Jonny Steventon
Product Manager
Over the past couple of years, ChatGPT has moved from novelty to expectation. Many founders now arrive at early conversations asking some version of the same question: “Can we add AI to this?” This article isn’t a step-by-step guide to wiring up an API. It’s a practical look at how teams should think about integrating ChatGPT into a product, where it works well, and where it often causes more complexity than value.
Start with the problem, not the model
ChatGPT isn’t a feature on its own. It only works when it solves a specific user problem. In practice, the strongest use cases tend to fall into a few categories:
helping users navigate complex products
reducing friction in repetitive tasks
supporting decision-making where rigid interfaces fall short
Where teams struggle is when AI is introduced too early, without a clear role. Dropping a chatbot into an app and hoping it “figures it out” rarely improves the experience. It usually adds noise, cost and maintenance overhead.
Before thinking about models, prompts or infrastructure, the most important question is simple: what problem is this meant to solve for the user?
Good product use cases we see working
When ChatGPT is integrated well, it feels like a natural extension of the product rather than a separate layer. Some examples that work particularly well:
contextual help that explains features in plain language
guided workflows where users don’t know what to do next
internal tools that summarise data or generate first drafts
support surfaces that handle common queries before escalation
In each case, ChatGPT is given a narrow, well-defined role. It’s not trying to do everything. It’s supporting the user at a specific moment in their journey.
What teams underestimate
Most of the work isn’t technical. It’s product and delivery work. Teams often underestimate:
how prompts need to evolve over time
how users will phrase requests unpredictably
how quickly usage costs can grow
how important fallback behaviour becomes when AI responses aren’t good enough
There’s also a tendency to overestimate how much “memory” the system should have. In many cases, short-lived context and clear constraints produce better results than long conversational histories.
Integrating ChatGPT responsibly means designing for failure states, setting clear boundaries, and accepting that AI output still needs guardrails.
Cost, privacy and operational reality
ChatGPT pricing is usage-based, which means costs scale with success. This is often overlooked early on. It’s important to:
model realistic usage scenarios
cap or rate-limit interactions
monitor token consumption closely
be explicit about what data is sent and stored
For products handling sensitive or regulated data, integration decisions should be made carefully and in line with legal and security requirements. AI shouldn’t become a shortcut that introduces risk later.
Build it like a product, not a demo
The biggest difference between successful and unsuccessful integrations is intent. Successful teams treat AI as part of the product roadmap. They test assumptions, observe real usage, and iterate deliberately. Unsuccessful teams treat it as a quick win or marketing feature. ChatGPT works best when it’s designed, shipped and improved like any other product capability. That means starting small, learning quickly and being clear about what success looks like.
Final thoughts
ChatGPT can be a powerful addition to the right product. But it isn’t a shortcut to product-market fit, and it doesn’t replace good UX, clear thinking or disciplined delivery. When integrated thoughtfully, it can reduce friction, support users and unlock new workflows. When rushed or bolted on, it often creates more problems than it solves.
As with most things in product development, the difference comes down to clarity, intent and execution.
