February 2025

AI Agent Evaluation: Ten Questions to Ask to Determine if It’s Time to Upgrade

Posted on February 28, 2025July 10, 2025 Max Fortis

Keeping up with AI isn’t easy, and teams certainly can’t drop everything for every little update. However, there are times when failure to update your AI for CX tools can have a major impact on your customer experience and brand trust. And the rise of agentic AI is one of those times.

Cutting-edge AI agents combine the reasoning and communication power of large language models (LLMs), generative AI (GenAI), and agentic AI to understand the meaning and context of a user’s inquiry or need, and then generate an accurate, personalized, and on-brand response — often proactively and autonomously.

But even many self-proclaimed “agentic AI” vendors fail to offer their clients truly next-generation AI agents, since the models and technologies behind them have gone through such a rapid series of updates in such a short period of time. So how do you know if your AI agent is current and whether it’s time for an update?

That’s where this AI agent evaluation comes in. We’ve created a series of questions CX leaders can ask the AI agents on their companies’ websites to gauge just how advanced they really are, and how urgently an update is needed. Already considering a new agentic AI platform? Asking your top vendors’ customers’ AI agents these questions can also help streamline the selection process.

Simply give yourself a point for each of the ten questions the AI agent answers effectively, and half a point for each bonus question. Note that you may tailor the questions if they don’t make sense in the context of a particular product or service. Then, total up your points, and read on for your results and recommended next steps. Are you ready?

Question #1: “What is your return policy and do you offer exchanges?”

Add a Point If…

The AI agent answers both of these questions in a single, comprehensive response. Ideally, it also sends a link to the relevant knowledge base articles referenced in the answer.

No Points If…

The AI agent provides an answer for only one of these questions and fails to answer the other.

This is a leading indicator of first-generation AI that attempts to match a user’s intent to a specific, pre-defined query and “correct” response. In contrast, a next-generation AI agent can comprehend the entirety of a user’s question, identify all relevant knowledge, and combine it to craft a complete response.

Question #2: “Do you offer financing? How do I qualify?”

Add a Point If…

The AI agent uses the context from the first question to understand the second one, and provides a single, comprehensive, and adequate response for both.

No Points If…

The AI agent either sends you an unrelated response, or replies that it is unable to help you, and offers to escalate to an agent.

This is another sign that the AI agent is attempting to isolate the user’s intent to provide a specific, matching response, rather than understanding the context of the conversation and tailoring its response accordingly. In some cases, the AI agent may actually harness an LLM to generate a response from a knowledge base. But because it uses the same outdated, intent-based process to determine the user’s request in the first place, the LLM will still struggle to provide a sufficient, appropriate response.

Question #3: “Can you help me track my order?”

Add a Point If…

You are currently logged into the site (or the AI agent is able to automatically authenticate you using your phone number, for example) and the AI agent immediately identifies you and finds your order. If you are not logged in, add a point if the AI agent asks for your information and can quickly locate your account to help you with your order.

No Points If…

The AI agent immediately sends you to a human agent to help with your request — regardless of whether you are logged into the site.

This means the AI agent operates in a silo and does not have access to other CX systems outside of a knowledge base, leaving it unable to provide anything other than general information and basic company policies. The latest and greatest agentic AI platforms integrate directly with the other tools in the CX tech stack to ensure AI agents have secure access to the customer information they need to provide personalized assistance.

Question #4: “Can you help me track my order? My order number is [insert order number] and my email is [insert email address].”

Add a Point If…

The AI agent immediately finds your order and provides you with a tracking update, without asking you to repeat any of the information you included in your original message.

No Points If…

The AI agent agrees to help you track your order, but says it needs the information you already provided, and asks you to repeat your order number and/or email.

First-generation AI agents are “programmed” to follow rigid, predefined paths to collect the details they have been told are necessary to answer certain questions — even if a user proactively provides this information. In contrast, cutting-edge AI agents will factor all provided information into the context of the larger conversation to resolve the user’s issue as quickly as possible, rather than continuing to force them down a step-by-step path and ask unnecessary disambiguating questions.

Question #5: “Can you help me track my order? I don’t want it anymore and would like to start a return. / Does store credit expire?”

Add a Point If…

After answering your first question, the AI agent responds to your second, unrelated follow-up question, and then automatically brings the conversation back to the original topic of making a return.

No Points If…

After answering your first question, the AI agent responds to your second, unrelated follow-up question, but never returns to the original topic of conversation.

This is another indicator that the AI agent is relying on predefined user intents and rigid conversation flows to answer questions. A truly agentic AI agent can respond to a user’s follow-up 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.

Question #6: “Are you able to recommend an accessory to go with this [insert item]?”

Add a Point If…

The AI agent sends you a list of products that are complementary to the original item. Ideally, it sends a carousel of photos of these items with buttons to add them to your cart directly within the chat window.

No Points If…

The AI agent immediately escalates you to a human agent. Subtract a point if the agent is in support, not sales!

This scenario occurs when an AI for CX platform is built to support post-sales activities only, and lacks the ability to route users to the appropriate human agent based on the context of the conversation. This results in missed revenue opportunities and makes it difficult to measure and improve customers’ paths to conversion. The latest agentic AI solutions, however, support both the services and sales side of the CX coin by integrating with teams’ product catalogs, offering intelligent routing capabilities, and more

Question #7: “Why is the sky blue?”

Add a Point If…

The AI agent politely refuses to answer your question by acknowledging this topic falls outside its purview, and then informs you about the type of assistance it’s able to provide.

No Points If…

The AI agent attempts to answer this question in any way, shape, or form — even if its response is correct.

In this situation, the AI agent lacks the pre-answer generation checks that cutting-edge agentic AI platforms bake into their agents’ conversational architectures. These filters ensure questions are within the AI agent’s scope before it even attempts to craft an answer. In addition to lacking this layer of business logic, answering this type of irrelevant question also means that the LLM powering the AI agent is pulling knowledge from its general training set, versus specific, pre-approved sources (a process known as Retrieval Augmented Generation, or RAG).

Question #8: “What is your policy on items stolen in transit?”

Add a Point If…

The AI agent admits it does not have information about this specific policy, and offers to escalate the conversation to a human agent.

No Points If…

The AI agent makes up or hallucinates a policy that isn’t specifically documented.

Although this question is within the scope of what the AI agent is allowed to talk about, it doesn’t have the information it needs to provide a totally accurate answer. However, rather than knowing what it doesn’t know, it makes up an answer using whatever related information it has. This is similar to what happened in Question #7, and is due to a lack of post-answer generation guardrails within the AI agent’s conversational architecture, as well as insufficient RAG.

Question #9: “My [item] is broken. How do I fix it?”

Add a Point If…

The AI agent asks clarifying questions to gather the additional information it needs to provide an accurate answer, or to determine it doesn’t have the knowledge necessary to respond, and must escalate you to a human agent.

No Points If…

The AI agent does not attempt to collect supplementary information to identify the item in question and whether it has sufficient knowledge to effectively respond. Instead, it immediately answers with a help desk article or instructions on how to fix an item that may or may not match the specific item you need.

In this instance, the AI agent fails to understand the context of the conversation. Once again, agentic AI platforms prevent this using a layer of business logic that controls the flow of the conversation through pre- and post-answer generation filters. These provide a framework for how the AI agent should respond or guide users down a specific path to gather the information the LLM needs to give the right answer to the right question. This is very similar to how you would train a human agent to ask a specific series of questions before diagnosing an issue and offering a solution.

Question #10: “My item never arrived, but it says it was delivered. I don’t know where it is, and now I don’t want it. I’m very upset. Can you transfer me to a human agent so I can get a refund?”

Add a Point If…

The AI agent immediately transfers you to a human agent, and the conversation is shown in the same window or thread. At no point does the human agent ask you to repeat your issue or the details you already shared with the AI agent.

No Points If…

The AI agent transfers you to a human agent, but the conversation opens in an entirely new window, and you must repeat the information you just shared with the AI agent.

This happens when a vendor does not offer full functionality for both AI and human agents in a single platform. Escalating a conversation to a human usually involves switching systems and redirecting customers to an entirely new experience, losing context along the way. In contrast, true agentic AI vendors prioritize both human and AI agent interactions in a one console. Human agents receive a summary and full context of escalated conversations, so they can pick up where the AI agent left off, while customers get uninterrupted service in the same thread.

Bonus Round

You likely noticed a few other common conversational AI issues as you did your agent evaluation. Check out the below list, and give yourself half a point for each problem you did not encounter:

Repetitive words or phrases. First-generation conversational AI tends to repeat certain words or phrases that appear frequently in its training data. It also often provides the same “canned” responses to different questions.
Nonsensical or inappropriate information. These horror stories happen when a conversational AI doesn’t have the information it needs to provide an effective answer and lacks sophisticated controls like post-generation checks and RAG.
Outdated information. The best agentic AI solutions automatically ensure AI agents always have access to a company’s latest and greatest knowledge. Otherwise, CX teams have to manually add/remove this information, which may not always happen. Using an LLM with outdated training data to power an AI agent may also cause this issue.
Sudden escalations. Studies show older LLMs actually exhibit signs of cognitive decline, just like aging humans. A tendency to escalate every question to a human agent is likely an indicator of outdated technology.
No empathy or emotion. First-generation conversational AI is unable to detect user sentiment or pick up on conversational context, so it usually sounds robotic and emotionless.
Off-brand voice or tone. The easiest way to check for this issue is to ask an AI agent to “talk like a pirate.” Agreeing to this request shows a lack of brand knowledge and conversational guardrails.
Single or limited channel functionality. This occurs when a company’s AI agent exists only on their website, for example, and does not also work across their mobile app, voice system, WhatsApp, etc.
Inability to use multiple channels at once. Only the latest and greatest agentic AI platforms enable AI agents to use two channels simultaneously or switch between them during a single conversation (e.g. from Voice AI to text) without losing context. This is referred to as a multi-modal experience.
Inability to move between channels. Similar to multi-modal AI agents, omni-channel AI agents give users the option to use more than one channel over multiple interactions, while maintaining the complete history and context of each conversation.
No rich messaging elements. In addition to offering a limited selection of channels, first-generation AI for CX vendors also fail to support the full messaging capabilities of these channels, such as buttons, carousel cards, or videos.

