Who this is for: founders and CEOs of AI-first companies; boards and investors backing AI-heavy roadmaps; CHROs and functional leaders about to hire VPs and C-level roles in product, engineering, data, go-to-market and operations. If you expect your next senior hire to unlock AI as a growth and efficiency lever, this is for you.
Why this is relevant now
The numbers are stark. Roughly 71% of CEOs now label AI a top investment priority, and roughly 69% plan to allocate between 10% and 20% of their budgets to AI over the next 12 months. Yet most of that investment is going into technology infrastructure, not leadership capability. The result is predictable: fewer than 10% of companies report significant business value from AI investments, and over 85% of AI projects never make it to production.
This creates a gap. Companies are throwing budget at AI, but most executive hiring processes are still optimised for a pre-AI world. You hire strong operators who cannot translate AI into leverage, or AI-native technologists who cannot lead a business. The opportunity in the next 24 months is simple: treat every executive hire as an AI operating system decision.
The new question boards should ask
Traditional search briefs start with familiar questions: what stage experience do we want, which logos should be on the CV, what is the industry background. In the AI era, these are now second order. The first question boards and CEOs should ask is different:
Can this executive design, govern, and continuously upgrade how our organisation actually uses AI?
McKinsey's research on AI high performers shows they are 3x more likely to have senior leaders demonstrating strong ownership of AI initiatives compared to typical companies. Leadership readiness is not a soft factor. It is the performance variable. If AI leadership is the number one strategic priority, then your next VP or C-level hire is no longer just a domain decision. It is a system design decision.
A new lens for executive hiring in the AI era
Most writing on AI and hiring focuses on tools. The more interesting shift for executives is not which tools you use. It is how you define the role itself. Think of AI-era executive hiring through four lenses.
1. From domain expertise to AI leverage profile
Executives have always been hired for domain expertise. In AI-intensive environments, domain is now the starting point, not the endpoint. A modern brief should describe three layers: the domain mandate (what business problem this leader owns), the AI leverage profile (where AI can realistically create advantage in that mandate over the next 12 to 24 months), and operating constraints (data quality, regulatory context, technical debt, organisational maturity).
You are not just hiring a VP of Product. You are hiring a VP of AI leverage for product, even if the title stays the same.
2. From pedigree to AI-first leadership behaviours
Big tech logos and elite schools still matter, but they are weaker predictors of success in AI-driven organisations. The behaviours that separate high performers are different: personal adoption (leaders who actively use AI tools in their own work, not just sponsor initiatives), translational thinking (turning ambiguous AI potential into clear use cases and measurable outcomes), psychological safety (communicating about AI in a way that reduces fear and mobilises talent), and ethical instinct (defaulting to explainability and governance instead of speed at any cost).
3. From one-time hire to continuously upgraded AI operating system
Executive hiring has historically been treated as a point decision. In an AI-heavy environment, the half-life of a role definition shrinks. The technology itself is evolving month to month. What made someone a great VP of Product in 2023 is different from what makes them great in 2026.
That has two implications. You recruit for upgradeability: curiosity, learning velocity, and the willingness to reinvent their own job become as important as prior achievements. And you build post-hire feedback loops that measure leadership impact against actual AI outcomes, not just traditional performance metrics.
4. From intuition only to human plus data search
Executive search is shifting from relationship-only models to data-enhanced headhunting. AI enables global market mapping across geographies and non-traditional backgrounds. Skills-based shortlisting emphasises competencies over job titles. Cycle times compress. The question is not whether to use AI in search; it is where to keep humans in the loop. Use AI for breadth, global mapping, and pattern detection. Keep humans for depth, nuanced qualification, and closing.
Three practical shifts for your next AI-era executive hire
Rewrite the brief as an AI leverage document
Move beyond a traditional job description. Create a short AI leverage brief for the role: the top three business outcomes this leader must unlock that directly depend on AI; two or three existing workflows where AI can realistically move the needle in year one; governance guardrails and ethical non-negotiables. This immediately changes who looks strong on paper.
Test for AI-first leadership in the process
Design one or two assessments that reveal how the person thinks about AI in practice. A case discussion: give a simple scenario and ask them to outline their approach. A tool usage question: ask how they currently use AI in their own work, and what they have tried that failed. A communication test: have them explain the AI strategy from the perspective of a frontline employee who is worried about their job.
Commit to post-hire AI leadership development
Even the best hires will not arrive fully formed for what AI will look like in three years. Give new executives an AI onboarding track alongside standard onboarding. Pair them with internal or external AI advisors for their first six months. Include AI-related objectives in their first year scorecard. This closes the loop between hiring and capability building.
The quiet competitive advantage
Most companies are still hiring executives with an implicit assumption: AI is a topic for a separate team, a lab, or a chief AI officer. In reality, every executive role is now partly a chief AI officer role for their function. High performers are three times more likely to have senior leaders who own AI transformation. Transformation fails when it is delegated, not when it is led.
The organisations that recognise this and redesign their hiring around it will ship faster, attract better leadership, and turn AI hype into durable advantage. The companies that figure this out in 2026 will be running different organisations by 2028.