For years, the direction of travel in tech has been towards specialization. But as AI makes expertise more accessible and allows individuals to cover more ground, another kind of profile is becoming increasingly valuable: the person who can step into a broad problem, figure out what matters, and own it from end to end.

Specialization has long made sense in tech. As products became more complex and disciplines matured, companies needed people with increasingly deep expertise in increasingly specific areas. And they still do. Experienced engineers, AI and data specialists, cybersecurity professionals and other highly technical profiles remain some of the hardest people to hire.

But alongside that demand, we are seeing something else emerge in conversations with companies: growing demand for people who can operate across a wider surface area. People who learn quickly, who can move between strategic and operational work, and understand enough outside their immediate discipline to connect the dots. And, perhaps most importantly, who can take an ambiguous problem and own it rather than wait for a clearly defined task.

In other words, strong generalists.

AI changes how much ground one person can cover

The idea that AI could make smaller teams capable of doing more has been discussed for some time. What is becoming more interesting now is what that could mean for the people inside those teams.

We are not yet seeing AI translate into a straightforward, measurable drop in hiring across our clients. What we are seeing is companies becoming more deliberate about the hires they make. Instead of simply asking who they need to add, more conversations are starting with what the team actually needs to accomplish, which capabilities are missing, and how much of that work needs another person at all.

The practical consequence is often a higher bar for breadth and ownership. Companies increasingly value people who can take responsibility for a larger area rather than execute one narrowly defined part of it. AI allows relatively small teams to cover more ground, but doing so puts a premium on people who know how to direct that capacity effectively.

There is broader evidence that work is beginning to move in this direction. Microsoft's 2025 Work Trend Index, based on 31,000 workers across 31 countries alongside LinkedIn labour-market data and Microsoft 365 signals, describes organizations moving towards more fluid, outcome-driven structures in which people work alongside AI agents rather than within rigidly defined task boundaries. Microsoft found that 46% of leaders said their organizations were already using agents to fully automate workstreams or business processes.

Its 2026 research takes that idea further: as AI handles more tactical execution, the human role increasingly shifts towards setting direction, defining standards and evaluating outcomes. That distinction matters. AI can expand what one person is capable of doing, but someone still needs to decide what should be done, understand the context around it, judge whether the output is good enough and connect the work to a wider objective.

Those are not particularly narrow skills.

The valuable generalist is not someone who is average at everything

There is an important distinction here. Generalist can easily sound like someone who knows a little about many things but has no real expertise in any of them. That is not the profile we are talking about.

The generalists becoming valuable are closer to people with a strong foundation in one or more areas who can apply that expertise across a broader set of problems. They can go deep when necessary, but they are not dependent on staying within a tightly defined job description to create value. Some of the profiles that remain hardest to find are exactly these people: strong generalists who can own an area, operate independently and step into unfamiliar problems without needing every step mapped out for them.

The same qualities appear when you try to define what "the best talent" actually means. Once you move beyond the phrase itself, the criteria tend to become much more concrete: learning speed, problem-solving, ownership, communication, collaboration, business understanding and the ability to execute. That combination may become more useful as the cost of accessing knowledge falls.

If AI can help an employee write code outside their primary language, analyze unfamiliar information, produce a first draft, research a new market or quickly get up to speed on a topic, knowing everything yourself becomes less important. Knowing what to ask, how to evaluate the answer and what to do next becomes more important.

The skills data points in the same direction

This does not mean technical expertise is losing value. In fact, the opposite is true. The World Economic Forum's Future of Jobs Report 2025 found that AI and big data are expected to be the fastest-growing skills through 2030, followed by networks and cybersecurity and technological literacy. Technology-related roles including AI and machine learning specialists, big data specialists and software developers are also among the fastest-growing jobs.

But look at the other skills employers expect to become more important and the picture gets broader. Creative thinking, resilience, flexibility and agility, curiosity and lifelong learning, leadership and analytical thinking all rank among the skills rising in importance. Nearly 40% of workers' core skills are expected to change by 2030. That combination is telling.

The future does not appear to belong exclusively to specialists or generalists. Companies need deep technical expertise at the same time as they need people capable of learning, adapting and operating across changing problems. AI may simply shift where the balance sits.

Specialists are not going anywhere

None of this is an argument for replacing specialists with generalists.

Some problems require genuine depth, and the market reflects that. Senior technical talent and specialists in areas such as AI and data remain highly sought after. But the assumption that increasingly sophisticated technology will inevitably make jobs increasingly narrow may be worth questioning.

AI can provide access to knowledge, execution capacity and technical capabilities that once required several different people. As that happens, the scarce resource may increasingly become the person capable of bringing those pieces together: understanding the wider problem, knowing where expertise is needed, making decisions with incomplete information and taking responsibility for the outcome.

The specialist still knows something few others know. The high-ownership generalist knows how to make a lot of different things work together. In smaller, AI-enabled teams, companies are likely to need both. But the second profile may be becoming much more valuable than we have traditionally given it credit for.