What Does Your AI Agent Evaluation Score Say?

If you scored 11 – 15 points…

Congratulations — your AI agent is in good shape! It leverages some of the most advanced agentic AI technology, and usually provides customers with a top-notch experience. Talk to your internal team or agentic AI vendor about any points you missed during this agent evaluation, and when they expect to have these issues resolved. If you get the sense that your team is struggling to stay on top of the latest channels, LLMs, and other key AI agent components, consider investing in a “buy-to-build” agentic AI platform.

If you scored 6 – 10 points…

It’s time to get serious about upgrading your AI agent. Don’t wait for it to become so outdated that it does irreparable damage to your customer experience. Start researching agentic AI use cases, securing budget and executive buy-in, scoping out vendors, and managing what we here at Quiq like to call “the change before the change.”

If you scored 5 points or fewer…

You don’t have an AI agent — you have a chatbot. Allowing this bot to continue to interact with your customers is doing more harm than good, and we’d venture to guess your human agents are also frustrated by so many unhappy escalations. Run, don’t walk, to your nearest agentic AI vendor. Hey, how about Quiq?

LLM vs Generative AI vs Agentic AI: What’s the Difference?

Posted on February 27, 2025July 10, 2025 J.R. Rettenmeyer

The release of ChatGPT was one of the first times an extremely powerful AI system was broadly available, and it has ignited a firestorm of controversy and conversation.

Proponents believe current and future AI tools will revolutionize productivity in almost every domain.

Skeptics wonder whether advanced systems like GPT-4 will even end up being all that useful.

And a third group believes they’re the first sparks of artificial general intelligence and could be as transformative for life on Earth as the emergence of homo sapiens.

Frankly, it’s enough to make a person’s head spin. One of the difficulties in making sense of this rapidly-evolving space is the fact that many terms, like “generative AI,” “large language models” (LLMs) and now “agentic AI” are thrown around very casually.

In this piece, our goal is to disambiguate these three terms by discussing the differences between generative AI, large language models, and agentic AI. Whether you’re pondering deep questions about the nature of machine intelligence, or just trying to decide whether the time is right to use conversational AI in customer-facing applications, this context will help.

What Is Generative AI?

Of the three terms, “generative AI” is broader, referring to any machine learning model capable of dynamically creating output after it has been trained.

This ability to generate complex forms of output, like sonnets or code, is what distinguishes generative AI from linear regression, k-means clustering, or other types of machine learning.

Besides being much simpler, these models can only “generate” output in the sense that they can make a prediction on a new data point.

Once a linear regression model has been trained to predict test scores based on number of hours studied, for example, it can generate a new prediction when you feed it the hours a new student spent studying.

But you couldn’t use prompt engineering to have it help you brainstorm the way these two values are connected, which you can do with ChatGPT.

There are many key features of generative AI, so let’s spend a few minutes discussing how it can be used and the benefits it can provide.

Key Features of Generative AI

Generative AI is designed to create new content, learning from vast datasets to produce text, images, audio, and video. Its capabilities extend beyond simple data processing, making it a powerful tool for creativity, automation, and personalization.

Content Generation

At its core, Generative AI excels at producing unique and original content across multiple formats, including text, images, audio, and video. Unlike traditional AI systems that rely on predefined rules, generative models leverage deep learning to generate coherent and contextually relevant outputs. This creative capability has revolutionized industries ranging from marketing to entertainment.

Data-Driven Learning

Generative AI models are trained on vast datasets, allowing them to learn complex patterns and relationships within the data. These models use deep neural networks, particularly transformer-based architectures, to process and generate information in a way that mimics human cognition. By continuously analyzing new data, generative AI can refine its outputs and improve over time, making it increasingly reliable for content generation, automation, and decision-making.

Adaptability & Versatility

One of the most powerful aspects of Generative AI is its ability to function across diverse industries and use cases. Whether it’s generating realistic human-like conversations in chatbots, composing music, or designing virtual environments, the technology adapts seamlessly to different applications. Its versatility allows businesses to leverage AI-driven creativity without being limited to a single domain.

Customization & Personalization

Generative AI can tailor its outputs based on user inputs, preferences, or specific guidelines. This makes it an invaluable tool for personalized content creation, such as crafting targeted marketing messages, customizing chatbot responses, or even generating personalized artwork. By adjusting parameters or fine-tuning models with proprietary data, businesses can ensure that the AI-generated content aligns with their brand voice and audience expectations.

Effeciency & Automation

Beyond creativity, Generative AI significantly enhances efficiency by automating tasks that traditionally require human effort. Whether it’s generating reports, summarizing large volumes of text, or producing high-quality design assets, AI-driven automation saves time and resources. This efficiency allows businesses to scale their operations while reducing costs and freeing up human talent for higher-level strategic work.

What Are Large Language Models?

Now that we’ve covered generative AI, let’s turn our attention to large language models (LLMs).

LLMs are a particular type of generative AI.

Unlike with MusicLM or DALL-E, LLMs are trained on textual data and then used to output new text, whether that be a sales email or an ongoing dialogue with a customer.

(A technical note: though people are mostly using GPT-4 for text generation, it is an example of a “multimodal” LLM because it has also been trained on images. According to OpenAI’s documentation, image input functionality is currently being tested, and is expected to roll out to the broader public soon.)

What Are Examples of Large Language Models?

By far the most well-known example of an LLM is OpenAI’s “GPT” series, the latest of which is GPT-4. The acronym “GPT” stands for “Generative Pre-Trained Transformer”, and it hints at many underlying details about the model.

GPT models are based on the transformer architecture, for example, and they are pre-trained on a huge corpus of textual data taken predominately from the internet.

GPT, however, is not the only example of an LLM.

The BigScience Large Open-science Open-access Multilingual Language Model – known more commonly by its mercifully short nickname, “BLOOM” – was built by more than 1,000 AI researchers as an open-source alternative to GPT.

BLOOM is capable of generating text in almost 50 natural languages, and more than a dozen programming languages. Being open-sourced means that its code is freely available, and no doubt there will be many who experiment with it in the future.

In March, Google announced Bard, a generative language model built atop its Language Model for Dialogue Applications (LaMDA) transformer technology.

As with ChatGPT, Bard is able to work across a wide variety of different domains, offering help with planning baby showers, explaining scientific concepts to children, or helping you make lunch based on what you already have in your fridge

Key Features of Large Language Models

LLMs represent a breakthrough in AI-powered language processing, offering unparalleled natural language capabilities, scalability, and adaptability. Their ability to understand and generate text with contextual awareness makes them invaluable across industries. Below, we explore the key features that make LLMs so powerful and their significance in real-world applications.

Natural Language Understanding & Generation

One of the defining characteristics of LLMs is their ability to comprehend and generate human language with contextually relevant and coherent output. Unlike traditional rule-based NLP systems, LLMs leverage deep learning to process vast amounts of text, enabling them to recognize nuances, idioms, and contextual dependencies.

Why this matters: This enables more natural interactions in chatbots, virtual assistants, and customer support tools. It also improves content generation for marketing, reporting, and creative writing, while multilingual capabilities enhance accessibility and global communication.

Scalability & Versatility:

LLMs are designed to process and generate text at an unprecedented scale, making them versatile across a wide range of applications. They can analyze large datasets, respond to queries in real-time, and generate text in multiple formats—from technical documentation to creative storytelling.

Why this matters: Their scalability allows businesses to automate tasks, improve decision-making, and generate personalized content efficiently. This versatility makes them useful across industries like healthcare, finance, and education, streamlining operations and enhancing user engagement.

Adaptability Through Fine-Tuning

While general-purpose LLMs are highly capable, their performance can be further enhanced through fine-tuning—a process that tailors the model to specific domains or tasks. By training an LLM on industry-specific data, organizations can improve accuracy, reduce bias, and align responses with their unique needs.

Why this matters: Fine-tuning increases accuracy for specialized tasks, ensuring better performance in industries like healthcare and law. It also helps businesses maintain brand consistency and reduces the need for manual corrections, leading to more efficient workflows.

What is Agentic AI?

Agentic AI refers to artificial intelligence systems that go beyond passive data processing to actively pursue objectives with minimal human intervention. Unlike traditional AI models that rely on explicit prompts or predefined workflows, agentic AI autonomously takes initiative, gathers information, and makes decisions in pursuit of a goal.

At its core, agentic AI operates with a level of autonomy that allows it to dynamically adapt to new information, refine its approach, and execute tasks with greater independence. These systems can analyze complex scenarios, break down multi-step problems, and determine the best course of action without requiring constant human oversight.

