. It became clear that something more flexible and intelligent was needed to truly transform customer service.

The rise of AI and speech-enabled systems

As businesses encountered the limitations of rules-based systems, the next chapter in the evolution of Voice AI introduced speech-enabled systems. These systems were a step forward, as they allowed customers to interact more naturally with technology by transcribing spoken language into text. However, while they could accurately convert speech to text which solved one issue, they still struggled with a critical challenge—they couldn’t grasp the underlying meaning or the sentiment behind the words.

This gap led to the emergence of the first generation of AI, which represented a significant improvement over simple chatbots. This intelligence improved more helpful for customer interactions, but they still fell short in providing the seamless, human-like conversations that CX leaders envisioned. While customers could speak to AI-powered systems, the experience was often inconsistent, especially when dealing with complex queries. The advancement of AI was another improvement, but it was still limited by the rules-based logic it evolved from.

The challenge stemmed from the inherent complexity of language. People express themselves in diverse ways, using different accents, phrasing, and expressions. Language rarely follows a single, rigid pattern, which made it difficult for early speech systems to interpret accurately.
These AI systems were a huge leap in progress and created hope for CX leaders. Intelligent systems that can adapt and respond to users’ speech were powerful, but not enough to make a full transformation in the CX world.

The AI revolution: From rules-based to next-gen LLMs

The real breakthrough came with the rise of LLMs. Unlike rigid rules-based systems, LLMs use neural networks to understand context and intent, creating true natural, fluid human-like conversations. Now, AI could respond intelligently, adapt to the flow of interaction, and provide accurate answers.

For CX leaders, this was a pivotal moment. No more frustrating dead ends or rigid scripts—Voice AI became a tool that could offer context-aware services, helping businesses cut costs while enhancing customer satisfaction. The ability to deliver meaningful, efficient service marked a turning point in customer engagement.

What makes Voice AI work today?

Today’s Voice AI systems combine several advanced technologies:

  • Speech-to-Text (STT): Converts spoken language into text with high accuracy.
  • AI Intelligence: Powered by NLU and LLMs, the AI deciphers customer intent and delivers contextually relevant responses.
  • Text-to-Speech (TTS): Translates the AI’s output back into natural-sounding speech for smooth and realistic communication.

These technologies work together to enable smarter, faster service, reduce the load on human agents and provide an intuitive customer experience.

The transformation: What changed with next-gen Voice AI?

With advancements in NLP, ML, and omnichannel integration, Voice AI has evolved into a dynamic, intelligent system capable of delivering personalized, empathetic responses. Machine Learning ensures that the system learns from every interaction, continuously improving its performance. Omnichannel integration allows Voice AI to operate seamlessly across multiple platforms, providing a unified customer experience. This is crucial for the transformation of customer service.

Rather than simply enhancing voice interactions, omnichannel solutions select the best communication channel within the same interaction, ensuring customers receive a complete answer and any necessary documentation to resolve their issue – via email or SMS.

For CX leaders, this transformation enables them to offer real-time, personalized service, with fewer human touchpoints and greater customer satisfaction.

The four big benefits of next-gen Voice AI for CX leaders

The rise of next-gen Voice AI from previous-gen Voice AI chatbots offers CX leaders powerful benefits, transforming how they manage customer interactions. These advancements not only enhance the customer experience, but also streamline operations and improve business efficiency.

1. Enhanced customer experience

With faster, more accurate, and context-aware responses, Voice AI can handle complex queries with ease. Customers no longer face frustrating dead ends or robotic answers. Instead, they get intelligent, conversational interactions that leave them feeling heard and understood.

2. 24/7 availability

Voice AI is always on, providing customers with support at any time, day or night. Whether it’s handling routine inquiries or resolving issues, Voice AI ensures customers are never left waiting for help. This around-the-clock service not only boosts customer satisfaction, but also reduces the strain on human agents.

3. Operational efficiency

By automating high volumes of customer interactions, Voice AI significantly reduces human intervention, cutting costs. Agents can focus on more complex tasks, while Voice AI handles repetitive, time-consuming queries—making customer service teams more productive and focused.

4. Personalization at scale

By learning from each interaction, the system can continuously improve and deliver tailored responses to individual customers, offering a more personalized experience for every user. This level of personalization, once achievable only through human agents, is now possible on a much larger scale.
However, while machine learning plays a critical role in making these advancements possible, it is not a “magical” solution. The improvements happen over time, as the system processes more data and refines its understanding. Although this may sound simplified, the gradual and ongoing development of machine learning can indeed lead to highly effective and powerful outcomes in the long run.

