There’s no shortage of professionals who know their way around AI. Prompts, tools, frameworks — it’s becoming table stakes. As a result, knowing how to use AI is no longer the differentiator. The real shift? Organizations are now prioritizing people who can architect the why behind it, valuing strategy over skillset, and execution over experimentation.
The stakes are higher, the tools are more innovative, and the expectations have changed. AI generalists were the MVPs of the early wave. Today, organizations need operators, orchestrators, and strategy leaders — people who can align AI efforts with business outcomes, design responsible governance frameworks, and turn potential into practical value.
We recently explored the real AI skill gap, a growing need to bridge the gap between innovation and execution. Now we’re zooming in on the people stepping into that space.
From Tool Fluency to Strategic Literacy
The earliest adopters of AI unlocked quick wins: automating content, speeding up analysis, and reducing busywork. That baseline productivity still matters. However, the organizations leading the next wave are looking to take this one step further.
The people moving the needle are the ones who can translate AI’s capabilities into cross-functional impact. That means:
- Spotting high-leverage opportunities across functions. Some opportunities can be automating client onboarding in services, streamlining supply chain forecasting in operations, or accelerating recruiting cycles in HR. This is about identifying bottlenecks or repetitive processes that, once automated or augmented by AI, can free up significant time and resources and create lasting value across multiple teams.
- Asking should we just as often as can we. While technological feasibility is important, strategic leaders interrogate the appropriateness of AI in a given context. For example, before implementing AI-powered customer support, they consider whether it enhances the customer experience or risks alienating users who expect a human touch.
- Defining the right KPIs, from efficiency to experience to innovation. Instead of tracking generic metrics, effective operators align AI initiatives with business goals, such as reducing customer churn through smarter outreach, or cutting the time to produce market research. They measure not just efficiency, but also improvements in quality, customer satisfaction, and innovation velocity.
- Navigating risk, ethics, and internal readiness. This involves more than compliance checklists. Leaders ensure AI is deployed responsibly; for instance, they establish robust review processes around bias in hiring algorithms or train staff to understand AI’s limitations. They strike a thoughtful balance between speed and accountability, ensuring that rapid AI adoption doesn’t compromise ethical standards, data privacy, or the long-term objectives of the organization.
TL;DR: Strategic literacy means knowing how to make AI useful, not just impressive.
New AI Roles are Emerging and Reshaping the Org Chart
We’ve seen a surge in executive titles and leadership roles (think Chief AI Officer, Head of AI Strategy, and Director of AI Enablement, among others). These titles may vary, but the purpose is the same: to orchestrate people, policy, and platforms into something cohesive.
Between Q2 2022 and Q2 2024, the number of AI-related jobs nearly tripled from 3,700 to close to 11,000 per quarter. And the biggest spike happened at the top: executive-level AI roles like VP of AI or Chief AI Officer increased by more than 400% during that period.
These positions are being hired not to oversee experiments but to drive AI initiatives that impact long-term business strategy. The takeaway? AI isn’t just a trend anymore nor a one-team project. Instead, it’s becoming mission-critical to how organizations think, plan, and grow. It reflects a broader shift in how companies think about AI as a long-term investment that requires formal leadership and strategic intent.
Upskilling Isn’t Optional, But It’s Not One-Size-Fits-All
One of the most interesting effects of AI adoption is how quickly it’s reshaping expectations for individual roles, even ones that aren’t traditionally technical. The ability to interact with AI tools, evaluate results, and spot inaccuracies is becoming a core skill in various fields, from marketing to operations.
This shift is accelerating demand for what many call AI literacy. More than just knowing how to use a tool, it’s about becoming comfortable utilizing AI with critical thinking, transparency, and adaptability. Understanding the limitations of generative models, asking better questions, and knowing when to bring in a human lens are all part of that learning curve.
Organizations are starting to meet that curve in a variety of ways, from internal AI bootcamps to cross-departmental task forces. As teams experiment, they learn that enablement matters just as much as access. Giving people the right tools is one thing; ensuring they understand how to use them responsibly and effectively is another.
And the payoff is real: When employees feel confident using and adopting generative AI, organizations can see measurable gains in speed, quality, and productivity. Well-supported employees are nearly three times as likely to report improvements in both the pace and caliber of their work and may even be up to 8% more productive overall.
Why It Matters Now
We’re well past the phase where AI was a novelty or even a competitive edge. It’s now becoming foundational, and that means the talent companies invest in now will determine whether they’re building real capability or just chasing the next wave of tools.
The future of AI at work is changing. The people who can define not just how to use AI – but why – are shaping what comes next. And that deeper understanding is what makes the difference between good ideas and sustainable impact.