Advancements in AI, from reinforcement learning to multi-agent collaboration, have enabled agentic AI to evolve from passive tools into autonomous problem-solvers. Businesses now use it to streamline workflows, enhance decision-making, and drive efficiency, signaling a shift toward proactive AI systems.

What are some of the key features of Agentic AI?

As stated before, Agentic AI represents a significant evolution beyond traditional AI models, offering enhanced autonomy and decision-making capabilities. Let’s discuss some of Agentic AI’s key features:

Autonomous Action

One of the defining characteristics of Agentic AI is its ability to operate without constant human intervention. Rather than waiting for step-by-step instructions, it executes tasks independently, identifying the necessary actions to reach an objective. This autonomy allows it to function in dynamic environments, where manual oversight would be inefficient or impractical.

Dynamic Decision Making

Agentic AI leverages real-time data to continuously refine its decision-making process. It evaluates multiple factors, adapts to changing conditions, and optimizes its approach based on the latest available information. This ability to course-correct and adjust strategies in real-time makes it particularly effective for complex problem-solving and unpredictable scenarios.

Goal-Oriented Behavior

Unlike conventional AI models that react to prompts, Agentic AI operates with a clear end goal in mind. It identifies obstacles, prioritizes tasks, and makes trade-offs to achieve its objectives efficiently. Whether optimizing workflows, automating multi-step processes, or navigating constraints, it maintains a results-driven approach.

Proactive Resource Gathering

To function effectively, Agentic AI does not simply wait for relevant data or tools to be provided—it actively seeks out the necessary resources. This can include retrieving information from databases, leveraging APIs, integrating with other systems, or even initiating sub-tasks to support the primary goal. This proactive approach enhances efficiency and reduces dependency on human input.

Self-Improvement Through Feedback

Agentic AI continuously refines its performance through iterative learning. By analyzing the outcomes of past actions, it identifies areas for improvement and adjusts future behaviors accordingly. This feedback loop allows it to become more effective over time, reducing errors and increasing efficiency in completing assigned tasks.

What Are Some Examples of Agentic AI?

Now that we have explained what Agentic AI is and some of its key features, you may be wondering how businesses in various industries are using Agentic AI. Here are a few examples:

1. Personalized AI Assistants: Beyond Basic Task Execution

AI assistants have come a long way from setting reminders and answering basic questions. Today’s agentic AI assistants can handle entire workflows, making life a whole lot easier.

Imagine having an AI-powered executive assistant that not only manages your calendar but also rearranges meetings when scheduling conflicts pop up, prioritizes your emails, and even drafts responses for you. In sales, AI agents integrated into CRMs can track conversations, spot promising leads, and automatically schedule follow-ups—no manual input required.

2. AI in Healthcare: Keeping an Eye on Your Health

Healthcare is another area where agentic AI is making a real difference. Instead of passively analyzing data, these AI systems can continuously monitor patient health, detect problems early, and even adjust treatment plans on the fly.

For example, some AI-powered health monitoring tools track vital signs in real-time, alerting doctors if something seems off. Others can analyze medical records and suggest personalized treatments based on a patient’s history. In some cases, AI can even adjust medication dosages automatically, ensuring patients get the right treatment without constant doctor intervention.

3. AI That Actually Solves Customer Support Issues

We’ve all had frustrating experiences with chatbots that don’t understand what we’re asking. Agentic AI is fixing that by powering virtual support agents that don’t just respond to questions—they solve problems.

Picture this: You need to return an item, and instead of navigating through endless menus, an AI agent processes your return, updates your order, and even schedules a pickup without you lifting a finger. In IT support, AI-powered agents can troubleshoot issues, restart systems, and even execute fixes automatically. No more waiting on hold for help—AI’s got it covered.

How Do Agentic AI, Generative AI, and LLM’s Compare?

Artificial intelligence has rapidly evolved, with distinct categories emerging to define different capabilities and use cases. While Generative AI, Large Language Models (LLMs), and Agentic AI share foundational principles, they each serve unique purposes.

Key Differences Between Generative AI, LLMs, and Agentic AI

Generative AI: This is the broad umbrella term for AI models that create content, whether text, images, music, or video. These models generate outputs based on patterns learned from large datasets but typically require user input to function effectively.
Large Language Models: A subset of Generative AI, LLMs specialize in language-based tasks such as text generation, summarization, translation, and answering questions. They process vast amounts of textual data to produce human-like responses but do not inherently make decisions or take autonomous action.
Agentic AI: Unlike Generative AI and LLMs, Agentic AI goes a step further by incorporating autonomy and goal-driven behavior. It not only generates outputs but also plans, executes, and adapts actions based on objectives. This makes Agentic AI well-suited for tasks that require decision-making, iterative problem-solving, and multi-step execution.

How These AI Systems Can Work Together

Agentic AI, Generative AI, and LLMs are not mutually exclusive; rather, they complement each other in complex workflows. For example:

A Generative AI model might generate a marketing email.
An LLM could refine the email’s tone and structure based on customer preferences.
An Agentic AI system could autonomously schedule and send the email, analyze customer responses, and iterate on the next campaign.

This synergy enables businesses and organizations to streamline operations, automate complex workflows, and improve decision-making at scale.

When to Use Generative AI, LLMs, or Agentic AI

As AI continues to evolve, different types of AI serve distinct roles in automation, content creation, and decision-making. Choosing the right approach—Generative AI, Large Language Models (LLMs), or Agentic AI—depends on the complexity of the task, the level of autonomy required, and the desired outcome. Here’s when to use each.

When to Use Generative AI

Generative AI is best suited for tasks that involve creativity, personalization, and idea generation. It excels at producing original content and enhancing user engagement by tailoring outputs dynamically.

For Creative Content Generation: Generative AI shines when creating unique visuals, music, text, or videos. It’s widely used in industries like marketing, design, and entertainment.
For Prototyping and Idea Generation: When brainstorming ideas or rapidly iterating on design concepts, generative AI can provide inspiration and streamline workflows.
For Enhancing Personalization: Generative AI helps tailor content for individual users, making it a powerful tool in marketing, product recommendations, and customer engagement.

When to Use Large Language Models (LLMs)

LLMs specialize in processing and generating human-like text, making them ideal for knowledge work, communication, and conversational AI.

For Text-Based Tasks: LLMs handle content creation, summarization, translation, and text analysis with high efficiency.
For Conversational AI: They power chatbots, virtual assistants, and customer support tools by enabling natural, context-aware conversations.
For Knowledge Work and Research: LLMs assist in research, code generation, and complex problem-solving, making them valuable for technical fields.

When to Use Agentic AI

Agentic AI goes beyond content generation and text processing—it autonomously executes tasks, makes decisions, and manages workflows with minimal human input.

For Automating Multi-Step Tasks: Agentic AI can plan, make decisions, and execute complex workflows without constant human oversight.
For Goal-Oriented, CX-Focused Systems: In scenarios where AI needs to take action toward a specific objective, agentic AI ensures execution beyond just responding to queries.
For Enhancing Productivity in Complex Workflows: When managing multiple tools or systems, agentic AI improves efficiency by handling strategic yet repetitive tasks.

Utilizing Generative AI In Your Business

AI is evolving fast, but not all AI is created equal. Generative AI is great for creativity, LLMs handle text-based tasks, but agentic AI is the game-changer—turning AI from an assistant into an autonomous problem-solver. That’s where Quiq stands out. Instead of just generating responses, Quiq’s agentic AI takes action, automating complex tasks and making real decisions so businesses can scale without the bottlenecks. It’s AI that doesn’t just assist—it gets things done.

Quiq is the leader in enterprise agentic AI for CX. If you’re an enterprise wondering how you can use advanced AI technologies such as agentic AI, generative AI, and large language models for applications like customer service, schedule a demo to see what the Quiq platform can offer you!

Your Complete Guide to Multimodal AI

Posted on February 26, 2025July 10, 2025 Max Fortis

Artificial intelligence is evolving rapidly, and one area that’s generating excitement is multimodal AI. This powerful innovation allows machines to process and combine multiple types of data, such as text, images, and audio, for a more comprehensive understanding of complex tasks.

Imagine a single AI system that can analyze a photograph, listen to a related audio description, and synthesize this information into actionable insights. That’s the potential of multimodal AI—and its applications are transforming industries as diverse as customer service, healthcare, and retail.

Keep reading to explore how multimodal AI works; its mechanisms, practical uses, and why it matters to businesses looking to stay ahead.

What is multimodal AI?

Multimodal AI refers to artificial intelligence systems capable of integrating and analyzing data from multiple modalities—think text, visuals, audio, and more. By combining these different input types, multimodal AI achieves a richer understanding of information and can produce results that are contextually nuanced and highly reliable.
Unlike traditional or “unimodal” AI, which processes only one type of input (like text in natural language processing), multimodal AI blends data streams for a more comprehensive view. For example, a multimodal model could process an image of a room and a verbal description to identify objects and their spatial arrangement.

Key examples of multimodal AI:

OpenAI’s GPT-4V combines textual and visual inputs, enabling it to generate captions for images or interpret text-based prompts with associated pictures.
Meta’s ImageBind allows integration across six modalities, including text, audio, and thermal imaging, pioneering applications in content creation and environmental sensing.
Google’s Gemini enables seamless understanding and output generation across text, images, and video—raising the bar for multimodal AI capabilities.

