Two lenses for thinking about AI adoption, organisational design and human value

Most organisations are now talking about AI.

The conversation often starts with tools: What should we use? Who is already experimenting? How do we help more people become confident? Should we appoint AI champions to encourage uptake?

These are worthwhile questions. But are they enough?

In a recent leadership session, we were exploring some of the significant shifts likely to affect the organisation over the next year. Unsurprisingly, AI was one of them.

The initial instinct was to identify the people already enthusiastic about AI and empower them to help others get started. That makes sense. Enthusiasts can demonstrate what is possible, build confidence and help new practices spread.

But as the conversation developed, another question emerged:

Could focusing primarily on individual adoption lead us towards a relatively narrow view of AI?

Lens one: helping people do their current work better

The first lens is about enabling people to use AI within the roles and processes they already have.

AI can help someone prepare a first draft, summarise information, explore ideas or complete parts of their work more efficiently. Building confidence in these tools can reduce friction and give people more time for other things.

There is real value in this.

It also helps people develop a practical understanding of AI. As they experiment, they begin to see possibilities that might not be visible from the executive table or within a centrally developed transformation plan.

But there is a risk.

If this becomes the organisation’s main lens, AI can end up being treated primarily as a personal productivity tool. The underlying work remains largely unchanged. People are simply expected to do it faster, produce more, or operate more efficiently.

That may accelerate current practice without fundamentally changing it.

Lens two: reconsidering how the organisation works

The second lens operates at an organisational level.

Rather than asking only, “How can AI help people perform their current roles?”, it asks:

• Which activities are repeatable or replicable?
• Where could AI take on more of the process?
• Which parts of the work genuinely require human judgement?
• Where do people add value that technology cannot?
• If capacity were released, where would we most want to redirect it?

This shifts the conversation from individual productivity to organisational design.

The aim is not simply to remove people from processes. It is to become more deliberate about where human attention is most valuable.

In public-facing organisations, that may include the moments where people need to exercise judgement, understand context, provide reassurance, challenge an assumption or bring warmth and compassion to an interaction.

AI may make that human contribution more important, not less.

The opportunity is to automate or simplify routine tasks, freeing people to have the capacity to be genuinely present where it is needed.

Perhaps organisations need both lenses

These two perspectives are not competing strategies.

Individual experimentation builds capability, curiosity and awareness. It helps people discover what is possible and can create momentum from the bottom up.

The organisational lens helps ensure that all this activity contributes to something bigger than doing more of the same, only faster.

The challenge is to hold both at once:

How do we encourage people to experiment with AI in their current work, while also reconsidering how work, roles and services could operate differently?

This becomes particularly important because the technology is changing so quickly.

A large, centrally designed transformation can take considerable time and investment. By the time it is implemented, some of its assumptions may already be outdated. But leaving everything to individual experimentation may produce fragmented activity, duplicated effort and unmanaged risk.

The alternative may be a more iterative approach: building capability across the organisation while creating mechanisms for promising ideas to be tested, learned from and scaled.

Freedom to experiment, within meaningful guardrails

That raises another question.

How open should an organisation be?

People need enough freedom to explore different tools and find better ways of working. Yet organisations also have genuine responsibilities around privacy, data security, safety and public trust.

Closing everything down may feel safe, but it can also prevent the organisation from learning.

Opening everything up creates a different set of risks.

So the leadership task may not be to eliminate risk, but to make deliberate choices about it.

That could mean creating safe environments in which teams can experiment with particular pieces of work, access different tools and receive appropriate technical guidance. It could also mean ensuring technology teams are supported to manage risk thoughtfully, rather than being expected to remove it entirely.

For senior leaders, this requires enough understanding of AI to make informed choices. Not technical mastery, but an appreciation of the possibilities, trade-offs and organisational implications.

A leadership conversation, not just a technology conversation

AI is often positioned as the responsibility of technology teams or a small group of enthusiasts.

But the deeper questions belong at the leadership table.

• Where do we want human capability directed?
• Which parts of our work should remain distinctly human?
• How might roles, teams and services change?
• What level of experimentation are we prepared to support?
• And how will we learn quickly enough to adapt as the possibilities continue to evolve?

There may not yet be definitive answers. In fact, acting as though there are could lead organisations to lock in solutions too early.

But there is value in asking better questions.

Perhaps the starting point is to look at AI through both lenses: helping people use it to improve today’s work, while also exploring how it could reshape the organisation around the work that humans are uniquely equipped to do.

Because the real opportunity may not be helping people do everything faster.

It may be creating more capacity for the work that matters most.