AI is accelerating product development - but without design, it’s accelerating risk
AI is changing how products are built.
Teams can generate ideas faster, prototype in hours, and ship features at a pace that would have been unrealistic not long ago. There’s real energy around it. People are experimenting, going to events, trying new tools, running hackathons. It’s exciting, and rightly so.
But alongside that, I’m seeing something else.
A lot of rushed, reactive decisions. A lot of effort going into AI initiatives that don’t quite deliver what people expected.
And it usually starts in the same place.
“We need AI.”
“We need AI” — but why?
There’s a growing pressure at a leadership level. CEOs are hearing about AI everywhere, seeing competitors talk about it, and understandably feeling like they need to respond. That pressure gets passed down to CPOs and product teams.
At the same time, product teams are excited. They’re exploring what’s possible, finding new tools, building things quickly.
So you end up with two forces moving at once:
pressure from the top
energy from within the team
And both are completely understandable.
But neither of them, on their own, answers the most important question:
What problem are we actually trying to solve?
When everyone is moving, but not necessarily in the same direction
What it often feels like inside these organisations is movement without alignment.
People are busy. Things are being built. There’s momentum.
But it can also feel like people are running in slightly different directions.
That’s where the role of product leadership becomes critical.
The CPO often ends up in the middle, acting as the calm, rational voice. Not trying to shut down the energy, but trying to introduce a moment of pause. A chance to step back and decide where all of this should be heading.
One of the best ways I’ve heard it described is like a ship.
You can absolutely go at full speed. In fact, you probably should.
But if your direction is slightly off, the faster you go, the harder it becomes to correct course.
Taking a moment to check the direction doesn’t slow you down. It prevents you from going fast in the wrong direction.
Just because you can, doesn’t mean you should
AI makes it easier than ever to build.
But that creates a new kind of risk.
Because when the barrier to building is lower, the temptation to build increases, even when the value isn’t clear.
This is where I keep coming back to a simple idea:
Just because you can, doesn’t mean you should. Jurassic Park anyone?
Without clarity on the problem, AI becomes a layer of complexity rather than a source of value.
The real problem is the lack of pause
From the conversations I’ve had recently with product leaders, this is a consistent theme.
There’s a lot of activity around AI.
But not enough time spent understanding the problem space.
This is where product design thinking becomes critical. Not in a theoretical sense, but in a very practical one.
It’s about taking a step back and asking:
what are we actually trying to solve?
what does success look like?
what is the return we’re trying to achieve?
It’s the fundamentals. The double diamond. Exploring the problem properly before jumping to solutions.
Because without that pause, what are you actually creating?
AI increases the cost of getting it wrong
On the surface, experimenting with AI doesn’t seem that costly. It’s cheaper than traditional development right?
You can prototype fast, test ideas, and explore what’s possible without a huge upfront investment. Compared to building something from scratch, it can feel like a low-risk way to move forward.
But that’s not quite the reality. AI doesn’t just add capability. It adds complexity.
Suddenly you have more features, more moving parts, and more decisions to manage. What starts as experimentation can quickly turn into feature bloat, with products becoming harder to understand and harder to use.
At the same time, there’s a lot of energy in the team. People are excited. Leaders are excited. The idea feels promising. But when those features don’t land, that energy turns into disappointment.
And it’s not free. There’s still time being invested. Still budget being spent. Still opportunity cost in what the team could have been focusing on instead. When AI is applied without a clear problem, it often ends up being money down the drain.
I’ve seen businesses get caught up in building something because it’s interesting or impressive, rather than because it’s useful.
And that’s the real question that often gets missed:
Is this actually helping the business?
Because if it’s not, you don’t just have a feature that didn’t work. You have something that now needs to be rethought, reworked, or removed entirely.
And by that point, you’re no longer experimenting.
You’re fixing.
And what could you have created in that time instead?
What good looks like
The businesses getting this right aren’t the ones moving the fastest.
They’re the ones that are able to create a moment of clarity before they move.
They take the time to understand the problem space properly. They define what success looks like. They align around what they’re actually trying to achieve.
Then they move quickly.
AI becomes powerful in that context. Not because it’s new or impressive, but because it’s being applied deliberately.
Final thought
AI creates both opportunity and pressure.
The pressure is to move quickly, to keep up, to not be left behind.
But speed without direction leads to:
wasted investment
disappointed teams
products that feel complex rather than valuable
The businesses that succeed won’t be the ones that use AI the most.
They’ll be the ones that take a moment to understand what they’re trying to achieve, and use it with intent.
Exploring AI, but want to make sure you’re solving the right problem first?
We help businesses step back, define what matters, and apply the right thinking before building, so you can move forward with clarity and confidence.
👉 Book a call with our team to talk about how we can help.
FAQs
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A: Starting with the technology instead of the problem. Without a clear understanding of what needs to be solved, AI features often fail to deliver value.
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A: By taking time to define the problem, align on success metrics, and then exploring how AI can support that, rather than starting with AI itself.
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A: No. It increases the need for design, as it introduces more complexity and requires clearer thinking about how products should behave.
