From concept to launch: how to build and ship AI-powered products
by Yemi Fenuyi ✦ 28 April 2025 ✦ 5 Min Read
AI is changing how we build digital products and solve user needs. When done right, AI-powered products grow customer loyalty, unlock new revenue streams, and reach the market faster and with more confidence.
But too often, businesses fall into one of two traps, unable to turn AI’s potential into something useful, usable, and valuable to their customers.
Most businesses get stuck in the idea phase, without the time, resource, or direction to move from concept to launch.
Others, in a rush to keep up with competitors, bolt AI onto existing products without a clear purpose or user need.
To get AI-powered products live, follow this 3-step approach we've used to help clients launch products and features their customers love.
1. Start with the problem, not the tech
Too many teams start with AI and then try to find a use case. They tack AI onto their existing product without asking the most important question: what’s the best way to solve this problem?
That’s backwards. The best AI products start with a clear problem that AI is the best way to solve.
For one of our partners, the initial assumption was that they needed to modernise their content to win over big tech clients and expand their offering with more resources.
But when we dug deeper, we found the content itself wasn’t the issue. It was already great. The real problem was how hard it was to access. Clients had to dig through long PDFs and navigate a clunky website instead of getting instant, accurate answers.
That’s where AI made sense. Not as a shiny feature, but as a way to remove friction, surface answers faster, and create a better customer experience.
Ask yourself:
What’s frustrating about how users currently solve this problem?
Is AI actually the best solution, or is there a simpler fix?
What would this experience look like if AI worked perfectly?
The best AI products start with a clear problem that AI is the best way to solve.
2. Use AI to build testable prototypes (not just wireframes)
Most teams still design products using static wireframes. But AI is making it easier than ever to create working prototypes quickly. And it is no longer just designers who can do it. Anyone on a product team can get involved.
This shift allows teams to rapidly build testable prototypes and gain richer insights from usability testing. And when your product includes AI, it means you can test real AI functionality early in the process, not just imagine how it might work.
With one client, instead of relying on wireframes to show "how AI might work," we built an AI-powered prototype in one week and put it in front of real users.
It helped us:
Let users test real AI-driven interactions, not just a static UI
Iterate quickly based on real-world feedback
Prove that AI was the right solution
AI is making it easier than ever to create working prototypes quickly. And it is no longer just designers who can do it. Anyone on a product team can get involved.
3. Build trust into AI from the start
AI products fail when users don’t trust the outputs. If they don’t believe the AI is accurate or reliable, they’ll default to other solutions. That’s why trust has to be designed into the product.
Ways to build trust into a product:
AI traceability: Showing users why the AI gave a certain answer.
Human-in-the-loop checks: Ensuring experts reviewed AI-generated responses.
AI validating AI: Using a second model to check for inconsistencies.
Trust isn’t just about accuracy. It’s about setting the right expectations so users feel confident using AI.
For one of our clients, we built in trust by:
Inputting clear limitations on what AI could and couldn’t do.
Adding feedback loops so users could flag inaccurate responses.
Designing AI as a support tool, not a fully automated replacement for human expertise.
AI products fail when users don’t trust the outputs. If they don’t believe the AI is accurate or reliable, they’ll default to other solutions.
AI only works when it solves a real problem.
Just because AI can do something doesn’t mean it should.
The key is knowing when AI is the right tool, testing it with real users early, and designing for trust from the start.
The better teams get at recognising when AI isn’t the answer, the better they’ll be at knowing when it is.