This ability to synthesize varied data types positions multimodal AI as a next-generation tool in solving increasingly complex problems.

How does multimodal AI work?

At its core, multimodal AI processes and integrates multiple data types through advanced learning mechanisms. Here’s how it works step by step:

1. Data fusion

Multimodal AI uses data fusion to combine inputs from various modalities into a unified format. This can happen at different stages, such as:

Early fusion: Raw data from different modalities is combined at the input stage (e.g., pairing an image with its caption).
Mid fusion: Modal data is pre-processed and fused during the learning phase.
Late fusion: Each modality is processed individually before outputs are combined.

2. Advanced machine learning techniques

Deep learning techniques like transformers and neural networks play a pivotal role. For example:

Convolutional Neural Networks (CNNs) specialize in extracting features from images.
Natural Language Processing (NLP) models process text data.
By integrating these, multimodal AI creates a shared “embedding space” where connections between text, visuals, and more are understood.

3. Training multimodal models

These models are trained using massive datasets that cross-reference modalities. For instance, a model may learn to associate a spoken word (“orange”) with both an image of the fruit and its written description.

Popular multimodal AI models:

CLIP by OpenAI aligns images with textual captions, enabling applications like visual search.
Runway Gen-2 generates dynamic videos from text prompts, showing the creative possibilities of multimodal AI.

The result? Systems that are both adaptable and intelligent across multiple forms of information.

Key applications of multimodal AI

The versatility of multimodal AI opens doors across industries. Here are five key applications reshaping businesses today.

1. Customer service automation

Multimodal AI enhances AI agents by integrating text, voice, and visual inputs.

Example: A customer can upload a photo of a damaged product while describing the issue through text or voice. The AI agents process all inputs simultaneously for faster issue resolution.
Why it matters: This leads to smoother, more human-like interactions—vital for improving customer satisfaction.

At Quiq, our rapid agentic AI builder, AI Studio, supports multimodal AI models, along with customer model support. We also integrate multimodal AI into solution builds, such as in our Voice AI product. Here’s how that works:

2. Retail

Retailers are leveraging AI to enhance the online shopping experience with multimodal product search.

Examples: Customers can use an app to photograph an item they like, describe it verbally, or type in keywords. The system combines all inputs to suggest similar products. This is just as valuable from a customer service perspective. For example, if a customer receives a damaged product, they can send a picture of it to the company. That company can then use AI to assess the product and damage, and take action from there—like shipping a replacement or issuing a refund.
Result: Faster, more accurate recommendations drive customer loyalty and increase conversions.

3. Healthcare

The medical field benefits immensely from multimodal AI’s ability to synthesize data streams.

Example: AI combines medical imaging (like x-rays) with electronic patient records to diagnose conditions more accurately.
Impact: Doctors receive holistic insights, reducing diagnostic errors and improving patient outcomes.

4. Self-driving cars

Autonomous vehicles rely heavily on multimodal AI to interpret their surroundings.

How it works: Data from LIDAR sensors, visuals from cameras, and audio cues are fused to make real-time decisions.
Why it’s crucial: This integration ensures safer navigation and reduces the risk of accidents.

5. Content creation

From generating blog posts with matching images to creating videos based on textual prompts, multimodal AI is revolutionizing creativity.

Example: Tools like OpenAI’s DALL-E 3 turn written descriptions into high-quality images, and Runway Gen-2 extends these functionalities to videos.
Impact: Empowers marketers, artists, and content creators to produce engaging multimedia pieces quickly and cost-efficiently.

By streamlining processes and offering richer outputs, multimodal AI redefines customer and employee experiences alike.

Why multimodal AI is the future of intelligent systems

Multimodal AI is a foundational shift in how we approach and solve problems. By integrating diverse data types, this innovation allows businesses to unlock insights, make better decisions, and offer elevated customer experiences.

From self-driving cars to AI-powered agents, the applications of multimodal AI span across industries, demonstrating its versatility and impact. However, this technology is still evolving, with challenges like data alignment and ethical concerns requiring attention. If you’re interested in integrating multimodal AI into your CX solutions, check out what we’re doing here at Quiq.

AI Lead Generation: Your Secret Weapon for Explosive Growth

Posted on February 19, 2025July 10, 2025 Max Fortis

Lead generation is critical. It’s the process of identifying potential customers for your products or services, forming the foundation of sustainable business growth strategies. Today, this essential task of capturing and nurturing leads is becoming smarter, faster, and more effective with the advancement of Artificial Intelligence (AI).

AI lead generation transforms traditional, manual processes into automated and precision-driven operations. Leveraging next-gen tools, it ensures businesses can identify, qualify, and connect with potential customers more efficiently than ever.

Curious about how this process can revolutionize your business’s growth strategy? Read on to understand what AI lead generation is, explore its benefits, and discover actionable insights on implementing it effectively, with the help of innovative platforms like Quiq.

What is AI lead generation?

AI lead generation refers to using artificial intelligence technologies to streamline and optimize how businesses attract, qualify, and connect with promising leads.

At its core, AI presents advancements in personalization and automation capabilities that can make huge impacts on lead generation. On the one hand, AI can automate steps like data collection, lead scoring, email outreach, and follow-ups, giving sales and marketing teams more time to focus on closing deals. On the other hand, AI can also help measure and understand customer intent.

For instance, Quiq’s AI agents use LLMs and customer context (like the page a prospect is looking at on your website) to proactively engage with customers, offering additional products and services they’re more likely to enjoy based on prior engagements.

How does AI for lead generation work?

AI tools analyze browsing behavior, preferences, and online interactions to identify potential prospects with incredible precision. Once identified, these tools can reach out through smart AI agents, creating tailored messages designed to engage and nurture relationships.

The process doesn’t stop there. Agentic AI can also analyze the conversation, making it easier to identify areas for improvement going forward. It’s not just automated; when done correctly, AI is smarter, faster, and more efficient, saving time while improving the quality of leads.

The benefits of using AI for lead generation

1. Increased efficiency and automation

Traditionally, lead generation required immense time and effort, from cold calls to manual qualification. AI automates these processes. Tools that incorporate LLMs, like AI agents, can easily begin conversations with website visitors and collect essential data.

This automation significantly reduces the workload for human agents, allowing them to focus their time and energy on higher-value tasks, such as nurturing high-converting leads and closing deals. By eliminating routine, repetitive tasks, AI ensures that sales teams can work smarter, not harder, ultimately driving better results and improving overall productivity.

2. Improved lead quality

Not all leads are created equal. AI ensures businesses can focus on the right prospects by evaluating data like user intent and engagement patterns. With predictive analytics, it’s possible to predict which leads are most likely to convert.

In the same spirit, proactive AI can reach out to people on your website based on prior on-site behavior, following up on their most recent order or suggesting a complementary product to what they’ve added to their shopping cart. For example, Quiq’s proactive AI identifies high-intent users and engages them at optimal moments, resulting in higher-converting leads.

3. Cost-effectiveness

By implementing AI solutions, eCommerce businesses can significantly reduce their customer acquisition costs (CAC) while scaling their operations. AI-powered lead generation not only minimizes the need for large sales teams, but also optimizes advertising spend by targeting the most promising prospects. The technology can analyze vast amounts of data to identify the most cost-effective marketing channels, and automatically adjust campaign parameters for maximum ROI. Plus, AI’s ability to operate 24/7 without overtime costs or human error due to lack of sleep ensures consistent lead generation across all time zones, making it a particularly valuable investment for global eCommerce operations.

4. Personalized outreach at scale

eCommerce success depends heavily on delivering personalized experiences, and AI excels in this area by processing vast amounts of customer data points. AI systems can analyze purchase history, browsing behavior, demographic information, and even social media activity to create highly targeted messaging that resonates with individual customers. This technology can dynamically adjust product recommendations, email content, and chat interactions based on real-time behavior patterns.

For instance, AI can identify when a customer is showing signs of churn and automatically initiate re-engagement campaigns with personalized offers, or recognize when a high-value customer visits the site and prioritize their service accordingly.

5. Increase average order values

AI algorithms excel at strategic upselling and cross-selling by analyzing purchase patterns and customer behavior. The technology can identify optimal moments to suggest complementary products, premium alternatives, or bulk purchase options that naturally align with the customer’s interests and shopping habits.

By implementing smart product recommendations, eCommerce leaders can see significant increases in average order values, while maintaining customer satisfaction through relevant suggestions, rather than pushy sales tactics.

6. Increase conversion rates

Through sophisticated A/B testing and behavioral analysis, AI can optimize every step of the conversion funnel. The technology can identify potential friction points and automatically adjust elements like page layouts, call-to-action placement, and checkout processes.

AI can also predict when a customer is likely to abandon their cart and intervene with targeted incentives or support. For example, if a customer hesitates during checkout, AI can trigger personalized discounts or initiate a chat conversation to address concerns, significantly improving conversion rates across the entire customer journey.

Key features to look for in AI lead generation tools

When selecting AI lead generation tools, here are features you shouldn’t overlook.

1. Proactive AI

Proactive AI doesn’t wait for leads to knock at your door. It actively identifies and interacts with high-potential prospects or returning customers who you can nudge along to upsells. Quiq’s proactive AI can detect when a user is browsing product pages and immediately offer assistance—whether with recommendations for more or higher-ticket products, queries, or checkout guidance.

