AI adoption elevates demand for managerial roles
Anyone expecting AI to reduce the need for managers hasn’t been paying close attention. What’s actually happening is the opposite. As AI rolls deeper into real enterprise functions, code generation, logistics, forecasting, customer service, what emerges is an entirely new set of complexities that must be coordinated at a high level. The routine work might get automated, but the strategy, integration, and oversight get more important.
You don’t deploy AI systems and walk away. You deploy, adapt, monitor, assess, and iterate. That requires skilled managers who understand both how AI operates and how human teams need to interact with it. These are the people who align machine capability with business need. In short, they don’t disappear, they become mission-critical.
For the C-suite, this point matters operationally and strategically. Operationally, you’ll need more managers in the room who know how to supervise AI-influenced workflows. Strategically, you need leaders who understand that AI isn’t just another automation tool, it’s a system that evolves and demands dynamic integration with human decision-making. The more you invest in AI, the more you’ll need people who can direct and course-correct intelligently.
Research backs this up. Professors Mireia Gine and her team at IESE Business School analyzed 370 million U.S. job postings from 2010 to 2022. The data shows a consistent rise in demand for management roles as AI adoption expands. Their results are clear: firms adopting AI become more management-intensive. So if your hiring and talent planning hasn’t factored in this shift, it’s already outdated.
We’re entering a management-first era. Not because we have more red tape, but because we have faster systems, broader possibilities, and more interactions between human and machine intelligence. That doesn’t shrink leadership, it scales it.
AI is redefining managerial skill sets toward cognitive and creative capacities
When AI enters the organization, it doesn’t just change workflows, it changes what we expect from leaders. The old model of skill division, hard vs. soft skills, isn’t useful anymore. What matters now is cognitive strength. That includes strategic thinking, the ability to solve unforeseen problems, and the capability to guide teams through fast, data-driven decisions.
Managers are now expected to bring creativity into operational planning. Not creativity in the abstract, but in applied ways, figuring out new workflows, designing more effective coordination between AI and human teams, and focusing hard on how to drive real revenue outcomes. This is already being reflected in hiring. The roles you post today have to prioritize these thinking skills, or you’re hiring for yesterday’s challenges.
The nature of management has fundamentally shifted, and AI is pushing that evolution faster. According to Mireia Gine and her colleagues from IESE Business School, job postings from the last decade show a marked rise in demand for leadership skills that deal with complexity, specifically creativity, stakeholder coordination, and strategic revenue growth. Their team analyzed 370 million U.S. job ads between 2010 and 2022, and the pattern was clear: companies moving into AI want managers who can think better, not just do more.
Here’s what that means for executives: your training programs, career paths, and leadership pipelines must now reflect this shift. If future managers in your company are still being evaluated by technical delivery or time management alone, you’re falling behind. What you need are minds capable of leading mixed-expertise teams into uncharted environments. That’s the difference between managing and navigating.
Mireia Gine reinforced this in her research context. She pointed out that AI doesn’t behave like traditional tech, it mimics recognition, learns actions, and supports decision-making. If managers don’t adapt to this, it doesn’t just slow innovation, it risks reversing progress. Leading teams in an AI-aligned workplace means creating processes that AI can amplify, not inherit flaws from. That’s a leadership issue, not a tooling one.
AI integration transforms the roles of engineering and product managers
In software and product teams, AI isn’t just another integration, it’s changing how leaders work. Automation is taking over high-volume engineering tasks like code scaffolding, bug detection, and testing workflows. That doesn’t reduce leadership needs; it sharpens them. Engineering and product managers now need to focus less on execution and more on ensuring quality, driving decisions, and aligning machine output with human accountability.
Managers in these environments aren’t just shipping features, they’re managing mixed systems where some contributors are human and others are driven by algorithms. That raises responsibility. You can’t just delegate and move on. You need to know how the AI works, how your team is interacting with it, and whether the final product meets both functional and ethical standards. This demands stronger evaluative thinking, judgment, and a much deeper understanding of system dynamics.
Rachel Cohen, COO at the software agency Silicon Society, made this shift clear. She pointed out that engineering managers now have to know how to inspect and assess code written by AI. It’s no longer just about assigning a ticket and checking delivery, it’s about understanding how automation fits into quality and mission. That’s a different kind of oversight and a different style of leadership.
Frank Fusco, CEO of Silicon Society, added to that by emphasizing outcome-focused leadership. With routine programming increasingly automated, the primary value of technical managers comes from defining what to build, why it matters, and how to allocate both human and machine time wisely. This isn’t about writing cleaner code, it’s about making higher-return decisions.
Executives need to recalibrate how they evaluate their technical managers. Credentials and delivery records remain useful, but limited. Going forward, success in these roles will depend on strategic clarity, communication with diverse contributors (human and non-human), and a sharp ability to detect risks before they scale. If you’re not actively developing these capacities on your teams, you’re already playing catch-up.
Key takeaways for decision-makers
- AI drives up managerial demand: As companies automate routine tasks with AI, complexity and oversight needs grow, executives should anticipate higher demand for managers who can integrate, monitor, and align AI systems with business operations.
- Management skills are evolving: AI adoption shifts the core skills managers need; leaders should prioritize hiring and developing talent with strong cognitive abilities, including creative problem solving, stakeholder coordination, and strategic thinking.
- Engineering and product leadership is being redefined: With AI automating technical execution, engineering and product managers need to focus on decision-making, quality assurance, and hybrid human-AI workflows, roles must evolve accordingly to sustain value.
