Head of AI Executive Search

Applied AI Leadership vs Research Leadership

The majority of growth-stage technology companies that are hiring a Head of AI need applied AI leadership — a person who can take language models, computer vision systems, or other AI capabilities and ship them as product features that create customer value. This profile is different from an AI research leader, who is optimised for advancing the state of the art rather than shipping production systems. Research leaders from academia or large AI labs frequently struggle to operate in product-shipping environments because the incentive structures and success metrics are fundamentally different.

Applied AI leadership requires the ability to make pragmatic decisions about model selection and architecture under time and resource constraints. The applied AI leader at a growth-stage company cannot spend six months training a custom model when a fine-tuned foundation model with the right prompting infrastructure would deliver 90% of the outcome in six weeks. The candidates who excel in this environment have a specific operating temperament: they are comfortable making good-enough decisions quickly rather than perfect decisions slowly.

The AI Leadership Candidate Market in 2026

The Head of AI candidate market is simultaneously oversupplied with candidates who have AI exposure and undersupplied with candidates who have the combination of technical depth, product judgment, and leadership experience that the role actually requires. The most credible candidates are currently employed at technology companies or AI-native startups and are not publicly available. Reaching them requires sourcing from the applied ML and AI engineering communities, not from LinkedIn searches for "AI" in job titles.

Compensation for AI leadership roles has increased substantially since 2023. Head of AI base salary at a growth-stage company typically ranges from $280K–$420K depending on scope and depth of AI experience. Total compensation packages frequently include substantial equity and, in some cases, research budgets or compute credits as non-cash components that matter to AI candidates who are evaluating the resources they will have to do their best work.

What AI Leaders Evaluate in an Opportunity

Strong AI leadership candidates evaluate opportunities on three dimensions that most companies underinvest in presenting: the technical infrastructure they are inheriting (what data, what compute, what existing models or systems), the product problem the AI function will be solving (is it genuinely interesting and consequential), and the organisational authority they will have to make technical decisions without excessive oversight. AI leaders who join companies and discover that the CEO makes model architecture decisions are typically gone within 12 months.

Majhi Group for Head of AI Search

Majhi Group places AI leaders at growth-stage technology companies that are building AI-native products or integrating AI into existing product lines at the leadership level. We source from the applied ML and AI engineering community and assess candidates on product-shipping track record, not just research credentials. We run a 20-minute confidential search assessment covering your AI product vision, infrastructure, and the technical leadership profile your company needs to execute it.