Why AI Needs Context – and why leaders must own it

Over the past months, AI has moved from promise to pressure. Expectations are high. Investments are accelerating. Boards want answers. Organisations want results. And in many places, the same question keeps returning: Why is this harder than we thought?

The honest answer is that AI rarely fails because the technology is insufficient. It fails because context is missing.

Models can be trained. Platforms can be deployed. Capabilities can be purchased. But context cannot be outsourced. It must be created – deliberately – by leadership.

The more powerful AI becomes, the more visible this gap gets.

Many leaders feel caught in a familiar tension. On one hand, they are expected to move fast – to explore new possibilities, experiment, and keep pace with a rapidly changing technological landscape. On the other hand, they are responsible for stability, quality, and consequences. Not just whether something works, but whether it works in reality.

Some describe this as ambidextrous leadership – a concept introduced by Charles O’Reilly and Michael Tushman in Harvard Business Review 20 years ago – which means the ability to move forward without losing balance. I think of it more simply as responsibility.

AI amplifies whatever context it is given. Clear priorities become clearer. Confusion becomes more expensive. When organisations struggle with AI, it is often because no one has taken ownership of the questions that sit before the technology: What problem are we actually trying to solve? What decisions will this affect? Who carries the risk? And what does success look like once the excitement has settled?

These are not technical questions. They are leadership questions.

And they require something that doesn’t scale easily: judgment.

In practice, the most difficult phase of any AI initiative comes after the pilot. After the initial enthusiasm. When trade-offs appear. When edge cases emerge. When it becomes clear that reality is messier than the model assumed.

This is where many organisations lose momentum – not because they lack ambition, but because they underestimate the human effort required to stay with complexity long enough for it to make sense.

In Finland, there is a word for this kind of quiet determination: sisu. It is not about heroics or stubbornness. It is about staying present, holding responsibility, and continuing with care when the easy answers are gone.

Responsible AI demands that kind of endurance. The willingness to adjust rather than abandon. To refine rather than rush. To accept that meaningful progress often happens slowly – not because we are hesitant, but because we are careful.

At Context&, this understanding shapes how we work with customers. We don’t start with technology. We start with their world – their industry, their constraints, their decisions, their people. Only then do we introduce AI as a tool, not a solution in itself. Because without context, even the most advanced technology struggles to deliver value that lasts.

AI will continue to accelerate. There is no turning back from that. But acceleration alone does not create direction. Leaders do.

That succeeding with AI is not about choosing between speed and responsibility. It is about holding both at the same time – moving forward with confidence, while creating the context that allows technology to serve real decisions, real people, and real outcomes.