A senior executive is caught in an artificial intelligence pressure cooker. On the one hand, boards of directors and CEOs see relentless headlines Layoffs caused by artificial intelligence at big tech companies and ask a simple and relevant question: “Where are our savings?” This creates enormous pressure on CIOs to realize financial returns from AI, with the implication that the main path to those returns is through workforce reduction. On the other hand, there is the sobering reality of execution. Nearly half of CIOs report AI doesn't meet their needs return on investment (ROI) expectations.
This gap exists because senior management is operating from a flawed premise. The idea that AI is already driving widespread job losses driven by productivity gains is dangerously misleading for most organizations. The primary challenge for technology leaders is to shatter this myth and move executive teams into a more analytical, data-driven reality.
The most dangerous strategic mistake a CIO can make today is mistaking a pivot to a new business model for simply improving efficiency.
There are three different AI firing strategies, each based on three very different talent models. The execution strategy must align with the desired outcome, and for the vast majority of businesses, headlines are simply irrelevant.
Repositioning of full-time employees
First, an analysis of what is actually happening in the companies that generate the news cycle. High-profile layoffs at companies such as IBM, Salesforce and major consulting houses are not evidence of a simple automation-driven job apocalypse.
These steps are not performance related at all; this is a commercialized strategy known as expertise redistribution.
This is the “Talent Remix”. These organizations are strategically reallocating human capital, divesting from underperforming or legacy lines of business to fund a major pivot to new, clean AI revenue streams. IBM, for example, said that while some back office positions were replaced, overall employment at the company actually increased, spurring investment in its artificial intelligence consulting services. Salesforce laid off 1,000 employees while simultaneously creating 2,000 new sales positions specifically to sell its new artificial intelligence products.
This is a commercial pivot to capture new markets. Gartner's workforce analysis for the first half of 2025 confirms this. Of the more than 241,000 work events studied, 79% were not related to AI at all. Crucially, 17% of cases were due to this “repositioning” strategy, while less than 1% were due to AI-induced performance cuts.
The takeaway for CIOs is clear: unless your business is focused on selling software, hardware, or AI consulting services, this is not your strategy.
Hiring restrictions
For most businesses, the most common and immediate impact of AI on talent is not layoffs, but limited hiring.
This strategy is enabled by a talent template called Experience Hunger. This mechanism is rooted in human behavior: organizations use AI assistants for their most experienced and complex employees (engineers, analysts, consultants) to make them more productive. When a new task arises, a senior employee finds it faster to complete the job on his own with his AI assistant than to mentor a junior in the process.
The natural apprenticeship model is failing. As a result, when demand for work increases, the organization feels less pressure to increase the number of junior staff.
This provides a real but limited financial benefit: cost savings. The organization is not reducing its existing headcount; it's avoiding hiring new ones. This is a crucial difference. This prevents future costs from being added, but does not create cashable savings from current payroll that could be collected and redistributed.
There is a real risk here. This strategy will starve the future talent channel, creating a critical vulnerability because AI will not replace roles that require recognition of experience, the very experience it no longer cultivates.
Staff reduction
This brings us to the strategy most executives believe they are asking for: downsizing.
This strategy is based on a model called Experience Compression, where AI radically improves the skills of junior staff in low- and medium-complexity roles. A classic example is the contact center, where an artificial intelligence tool helps a new agent solve complex problems, making them as effective as a senior agent.
However, in practice, this goal turns out to be very unattainable and has not yet been realized on a large scale. The obstacles are enormous.
First, the performance gains are simply not big enough. Eliminating roles requires a 30–65% increase in functional productivity. Current research shows that even one of the most successful use cases—customer service—results in gains of between 14% and 34%. This is often below the minimum threshold required to reduce headcount.
Second, any expected benefits are lost due to performance leakage. A 10% improvement in efficiency for one team member often means only a 1% improvement in process due to workflow bottlenecks and coordination overhead.
Most importantly, sustainable cost savings are achieved only by transforming work processes, not by prematurely adding staff. This requires a deep, fundamental redesign of processes before any cuts are made. The effort and cost associated with process redesign is often one to three times greater than the cost of implementing the AI technology itself. Attempting large-scale layoffs without this fundamental work is a recipe for operational instability.
Framework for Strategic Action
The CIO's mandate is to lead senior management from pressure to precision. This requires a new basis for action.
- Diagnostics and alignment: The first step is diagnosis. CIOs must determine what AI talent strategy aligns with the organization's current strategic goals and ensure alignment of that reality among fellow executives. This includes setting clear expectations regarding deadlines. “Repositioning” strategies are currently being implemented; “Containment” strategies are being used now and are likely to increase; “Reduction” strategies have not yet been applied on a large scale.
- Prioritize your talent template: The second and most important step is to create an appropriate talent model before executing an AI talent strategy. Layoffs or hiring restrictions should begin with the establishment of a suitable talent pool. Implementing a strategy without a basic HR structure in place often leads to operational instability.
- Counteracting fasting: Third, organizations must consciously resist experience starvation, which is the likely outcome for most. As senior employees solve more problems using AI, junior talent pipelines are at risk. Best practice involves creating GenAI-powered simulations that allow protégés to practice complex, domain-specific scenarios in a safe environment, gaining vital experience before real decisions are made.
- Transition to financial efficiency: Finally, for technology leaders faced with an overwhelming mandate to cut costs in the short term, productivity initiatives are an unreliable path. Layoffs won't lead to savings fast enough. A more effective answer is “financial efficiency,” where AI is used not to make people faster, but to make finance and cash more efficient. This includes applications such as optimizing supplier contracts or working capital. This approach targets budget lines directly, delivering measurable impact without prematurely reducing headcount.
Strategic Imperative
AI is revolutionizing the workforce. Every leadership team will need an AI-assisted layoff strategy, even if that strategy is a deliberate decision not to pursue layoffs. In the current conditions, this must be a conscious, reasoned choice. If an organization decides to act on AI-powered talent change, the approach must be consistent with its core business strategy and its underlying talent models. To retreat from this issue under the guise of human-centrism is a mistake. Having a sound strategy is the most humane approach for an organization. Without this, any action taken becomes simply a reaction.
Nate Court is a senior director analyst at Gartner.
Gartner analysts will continue to explore how artificial intelligence is changing the world's enterprise structure, talent and leadership. Gartner IT/Xpo Symposium in Barcelona, 10–13 November 2025
