​Layoffs Are Not An AI Strategy: How Enduring Companies Build Workforce Resilience

During the pandemic, a major aviation group faced a problem that many companies confronting AI will recognize. Many employees could no longer carry out the jobs they had been hired to do, at least not at the same scale. Pilots were grounded, and cabin crew had far fewer flights to operate. The immediate pressure was to reduce costs, but the group also had to consider what would happen when demand returned.

I saw this challenge firsthand through an internal workforce mobility program, which supported affected employees with career profiling, training and opportunities to move within the organization. The work started with a simple question: Beyond their current job titles, what were these people capable of doing?Answering this question revealed transferable capabilities, from pilots’ analytical decision-making to cabin crew’s customer management and judgment under pressure. Internal program data indicates 1,378 participants moved into other roles within the organization, a reported redeployment rate of 87%.

While other organizations should not assume they can reproduce that result, the experience demonstrated the options available when a company understands its people as more than the positions they currently occupy.

AI is creating the same decision at greater speed.

The pandemic was an acute shock. AI is creating a slower but potentially bigger version of the same problem. Tasks are changing before organizations have redesigned the jobs around them, while skills are losing relevance faster than many companies can develop replacements.

The World Economic Forum estimates that 39% of workers’ existing skills will change or become outdated between 2025 and 2030. It also reports that 63% of employers already see skills gaps as a major barrier to transformation. Companies therefore face two pressures at once: They are expected to realize savings from AI now while building the capabilities they will need over the next three to five years.

Layoffs produce immediate, measurable savings, making them attractive when leaders are under pressure to demonstrate returns from AI. Sometimes they are also necessary. Companies should not preserve work that no longer creates value, and not every employee can or wants to move into a future role.The problem is deciding who goes without understanding what is being lost. Job titles are poor proxies for capability. Eliminating a position may also remove operating knowledge, customer relationships, judgment and skills the company later has to recruit and rebuild.​​

The lesson is not to avoid automation. Companies should use AI where it improves speed, quality or cost. But they should not treat headcount reduction as proof that an AI strategy is working.

Enduring companies need workforce resilience: the ability to develop, move and supplement capabilities as technology, customer demand and business models change.

Know what the future business needs before deciding who goes.

Leaders should begin with the future work rather than an arbitrary headcount target. They should identify which tasks AI will perform, how jobs will change and which human capabilities will become more valuable.

Most jobs combine different types of work. AI may remove routine tasks while leaving the need for domain knowledge, judgment, relationship management or accountability. Automating part of a role does not necessarily make the whole role redundant.

A workforce-resilience audit should answer three questions.

1. What capabilities will the future organization require?

Translate the future operating model into specific capabilities and realistic proficiency levels. This creates a clearer link between AI investment and the work people will still need to perform.

2. Which capabilities already exist?

Map employees against that future framework, looking beyond formal titles and distinguishing proven competence from the potential to learn. This reveals where valuable capabilities may be hidden and where genuine gaps remain.

3. Who can and wants to move?

Technical fit is only part of sustainable redeployment. Career interests, work preferences and motivation affect whether a move will last. Leaders need to understand whether someone could perform a future role and whether that role is credible for the individual.

These questions should be answered before a major restructuring, not after severance decisions have been made. They must also be revisited regularly because a one-off skills inventory will quickly become outdated as business strategy and technology change.

Map what exists, then compare the real costs.

Capability mapping does not need to begin as an exhaustive exercise. Existing job descriptions, CVs and HR records can provide an initial view, followed by targeted assessments where evidence is weak or the decision is significant.

The commercial comparison must also be honest. Weigh the cost of redeployment against severance, recruitment, onboarding, time to productivity and the loss of institutional knowledge. Also consider the probability that someone will succeed in a new role.

This process will not always produce an internal answer. In some cases, an external hire or workforce reduction will remain the better choice. The purpose of this kind of workforce intelligence is not to justify redeployment at all costs. It is to show you where you can build or move capability and where you genuinely need to buy it.

​This is the true value of workforce resilience.

Workforce resilience is not only about protecting employees. A company that can see and move its capabilities can redirect people toward new products and markets, retain knowledge that would otherwise leave and reduce the expense of repeatedly firing and hiring.

AI will not be the final disruption companies face this decade. Enduring organizations will still make difficult decisions, including layoffs. Their advantage will come from knowing why reductions are necessary, what capabilities they must preserve and where their people can contribute next.

That is the difference between using AI to meet a short-term cost target and building a company that can remain profitable through repeated change.​

This article was first published in Forbes.