What is user intent in AI? By understanding user intents, AI can answer customers’ questions and share relevant information with them.

AI has transformed the way people search, shop, and communicate with businesses online. From providing 24/7 connectivity to offering seamless integration across channels, AI-powered agents and chatbots have become the front line of customer engagement. But here’s the catch: the success of these interactions depends heavily on one thing — how well the AI understands user intent.

A recent study by Aspect found that customers evaluate chatbot technology on factors such as friendliness (65%), ease of use (65%), speed (62%), interaction success (55%), and accuracy (55%). These numbers reveal something important: it’s not just about having an AI in place; it’s about whether that AI can truly understand what the user is asking and respond in a way that feels natural. And this is only possible when user intent is defined and recognised in AI systems.

Think about it: when a human agent interacts with a client, even they sometimes struggle to understand the question. That’s why agents undergo regular training to handle queries with empathy and clarity. Similarly, AI agents also need “training” — not just to process language, but to interpret variations in phrasing, tone, and even emotion.

For example, a customer saying “Can I change my card?” might mean replacing a lost debit card, or updating payment details on file. Unless the AI correctly identifies the intent behind that query, the response could easily miss the mark.

This is why intent becomes the foundation of conversational AI. Whether answering a question, sharing information, or guiding a user through a transaction, the ability to identify and act on user intent is what separates a helpful AI agent from a frustrating one.

In the next section, let’s break down what exactly “intent” means in conversational AI — and look at some examples of how it shapes user experiences.

What is intent in AI?

What do you expect the conversational AI and AI Agents to do?

You want it to answer questions as accurately and quickly as possible. This is possible when the AI understands the customer’s intention behind the question. 

This is the first question to ask when you plan to design the user intent in AI. By answering this, it will guide how the platform performs and assists customers. 

Uberall suggests that 80% of customers who have used chatbots have rated them with a positive experience. This success in customer experience shows how well the AI is trained on customer intent. 

In simple terms, intent refers to the goal that the customer has in his mind while using the chatbot. The AI is designed to understand the customer’s message and then offer the appropriate response. 

In other words, AI actively listens to customers and responds accordingly. 

Working on a case-to-case basis, the intent-based chatbot is designed on natural language processing (NLP) and natural language understanding (NLU). The identification of the intent is based on the intent classification.

Usually, the most common user intent recognition is grouped in intents. For example, in the eCommerce industry, the intent will be classified as purchase, return, quality, billing, order status, subscribe, information, downgrade, unsubscribe, demo request, etc.

Reasons why user intents are so hard to detect

According to CNBC, 75-90% of the queries are expected to be handled by chatbots by 2022. This clarifies that AI-powered chatbots are one of the primary interaction tools to be adopted in the future. 

But designing the intent-based AI can be challenging to some extent. The main challenges that the company may face while creating one are:

1. Natural language is complex

Understanding the natural language is an extensive process that includes understanding voice inputs, the meaning of the words, sentence structure, meaning of sentences, and the context. 

Defining the complete basis of understanding the intent is a complex process. In simple terms, for user intent recognition, a series of steps involving in-depth information analysis is a must.

2. Differentiating user intent is difficult

Defining the user intent is useless until the manner to fulfil the intent is identified. Say a user is looking for an apple. The intent is to get information on apple, but one can interpret it as apple the fruit or Apple the product. 

A company needs to train the AI with proper classification. One needs to clearly state the intention based on intent classification and diversify the response based on the same.

3. Knowing keywords is imperative

When conversing with conversational AI, or any chat-based service, in that case, users don’t type in complete sentences. Usually, they prefer to use keywords or key phrases. And, in some cases, they use emojis or symbols. Understanding this keyword is not simple. 

Sometimes, the system might reject the query based on the lack of identification of the keyword.

4. Multiple ways, same intent

While developing the AI-powered chatbot, the developer can not input all the intents at once. It means there could be hundreds of ways to ask something. For example, to check the order status, the user could ask for an update, where is my order, when will my order reach, etc. 

All these reach the single intent of finding the order’s status, which might or might not be accurate. Thus, identifying the same intent by adopting multiple ways is essential.

Why Understanding User Intent Matters for AI Systems

When customers interact with an AI agent or chatbot, they expect the same level of understanding they would get from a trained human agent. If the AI misinterprets the request, the experience quickly turns frustrating. This is why understanding user intent is not just a technical feature — it’s the foundation of effective AI-driven conversations.