The future of Voice AI: Next-gen experience in action

Voice AI’s future is already here, and it’s evolving faster than ever. Today’s systems are almost indistinguishable from human interactions, with conversations flowing naturally and seamlessly. But the leap forward doesn’t stop at just sounding more human—Voice AI is becoming smarter and more intuitive, capable of anticipating customer needs before they even ask. With AI-driven predictions, Voice AI can now suggest solutions, recommend next steps, and provide highly relevant information, all in real time.

Imagine a world where Voice AI understands customer’s speech and then anticipates what is needed next. Whether it’s guiding them through a purchase, solving a complex issue, or offering personalized recommendations, technology is moving toward a future where customer interactions are smooth, proactive, and entirely customer-centric.

For CX leaders, this opens up incredible opportunities to stay ahead of customer expectations. Those adopting next-gen Voice AI now are leading the charge in customer service innovation, offering cutting-edge experiences that set them apart from competitors. And as this technology continues to evolve, it will only get more powerful, more intuitive, and more essential for delivering world-class service.

The new CX frontier with Voice AI

As Voice AI continues to evolve from the simple Voice AI chatbot of yesteryear, we are entering a new frontier in customer experience. What started as a rigid, rules-based system has transformed into a dynamic, intelligent agent capable of revolutionizing how businesses engage with their customers. For CX leaders, this new era means greater personalization, enhanced efficiency, and the ability to meet customers where they are—whether it’s through voice, chat, or other digital channels.

We’ve made more progress in this development, but it is far from over. Voice AI is expanding, from deeper integrations with emerging technologies to more advanced predictive capabilities that can elevate customer experiences to new heights. The future holds more exciting developments, and staying ahead will require ongoing adaptation and willingness to embrace change.

Omnichannel capabilities is just the beginning

One fascinating capability of Voice AI is its ability to seamlessly integrate across multiple platforms, making it a truly omnichannel experience. For example, imagine you’re on a phone call with an AI agent, but due to background noise, it becomes difficult to hear. You could effortlessly switch to texting, and the conversation would pick up exactly where it left off in your text messages, without losing any context.

Similarly, if you’re on a call and need to share a photo, you can text the image to the AI agent, which can interpret the content of the photo and respond to it—all while continuing the voice conversation.

Another example of this multi-modal functionality is when you’re on a call and need to spell out something complex, like your last name. Rather than struggle to spell it verbally, you can simply text your name, and the Voice AI system will incorporate the information without disrupting the flow of the interaction. These types of seamless transitions between different modes of communication (voice, text, images) are what make multi-modal Voice AI truly revolutionary.

Voice AI’s future is already here, and it’s evolving rapidly. Today’s systems are approaching a level where they are almost indistinguishable from human interactions, with conversations flowing naturally and effortlessly. But the advancements go beyond merely sounding human—Voice AI is becoming smarter and more intuitive, capable of anticipating customer needs before they even express them. With AI-driven predictions, these systems can now suggest solutions, recommend next steps, and provide highly relevant information in real-time.

Imagine a scenario where Voice AI not only understands what a customer says, but also predicts what they might need next. Whether it’s guiding them through a purchase, solving a complex problem, or offering personalized product recommendations, this technology is leading the way toward a future where customer interactions are smooth, proactive, and deeply personalized.

For CX leaders, these capabilities open up unprecedented opportunities to exceed customer expectations. Those adopting next-generation Voice AI are positioning themselves at the forefront of customer service innovation, offering cutting-edge experiences that differentiate them from competitors. As this technology continues to advance, it will become even more powerful, more intuitive, and essential for delivering exceptional, customer-centric service.

Voice AI’s exciting road ahead

From the original Voice AI chatbot to today, Voice AI’s evolution has already transformed the customer experience—and the future promises continued innovation. From intelligent human-like conversations to predictive capabilities that anticipate needs, Voice AI is destined to change the way businesses interact with their customers in profound ways.

The exciting thing is that this is just the beginning.

The next wave of Voice AI advancements will open up new possibilities that we can only imagine. As a CX leader, the opportunity to harness this technology and stay ahead of customer expectations is within reach. It could be the most exciting time to be at the forefront of these changes.

At Quiq, we are here to guide you through this journey. If you’re curious about our Voice AI offering, we encourage you to watch our recent webinar on how we harness this incredible technology.

One thing is for sure, though: As the landscape continues to evolve, we’ll be right alongside you, helping you adapt, innovate, and lead in this new era of customer experience. Stay tuned, because the future of Voice AI is just getting started, and we’ll continue to share insights and strategies to ensure you stay ahead in this rapidly changing world.

National Furniture Retailer Reduces Escalations to Human Agents by 33%

Posted on Mike Zinne

A well-known furniture brand faced a significant challenge in enhancing their customer experience (CX) to stand out in a competitive market. By partnering with Quiq, they implemented a custom AI Agent to transform customer interactions across multiple platforms and create more seamless journeys. This strategic move resulted in a 33% reduction in support-related escalations to human agents.