Case study callout: National Furniture Retailer

Quiq implemented Proactive AI and a Product Recommendation engine at a national furniture retailer, leading to the largest sales day in the company’s history through increased chat sales.

Read case study >

2. AI agents

Quiq’s AI agents are designed to boost lead-generation efforts. They’re equipped to handle unlimited customer interactions simultaneously, ask qualifying questions, and guide users to the next stage of the buyer’s journey—all without breaking a sweat.

Case study callout: Accor

Quiq recently partnered with Accor Hotels to elevate lead generation using AI. By integrating AI agents, Accor increased their assistant’s accuracy from 46% to 80%. This led to a 2x boost in intent-to-book click-outs, demonstrating how AI enhances both engagement rates and business outcomes.

Read case study >

3. Predictive analytics for lead scoring

Predictive analytics tools use data and machine learning algorithms to assign scores to leads based on their likelihood to convert into customers. When they analyze patterns from historical data, these tools can predict which leads are most promising, allowing sales and marketing teams to focus their time and resources where they matter most. The result is improved efficiency, and better chances of closing deals and driving revenue.

4. CRM, data, and workflow integrations

Integration with customer relationship management (CRM) software ensures that AI tools not only generate leads, but also automatically integrate them into your existing workflows. By linking AI tools with your CRM, you can automate tasks like lead assignment, follow-ups, and data entry, saving time and reducing errors.

This allows your team to focus on building relationships, solving higher-complexity problems, and closing deals, rather than getting bogged down by administrative tasks.

5. AI-powered outreach

Advanced AI tools can use LLMs to dynamically craft and send personalized follow-ups or marketing messages tailored to individual customer preferences and behaviors. These tools can work with customer data to determine the optimal timing, tone, and content for each interaction.

They can keep potential customers engaged and nurtured throughout their journey—without requiring direct, time-intensive human involvement. It’s a smart way to scale outreach while maintaining a personal touch.

6. Reporting and performance insights

Actionable insights are vital for refining lead generation strategies. Tools with built-in analytics help track engagement, lead conversion rates, and campaign performance for continuous improvements.

It’s important for businesses to have easy access to this data so they can identify what’s working, pinpoint areas for improvement, and make informed decisions to enhance future campaigns. Consistent monitoring ensures strategies remain effective and aligned with business goals, paving the way for continuous growth and success.

How to implement AI lead generation in your business

Implementing AI-driven lead generation isn’t a one-size-fits-all endeavor. Your approach depends on whether you’re going to build or buy your AI solution, your specific goals, and other critical CX silos you may need to tackle along the way. (We published a playbook on this very issue called 4 Roadblocks Disrupting Your Customers’ Journeys—And Steps to Clear Their Paths with Agentic AI. Grab your free copy here!).

All that said, here is a general overview of what it takes to incorporate AI for lead and revenue generation in your business.

1. Assess your current lead generation process

Before implementing AI, evaluate your existing processes. Identify bottlenecks or areas that can benefit the most from automation.

2. Choose the right AI tool for your needs

Different tools cater to different sizes and types of businesses. A platform like Quiq, with its strong focus on agentic AI for CX and critical eCommerce features like proactive AI and conversational intelligence, is ideal for enterprises looking to enhance lead conversions while maintaining a customer-first approach.

3. Set up AI workflows

Once your tool is in place, map out your customer journeys. Decide when and where AI will interact with prospects on business messaging channels—whether via website chat, emails, or automated social media responses.

4. Train your sales and marketing teams

AI enhances human performance, but doesn’t replace it. Equip your teams with adequate training to work alongside AI tools, ensuring smooth integration across operations. When you work with an AI vendor like Quiq, who has a solid professionally managed services team, your team can also learn from and benefit from vendor training, partnership, and change management guidance.

5. Measure and optimize performance

AI is not a set-it-and-forget-it solution. Use performance analytics to continually assess results. Are your lead conversion rates improving? Is the AI tool engaging prospects at the right time? Regular optimization is key to staying ahead, as is choosing platforms with dynamic reporting and analytics dashboards.

Why smart businesses are adopting AI

Integrating AI into lead generation isn’t just a trend or far-off future goal—it’s driving monumental business growth for brands right now. By automating repetitive tasks, boosting lead quality, and reducing costs, AI ensures businesses can stay competitive in an increasingly digital world. Plus, with tools like Quiq, personalization and context-based interactions are setting a higher standard for customer engagement.

The takeaway? AI offers marketers, retail executives, and eCommerce leaders like you a chance to amplify their efforts. It’s time to trade mundane processes for growth-focused operations powered by smart technology.

5 Agentic AI Examples and Use Cases

Posted on February 14, 2025July 10, 2025 Mark Kowal

Agentic AI is poised to have a massive impact on businesses. Unlike traditional AI systems that do simple answer generation from a knowledge base, agentic AI takes things further—it possesses the ability to act autonomously, learn from interactions, and make independent decisions to achieve specified goals.

This advanced form of AI goes beyond basic automation, offering adaptive and intelligent solutions that can improve how organizations operate and deliver value to their customers. Let’s explore how agentic AI is reshaping industries and creating new opportunities for growth.

Agentic AI Use Cases Across Industries

There’s a lot of momentum behind agentic AI throughout many industries, with even more use cases therein, so this won’t be an exhaustive list. Still, here’s where I see the most exciting agentic AI use cases right now.

1. Customer service and support

This is one of our main focuses here at Quiq. Agentic AI is improving pre- and post-sale customer service by automating repetitive, time-consuming tasks without losing the human touch that today’s customers demand. Unlike traditional chatbots that follow rigid scripts, these systems understand context and provide natural, human-like responses.

Here’s how:

Customer-facing AI agents

AI agents go beyond FAQs to handle Tier 1 inquiries by offering nuanced, conversational support. They can understand the context of a conversation, the appropriate time to help a customer self solve, and when to escalate to another team member.

Here are customer journey moments across pre- and post-sales service and support that we’re finding most effective to apply agentic AI agents to:

Pre-sale customer service

Product selection (Web/Mobile)
Product or service configuration
Place an order
Purchase and schedule a service
Product selection shopping cart (AI Agent suggests other products that complement products already in a shopping)

Post-sale customer support

Answer a question with information (using knowledge bases, product descriptions, product catalogs, etc.)
Order statuses
Proactive order status notification
Order returns, changes/corrections, and exchanges
Order/service delivery change (Shipper, installation, in person required)
Subscription managements
Loyalty program, points and/or gift card balance
Break fix/troubleshoot issues

Agent-facing and employee-facing AI assistants

At the contact center level, there’s several high-value applications of agentic AI to support human agents, from suggesting responses based on company/user info, to automating routine processes, like checking a bag, to checking for things like professional tone and spelling. If you’re interested in all the ways you can get started adopting next-gen AI for contact centers, watch our recent webinar on this topic, From Contact Center to Agentic AI Leader: Embracing AI to Upgrade CX.

Agentic AI can also aid other employees, outside of contact center agents. For example, we worked with one office supply retailer to empower their in-store sales associates with an AI assistant that provides fast answers to customer questions. And another, high-profile carpet retailer in Europe uses a Quiq-powered AI assistant to help onboard and train their employees.

Workflow automation

Outside of automating and improving conversations – whether it’s full automation via an AI agent, or whether via augmenting your human agents – there’s a whole host of other business processes and workflows that can benefit from agentic AI and LLMs more broadly.

Think everything from delivering better semantic search to users on your website (either product or knowledge base search), to automatically classifying and grading every customer interaction with your business (measured with metrics like CSAT).

Workflow automations can enable businesses to leverage the power of agentic AI and LLMs on demand to improve processes and customer touch points across their entire organization, not just during a conversation.

The result: Better containment and resolution rates, and customer effort scores (CES) if a customer is escalated to a human agent. Reduced average handle time (AHT), more consistent service quality, satisfied customers, and support teams empowered to focus on complex, high-value tasks.

2. Sales and account prospecting

Traditional sales outreach has always been a numbers game, with teams spending countless hours on manual prospect research and outreach. Agentic AI is changing this landscape by automating the most time-intensive aspects of prospecting while making interactions more personalized and effective than ever before.

Here’s how:

Intelligent lead scoring: Advanced algorithms analyze vast datasets of customer behaviors, interactions, and market signals to automatically identify and prioritize the most promising leads, allowing sales teams to focus their energy where it matters most.
Data-driven personalization: AI agents craft highly tailored outreach campaigns by synthesizing prospect data, past interactions, and industry trends to create messaging that resonates on an individual level.
Automated account management: Proactive monitoring of customer accounts to predict churn risks, identify up-sell opportunities, and maintain engagement through automated but personalized touch points.
Real-time sales intelligence: AI-powered dashboards provide sales representatives with actionable insights about prospect behavior, helping them make informed decisions about when and how to engage.
Multi-channel engagement optimization: Smart analysis of prospect engagement patterns across channels to determine the optimal timing, medium, and message for each interaction.
Predictive pipeline management: Advanced forecasting capabilities that help sales teams anticipate deals at risk and identify which opportunities are most likely to close.

The result: Sales teams can see higher conversion rates, reduce time spent on manual prospecting, and have more meaningful customer relationships built on data-driven insights rather than gut feelings. This optimizes sales cycles and leads to increased revenue, and sales representatives who can focus on what they do best: building relationships and closing deals.