1. Improves Accuracy and Relevance

The difference between “I lost my card” and “I want to change my card” may seem subtle, but the intent behind each is very different. By recognising the user’s actual goal, AI systems deliver accurate, context-aware responses instead of surface-level keyword matches. This makes interactions smoother and more relevant.

2. Enhances User Experience

Customer patience is thin when technology doesn’t deliver. In fact, Google found that 48% of users abandon a site if they don’t find relevant information quickly. An AI agent that understands intent can answer the right question the first time, reducing back-and-forth clarifications and leaving the customer with a positive impression.

3. Drives Business Outcomes

Understanding intent isn’t just about customer satisfaction — it directly impacts the bottom line. When AI agents respond with accuracy and empathy, businesses see higher conversion rates, reduced call handling times, and lower support costs. For instance, an e-commerce user who says “I’m looking for a birthday gift” signals an intent to purchase, and a well-trained AI can guide them to curated products, increasing chances of a sale.

4. Builds Trust and Consistency

When responses feel personalised and on-point, customers begin to trust the AI as a reliable touchpoint for the brand. This trust is what fuels long-term loyalty and repeat engagement.

In short, the importance of user intent in AI systems comes down to one truth: the better an AI understands intent, the better it can create experiences that are accurate, human-like, and business-driven.

Why does AI intent matter to customer support?

As per Chatbots Life, the USA, India, Germany, the UK, and Brazil are the top 5 countries to adopt chatbots on a large scale. Also, Drift suggests that 95% of the consumers believe that chatbots will benefit overall customer service. 

To meet these expectations, companies cannot rely on simple keyword-based chatbots. They need the power of AI to ensure the customer experience is as smooth as butter. 

Understanding user intent with the help of AI helps the business in terms of improved customer service. We list some benefits below.

• Initiates the platform for natural conversation.
• Allows flexibility with the questions asked.
• Holds longer conversations with the customers, keeping them positively engaged.
• Offers the opportunity to take advantage of sales opportunities.
• Helps to scale customer support and, hence, grow the business.
• Simplifies the maintenance and recording of data, ensuring consistency.
• Improves the conversion rate based on the number of satisfying interactions.
• Records data for better insights and statistics.

3 Types of User Intent in AI

When people interact with an AI agent, the words they use are only part of the story. Behind every question lies a purpose — the real reason they reached out. AI systems need to understand this purpose, or intent, to respond in a way that feels natural and helpful. Broadly, user intent falls into three categories: informational, navigational, and transactional.

1. Informational intent

Informational intent appears when users are simply looking for knowledge. Someone might ask, “How does two-factor authentication work?” or “What are the benefits of Voice AI Agents?” At this stage, the person isn’t buying or taking immediate action — they’re exploring, learning, and gathering confidence. For AI agents, recognising this is crucial because it allows them to provide clear, accurate answers without overselling. If the response feels pushy or irrelevant, the user may abandon the interaction altogether.

2. Navigational intent

Navigational intent is more direct. Here, the user knows what they want but needs help getting there quickly. A customer might say, “Show me my order history” or “Take me to Verloop.io’s pricing page.” The intent isn’t to understand what those things are, but to access them efficiently. If the AI understands this, it acts like a smart guide, reducing clicks, saving time, and making the experience seamless. If it misses the point, it risks frustrating the customer by delivering explanations instead of access.

3. Transactional intent

Transactional intent is where intent becomes opportunity. This is when a customer is ready to act — to buy, book, or commit. A user asking, “Renew my insurance policy” or “Book a demo for Verloop.io Voice AI Agent” is signalling that they’re at the decision stage. AI systems that identify this correctly can immediately trigger the right workflow, guiding the user smoothly through checkout or scheduling. A failure here doesn’t just create friction — it can cost a business a conversion.

Understanding these three types of intent allows AI agents to adapt responses based on context. Informational queries call for clarity, navigational queries demand efficiency, and transactional queries require action. By mapping language to intent, businesses can ensure their AI doesn’t just “reply,” but converses in a way that drives satisfaction and results.

How AI Detects and Interprets User Intent

Have you ever asked a chatbot a simple question, only to get an answer that completely missed the point? Maybe you typed “Change my card” and the bot sent you information about design customisation instead of updating your payment details. Frustrating, right? That’s because the system didn’t fully understand your intent — it caught the words but missed the meaning.

