AI Studio Live: Real Customer Questions, Real Solutions by Quiq Experts (Webinar Recap)

At Quiq, we understand that implementing AI in your customer experience strategy can sometimes feel like navigating uncharted waters. To help our customers overcome these challenges, we hosted an AI Studio Live webinar—a hands-on session designed to address real customer questions about using AI in business. This interactive session was led by Quiq experts Mark Kowal (Senior Director of Product Marketing), John Anderson (Conversational Architect), and myself—Max Fortis (Product Manager).

During the webinar, we tackled the most common and pressing questions directly from our customer community. We grouped these questions into four key areas and offered practical advice, demos, and solutions in Quiq’s AI builder platform, AI Studio.

Want complete answers to each question with the in-depth product demos John provided during the session? You can watch the replay on demand here. Otherwise, here are the highlights of what we covered.

Questions that shape the future of AI integration

The questions we received from participants in this webinar reflect deep-seated challenges businesses face when building and deploying AI solutions. During the session, we grouped the questions into three key categories:

  1. Preparing Data – How do we ensure data readiness for AI?
  2. Building with Data – Once data is ready, how do we leverage it effectively in AI agents?
  3. Building with Large Language Models (LLMs) – What are the best practices for navigating the rapidly evolving landscape of LLMs?

These categories encapsulate key building blocks of any AI deployment strategy. Below, we’ll break down the key topics and solutions covered during each section.


1. Preparing data for AI success

When it comes to AI, one thing is certain—your model is only as good as the data it has access to. To ensure your AI agent performs at the highest level, data must be transformed, enriched, and synchronized with precision.

Common questions about data preparation

Here are some examples of critical questions we tackled within this category:

  • “How can I remove unwanted JSON or HTML tags from my dataset?”

Understanding what data to exclude can significantly impact performance. For instance, removing excessive metadata such as phone numbers or “contact us” labels from help articles improves how your agent retrieves relevant answers.

  • “What are best practices for improving search results?”

Creating augmented datasets, assigning topic tags, and adding metadata like summaries or descriptions can amplify the effectiveness of your knowledge pool.

  • “How can I transform my data while keeping it synchronized?”

When data transformations create multiple sources of truth, it introduces inefficiencies. Applying rules-based synchronization on Quiq’s AI Studio ensures no data is decoupled during updates.

Highlighted solution demonstrations

John Anderson showcased the flexibility of Quiq’s AI Studio for addressing these issues.

  • Leveraging transformations, he demonstrated stripping out unnecessary elements like embedded HTML or links while enhancing datasets with topic-based metadata.
  • With automatic synchronization, data can be updated and transformed on an ongoing basis, resulting in consistent, high-quality information that agents could rely on.

💡 Pro Tip: Sync datasets regularly and build robust rules to preserve accuracy over time.


2. Building with data in AI Studio

Once your data is prepared, the next challenge is to figure out how to use it effectively. Different user needs, markets, and data sources require careful planning to guarantee relevant and accurate results from agents.

Common challenges for data utilization

Webinar attendees were particularly curious about these scenarios:

  • “We have users from multiple markets. How do we ensure the agent uses market-relevant knowledge sources?”

The solution lies in conditionally segmenting datasets. For example, a single agent can serve Australian and US customers using conditional logic, which ensures that region-specific knowledge is applied based on the user’s locale.

  • “Can two datasets be used together, like a core product catalog supplemented by promotional content?”

Yes—Quiq’s AI Studio quickly combines multiple datasets for dynamic applications. Supplemental knowledge bases, such as blog content or seasonal catalogs, can be accessed opportunistically depending on the interaction context.

Demonstrated use cases

John highlighted how search behaviors can incorporate multiple datasets. During one demo, he adjusted search logic to demonstrate the differences between pooled vs. isolated queries.

  • Scenario 1: Combining a product catalog with a promotional dataset allowed the AI to deliver direct responses on availability with added context about special offers.
  • Scenario 2: Isolating each dataset showcased accurate queries tailored to specific needs (e.g., products vs. how-to articles).

💡 Pro Tip: Use dynamic search behaviors for scenario-specific queries without cluttering your AI workflows.


3. Building with large language models (LLMs)

The excitement surrounding LLMs like GPT-4o, Gemini—and most recently as of the time of writing this article, DeepSeek—comes with an undeniable amount of complexity and questions. How do you make the right choices for modeling, scaling, and updates in such a fast-moving environment?

Key questions tackled

These were some of the most common LLM dilemmas posed by attendees:

  • “How do I decide what model is best for a specific use case?”

The appropriate model depends on factors like performance needs, accuracy, and cost-efficiency. Balancing these trade-offs is essential. That said, it’s a process of trial and error to really cue in on the best model for the job.

  • “What is atomic prompting, and when should I use it?”

Atomic prompting involves breaking prompts into individual parts to resolve multiple queries efficiently. This can reduce computational strain,improve precision, and increase traceability.

  • “How can I test updates without disrupting live agents?”

Testing updates in isolation with tools like Quiq’s Debug Workbench allows businesses to debug prompts, assess new models, and replicate conditions—all without publishing in-progress changes.

Demonstrated solutions

John dove into prompt engineering to showcase techniques such as atomic prompting (decomposing tasks into manageable chunks) and model selection through Quiq platforms. He underscored testing’s critical role by showcasing before-and-after scenarios of prompt changes, confidently ensuring accuracy and compliance.

Importantly, we built AI Studio to be model agnostic to accommodate innovative advancements in this space (see this LinkedIn post from our CEO Mike Myer about this in the context of DeepSeek’s release).

💡 Pro Tip: Create tests using both real and simulated conversations to ensure you’re capturing the full range of scenarios necessary..


Taking AI beyond the traditional use case

While most of the webinar focused on the above categories, we also fielded additional questions, such as:

  • “How do we deploy AI agents across platforms with varying capabilities?”

The advice? Build once, ensuring functionality across all platforms (e.g., SMS, voice, web chat). Tailoring formats for channel-specific features (e.g., carousel cards for chat) ensures consistency in user experience.

  • “What is Retrieval Augmented Generation (RAG) with customer data?”

John clarified how RAG applies beyond knowledge bases to dynamic APIs—e.g., personalized product recommendations.

Next steps for your AI journey

Harnessing the full power of AI starts with asking the right questions—and our webinar made it clear that the Quiq community is full of thoughtful, innovative inquiry. With robust tools like AI Studio and guidance from our expert team, businesses can prepare their data, leverage LLMs effectively, and scale AI to meet growing demands.

Looking to bring these strategies to life? Test drive Quiq’s AI Studio for free, and see how you can elevate your customer experience. Our platform allows you to build smarter, more contextual AI agents—all while simplifying the complexities of AI for your team.

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

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)

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

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

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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.

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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.

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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

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.