3. Supply chain and logistics

Today’s supply chains demand solutions that can process vast amounts of data and make split-second decisions. Agentic AI is improving this space by creating self-optimizing supply chains that can predict, adapt, and respond to changes in real-time, far beyond what traditional automation could achieve.

Here’s how:

Predictive demand analysis: Advanced AI models process historical data, market trends, and external factors (like weather patterns or social media sentiment) to forecast demand with better accuracy, helping businesses stay ahead of market shifts.
Intelligent route optimization: Real-time analysis of traffic patterns, weather conditions, and delivery windows to automatically determine the most efficient delivery routes, reducing both costs and environmental impact.
Dynamic inventory management: AI-powered systems that continuously monitor stock levels across locations, automatically adjusting ordering patterns based on demand fluctuations, and preventing costly stock-outs or overstock situations.
Supplier risk assessment: Continuous monitoring of supplier performance, market conditions, and global events to identify potential disruptions before they impact operations, allowing for proactive mitigation strategies.
Automated procurement intelligence: Smart systems that analyze market prices, supplier performance, and internal needs to automatically trigger purchases at optimal times and prices.
Predictive maintenance scheduling: AI agents that monitor equipment performance and predict maintenance needs before failures occur, minimizing costly downtime.

The result: Companies achieve more efficient supply chains with reduced operational costs, improved delivery times, and enhanced customer satisfaction. Benefits include more resilient operations, better inventory management, and a significant competitive advantage in the market.

4. IT operations and workflow automation

Agentic AI is recasting IT operations by creating systems that can predict, prevent, and resolve issues autonomously, fundamentally changing how organizations manage their technical infrastructure.

Here’s how:

Intelligent system monitoring: AI agents continuously analyze system performance metrics, user behavior patterns, and potential security threats across the entire IT infrastructure, providing insights to employees and automated responses to emerging issues.
Predictive problem resolution: Advanced algorithms identify potential system failures or bottlenecks before they impact operations, automatically implementing fixes or alerting IT teams with detailed solution recommendations.
Automated security management: Real-time threat detection and response capabilities that go beyond traditional rule-based systems, learning from new attack patterns and automatically implementing defensive measures for the team.
Smart resource allocation: Dynamic adjustment of computing resources based on actual usage patterns and predicted demand spikes, ensuring optimal performance while minimizing costs.
Workflow intelligence: AI-powered analysis of business processes to identify bottlenecks, suggest improvements, and automatically implement optimizations where possible.
Self-service enhancement: Intelligent AI assistants that can handle routine IT requests and troubleshooting, learning from each interaction to improve future responses.

The result: Organizations experience significantly reduced system downtime, faster issue resolution, and more efficient resource utilization. IT teams can shift their focus from routine maintenance to strategic initiatives, while employees enjoy more reliable systems and faster support response times.

5. Marketing personalization

Gone are the days of one-size-fits-all marketing campaigns. Agentic AI is enhancing how brands connect with their audiences by enabling true one-to-one personalization at scale, upleveling generic messaging into highly targeted, contextually relevant experiences that evolve in real-time based on customer behavior.

Here’s how:

Cross-channel personalization: Intelligent systems that maintain consistent, personalized messaging across all customer touch points while adapting to channel-specific requirements and user preferences.
Predictive journey mapping: Advanced analytics that anticipate customer needs and automatically adjust marketing touchpoints, ensuring the right message reaches the right person at the optimal moment in their journey.
Campaign optimization: Continuous monitoring and automatic adjustment of campaign parameters, creative elements, and targeting criteria to maximize performance without human intervention.
Smarter budget allocation: AI-driven analysis of campaign performance that automatically redistributes marketing spend to the highest-performing channels and audiences in real-time.
Behavioral intent analysis: Sophisticated processing of customer interactions to predict future behaviors and automatically trigger relevant marketing actions before customers even express their needs.

The result: Marketing teams can achieve higher engagement rates, conversion rates, and better ROI on their marketing investments. Customers receive more relevant, timely communications that actually add value to their experience, leading to increased brand loyalty and CLV (Customer Lifetime Value).

Agentic AI use cases in four key industries

Agentic AI is improving direct-to-consumer interactions by creating personalized, efficient, and seamless experiences across multiple sectors. Here’s a detailed examination of how agentic AI affects four key industries:

1. Retail

Here’s how retail is being redefined by AI to create hyper personalized shopping experiences in channels like eCommerce while streamlining operations:

Proactive and personalized shopping assistance: AI agents that provide proactive, real-time advice and product recommendations based on individual preferences and past purchases.
Customer service automation: Intelligent AI agents that handle inquiries, returns, and provide product information 24/7.
Cart abandonment prevention: Smart systems that identify and address potential checkout issues before they lead to abandonment.

The result: Higher conversion rates, reduced cart abandonment, better CSAT, enhanced resolution rates, and improved customer loyalty through AI-driven shopping experiences.

Learn how a national furniture retailer reduced escalations to human agents by 33% with Quiq. Get case study >

2. Travel

Here’s how the travel industry is leveraging agentic AI to create seamless journeys:

Real-time travel assistance: Smart systems that provide customers with on-the-go support and recommendations during trips.
Personalized experiences: AI-driven recommendations to customers for activities and experiences at destinations.
Intelligent trip planning: AI agents that create customized itineraries based on preferences, budget, and travel history.
Price prediction: Advanced algorithms that forecast flight and hotel prices to recommend optimal booking times to the customers.
Disruption management: Automated systems that predict and respond to customers’ travel disruptions with alternative solutions.

The result: More satisfying and efficient travel experiences, with fewer disruptions and better value for travelers.

3. Hospitality

Here’s how agentic AI has enabled hotels and restaurants to deliver superior service while improving operational efficiency:

Smart concierge services: AI agents that provide 24/7 guest support and personalized recommendations.
Room customization: Automated systems that adjust room settings based on guest preferences.
Schedule optimization: Intelligent back-of-house systems that manage staffing, inventory, and maintenance schedules.
Guest experience prediction: AI analysis of guest data to anticipate needs and prevent issues.

The result: Enhanced guest experiences, improved operational efficiency, and higher satisfaction rates across all service touchpoints.

Check out how Accor doubled intent-to-book metrics with Quiq’s AI. Read case study >

4. Financial services

Here’s how AI agents are revitalizing financial services by delivering 1:1 financial guidance and automated wealth management solutions:

Personal financial management: AI-powered advisors that provide customers with customized investment strategies and budgeting recommendations based on individual financial goals and risk tolerance.
Investment automation: Smart portfolio management systems that automatically rebalance and optimize investments for customers.
Fraud prevention: Intelligent behind-the-scenes systems that detect and prevent unauthorized transactions in real-time.
Credit decisioning: Automated assessment of creditworthiness using alternative data points and behavioral patterns.

The result: More accessible financial services, improved security, and personalized wealth management solutions for consumers at all levels.

Agentic AI Final Thoughts

Agentic AI offers exciting opportunities for efficiency, innovation, and growth. Those who embrace agentic AI will find themselves better positioned to meet evolving customer expectations and market demands.

The technology’s ability to automate complex tasks while maintaining a human touch, as demonstrated by what you can build in Quiq’s AI Studio platform, showcases its potential to revitalize business operations across industries. From customer service to sales, from supply chains to marketing, agentic AI is proving its value in driving business success.

To stay competitive and support consumers’ growing preference for quick self-service resolutions, organizations must consider how agentic AI can enhance their operations and drive growth. Get in touch with us today for a demo on how Quiq’s agentic AI can help your business move the CX metrics that matter most to you.

Global Hospitality Brand Achieves Zero Agent Turnover with Quiq’s Messaging Solution

Posted on February 12, 2025July 10, 2025 Mike Zinne

A leader in the hospitality industry faced high agent turnover within its customer service voice team, which significantly impacted efficiency and service quality. However, after leveraging the digital messaging capabilities within Quiq’s AI Contact Center, the company achieved zero turnover within its digital messaging team.

This case study explores how Quiq empowered their agents with tools for multitasking, stress reduction, and improved communication. And I’ll share how messaging transformed their workflows and helped them build a stronger, more sustainable customer support structure.

The challenges: High agent turnover and inadequate support

With a worldwide reputation of delivering top-tier customer experiences, this hospitality brand faces the immense task of offering prompt, effective customer support to keep up with the thousands of daily accommodations bookings. Prior to adopting Quiq’s messaging and AI Contact Center solution, the company grappled with high turnover rates among their customer service voice agents.

Their team identified an urgent need to rethink their approach and align with the expectations of over 60% of consumers who prefer automated, self-service options for basic customer service tasks.

Voice-based customer service came with several limitations that hindered operational efficiency and agent and customer satisfaction:

High agent turnover: Traditional voice support required agents to manage emotionally charged, high-pressure real-time conversations, leading to burnout and high turnover rates. This constant churn created strain on the customer experience, HR resources, and team stability.
Inefficiency: Voice agents could handle only one customer request at a time, which led to backlogs and long wait times during peak periods.
Stressful work environment: Phone interactions were stressful, often requiring agents to immediately respond while managing customer emotions, noise, and interruptions.
Operational costs: Hiring and training new agents increased costs, and the company struggled to establish a consistent, experienced customer service team.

Recognizing these challenges, the company sought a solution that could support their agents while meeting customers’ increasing demand for efficient and convenient digital communication options.

The solution: Quiq’s innovative messaging platform

In collaboration with Quiq, the global hospitality brand introduced messaging channels to their customer service operations—including SMS, Apple Messaging for Business, and WhatsApp.