This is the real challenge for AI: recognising that human language is messy, full of context, and often ambiguous. People don’t always speak in perfectly formed sentences. They say things like “It’s not working again” without explaining what “it” is, or they mix two requests in one go, like “Book my flight and cancel yesterday’s hotel.” For an AI agent to respond naturally, it needs more than keyword spotting. It needs to understand the goal behind the words.

So how does AI actually do this? The answer lies in a combination of technologies working together — from the basics of Natural Language Processing (NLP) to the advanced capabilities of Large Language Models (LLMs), sentiment detection, and context analysis.

Key Ways AI Understands Intent

Natural Language Processing (NLP)

NLP is where it all begins. It breaks down language into tokens (words and phrases), analyses grammar, and identifies patterns. For example, if a customer says, “It’s not working again,” NLP helps the AI understand that “it” is the subject and “not working” signals a problem. But on its own, NLP is limited — it can flag the words but doesn’t always grasp the why.

Large Language Models (LLMs)

This is where the depth comes in. LLMs are trained on vast datasets and can interpret meaning in context. Consider the phrase “Change my card.” Without context, this is ambiguous: is the user updating their payment method or replacing a lost debit card? LLMs use conversation history and contextual cues to make the right call, reducing costly misunderstandings.

Entity Recognition

Humans naturally reference specifics — names, dates, numbers, or products. AI uses entity recognition to catch these details. If a user says, “Cancel my booking for 12th September at Hyatt,” the system needs to recognise “12th September” as a date and “Hyatt” as a hotel to act correctly. Miss this step, and the response becomes vague or irrelevant.

Sentiment Analysis

Words alone don’t always reveal intent — tone matters too. A message like “Thanks for your help” could be genuine or sarcastic, depending on the situation. Sentiment analysis allows AI to pick up on emotions such as frustration, urgency, or satisfaction. This helps the AI adjust tone and even decide when to escalate to a human agent.

Handling Multi-Intent Queries

People often multitask even in their requests. Think of a user saying, “Book my flight and cancel yesterday’s hotel.” That’s two separate tasks in one sentence. A strong AI agent can split this into distinct workflows and execute both, instead of ignoring one or giving a confusing answer.

Contextual Learning

Just as a human agent remembers past interactions, AI also benefits from memory. Contextual learning allows it to use prior conversations and user history to shape responses. If a customer frequently asks about “order status,” the AI can prioritise that context when they next reach out. This creates a smoother, more personalised experience.

Bringing all these layers together allows AI to move from simply hearing words to actually understanding intent. It’s the difference between a bot that frustrates and one that feels human-like. Platforms like Verloop.io’s Voice and Chat AI Agents are built on this layered approach — combining NLP, LLMs, sentiment, and contextual cues — to ensure conversations stay relevant, empathetic, and productive.

So next time you chat with an AI and it gets your request right the first time, think about the invisible intelligence at work. It’s not luck. It’s intent recognition in action.

Challenges in Understanding User Intent

Even with advanced NLP and LLMs, AI systems still struggle to consistently understand human intent. The problem lies in how people actually communicate — often vague, emotional, or inconsistent. Let’s look at the key challenges in detail.

1. Ambiguity in Language

Human language is full of ambiguity. When a customer says “Cancel my booking,” the AI doesn’t automatically know what kind of booking it is — a flight, hotel, or doctor’s appointment? Without enough context, the system risks guessing wrong. Unlike humans, who can often infer intent from tone, prior conversation, or situational context, AI agents rely heavily on training data and conversation history. A misstep here can cost not just customer satisfaction but also business credibility.

2. Slang and Informal Speech

People don’t talk to AI the same way they write in formal text. A user might type “Need a quick fix, this app’s glitching again” or “Yo, what’s the ETA on my order?” These expressions aren’t standard and may not exist in the AI’s training data. If the AI fails to recognise slang or casual phrasing, it could respond awkwardly or not at all. This breaks the flow of conversation and makes the AI feel robotic rather than natural.

3. Cultural and Regional Variations

Language also shifts across geographies. In the UK, someone might say “top-up my balance,” while in the US, the same request is “add credit to my account.” To the AI, these are two very different sentences, but the intent is identical. If not trained on regional variations, the AI could miss the meaning. Similarly, idioms like “rain check” or “in a pickle” can confuse AI systems if they’ve never been exposed to such cultural expressions.

