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The hardest part of building AI products is no longer making them more capable.

It’s making them easier to trust.

That distinction matters because capability is increasingly becoming table stakes. Models can summarise complex information, identify patterns, generate recommendations and complete tasks that previously required hours of manual work.

But capability on its own does not create adoption.

People still need to understand what the system is doing, where the information came from and what they are expected to do next.

That is particularly important in regulated environments.

A compliance professional may be happy for AI to review thousands of obligations in seconds. They may even welcome a system that highlights potential gaps, compares policies and surfaces changes across multiple jurisdictions.

The harder question is whether they can confidently challenge the result.

Can they see why the system reached that conclusion?

Can they understand which evidence was used?

Can they identify what still requires human judgement?

And can they prove, later, that the decision was properly reviewed and governed?

These are not simply interface questions. They are adoption questions, operating model questions and ultimately trust questions.

Too often, product teams optimise for what the technology can do before understanding how people will work with it.

The result is a product that looks impressive in a demonstration but becomes difficult to use in the reality of a busy organisation.

The challenge is not just to make AI more intelligent - it is to make the experience around AI more understandable, more transparent and more useful in practice.

That means designing for the moments when people hesitate.

When an insight looks surprising.

When the underlying evidence is incomplete.

When two recommendations appear to conflict.

When a user needs to explain the decision to a colleague, a customer, a board or a regulator.

The best products do not hide these moments. They make them easier to navigate.

They give people enough context to move quickly, but enough control to pause, question and intervene when necessary.

This is where product design has a much bigger role to play.

Not by making complex systems look simple at any cost, but by helping people understand which complexity matters and which complexity can be removed.

In my experience, the strongest AI products are not always the ones with the longest feature lists.

They are the ones that make the right action feel clear.

That might mean showing the source behind a recommendation.

It might mean allowing a user to compare two interpretations side by side.

It might mean making uncertainty visible instead of presenting every answer with the same level of confidence.

Or it might mean designing a workflow that brings human review into the process at exactly the right point.

These details can look small from the outside.

They are not.

They determine whether a product becomes part of someone’s daily work or remains an interesting experiment that people tried once and quietly abandoned.

As AI continues to move into financial services, healthcare, legal work and other highly regulated industries, the organisations that succeed will not simply be the ones with access to the most powerful models.

They will be the ones that can turn capability into confidence.

That is a product challenge.

It is a governance challenge.

And increasingly, it is a leadership challenge too.

I’m excited by the teams working on this problem because the opportunity is much bigger than automating individual tasks.

It is about designing entirely new ways for people and intelligent systems to work together.

The technology is moving quickly.

The experience has to catch up.

#AI #ProductDesign #UX #RegTech #Compliance #HumanCentredDesign #Governance #ResponsibleAI #EnterpriseAI #RegWise

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