The Effect of AI on Expertise

AI discussions have been mostly around labour productivity.

Organisations have begun to track metrics around increased outputs, decreased costs, shorter timeframes for decision-making and greater efficiency or automation. These days, dashboards are mostly created with productivity in mind. Such measures matter. Productivity, competitiveness and effectiveness are constant pressures on organisations.

But maybe there is one more thing to be considered.

As technology handles more and more tasks, what happens to professionals' expertise?

Picture a junior analyst from ten years ago. Getting to this level of expertise typically requires hours spent researching, data collection and analysis, and slide preparation. It could be slow, even excruciating at times, but this experience gave professionals a look into how things worked. You built your judgement, critical thinking and even your confidence through practice.

In fact, nowadays AI enables us to accomplish a lot of different work within seconds.

Research can take less than a minute to complete. You can create drafts in a flash. Summarising data is easier now. It even makes creating presentations faster than before.

Short-term effects include higher productivity. But if fewer people are doing things to become good at them, how does expertise develop for the next generation?

There is simply no arguing with the benefits of having AI.

Organisations have already noted improvements in productivity and effectiveness. Experts realise that they can now free up time that was previously tied up in tedious labour. Now they can focus this time on the activities that require their attention.

Well, the issue is that historically, people have always acquired expertise through practice. We learnt by doing. They have learned from their mistakes and have repeated the process. They have studied the problems, scoured for options and slowly developed a degree of knowledge and expertise that you cannot pick up from reading manuals or taking courses.

With the advent of technology, it is possible that people's knowledge can be acquired in different ways.

This pattern has also repeated itself through history. For every innovation, we made it easier to do some specific work. Now there were capabilities that no longer mattered, and others we learned to need more than ever. It was slow, but the change happened.

AI is beginning to perform tasks that would otherwise be considered purely cognitive activities for humans, making it an especially salient example.

And so on the one hand, this is all how they used to talk about technology.

Now it is about capability.

For example, when AI assists research, people have to learn how to assess results. If AI reports, then they must interpret them with considerable skill. AI knows enough to counter objections, especially when it recommends something and people must spot potential risks.

Work does not go away.

Only its nature changes.

That presents challenges for leadership.

Organisations have spent a lot of money on the technology adoption. They measure productivity, efficiency, and ROI. In this case, what is absent and expected to be truly difficult is the event that this turns out during future capability development vis-à-vis rapid iteration of AI.

The problem is not that AI takes away expertise. The risk is that organisations think expertise keeps on evolving by itself.

In the past, previously finished work activities were used to develop knowledge. You gain some experience without even having to do work. The managers would have to form judgements via their decisions. And individuals specialising would reinforce their skills with cases of escalating difficulty.

If AI catches those activities under its umbrella, organisations should turn deliberately to craft capability.

It means seeking new development opportunities for the next leaders, experts, etc. We need to create different methods of mentoring the experts of the future. That's why organisations also need new learning programmes that, on the one hand, bring in both worlds best together.

You need it because ultimately expertise is the one thing that never changes: it's at the top of a stack for any organisation. Technology will make decision-making more straightforward, take better analytics and find a faster way to acquire the information needed. However, organisations rely on people to provide context and make decisions in grey areas that cannot be automated.

Maybe some organisations will not experience equal gain by using AI solutions.

Not simply firms that automate the majority of operations and activities. Instead, they might turn out to be those very organisations that find the right trade-off between productivity gains and capability building.

That balance will become even more important as AI continues to evolve and emerge.

New Challenges and Questions for Leaders

Instead of asking merely the following:

How much productivity do we get?

They may want to consider asking the following questions:

"What capabilities are we building?"

"What expertise are we developing?"

"Creating Future Experts through Experiences!"

Answers to these questions might influence organisational performance far more than productivity-based metrics do today.

Although technology is intended to assist individuals in working more quickly and efficiently, leadership helps organisations grow stronger.

The future may not belong to those organisations that deploy AI fastest but to those that build capabilities around it. Productivity is a short-term advantage. Capability is sustainable strength.

The challenge is making sure that the technology not only becomes more productive from a skill set perspective but also allows learning new skills.