4. Multi-Intent Queries

Customers often pack more than one request into a single message. For example: “Book my flight and also send me the receipt for last week’s hotel.” A simplistic AI might only pick up the first part and ignore the second, leaving the user frustrated. Handling multi-intent queries requires the AI to split one sentence into separate workflows — something that demands complex orchestration and context switching, which many platforms struggle to do smoothly.

5. Evolving Conversations

Intent isn’t fixed; it changes as the conversation progresses. A customer might begin with “What’s the price of your premium plan?” — clearly informational. But once they hear the answer, they might say, “Okay, sign me up.” That’s a transactional intent. Many AI systems still treat each input in isolation, failing to carry forward the evolving context. As a result, they can sound repetitive or disjointed, forcing users to restate what they mean.

6. Over-Reliance on Keywords

Basic chatbots often match intent by scanning for keywords like “cancel” or “order.” But this approach is shallow. A phrase like “Don’t cancel my order” could mistakenly trigger a cancellation workflow simply because of the keyword “cancel.” This kind of error not only annoys the customer but can also create costly operational mistakes.

Each of these challenges highlights a simple truth: people expect AI to listen, interpret, and respond like a human, but the complexity of language makes that extremely difficult. Without solving these pain points, AI agents risk becoming frustrating rather than helpful.

This is why platforms like Verloop.io invest in advanced NLP, LLM-based models, sentiment detection, and contextual memory — to minimise these gaps and ensure that AI agents handle ambiguity, slang, cultural nuance, and multi-intent queries with more precision.

Tools and Techniques to Improve AI Intent Recognition

If the challenges around intent recognition feel overwhelming, you’re not alone. Businesses across industries struggle with ambiguity, slang, evolving conversations, and misinterpretations. The good news? AI can be trained and equipped with the right tools to close these gaps. Much like human agents go through coaching to understand customers better, AI agents also need structured training and advanced techniques to interpret intent with accuracy.

So what helps turn a chatbot into a reliable AI agent that “gets it” the first time? Let’s break it down.

1. Training with Diverse Datasets

AI learns from examples. If the training data only contains formal queries like “Cancel my subscription,” the system will stumble when faced with “I wanna stop my plan.” By exposing AI to a wide variety of phrasing, slang, and regional variations, it becomes better at recognising the same intent across different expressions. Think of it as teaching a customer support rep to handle accents and informal language — practice broadens understanding.

2. Contextual AI and Retrieval-Augmented Generation (RAG)

One of the biggest breakthroughs in intent recognition is context retention. Instead of treating each question as separate, contextual AI uses memory and RAG techniques to link new inputs to past interactions. For example, if a user says, “How much is the premium plan?” and later follows up with “Okay, sign me up,” contextual AI connects both, recognising the shift from informational to transactional intent. Without context, the system might answer awkwardly, asking the user to clarify something they’ve already said.

3. Speech Profiling in Voice AI

In voice conversations, intent is not just about words — tone, pace, and stress also matter. A customer saying “It’s fine” quickly and with irritation often doesn’t actually mean it’s fine. Speech profiling allows Voice AI Agents to pick up these nuances, interpreting urgency, frustration, or hesitation. This helps the AI adjust tone or escalate to a human agent at the right moment, avoiding the all-too-common “robotic” feel.

4. Continuous Learning from Feedback

Just as human agents improve with coaching, AI agents improve with feedback loops. Every misinterpretation becomes an opportunity to refine the model. Businesses can tag incorrect responses, retrain the AI on those examples, and gradually reduce errors. Over time, this creates an AI agent that adapts to its specific audience rather than staying generic.

5. Multi-Channel Integration for Consistency

Customers don’t stick to one channel. They might begin on WhatsApp, continue on a website chat, and finish over a voice call. If the AI can’t carry intent seamlessly across these channels, the experience feels broken. Platforms like Verloop.io excel here by unifying intent recognition across web, WhatsApp, and Voice AI Agents, ensuring that the conversation feels continuous regardless of where it happens.

6. Real-Time Analytics and Optimisation

Finally, it’s not enough to launch AI and hope for the best. Real-time dashboards let businesses see how well intents are being recognised — which ones succeed, which fail, and where customers drop off. For example, if 30% of users saying “renew my plan” don’t get the right response, that’s a signal to retrain that specific intent. Verloop.io provides these analytics natively, giving teams the visibility they need to fine-tune campaigns and conversations.

