AI reshapes businesses

AI Is Reshaping Businesses to Rely on Senior Staff

Companies started by automating the work that is easiest to count. The larger opportunity is to improve the decisions that shape all the work below them: leaders who use AI should understand the work, exercise judgment and remain accountable for the result as it flows down.

What we see today is that AI is moving through the workplace unevenly. Most early implementations have been aimed at the "doing" part of an organisation: drafting routine material, handling service requests, writing standard code, summarising documents and processing transactions. These activities are attractive targets because they happen in high volume, their outputs are relatively easy to measure, and a company can often change the process without first changing who holds authority.

That helps explain the job impact so far. The work being altered first was typically carried out by larger groups of people whose individual employment cost is lower than that of senior managers and specialist decision-makers. The side-effect is now starting to be hard to ignore: a company reduces or slows hiring in those groups, leaving its leadership structure largely untouched. To date, AI flattened the amount of work at the bottom without flattening the organisation itself.

The value is moving from production to judgment

The larger opportunity sits in the "thinking" part of the business. In this context, thinking means the work of deciding what should be done, choosing between competing options, allocating money and people, modelling and planning for the future - and accepting the consequences. There are fewer people doing this work, but they are expensive, and one poor decision can create more cost than hundreds of efficiently completed tasks.

This work is harder to change because it is also harder to inspect. A company can count how many customer cases an AI assistant closes. It is much harder to say whether a product strategy was sound, whether a risk was judged correctly, or whether a proposed reorganisation will help. Outcomes arrive later, causes overlap, and senior people have more freedom to define success after the fact. These hard-to-measure decisions still contain substantial value, so the next stage of adoption requires better standards than a simple productivity count.

Judge the work and the understanding behind it

This month, Linus Torvalds made a point about individual work in a characteristically blunt response to a proposal about AI-assisted contributions. A rule against using AI for a particular task, he argued, makes no more sense by itself than banning compilers because a programmer ought to understand machine code, or banning editors because backspace can hide a typing mistake. Competence shows in what people produce and whether they understand it.

A blanket "No AI" rule regulates the process and can treat tool use as evidence of incompetence. This is not the right place to start. This distinction matters in the workplace more now, as LLM standards are so high. A useful policy realises everyone has AI tools, and can still state the expectations clearly:

• A professional standard requires each person to explain, check and own the result.
• The standard applies equally to an employee who submits unchecked AI analysis and a manager who makes an unsupported decision without AI.
• Accountability remains with the person responsible for the decision, whatever tools they used to reach it.

Some uses of AI still require firm limits. Confidential data, regulated decisions, copyright obligations and systems that can cause material harm require specific controls. Those controls should address each risk directly by specifying which information may not be disclosed, which claims need independent verification, which decisions require human approval, and who is responsible when something goes wrong. A blanket prohibition avoids that design work without making the underlying work more reliable.

If only a narrow group learns how to question a model, recognise a weak answer and incorporate a strong one into real work, AI will reinforce the existing division between those who decide and those who execute. Senior staff will gain leverage while junior staff lose both tasks and the experience through which judgment is usually developed. The company may become more top-heavy not only in headcount, but in capability.

People need safe tools, examples drawn from their own jobs, time to practise, and clear responsibility for checking the output. Junior employees also need work that develops judgment. If AI removes every first draft, basic analysis and routine diagnosis, organisations must deliberately replace the learning those tasks once provided. Otherwise they will automate the apprenticeship that produces their future experts.

The positive management goal

Useful tools tend to spread, and AI is the most useful tool knowledge workers have ever encountered. Organisations now have to decide where to direct that usefulness and how to guide staff toward critical thinking.

A weak adoption programme counts tasks completed faster and removes capacity from the easiest part of the organisation to measure. A stronger one also examines the decisions above those tasks. It asks which assumptions produced the work, who can challenge them, what evidence would change the decision, and whether AI can improve the quality or speed of that reasoning. It then gives people throughout the organisation the opportunity to learn the tool under standards that make understanding and accountability visible.

AI may make businesses more top-heavy in its first phase. That pattern comes from applying AI to doing while protecting thinking from the same scrutiny. Once the quick wins are exhausted, the harder work begins: using AI to improve judgment, widening the group able to exercise it, and evaluating people by the quality of what they understand and deliver.

Author: Michael Wells