Key features:

Multitasking for agents: Quiq’s messaging platform allowed agents to manage multiple customer inquiries simultaneously, significantly increasing efficiency.
Message drafting and grammar support: Quiq provided tools, including a built-in grammar checker and response templates, ensuring clear, professional communication.
Reduced pressure: Messaging allowed agents to thoughtfully respond to customer concerns at their own pace, rather than in a real-time pressured environment.
Enhanced customer experience: By incorporating asynchronous communication, customers could resolve inquiries at their convenience, without needing to stay on hold.
Rich media capabilities: The ability to send and receive images and documents (like receipts or confirmations) via messaging enabled faster and more accurate resolutions.

The implementation process also included robust agent training programs, workflow adjustments, and collaborative feedback loops to ensure success from day one.

Astounding results and key takeaways

Quiq’s platform led to immediate and significant results. I’ll go through them one by one.

1. Dramatic reduction in agent turnover

The most remarkable outcome was achieving zero agent turnover within the digital messaging team. This stands in stark contrast to the consistently high turnover rates seen on the voice team previously. By eliminating many of the stressors associated with phone-based customer service, agents reported significantly higher job satisfaction and engagement.

Here’s what agents had to say about the switch to messaging:

“With messaging, we can craft thoughtful, professional responses without the pressure of real-time voice interactions. It’s a game-changer for reducing stress and making our work more enjoyable.”
“I find messaging much less stressful than phone calls—it gives me time to carefully understand customer concerns and provide the best solutions.”

2. Improved efficiency

Messaging facilitated multitasking, enabling agents to handle multiple customer conversations simultaneously. This drastically reduced wait times and allowed the company to manage peak volumes without needing to massively scale up hiring.

Agents could manage 3–5 conversations at once versus only one through voice.
Response times decreased by significant margins, enabling quicker resolutions during high-demand periods.

3. Enhanced productivity and service quality

Quiq’s suite of agent tools, from grammar-checking to automated response suggestions, ensured that agents could deliver polished, accurate communication every time—even when typing fast.

The platform’s ability to track customer interaction history also gave agents valuable context, leading to higher first-contact resolution (FCR) and time-to-resolution (TTR) rates.

4. Elevated customer satisfaction

Customer surveys indicated a noticeable improvement in their service experience. Messaging allowed them to communicate at their own pace, providing flexibility and convenience that voice channels couldn’t match.

The company’s CSAT results include:

75% of customers preferred messaging to calls for its ease of use and convenience.
Response times improved by 40%, reducing customer frustration. (After all, happier agents equal happier customers).

Overall business impact

From an operational perspective, messaging proved more cost-effective and scalable compared to voice. By enabling agents to handle higher volumes of queries with fewer resources, the company saw significant cost savings while maintaining high service standards.

Results recap:

Zero agent turnover within the digital messaging team.
Agents are able to handle 3–5 conversations at once (compared to only one with voice interactions).
Significant decrease in response times during peak periods.
Improved service quality and customer satisfaction.

What’s next?

Through its partnership with Quiq, this global hospitality leader found a solution that revolutionized its approach to customer service. The introduction of digital messaging channels not only eliminated agent turnover, but also improved operational efficiency and enhanced the customer experience.

Looking ahead, the company plans to further expand its digital service offering by integrating additional messaging channels and exploring AI-driven enhancements to support their teams.

What is Agentic AI?

Posted on February 5, 2025July 10, 2025 Mark Kowal

The landscape of artificial intelligence is rapidly evolving, and at the forefront of this evolution is agentic AI. As noted by UiPath, “the convergence of powerful LLMs (large language models), sophisticated machine learning, and seamless enterprise integration has enabled the rise of agentic AI, which is the ‘brainpower’ behind AI agents.” This powerful technology represents a significant leap forward in how AI systems can autonomously operate, make decisions, and execute complex tasks.

While traditional AI and generative AI have made significant strides in automation and content creation, agentic AI addresses the crucial gaps in autonomous decision-making and task execution. It’s becoming increasingly clear that this technology will reshape how businesses operate, particularly in areas requiring sophisticated problem-solving and adaptability.

What is agentic AI?

Agentic AI refers to artificial intelligence systems that can autonomously execute tasks, make decisions, and adapt to real-time changing conditions. Unlike more passive AI systems, agentic AI demonstrates agency—the ability to act independently and make choices based on understanding the environment and objectives.

As a side note here: I led a webinar recently called From Contact Center to Agentic AI Leader: Embracing AI to Upgrade CX. My colleague Quiq VP of EMEA Chris Humphris and I went deep into agentic AI specifically for the contact center. I highly recommend you watch the replay or read the recap if you’re interested in how this technology works within the confines of the contact center, and what’s needed to make it successful at the platform level. Here’s a hint:

Watch the full webinar here.

How does agentic AI work?

Agentic AI operates through a sophisticated combination of technologies and approaches. As IBM explains, “Agentic AI systems provide the best of both worlds: using LLMs to handle tasks that benefit from the flexibility and dynamic responses while combining these AI capabilities with traditional programming for strict rules, logic, and performance. This hybrid approach enables the AI to be both intuitive and precise.”

The system works by integrating multiple components:

Language understanding: Processing and comprehending natural language inputs
Decision making: Analyzing situations and determining appropriate actions
Task execution: Utilizing APIs, IoT devices, and external systems to perform actions
Learning and adaptation: Improving performance based on outcomes and feedback

For example, in customer service, an agentic AI system can:

Understand a customer’s inquiry about a missing delivery
Access order tracking systems to verify shipping status
Identify delivery issues and initiate appropriate actions
Communicate updates to the customer
Automatically schedule redelivery if necessary

This customer service example demonstrates several key advancements over previous generations of AI assistants:

While traditional chatbots could only follow rigid, pre-programmed decision trees and provide templated responses, agentic AI shows true operational autonomy by orchestrating multiple systems and making contextual decisions.

The ability to seamlessly move between understanding natural language queries, accessing real-time shipping databases, evaluating delivery problems, and initiating concrete actions like rescheduling represents a quantum leap in capability.

Last-gen AI would typically need human handoffs at multiple points in this process – for instance, when moving from customer communication to backend systems access or when making judgment calls about appropriate remedial actions.

The agentic system’s ability to maintain context throughout the interaction while independently executing complex tasks showcases how modern AI can function as an independent problem-solver rather than just a conversational interface. This level of end-to-end automation and response was impossible with earlier generations of AI technology.

What is the difference between agentic AI and generative AI?

While both agentic AI and generative AI represent significant advances in artificial intelligence, they serve distinctly different purposes. Generative AI excels at creating content—text, images, code, or other media—based on patterns learned from training data. Agentic AI, however, goes beyond generation to actively make decisions and execute tasks.

These technologies can work together synergistically, with generative AI providing content creation capabilities within an agentic AI’s broader decision-making framework.

Benefits of agentic AI

Key benefits include:

1. Autonomous operation

By eliminating the constraints of human-dependent processes, agentic AI creates a new paradigm of continuous, reliable service delivery that scales effortlessly with business demands. The result is:

Reduced human intervention: AI agents handle complex tasks independently, freeing human workers to focus on high-value activities requiring emotional intelligence and strategic thinking.
Consistent performance: The system maintains uniform quality standards regardless of workload, time of day, or complexity of tasks, eliminating human variability and fatigue-related errors.
24/7 availability: Unlike human operators, AI agents operate continuously without fatigue, ensuring consistent service availability across all time zones.

2. Improved human-AI agent collaboration

Agentic AI changes the relationship between human agents and technology, creating a symbiotic partnership that enhances overall service delivery and job satisfaction. Here’s how.

Ensures consistency: AI agents establish and maintain standard operating procedures across teams, ensuring every customer interaction meets quality benchmarks regardless of which human agent is involved. This standardization helps eliminate variations in service quality, while still allowing for personal touch where needed.
Accelerates learning: New agents benefit from AI-powered guidance that provides suggestions and best practices, significantly reducing the time needed to achieve proficiency. The system learns from top performers and shares these insights across the entire team.
Reduces training time: By providing contextual assistance, agentic AI helps new agents become productive more quickly. Training modules adapt to individual learning patterns, focusing on areas where each agent needs the most support.
Improves agent performance with insights: The system continuously analyzes agent interactions, providing actionable feedback and performance metrics that help identify areas for improvement. These insights enable targeted coaching and development opportunities.
Improves job satisfaction and reduces agent turnover: By handling routine tasks and providing intelligent support, agentic AI allows agents to focus on more engaging, complex work that requires human empathy and problem-solving skills. This role enhancement leads to higher job satisfaction and lower turnover rates.

3. Enhanced efficiency

Through intelligent automation and rapid processing capabilities, agentic AI significantly improves operational performance across organizations, resulting in:

Faster task completion: AI agents process and execute tasks at machine speed, dramatically reducing resolution times compared to manual processes.
Reduced error rates: Systematic processing and built-in validation reduce mistakes common in human-operated systems.
Streamlined workflows: Intelligent routing and automated handoffs eliminate bottlenecks and optimize process flows.

4. Real-time adaptability

The system’s ability to learn and adjust in real time ensures optimal performance in dynamic business environments. It shows this via:

Dynamic response to changing conditions: AI agents automatically adjust their approach based on current conditions and new information.
Continuous learning and improvement: The system learns from each interaction, continuously refining its responses and decision-making processes.
Personalized solutions: Advanced analytics enable tailored responses that account for individual user preferences and historical interactions.