Each of these tools — from diverse training data to speech profiling and multi-channel integration — strengthens the AI’s ability to listen, interpret, and respond more like a human. The result isn’t just smoother conversations but real business impact: higher conversions, lower churn, and happier customers.

By adopting platforms like Verloop.io, businesses can put these techniques into practice without building from scratch. The combination of advanced LLMs, contextual understanding, and voice intent detection makes Verloop.io’s AI Agents equipped to handle the messy reality of human communication.

The Role of User Intent in the Customer Journey

Every customer journey — whether it’s buying a pair of shoes, booking insurance, or signing up for a SaaS platform — is driven by intent. The questions customers ask, the way they phrase them, and even the timing all reveal where they are in their journey. For AI agents, detecting intent isn’t just about responding correctly; it’s about guiding the customer from one stage to the next.

Think of intent as the compass that shows where the user is headed. At the awareness stage, people are curious and exploring. Later, they’re comparing options, and eventually, they’re ready to commit. If an AI agent can pick up on these shifts, it can play a pivotal role in shaping the customer experience.

1. Awareness Stage – Informational Intent

At the top of the funnel, customers are asking broad, knowledge-driven questions. A user might type, “What is Voice AI and how does it work?” They’re not ready to buy — they’re learning.

👉 For AI agents, recognising this informational intent means focusing on education rather than selling. The goal is to provide clear answers, perhaps sharing a blog, a short explainer, or even a demo video. By building trust here, the AI helps the customer take the first step forward in their journey.

2. Consideration Stage – Navigational Intent

As users move closer to making a decision, their queries become more specific. Instead of broad questions, they might say, “Show me Verloop.io’s pricing page” or “Compare premium and standard plans.”

This is navigational intent in action. The customer already knows about the product or service and is looking for details. AI agents need to act as smart guides, cutting down clicks and getting the user exactly where they need to go. A fast, seamless response here can significantly influence how confident the user feels about the brand.

3. Decision Stage – Transactional Intent

Finally comes the decision moment. The user might say, “Book a demo for Verloop.io Voice AI Agent” or “Renew my insurance policy now.” These are clear signals of transactional intent, and they represent business-critical opportunities.

At this stage, AI agents need to act with precision. Any friction — a misinterpreted request, a delayed handoff to a human, or unnecessary back-and-forth — could derail the conversion. A well-trained AI agent, however, can trigger workflows instantly: booking demos, processing renewals, or even escalating to a live agent when complexity arises.

By mapping user intent to different stages of the customer journey, businesses can design AI systems that don’t just answer questions but move users forward naturally. From awareness to decision, intent is the thread that ties conversations together — and when AI agents recognise it correctly, they transform from passive responders into active drivers of growth.

Platforms like Verloop.io put this into practice by combining chat and voice AI agents with contextual understanding. Whether the customer is exploring, comparing, or buying, the AI recognises intent and adapts accordingly — ensuring the journey is seamless, personalised, and results-driven.

The Future of User Intent in AI

So far, we’ve looked at how AI agents recognise intent today. But what happens when intent detection evolves beyond simply “reacting” to user queries? The future of AI lies in anticipating intent before the user even expresses it.

Think about it. Right now, when a customer types “I want to renew my policy,” the AI acts on the request. In the near future, the system may already know the policy is about to expire and proactively reach out with a reminder, tailored offers, or even an automated renewal option. This isn’t science fiction — it’s the direction AI is moving, powered by predictive intent recognition and generative AI agents.

Predictive Intent and Proactive Engagement

AI is becoming increasingly capable of analysing patterns in customer behaviour to anticipate needs. For example, an e-commerce AI agent that notices you browsing winter jackets might proactively suggest, “Would you like to see today’s discounts on thermal wear?” Similarly, a Voice AI Agent in banking could remind you of a due payment before you even ask about it. The shift here is from reactive service to proactive engagement, reducing friction and improving loyalty.

Generative AI and Conversational Depth

Large Language Models (LLMs) are also pushing the boundaries of how intent is interpreted. Instead of sticking to rigid categories like “informational” or “transactional,” AI can now understand layered, nuanced requests. A customer saying, “I want a healthy lunch option near my office that fits within $20 and delivers in 30 minutes” is expressing multiple intents wrapped in one. Generative AI allows agents to break that down, reason through it, and provide a personalised solution — something old keyword-based bots could never manage.

