Generalists are more valuable than specialists in the AI era
We’re at a point now where speed of execution matters more than long résumés. The old approach, hiring narrowly focused backend engineers or data scientists and building teams around their specialization, made sense when tech moved slowly. That time has passed.
AI is pushing everything into high gear. Technologies evolve in quarters, not years. You can’t hire a ten-year veteran of a tool that’s only been around for 18 months. Experience is useful, but it’s no longer the only competitive edge. What matters more is how quickly someone can learn and pivot in the face of unfamiliar problems.
Generalists aren’t just filling gaps; they’re redefining what work looks like. They move across domains; they don’t wait for someone to tell them what to do. They see a challenge, break it down, and build. It’s not about being good at everything, it’s about knowing enough across disciplines to make smart decisions without waiting for another layer of approval. That kind of momentum is what moves companies forward.
For those in the C-suite, this means reviewing how your business hires, supports, and promotes talent. If you’re still optimizing for depth over range, it’s probably time to recalibrate. What you really want are people with intellectual range, fast learning velocity, and a strong sense of ownership. These are the ones who bring clarity and progress when the path isn’t obvious.
Also worth noting: the shift isn’t just happening in startups. Even established engineering teams are seeing generalists outperform specialists thanks to their ability to adapt when plans change, which, in the AI era, is often.
AI has lowered technical barriers while elevating expectations for expertise
AI is changing not just how work gets done, but also who can do it. Tasks that once required years of coding experience or specialized training are now within reach for people willing to experiment and learn quickly. The tools are getting better, faster, and that means the barrier to entry is dropping.
It’s now possible for an engineer who never wrote front-end code to ship a full interface. And it’s just as common to see a product manager automate workflows that would have previously required a backend team. That’s progress. But as more people get access to powerful tools, the bar for what counts as real contribution goes up. Just using AI isn’t enough. You need to know how to use it to solve tougher, more complex problems that cross technical, operational, and strategic lines.
The paradox is simple: AI makes it easier to do more, but harder to stand out. Automation takes care of the repeatable stuff. What’s left are problems that don’t have fixed playbooks, problems that demand depth in multiple disciplines, strong reasoning, and execution in ambiguous conditions.
For executives, this means two things. First, stop thinking of AI as a replacement for talent. It’s a multiplier for the right kind of talent, people who can adapt, solve, and move fast. Second, give those people more room to work across boundaries. The old silos limit the value AI can create.
According to McKinsey, by 2030, up to 30% of U.S. work hours could be automated. They also estimate that 12 million Americans may need to switch jobs entirely. Ignore that, and your organization structure will be running a business model from the past while your competitors are scaling into the future.
Successful generalists combine depth with versatility and independent action
Being a generalist doesn’t mean knowing a little about everything. It means building deep knowledge in one or two areas, while staying fluent enough across disciplines to make smart, connected decisions. On high-performance teams, the people driving real progress understand technology, operations, product, and how those pieces fit together.
The best generalists operate with a high sense of ownership. They don’t just complete tasks, they take responsibility for outcomes. That includes defining what success looks like, adjusting when conditions change, and not waiting for permission to act. You want people who don’t freeze when the plan breaks, who know how to reassess and move forward with clarity.
Key traits matter here: adaptability, first-principles thinking, range, and strong communication. Adaptability means they can step into new domains without slowing down. First-principles thinking keeps them questioning default assumptions, which is critical when navigating new tools like AI. Communication and empathy are what allow them to align teams and create context, constantly keeping customer value in focus.
If you’re running a company, these traits aren’t optional anymore. They’re essential. Hierarchies built on rigid job scopes slow things down. If your team has to escalate every time a challenge crosses a boundary, progress stalls. You need people who see the full picture, and take action without requiring step-by-step guidance.
David Epstein, author of Range, made a sharp observation: having access to all the world’s information doesn’t help if people can’t connect it. He points out that too many professionals are trained to absorb knowledge but not taught how to integrate it. That disconnect is a liability in environments powered by AI and driven by constant shift. It’s not lack of information that slows companies down, it’s lack of integrative thinking.
Companies embracing adaptability and trusting generalists will thrive
If your organization is still optimizing for predictability, it’s already behind. The companies winning now are the ones built around curiosity, adaptability, and speed. That means hiring people who don’t just follow a roadmap but build new ones when none exist. Generalists who thrive in uncertain environments, those are the people you want in the room when priorities shift, resources tighten, or tech creates a new path overnight.
AI has made it clearer than ever that rigid structures, roles, approvals, workflows, limit your ability to move. Many organizations are still built around the idea that expertise lives in silos and movement requires permission. That model doesn’t scale in an environment where useful knowledge expires quickly and great decisions often need to be made with partial data.
What works instead is building teams of adaptable builders, people with end-to-end accountability. When people know what they own and are trusted to figure out how to deliver, momentum increases. Innovation speeds up. Failure rates go down not because people are cautious, but because they’re close to the work and can course-correct in real time.
This is also about how you hire. The résumé that was perfect for last year’s job probably isn’t what you need tomorrow. Credentials still matter, but not as much as learning velocity and agency. The best people grow into the roles your company doesn’t even have yet.
If you focus on outcome-oriented individuals who can move fast, use AI well, and work across boundaries, you’ll build a company that doesn’t just react to change, but uses it to accelerate. That mindset, trust in high-agency generalists, isn’t just useful. It’s required.
Key takeaways for decision-makers
- Generalists outperform specialists as AI accelerates change: Leaders should prioritize hiring adaptable, fast learners who can work across functions, as domain-specific expertise becomes less sustainable in rapidly shifting tech landscapes.
- AI raises the bar for meaningful contribution: Executives should enable cross-functional problem-solving and rethink role boundaries, as AI tools reduce technical barriers but increase the complexity of remaining challenges.
- The ideal talent profile is depth with range and ownership: Organizations should invest in individuals with deep expertise in one area, but with strong reasoning, initiative, and the ability to contribute beyond traditional role definitions.
- Companies built on adaptability will scale faster in the AI era: To stay competitive, decision-makers must move away from rigid organization structures and empower high-agency generalists to take ownership and deliver across changing business needs.
