Artificial intelligence is increasingly shaping the customer experience. The hype around AI coding assistants may have cooled, but the technology’s ability to simulate human interaction makes it uniquely suited to transforming how customers discover, buy, and receive support for products and services. Rather than thinking about what AI might do one day, it’s time to look at what it can already do across the customer journey and where businesses need to tread carefully.
Discovery: Personalisation done right
AI is already reshaping product discovery. Where traditional recommendation engines rely on simple pattern-matching: “people who bought this also bought that”, modern AI systems can interpret nuance and intent. They analyse a customer’s browsing patterns, preferences, and even tone in conversation to suggest products that genuinely fit their needs.
In retail, this means helping customers move from curiosity to consideration faster. An AI assistant can take a vague request like “I need a good laptop for travel” and turn it into specific, relevant options within seconds, drawing on real-time data about stock, specifications, and reviews.
The risk here lies in balance. Overly personalised recommendations can feel invasive, especially when they reveal how much data platforms hold. Companies must make it clear when and how AI is being used, and ensure that data-driven recommendations never cross into manipulation.
Selection: Smarter, more human
Once a customer begins comparing products, AI can make the process far smoother. Natural-language interfaces allow customers to ask follow-up questions such as “What’s the battery life if I use it for video editing?” and receive precise, contextual answers rather than wading through technical specs.
One of the significant advantages over this approach over traditional product queries is empathy. Large Language Models (LLMs) can interpret frustration, confusion, or excitement and adjust their tone accordingly. For example, if a customer sounds unsure, an AI assistant can slow down and explain options more carefully, just as a good salesperson would.
However, flexibility brings risk. Because LLMs generate text dynamically, there is always a possibility of an incorrect or misleading answer. In sales environments, that could translate into mis-selling or false promises. Guardrails are essential – AIs need access to verified product data, strict policy boundaries, and the ability to escalate queries to a human when uncertainty arises.
Purchase: Frictionless transactions
The checkout stage is where AI can deliver immediate, measurable value. Systems that integrate with payment APIs can complete transactions, apply discounts, and arrange delivery with minimal user effort. A customer could simply tell their AI assistant, “Buy the laptop you recommended, for delivery tomorrow,” and the entire process – selection, payment, confirmation – would happen automatically.
This approach, sometimes called “agentic commerce”, could eliminate the last fragments of friction from the buying process. Instead of typing, clicking, and re-entering data, customers would rely on trusted AI intermediaries to act on their behalf. Early indications are that AI driven commerce appears to have much higher conversion levels than traditional web-sites.
Yet automation must be matched by accountability. AI systems handling payments and orders must follow the same verification and compliance rules as human agents. Clear digital consent trails, transaction transparency, and customer oversight will be crucial to building trust.
Support: Always available, always learning
Post-purchase support is where AI can make the biggest difference versus current chatbots or FAQs. For years, basic chatbots frustrated users with scripted responses and limited understanding. The new generation of LLM-powered agents can resolve a far wider range of issues – from warranty claims to delivery tracking – at any time of day. Coming back to the topic of empathy, they can also adjust their approach to align with the customer needs or sentiments at that moment
A customer reporting a defective item could simply describe the problem in natural language. The AI would verify the purchase, check eligibility for replacement, and arrange a return – all without human intervention. It could also detect when a customer is angry or distressed and adjust its tone or escalate to a live agent before the situation deteriorates.
Still, companies must be cautious. An AI that “hallucinates” or overpromises, offering refunds or discounts not permitted by policy, creates liability just as a human employee would. Effective AI training should mirror employee onboarding: teaching systems company policies, escalation protocols, and when to defer to human judgment.
Between gimmick and game-changer
AI-driven customer experience sits at a crossroads. Done well, it eliminates friction, personalises engagement, and keeps service consistent across every stage of a customer’s journey. Done poorly, it becomes the latest in a long line of overhyped gimmicks.
The challenge for companies is to use AI for what it’s genuinely good at: interpreting human intent, responding in real time, and managing complex interactions at scale. With the right controls and transparency, AI won’t just improve customer service – it will redefine it.