Hyper-Personalisation and Consistency

As AI evolves, intent recognition will become the foundation for hyper-personalised experiences. Imagine AI agents that not only understand what you’re asking now but also remember your past interactions, tone preferences, and even speech patterns. This means a customer who prefers quick, concise answers won’t get long explanations, while someone who values detail will receive step-by-step responses. For businesses, this consistency in voice and tone strengthens brand trust over time.

The Role of AI Agents in the Future

In this future, AI agents won’t just handle support tickets or sales calls. They’ll act as always-on brand representatives — listening, empathising, and solving problems across voice, chat, and web. Platforms like Verloop.io are already laying the groundwork by combining LLM-powered chat and voice AI agents with real-time learning, enabling businesses to scale conversations while maintaining a human-like touch.

The future of user intent in AI isn’t about making chatbots smarter — it’s about making customer interactions seamless, predictive, and deeply personal. The more accurately AI can understand intent, the closer businesses get to creating experiences that feel less like “talking to a machine” and more like engaging with a trusted partner.

User intent in AI: the humanistic touch

The user intent in AI offers the advantage of transforming leads into customers. Identifying customer expectations and responding to offers can lead to the development of a satisfied customer base. 

Allowing the opportunity to facilitate the basic tasks and services in a timely and qualified manner, an intent-based chatbot is a perfect solution. 

In today’s world, people are looking for a personal, human connection. And this is not just limited to using the customer’s name. They are looking for a solution that understands them in the first go. 

While business profits from automation, using AI while conversing with users will ensure customers are understood accurately. This will also build a strong relationship with them. 

But like anything else, it’s easier said than done. The AI needs training on different intents and variations of intents before it can start conversing naturally. 

Verloop.io is one of the best partners that can offer you the seamless and effectively designed user intent in AI to personify the human in terms of feeling and understanding queries. 

Our AI has been trained on over 2 billion queries to understand user internet and reply to customers in human-like conversations. The AI is self-learning and can adjust the response based on customers replies.

FAQs

1. What is user intent in AI?

User intent refers to the underlying goal behind a customer’s query. For example, when someone types “Change my card”, the intent could be updating payment details or replacing a lost card. AI agents use natural language processing and contextual understanding to figure out what the customer really means.

2. Why is understanding user intent important for AI agents?

Without understanding intent, AI agents risk giving irrelevant or frustrating responses. Intent recognition ensures that the AI can respond accurately, improve user experience, reduce support costs, and even drive conversions by acting on transactional queries.

3. What are the main types of user intent?

The three most common types are:

• Informational intent – when users are seeking knowledge (e.g., “How does Voice AI work?”).

• Navigational intent – when users want to reach a specific place or feature (e.g., “Show me my order history”).

• Transactional intent – when users are ready to act (e.g., “Book a demo for Verloop.io Voice AI Agent”).

4. How does AI detect and interpret user intent?

AI agents use a mix of techniques including Natural Language Processing (NLP), Large Language Models (LLMs), sentiment analysis, entity recognition, and context memory. Together, these help AI go beyond keywords to truly understand the purpose behind a user’s words.

5. What challenges do AI systems face in recognising intent?

Common challenges include ambiguity in language, slang and informal speech, cultural variations, multi-intent queries, and evolving conversations where the user’s goal changes mid-interaction. Over-reliance on keywords also causes frequent misinterpretations.

6. How can businesses improve AI intent recognition?

Businesses can train AI on diverse datasets, use contextual AI with Retrieval-Augmented Generation (RAG), apply speech profiling for Voice AI agents, and implement feedback loops for continuous learning. Platforms like Verloop.io bring these techniques together to make intent recognition more accurate and conversational.

7. What role does user intent play in the customer journey?

User intent reveals where a customer is in their journey: exploring options, looking for specifics, or ready to buy. Recognising intent helps AI agents guide users naturally from awareness to decision, improving engagement and conversion rates.

8. What’s the future of user intent in AI?

The future lies in predictive intent and proactive engagement. Instead of waiting for a query, AI will anticipate needs (e.g., reminding about expiring policies or suggesting products before you ask). Generative AI agents will also manage layered, complex requests more effectively, creating hyper-personalised customer experiences.