Editorial
Nearly 80% of people surveyed said their organization uses AI in at least one business function. This widespread use of AI is quickly changing how companies work and grow. But the thing is, most discussions focus on technical aspects and specialists — models, engineers, data scientists and such. And yet, there’s another side here that is often overlooked. Namely, leadership.
Hiring AI talent today is not simply about bringing in “more coders.” It’s about finding the right balance between people who can build AI systems and those who can guide organizations through their use.
This talent dilemma is becoming one of the biggest challenges for companies in the age of AI. In this article, I want to explore why.
Table of Contents
- Why AI Talent Strategies Overlook Leadership
- What Happens When Leadership and Engineering Don’t Align
- How to Build the Right Mix of Technical & Strategic Talent
- AI Skills Every Business Leader Must Master
- Creating a Common Language for AI Success
Why AI Talent Strategies Overlook Leadership
When it comes to AI, the need for technical specialists comes across as obvious. You need people to design and train AI models and integrate them into existing systems. And it is easy to measure these needs: if a company lacks engineers, it can’t build or deploy AI solutions. The cause and effect are pretty clear here.
Leadership, on the other hand, is harder to quantify. Unlike model performance, the impact of a manager who understands AI is hard to see properly. As a result, many businesses put most of their energy into chasing technical hires, while underestimating the importance of having leaders who grasp both the technology and the broader business context.
And the constant hype around new models and breakthroughs only reinforces this imbalance. Far too many companies rush to experiment with the latest AI tools, but often forget the long-term task of embedding AI into processes safely, efficiently and at scale.
Related Article: AI Skills Training: Strategies for Technical Teams vs. End-Users
What Happens When Leadership and Engineering Don’t Align
From where I stand, both extremes — technical teams without context and leaders without technical depth — carry risks.
Developers who operate without context can create technically impressive but strategically inefficient solutions. Without a clear product owner to set the right goals and track business impact, organizations may end up with AI tools that don’t actually solve the right problems. Instead, they only result in wasted costs and missed opportunities.
That said, leaders without technical grounding also present a different problem. Why? Because they may underestimate the complexity of integrating AI. This can lead to unrealistic expectations or flawed decisions. One example of this is hasty layoffs because leaders assume that AI can fully replace human employees. A mistaken belief, because today’s artificial intelligence is not at a level where it can really substitute human expertise.
More often than not, successful AI integration requires reskilling existing teams, rethinking workflows and figuring out how to make people and AI work together best. And to do that, leaders need to really think things through.
To put it simply, coders without guidance risk building in the wrong direction, while leaders without technical literacy risk steering the integration efforts into the wrong direction. Neither is desirable.
How to Build the Right Mix of Technical & Strategic Talent
So, if both extremes are bad, what does the right balance between them look like? The way I see it, the optimal setup for the near future (roughly 3-5 years, at least) is likely to include three key roles:
- AI-literate leaders with strong product thinking — people who have a clear idea of what’s needed and can align projects with business goals while also keeping in mind ethical standards and regulatory requirements.
- Engineers and data scientists who build and maintain systems with awareness of the organizational context — and are periodically reminded of that context by the leaders.
- New process-focused roles, similar to how Scrum Masters once reshaped agile practices. I’d say it is very likely that we may soon see “AI coaches” who specialize in upskilling teams and helping/guiding consistent adoption of the tech in the companies that hire them.
Together, this triad will ensure that organizations aren’t just experimenting blindly with AI, but actually scaling it in sustainable, responsible ways.
AI Skills Every Business Leader Must Master
Now, to be clear: technical literacy for leaders doesn’t mean they need to be able to code and design an AI model from scratch. It means having enough knowledge to make informed decisions and ask the right questions when you don’t know something. Don’t just get swept along by hype.
Basic AI Fluency
Leaders should prioritize gaining at least a basic level of AI fluency, so they can understand what this technology can and cannot do. And since the development efforts are progressing very fast, they also need to keep an eye on updates and breakthroughs. What’s cutting-edge today may be outdated in a few months. And leaders need to know if something is no longer relevant.
Critical Thinking
Another important skill to cultivate is critical thinking. Not every AI solution is the right one. And choosing the one that suits your organization means you need to have a clear idea of what you need.
I’ve said it many times before, and I will say it again: implementing AI has to happen with a purpose. Don’t just chase after it out of fear of missing out. If you can’t build a real case for AI use in your company — with tangible benefits — then you likely don’t need it.
Strategic Patience
But if the need is actually there, make sure you pay attention to the balance between speed and quality when integrating AI. It can accelerate work dramatically, true, so the temptation to jump right in is understandable. But that doesn’t make it right — if anything, rushing through the process and doing it haphazardly may create risks that outweigh short-term gains.
Hybrid Team Management
Leaders must learn to manage not only employees but also AI agents themselves — ensuring they fit smoothly into workflows and complement human teams.
In this sense, leadership in the AI era is also about building. Not the technology directly, but rather building an environment where humans and AI can work together in the most efficient ways.
Related Article: The AI-Human Power Play: Leading Hybrid Teams in the Age of Automation
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Creating a Common Language for AI Success
Now that we’ve taken a look at what’s needed, many might ask: “But how do we get there?” How are organizations supposed to close the gap between engineers and managers when decent AI talent itself is already in short supply?
And the truth is: there is no short, ready-made answer here. So companies will need to create their own. The key is to build opportunities for the two camps to learn from one another. Some approaches that I myself can recommend would be:
- Continuous skill-building: Both technical and non-technical staff should have regular opportunities to update their AI knowledge. Ideally, while also sharing their mutual perspectives on what they learn with each other.
- All-hands or demo days: Sharing progress across teams ensures that breakthroughs in one area don’t stay siloed. It’s possible that what’s already being used by one team may be a useful breakthrough for another. Helping them stay in touch and aware will keep the whole company growing at a consistent pace.
- Rotations between teams: Giving leaders hands-on exposure to technical projects — and engineers a chance to see the business side — can build mutual understanding of both sides’ needs. As a result, the AI integration efforts can happen in a way that accounts for everyone’s realities.
Adopting at least some of these practices will help engineers and leaders find middle ground and learn how to work together to get better results.
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