Gartner: How CIOs can lead the expertise remix
The C-suite is caught in an AI strain cooker. On one facet, boards and CEOs see the relentless headlines of AI-driven layoffs at main expertise companies and ask a easy, urgent query: “The place are our financial savings?”. This creates immense strain on CIOs to understand monetary returns from AI, with the implicit assumption that the first path to that return is workforce discount. On the opposite facet is the sobering actuality of execution. Practically half of CIOs report that AI has not met their return on funding (ROI) expectations.
This disconnect exists as a result of the C-suite is working from a flawed premise. The narrative that AI is already enabling widespread, productivity-driven job cuts is dangerously deceptive for many organisations. As expertise leaders, the first mandate is to deconstruct this delusion and floor government groups in a extra analytical, data-driven actuality.
Probably the most harmful strategic error a CIO could make in the present day is to mistake a pivot to a brand new enterprise mannequin for a easy effectivity achieve.
There are three distinct AI layoff methods, every enabled by three solely totally different expertise patterns. An implementation technique should align with the specified end result, and for the overwhelming majority of enterprises, the headlines merely don’t apply.
Repositioning fulltime workers
First, an evaluation of what’s truly occurring on the corporations driving the information cycle. The high-profile layoffs at companies like IBM, Salesforce, and main consulting homes are usually not proof of a easy automation-driven job apocalypse.
These strikes are usually not about productiveness in any respect; they’re a commerce-driven technique often known as Expertise Redistribution.
This can be a “Expertise Remix”. These organisations are strategically reallocating human capital, reducing from low-performing or legacy enterprise strains to fund a large pivot towards net-new AI income streams. IBM, for instance, said that whereas some back-office roles had been changed, its complete employment truly elevated to gasoline funding in its AI consulting companies. Salesforce laid off 1,000 workers whereas concurrently creating 2,000 new gross sales roles particularly to promote its new AI merchandise.
This can be a business pivot to seize new markets. Gartner evaluation of workforce occasions within the first half of 2025 confirms this. Of the greater than 241,000 job occasions studied, 79% weren’t AI-related in any respect. Critically, 17% had been attributable to this “Reposition” technique, whereas lower than 1% had been attributable to AI-driven productiveness layoffs.
The takeaway for CIOs is stark: if your small business is just not pivoting to promote AI software program, {hardware}, or consulting companies, this technique is just not your technique.
Restraining hires
For almost all of enterprises, the commonest and instant expertise impression of AI is just not layoffs however Restrain Hiring.
This technique is enabled by a expertise sample termed Expertise Hunger. The mechanism is rooted in human conduct: organisations deploy AI assistants to their most skilled, high-complexity staff (engineers, analysts, consultants) to make them extra productive. When a brand new process arises, that senior worker finds it sooner to finish the work themselves with their AI assistant than to mentor a junior via the method.
The pure apprenticeship mannequin breaks down. Because of this, when demand for work will increase, the organisation feels much less strain so as to add junior headcount.
This delivers an actual, however restricted, monetary profit: value avoidance. The organisation is just not decreasing present employees numbers; it’s avoiding hiring new ones. This can be a essential distinction. It prevents future prices from being added, however it doesn’t create a cashable saving from the present payroll that may be harvested and redeployed.
There may be actual danger right here. This technique starves the longer term expertise pipeline, making a essential vulnerability, as AI is not going to exchange the roles that require the discernment of expertise, the very expertise it’s not cultivating.
Decreasing Headcount
This brings us to the technique that almost all executives consider they’re asking for: Scale back headcount.
This technique depends on a sample referred to as Expertise Compression, the place AI radically will increase the proficiency of junior employees in low- to mid-complexity roles. The traditional instance is a contact centre, the place an AI device guides a brand new agent via advanced points, making them as efficient as a senior agent.
In apply, nevertheless, this objective is proving extremely elusive and isn’t but being realised generally at scale. The hurdles are immense.
First, the productiveness good points are merely not massive sufficient. Eliminating roles requires a practical productiveness improve between 30% and 65%. Present analysis exhibits that even one of the vital profitable use circumstances, customer support, tops out at a 14% to 34% achieve. That is usually under the minimal threshold required for materials headcount discount.
Second, any anticipated good points are misplaced to “productiveness leakage”. A ten% effectivity achieve for one workforce member usually interprets to solely a 1% course of enchancment as a result of workflow bottlenecks and coordination overhead.
Most significantly, sustainable value financial savings solely stream from remodeled workflows, not from the untimely harvesting of headcount. This requires deep, foundational course of reorganisation earlier than any cuts are made. The hassle and price related to that course of redesign is commonly one to 3 instances as massive as the price of implementing the AI expertise itself. Making an attempt large-scale layoffs with out this foundational work is a direct path to operational instability.
A framework for strategic motion
The CIO’s mandate is to guide the C-suite from strain to precision. This requires a brand new framework for motion.
- Diagnose and Align: The primary motion is prognosis. CIOs should establish which AI expertise technique aligns with the organisation’s present strategic objectives and guarantee alignment on this actuality amongst government friends. This consists of setting clear expectations on timescales. “Reposition” methods are underway; “Restrain” methods are occurring now and can possible improve; “Scale back” methods are usually not but occurring at scale.
- Prioritise the Expertise Sample: The second, and most crucial, step is to create the corresponding expertise sample earlier than executing an AI expertise technique. Layoffs or hiring restraints should begin with creating the suitable expertise basis. Executing the technique with out the underlying expertise sample in place usually results in operational instability.
- Counteract Expertise Hunger: Third, organisations should intentionally counteract Expertise Hunger, which is a possible end result for many. As senior workers take in extra duties with AI help, junior expertise pipelines are threatened. Greatest apply includes creating GenAI-powered simulators, permitting protégés to apply advanced, domain-specific situations in a secure atmosphere, gaining very important expertise earlier than real-world choices come up.
- Pivot to Monetary Effectivity: Lastly, for expertise leaders dealing with a non-negotiable mandate for near-term value discount, productiveness initiatives are an unreliable path. Layoffs is not going to ship financial savings quick sufficient. The more practical reply is “monetary effectivity”, utilizing AI to not make individuals sooner, however to make funds and money extra environment friendly. This consists of purposes like optimising vendor contracts or working capital. This strategy targets price range line gadgets instantly, delivering measurable impression with out the friction of untimely headcount discount.
The strategic crucial
AI is essentially altering the workforce. Each government workforce will want an AI layoff technique, even when that technique is a deliberate resolution to not pursue layoffs. Within the present atmosphere, this should be a acutely aware, well-reasoned alternative. If an organisation decides to behave on AI-driven expertise modifications, the strategy should match its core enterprise technique and its foundational expertise patterns. Retreating from this query below the guise of human-centricity is a mistake. Having a deliberate technique is essentially the most humane strategy for the organisation. With out it, any actions taken develop into mere reactions.
Nate Suda is a senior director analyst at Gartner.
Gartner analysts will additional discover how AI is reshaping enterprise construction, expertise and management on the Gartner IT Symposium/Xpo in Barcelona, from 10–13 November 2025.
