4 recs for CIOs to stay on the human side of AI transformation

It’s been recently reported that up to 27 million corporate roles across the Global 2000 are meaningfully exposed to AI-driven elimination, displacement, or fundamental redesign over the next three years. According to the report, however, most organizations sitting on top of these exposures have no coherent plan for what they’re doing with AI, let alone what happens to the people in its crosshairs.

While few would argue that bringing AI into the enterprise is the right move, blindly following the pack and eliminating jobs to look good to Wall Street can have disastrous results. A better move is to be bold, fast and responsible, an approach that’s guided organizations like KPMG in their own AI transformations. 

To push back against the herd mentality and set up your organization for success in the long-term, here are four recommendations for CIOs to navigate the shifting AI and human workforce landscape.

Follow your strategy, not the market

Over the past decade or more, firms often used the excuse of being in a multi-year digital transformation journey to explain missed earnings and declining revenues. Today, AI transformation is being utilized to similar effect with a surge of 6,550% year-over-year mentions of AI agents in SEC filings. Here’s why cutting jobs without a plan can backfire.

By my calculation, out of the 27 million roles at risk, roughly 14% may be permanently lost, amounting to 3.78 million. A further 74% will be reskilled and upskilled, and 12% will be rehired as firms are forced to modify versions of the same roles to repair broken workflows. This means that the bulk of the work over the next three years, or about 86% of this total shift, will be in reskilling, upskilling and rehiring.

Rather than job replacement due to AI exposure, this seems more like any other automation and augmentation journey as we’ve seen previously with RPA and other technologies. According to Steve Hill, managing partner at AI, cybersecurity, and management consulting firm OakTruss Group, the adoption problems aren’t technical. “RPA taught us that people matter in ways that were unforeseen,” he says. “Agentic AI is the new bandwagon, but many failures will come from lack of attention to culture, change management, workforce trust, and clarity of purpose, not the models themselves.”

CIOs should therefore expect AI transformations to drag on just like digital transformations. Purchasing the tech will be the easy part, but fundamentally rethinking and redesigning all aspects of the business — including operations, processes, and products and services —will be a heavy lift requiring more critical thinking, and by more people.   

Manage your AI portfolio across the full innovation lifecycle

With current attention squarely on AI governance, it’s easy to focus on downstream aspects such as AI risk, compliance, trust, ethics, security, sovereignty, and sustainability. Of course, all this needs to be considered and planned well in advance, and the earlier in the innovation lifecycle, the better.

As attention moves to scaling, CIOs need to ensure they maintain the tools and processes to professionally manage the front-end of the innovation lifecycle as well. While excessive AI pilots and prototypes are criticized in today’s environment, the truth is they’re still essential to maintaining a healthy and continuous innovation pipeline from idea to value.

CIOs should ensure they have robust means to identify and prioritize AI-related ideas, inventory AI use cases across the enterprise, and track all their associated meta-data across finance, IT and governance, and risk and compliance.

To get started on identifying and prioritizing AI-related ideas, and to make it a core competency, look to techniques such as innovation workshops as well as the software-side of things. Workshops incorporate the human-side of AI transformation by way of highly-collaborative, interactive sessions bringing in a cross-functional set of subject matter experts and stakeholders that software alone can’t replicate.

Assess your team’s skills as well as your AI

Just as CIOs apply governance and associated guardrails around AI, they also need to examine their teams — where do you need to retrain, upskill, and hire? It often takes more skill to work with AI, and decide when to use and not use it, than perform the work in the first place.

The analogy of moving from a pyramid-shaped workforce to a diamond-shaped one can be misleading. While entry-level jobs can be replaced with AI, not all entry-level jobs are created equal. Recent MBA graduates, for example, may come into entry-level roles, but they have the business acumen and critical thinking skills the organization needs more of.   

Just because AI can give the impression for teams to think less, they shouldn’t. In fact, it’s important to look for team members who are self-motivated to think differently and examine AI outputs more at every step. For example, AI is notoriously bad at providing strategy advice and often produces trendslop instead. So do teams have the intelligence to analyze and interpret every AI output, and determine the signal from the noise, or do they just take it on face value and act on it?

Look for individuals who don’t just sit back and propagate AI slop in their workflows, decisions, and emails, but know where and when to use it and apply their own judgment. The regular assessment of your team’s skills is as essential as the regular assessment of your AI.

Automate when appropriate

In addition to having teams that know where and when to rely on AI, it’s important to take a similar approach for each AI use case and application across the enterprise. Determine when you need probabilistic versus deterministic code, when you need both, and when you need human-in-the-loop or not.

In high-risk AI situations, you may decide to prohibit the deployment of autonomous systems in core financial or customer-facing workflows unless the underlying model and its orchestration layer have successfully passed a pilot with documented safety metrics. As reported previously in KPMG’s Q1 2026 AI Pulse Survey, these types of restrictions are well underway, with 43% of organizations identifying high-risk use cases where autonomous agent decision-making isn’t allowed.

Overall, success in AI transformation is less about eliminating jobs and more about carefully rethinking and redesigning how work gets done, including where and when to use human skills and AI, and more often, where and when to carefully orchestrate both. The humans you plug into this new AI transformation need to be smarter than ever.