Rethinking workforce development for an
AI-First world

A version of this question is being asked in boardrooms and leadership team meetings across every industry right now. How do we build the workforce we need for an AI-driven future?

The question sounds straightforward. The answer is anything but.

After nearly three decades in this industry, I have watched organizations navigate wave after wave of technology change — the move to the cloud, the rise of digital, the shift to mobile. Each wave required adaptation. What’s happening now is different in one important respect: pace. The tooling is changing faster than any training program can keep up with, faster than most hiring plans can accommodate, and faster than many organizational structures were designed to handle.

The companies that get this right will not be the ones that simply hire more AI talent or deploy more AI tools. They will be the ones who fundamentally rethink how their workforce learns, operates, and grows.

The build-or-buy question now applies to talent

Most technology leaders are familiar with the build-or-buy decision in the context of solutions — do you build a capability internally or acquire it? That same logic now applies directly to talent.

The instinct for many organizations is to hire their way into AI readiness. Find people who already have the skills, bring them in, and move fast. There are cases where that makes sense — particularly for highly specialized roles like AI workflow architects, domain translation specialists, or AI output validation. These are skills that may not exist in your current organization and that serve as a critical precursor to broader adoption. In those cases, buying is the right call.

But for most of the workforce, the right answer is to build. Here’s why: the people already in your organization carry something that no external hire can bring on day one — deep institutional knowledge, client relationships, and hard-won domain expertise. What they need is not a replacement skillset. They need a new tool in the toolkit and a framework for continuing to develop as that toolkit evolves.

The mistake I see organizations make is treating AI training as a one-time event — a certification program, a workshop or a learning sprint. That approach no longer makes sense. The model that worked six months ago has already been superseded. The tools that developers rely on today will look different by year-end. Rather than build proficiency with a specific tool, organizations need to build muscle memory to keep learning as tools change.

AI-First — a different way of thinking

At Unisys, we’ve adopted an AI-First mindset, which is distinct from being AI-native. AI-native describes organizations or individuals who started with AI — they have limited to no other frame of reference. AI-First is a mindset anyone can adopt, regardless of tenure or background. It means that every time you encounter a problem, your first thought is: can AI do this differently, or better?

A simple example. I needed to work through a complex strategic decision and communicate it clearly to my leadership team. In the past, that would have meant pulling together a group of people, multiple rounds of input, and multiple iterations. Instead, I used our AI tooling to pressure-test my thinking by describing the scenario, the trade-offs, and what I was trying to land on. After several iterations of prompting,  I arrived at a decision and was able to make the case for it much better than a week of meetings would have produced — in minutes. That is a fundamentally different way of working.

That shift in mindset — applied consistently, across every function and every level of an organization — is what AI-First looks like in practice. It amplifies judgment and expertise.

Three buckets, one workforce challenge

When I think about how an organization moves to an AI-First model, I see three distinct groups of people, and each requires a different approach.

The first group are the early adopters — curious, self-directed, already experimenting on their own time. They make up roughly 15% of most organizations. Working with this group is relatively straightforward: give them access, give them space, and get out of the way.

The second group faces a more challenging reality. Their current roles — in areas like level one service desk support, routine data processing, or manual workflows — will be handled by AI systems sooner rather than later. For these individuals, the motivation to develop new skills is to remain relevant and advance in their careers. Organizations that have these types of employees and want to retain their expertise should be honest about the reality of their future employment and invest meaningfully in helping them move to higher-value work.

The third group will wait. They will assume this wave, like previous ones, will crest and recede. Some are close to the end of their careers and are calculating whether they can outlast the change. Some simply don’t believe the urgency is real. Over time, this group becomes a liability — not through any failure of character, but because the pace of change will outrun their willingness to adapt.

The goal is not to write off any of these groups prematurely. The goal is to build a framework that pulls as many people as possible into the first two buckets, as quickly as possible, while being clear-eyed about the consequences of standing still.

What investment looks like

Organizations serious about AI-First workforce development need to make that commitment tangible. That means creating protected time for learning — baked into utilization models, not bolted on as an afterthought. It means building sandboxes and labs where people can experiment without risk. It means tying compensation, rewards and advancement opportunities to demonstrated skill development, not just tenure or role performance.

It also means acknowledging that some of these investments will not deliver immediate ROI. In the early stages of a genuine capability shift, the return is not measurable in the traditional sense. The return is staying in the game — maintaining the ability to serve clients at the pace they now expect and the standard they increasingly demand.

Engaged, growing associates produce engaged, satisfied clients. That connection is not incidental. It is the foundation of a sustainable business.

Building what comes next

At Unisys, our approach to AI is built around three principles: helping clients develop the right foundation, transforming how work gets done, and orchestrating AI securely at scale. That same framework applies to the workforce question.

You have to build the foundation — a culture of curiosity and continuous learning. You must transform how people work — embedding AI-First thinking into daily operations. And you must orchestrate it at scale — with the governance, structure, and investment to make it sustainable across the organization.

The organizations that treat workforce development as a separate workstream from AI strategy will find that both efforts underperform. They are the same effort.

To learn more about how Unisys helps organizations develop the AI capabilities they need to compete, visit unisys.com/solutions/artificial-intelligence.