On the other end of the spectrum, the implementation of Proactive AI and a Product Recommendation engine led to the largest sales day in the company’s history through increased chat sales, showcasing the power of AI in improving efficiency and driving revenue.

Let’s dive into the furniture retailer’s challenges, how Quiq solved them using next-generation AI, the results, and what’s next for this household name in furniture and home goods.

The challenges: CX friction and missed sales opportunities

A leading name in the furniture and home goods industry, this company has long been known for its commitment to quality and affordability. Operating in a sector often the first to signal economic shifts, the company recognized the need to differentiate itself through exceptional customer experience.

Before adopting Quiq’s solution, the company struggled with several CX challenges that impeded their ability to capitalize on customer interactions. To start, their original chatbot used basic natural language understanding (NLU), and failed to deliver seamless and satisfactory customer journeys.

Customers experienced friction, leading to escalations, redundant conversations. The team clearly needed a robust system that could streamline operations, reduce costs, and enhance customer engagement.

So, the furniture retailer sought a solution that could not only address these inefficiencies, but also support their sales organization by effectively capturing and routing leads.

The solution: Quiq’s next-gen AI

With a focus on enhancing every touch point of the customer journey, the furniture company’s CX team embarked on a mission to elevate their service offerings, making CX a primary differentiator. Their pursuit led them to Quiq, a trusted technology partner poised to bring their vision to life through advanced AI and automation capabilities.

Quiq partnered with the team to develop a custom AI Agent, leveraging the natural language capabilities of Large Language Models (LLMs) to help classify sales vs. support inquiries and route them accordingly. This innovative solution enables the company to offer a more sophisticated and engaging customer experience.

The AI Agent was designed to retrieve accurate information from various systems—including the company’s CRM, product catalog, and FAQ knowledge base—ensuring customers received timely, relevant, and accurate responses.

By integrating this AI Agent into webchat, SMS, and Apple Messages for Business, the company successfully created a seamless, consistent, and faster service experience.

The AI Agent also facilitated proactive customer engagement by using a new Product Recommendation engine. This feature not only guided customers through their purchase journey, but also contributed to a significant shift in sales performance.

The results are nothing short of incredible

The implementation of the custom AI Agent by Quiq has already delivered remarkable results. One of the most significant achievements was a 33% reduction in escalations to human agents. This reduction translated to substantial operational cost savings and allowed human agents to focus on complex or high-value interactions, enhancing overall service quality.

Moreover, the introduction of Proactive AI and the Product Recommendation engine led to unprecedented sales success. The furniture retailer experienced its largest sales day for Chat Sales in the company’s history, with an impressive 10% of total daily sales attributed to this channel for the first time.

This outcome underscored the potential of AI-powered solutions in driving business growth, optimizing efficiency, and elevating customer satisfaction.

Results recap:

  • 33% reduction in escalations to human agents.
  • 10% of total daily sales attributed to chat (largest for the channel in company history).
  • Tighter, smoother CX with Proactive AI and Product Recommendations woven into customer interactions.

What’s next?

The partnership between this furniture brand and Quiq exemplifies the transformative power of AI in redefining customer experience and achieving business success. By addressing challenges with a robust AI Agent, the company not only elevated its CX offerings, but also significantly boosted its sales performance. This case study highlights the critical role of AI in modern business operations and its impact on a company’s competitive edge.

Looking ahead, the company and Quiq are committed to continuing their collaboration to explore further AI enhancements and innovations. The team plans to implement Agent Assist, followed by Voice and Email AI to further bolster seamless customer experiences across channels. This ongoing partnership promises to keep the furniture retailer at the forefront of CX excellence and business growth.

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.

Watch Now

How To Be The Leader Of Personalized CX In Your Industry

Posted on Cristina Bravo-Olmo

Customer expectations are evolving alongside AI technology, at an unprecedented pace. People are more informed, connected, and demanding than ever before, and they expect nothing less than exceptional customer experiences (CX) from the brands they interact with.

This is where personalized customer experience comes in.

By tailoring CX to individual customers’ needs, preferences, and behaviors, businesses can create more meaningful connections, build loyalty, and drive revenue growth.
In this article, we will explore the power of personalized CX in industries and how it can help businesses stay ahead of the curve.

What is Personalized CX?

Personalized CX refers to the process of tailoring customer experiences to individual customers based on their unique needs, preferences, and behaviors. This involves using customer data and insights to create targeted and relevant interactions across multiple touchpoints, such as websites, mobile apps, social media, and customer service channels.