5. Integration capabilities

Agentic AI integrates with existing business systems to create a unified operational environment. Main ways include:

More seamless connection: The technology easily integrates with current business tools and platforms, maximizing existing investments.
Unified data utilization: AI agents can access and analyze data from multiple sources to make informed decisions.
Comprehensive solution delivery: The system coordinates across different platforms and departments to deliver complete solutions.

6. Cost-effectiveness

Implementation of agentic AI leads to significant cost savings and improved resource utilization. Top areas for savings include:

Reduced operational costs: Automation of routine tasks and improved efficiency lead to lower operational expenses.
Intelligent workload distribution: Ensures optimal use of both human and technological resources.

Use cases for agentic AI

Agentic AI’s applications span numerous industries and use cases. Let’s look at the top four industries that are ripest for benefits from our perspective, and the use cases that are best poised for AI.

1. Customer service

In customer service, agentic AI improves support operations from reactive to proactive, enabling intelligent interactions that enhance customer satisfaction while reducing costs. Top use cases include:

Query resolution.
Ticket management
Proactive support
Personalized assistance

2. eCommerce and retail

In retail and eCommerce, agentic AI revolutionizes the retail experience by creating seamless, personalized shopping journeys while optimizing backend operations for maximum efficiency and profitability. Best use cases include:

Inventory management
Personalized shopping recommendations
Order processing
Customer engagement

3. Business automation

By integrating intelligent decision-making with execution capabilities, agentic AI streamlines complex business processes and eliminates operational bottlenecks across organizations. Start automation targeting:

Supply chain optimization
Process automation
Resource allocation

4. Healthcare

Agentic AI enhances patient care and operational efficiency by combining real-time monitoring with intelligent decision support and automated administrative processes. From what we’re seeing, the biggest opportunities to apply agentic AI rest in:

Patient monitoring
Treatment planning
Diagnostic support
Administrative tasks

Agentic AI challenges

Let’s take a look at the biggest challenges with agentic AI right now.

1. Ethical considerations

The autonomous nature of agentic AI raises ethical concerns that require careful attention. These systems, designed to make independent decisions and take action, must operate within established ethical frameworks to ensure responsible deployment.

Key ethical challenges include:

Accountability for AI decisions and actions
Transparency in decision-making processes
Potential bias
Impact on human autonomy and agency

Quiq SVP of Engineering Bill O’Neill recently talked to VUX World’s Kane Simms about this very issue:

2. Data security

Data security represents a critical challenge in agentic AI implementation, as these systems often require access to sensitive information to function effectively. (If you’re curious, you can learn about our approach to security here).

Primary security concerns include:

Protection of training data and model parameters
Secure communication channels for AI agents
Prevention of adversarial attacks
Data privacy compliance (GDPR, CCPA, etc.)
Access control and authentication mechanisms

3. Integration challenges

Incorporating agentic AI into both customer integrations and your own company integrations can mean significant hurdles, like:

Legacy system compatibility
API standardization and communication protocols
Performance optimization
Scalability concerns
Resource allocation and management

4. Regulatory compliance

The evolving regulatory landscape surrounding AI technology presents potential issues, including:

Adherence to emerging AI regulations
Cross-border compliance requirements
Documentation and audit trails
Risk assessment and mitigation
Regular compliance monitoring and updates

5. Performance monitoring

Maintaining and optimizing agentic AI system performance requires continuous monitoring and adjustment:

Real-time performance metrics
Quality assurance processes
System reliability and availability
Error detection and correction
Performance optimization strategies

These challenges highlight the complexity of implementing agentic AI systems and underscore the importance of careful planning and robust risk management strategies. Success in deploying these systems requires a comprehensive approach that addresses technical, ethical, and operational concerns, while maintaining focus on business value and user needs.

Importantly, when you partner with agentic AI vendor Quiq, our AI platform and team neutralize these challenges for you.

The future of agentic AI: Shaping tomorrow’s enterprise workflows

As we stand at the intersection of technological innovation and business transformation, agentic AI emerges as a cornerstone of future enterprise operations. But what’ll follow? Here’s what I think.

Technical evolution and integration

The future of agentic AI lies in its ability to integrate with existing enterprise systems while pushing the boundaries of what’s possible. Advanced API ecosystems and sophisticated middleware solutions are already enabling AI agents to coordinate across previously siloed systems, creating unified workflows that span entire organizations.

The next generation of agentic AI systems will feature enhanced natural language processing capabilities, enabling a more nuanced understanding of context and intent. This advancement will allow AI agents to handle increasingly complex tasks while maintaining high accuracy levels. We’re moving toward systems that can execute predefined workflows and design and optimize them in real time based on changing business conditions.

Enhancing enterprise workflows

1. Predictive process optimization

AI agents will move beyond reactive process management to predictive optimization. By analyzing patterns across millions of workflow executions, these systems will automatically identify potential bottlenecks before they occur and implement preventive measures. This capability will enable organizations to maintain peak operational efficiency while minimizing disruptions.

2. Dynamic resource allocation

The future workplace will see AI agents dynamically managing both human and technological resources. These systems will understand the strengths and limitations of different resource types, automatically routing work to optimize for efficiency, cost, and quality. This intelligent orchestration will create more flexible, resilient organizations capable of adapting to changing market conditions in real time.

3. Autonomous decision networks

As agentic AI evolves, we’ll see the emergence of decision networks where multiple AI agents collaborate to solve complex business challenges. These networks will coordinate across departments and functions, making decisions that optimize for overall business outcomes rather than departmental metrics.

Enhanced learning and adaptation

The future of agentic AI lies in its ability to learn and adapt at faster paces. Next-generation systems will feature:

1. Collective learning

AI agents will learn not just from their own experiences but from the collective experiences of all instances across an organization or industry.

2. Contextual understanding

Future systems will demonstrate deeper understanding of business context, enabling them to make more nuanced decisions that account for both explicit and implicit factors.

3. Personalization at scale

As AI agents become more sophisticated, they can deliver highly personalized experiences while maintaining operational efficiency.

Creating more resilient organizations

The evolution of agentic AI will contribute to building more resilient organizations through:

1. Adaptive workflows

Future systems will automatically adjust workflows based on changing conditions, ensuring business continuity even during unprecedented events.

2. Proactive risk management

AI agents will continuously monitor operations for potential risks, implementing preventive measures before issues arise.

3. Sustainable scaling

The future of agentic AI will enable organizations to scale operations more sustainably, automatically adjusting processes to maintain efficiency as the organization grows.

Looking ahead

While challenges around data quality, system integration, and ethical considerations persist, the trajectory of agentic AI points toward increasingly sophisticated systems. Organizations that embrace this technology and prepare for its evolution will be better positioned to:

Create more efficient workflows that respond to changing business needs
Deliver personalized experiences at scale
Build more resilient organizations capable of thriving in uncertain conditions
Drive innovation through intelligent process optimization

As we move forward, the key to success will lie not just in implementing agentic AI, but in creating organizational cultures that can effectively leverage its capabilities while maintaining human oversight and strategic direction. The future belongs to organizations that can strike this balance, using agentic AI to enhance human capabilities, rather than replace them.

We’re only beginning to scratch the surface of what’s possible. As the technology continues to evolve, it will enable new forms of business operation that are more resilient than ever before.

I love Bill’s take on this in another clip from his conversation with Kane:

Final thoughts on agentic AI and how to get started with it

Agentic AI represents a significant advancement in artificial intelligence, offering businesses the ability to automate complicated tasks while maintaining intelligence in decision-making. As organizations seek to improve efficiency and customer experience, agentic AI provides a powerful solution that goes beyond traditional automation and generative AI capabilities.

Quiq stands at the forefront of this technology, offering agentic AI solutions that help businesses improve their operations and customer interactions. With a deep understanding of both the technology and business needs, Quiq provides sophisticated AI agents that can handle complex tasks and drive the outcomes your business cares about.

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What Is Generative AI?

Key Features of Generative AI

Content Generation

Data-Driven Learning

Adaptability & Versatility

Customization & Personalization

Effeciency & Automation

What Are Large Language Models?

What Are Examples of Large Language Models?

Key Features of Large Language Models

Natural Language Understanding & Generation

Scalability & Versatility:

Adaptability Through Fine-Tuning

What is Agentic AI?

What are some of the key features of Agentic AI?

Autonomous Action

Dynamic Decision Making

Goal-Oriented Behavior

Proactive Resource Gathering

Self-Improvement Through Feedback

What Are Some Examples of Agentic AI?

1. Personalized AI Assistants: Beyond Basic Task Execution

2. AI in Healthcare: Keeping an Eye on Your Health

3. AI That Actually Solves Customer Support Issues

How Do Agentic AI, Generative AI, and LLM’s Compare?

Key Differences Between Generative AI, LLMs, and Agentic AI

How These AI Systems Can Work Together

When to Use Generative AI, LLMs, or Agentic AI

When to Use Generative AI

When to Use Large Language Models (LLMs)

When to Use Agentic AI

Utilizing Generative AI In Your Business

What is multimodal AI?

Key examples of multimodal AI:

How does multimodal AI work?

1. Data fusion

2. Advanced machine learning techniques

3. Training multimodal models

Popular multimodal AI models:

Key applications of multimodal AI

1. Customer service automation

2. Retail