Personalization can take many forms, from simple tactics like using a customer’s name in a greeting to more complex strategies like recognizing that they are likely to be asking a question about the order that was delivered today. The goal is to create a seamless and consistent experience that makes customers feel valued and understood.

Why is Personalized CX Important?

Personalized CX has become increasingly important in industries for several reasons:

1. Rising Customer Expectations

Today’s customers expect personalized experiences across all industries, from retail and hospitality to finance and healthcare. In fact, according to a survey by Epsilon, 80% of consumers are more likely to do business with a company if it offers personalized experiences.

2. Increased Competition

As industries become more crowded and competitive, businesses need to find new ways to differentiate themselves. Personalized CX can help brands stand out by creating a unique and memorable experience that sets them apart from their competitors.

3. Improved Customer Loyalty and Retention

Personalized CX can help businesses build stronger relationships with their customers by creating a sense of loyalty and emotional connection. According to a survey by Accenture, 75% of consumers are more likely to buy from a company that recognizes them by name, recommends products based on past purchases, or knows their purchase history.

4. Increased Revenue

By providing personalized CX, businesses can also increase revenue by creating more opportunities for cross-selling and upselling. According to a study by McKinsey, personalized recommendations can drive 10-30% of revenue for e-commerce businesses.

Industries That Can Benefit From Personalized CX

Personalized CX can benefit almost any industry, but some industries are riper for personalization than others.

Here are some industries that can benefit the most from personalized CX:

1. Retail

Retail is one of the most obvious industries that can benefit from personalized CX. By using customer data and insights, retailers can create tailored product recommendations and personalized support based on products purchased and current order status.

2. Hospitality

In the hospitality industry, personalized CX can create a more memorable and enjoyable experience for guests. From personalized greetings to customized room amenities, hospitality businesses can use personalization to create a sense of luxury and exclusivity.

3. Healthcare

Personalized CX is also becoming increasingly important in healthcare. By tailoring healthcare experiences to individual patients’ needs and preferences, healthcare providers can create a more patient-centered approach that improves outcomes and satisfaction.

4. Finance

In the finance industry, personalized CX can help businesses create more targeted and relevant offers and services. By using customer data and insights, financial institutions can offer personalized recommendations for investments, loans, and insurance products.

Best Practices for Implementing Personalized CX in Industries

Implementing personalized CX requires a strategic approach and a deep understanding of customers’ preferences and behaviors.

Here are some best practices for implementing personalized CX in industries:

1. Collect and Use Customer Data Wisely

Collecting customer data is essential for personalized CX, but it’s important to do so in a way that respects customers’ privacy and preferences. Businesses should be transparent about the data they collect and how they use it, and give customers the ability to opt out of data collection.

2. Use Technology to Scale Personalization

Personalizing CX for every individual customer can be a daunting task, especially for large businesses. Using technology, such as machine learning algorithms and artificial intelligence (AI), can help businesses scale personalization efforts and make them more efficient.

3. Be Relevant and Timely

Personalized CX is only effective if it’s relevant and timely. Businesses should use customer data to create targeted and relevant offers, messages, and interactions that resonate with customers at the right time.

4. Focus on the Entire Customer Journey

Personalization shouldn’t be limited to a single touchpoint or interaction. To create a truly personalized CX, businesses should focus on the entire customer journey, from awareness to purchase and beyond.

5. Continuously Test and Optimize

Personalized CX is a continuous process that requires constant testing and optimization. Businesses should use data and analytics to track the effectiveness of their personalization efforts and make adjustments as needed.

Challenges of Implementing Personalized CX in Industries

While the benefits of personalized CX are clear, implementing it in industries can be challenging. Here are some of the challenges businesses may face:

1. Data Privacy and Security Concerns

Collecting and using customer data for personalization raises concerns about data privacy and security. Businesses must ensure they are following best practices for data collection, storage, and usage to protect their customers’ information.

2. Integration with Legacy Systems

Personalization requires a lot of data and advanced technology, which may not be compatible with legacy systems. Businesses may need to invest in new infrastructure and systems to support personalized CX.

3. Lack of Skilled Talent

Personalized CX requires a skilled team with expertise in data analytics, machine learning, and AI. Finding and retaining this talent can be a challenge for businesses, especially smaller ones.

4. Resistance to Change

Implementing personalized CX requires significant organizational change, which can be met with resistance from employees and stakeholders. Businesses must communicate the benefits of personalization and provide training and support to help employees adapt.

Personalized CX is no longer a nice-to-have; it’s a must-have for businesses that want to stay competitive in today’s digital age. By tailoring CX to individual customers’ needs, preferences, and behaviors, businesses can create more meaningful connections, build loyalty, and drive revenue growth. While implementing personalized CX in industries can be challenging, the benefits far outweigh the costs.