In January 2021, "AI" or "machine learning" appeared in approximately 1.4% of US senior executive job postings (Director and above). By Q3 2023, that figure had grown to 12.3% — a 9x increase in two and a half years. The growth wasn’t primarily new jobs that hadn’t existed before; it was existing job categories getting new labels, existing functions getting AI-adjacent portfolio additions, and a small but genuinely distinct set of new senior roles created specifically to navigate the AI moment.
Understanding which of these three categories any specific "AI" role falls into is the most important analytical task for a senior professional evaluating opportunities in 2023. The compensation, the career trajectory, and the day-to-day work are categorically different across the three categories, even though the job titles sometimes look identical.
What the AI labels actually pay
In our 2023 placements, roles with "AI" or "ML" in the senior title fell into a clear three-tier compensation structure:
Tier 1: Roles at AI-native companies. Senior engineering and product leaders at foundation-model companies, AI infrastructure companies, and pure-play ML tooling companies. These roles, for the right candidates, commanded total compensation packages 2x to 3x above non-AI equivalents, driven by equity grants at rapidly-appreciating company valuations. We placed VP-level candidates at Tier 1 companies in 2023 with total comp ranging from $600K to $1.8M — a range that would have been implausible for the same titles at non-AI companies. This tier is real, well-compensated, and narrow: it requires deep ML infrastructure or research background and experience shipping AI-native products at scale, not just AI familiarity.
Tier 2: AI leadership at established tech companies. Roles like "VP of AI," "Head of AI Products," "Director of Machine Learning" at companies that existed pre-AI and are now integrating AI capabilities into existing products. These roles pay a premium of 20% to 40% over comparable non-AI equivalent titles at the same companies, justified by the scarcity of people who can bridge deep ML technical knowledge with product and organizational leadership. The compensation is strong but the range is wide, depending heavily on the candidate’s specific technical depth.
Tier 3: AI-adjacent roles at non-tech companies. "Chief AI Officer" or "VP of AI Strategy" roles at financial services, healthcare, manufacturing, or consumer companies that are building AI capabilities but are not fundamentally AI businesses. These roles paid a premium of 10% to 20% in 2023 versus comparable non-AI leadership titles at the same companies. The premium is real but modest, and it erodes quickly as the "AI strategy" function matures into an operational one and stops being a distinct senior position.
Where the premiums are real
The AI compensation premium is most durable where it reflects genuine scarcity. The number of people who have led ML infrastructure at scale, who have run research teams producing novel capabilities, or who have successfully shipped consumer AI products with 100 million users is small and grows slowly. When these people receive premium compensation, the market is accurately pricing a rare input. The premium will persist as long as the scarcity persists.
A useful heuristic from our 2023 placement experience: if a company can describe exactly what skills they need and exactly why they’re rare, the premium they’re offering is probably real and likely to persist. If a company can only describe the premium in terms of market trends ("AI is very important right now"), the premium may not be durable.
Where the premiums are marketing
The most common over-labeled AI roles in 2023 were in consulting, corporate strategy, and business operations functions that were adding "AI" to their titles without meaningfully changing the underlying work. A "Director of AI Strategy" role that involved producing AI adoption roadmaps for executive audiences was, at many companies, doing essentially the same work as a Director of Innovation or Director of Digital Transformation had done in prior years. The AI label added 10% to the compensation; it didn’t add 10% to the skills required.
For senior professionals evaluating AI-labeled roles, the right diligence question is: what percentage of my time will I spend doing work that requires specific AI or ML knowledge versus work that could be done by any experienced strategy or product leader? If the answer is less than 30%, the AI label is primarily marketing.
Career implications
The most important career implication of the AI title wave for senior professionals who are not deeply technical: building genuine AI understanding — not just familiarity with AI terminology — has become a meaningful career differentiator across almost every senior professional function. The CFOs who understood how AI would affect their financial close processes and risk modeling frameworks were more valuable in 2023 than those who didn’t. The General Counsels who could engage substantively on AI regulatory risk were more valuable than those who couldn’t. The Head of Sales who understood how AI SDR tools changed their pipeline economics was more valuable than those who treated AI as an IT decision.
This doesn’t require becoming an ML engineer. It requires developing enough genuine understanding to be a sophisticated buyer and overseer of AI capabilities within your function. The distinction between that functional AI fluency and the deep technical capability that commands Tier 1 premiums is real, and confusing them is one of the most common career errors we see in the 2023 senior professional cohort. For the current 2026 state of this market, see our VP Engineering compensation report, which details how the AI premium has evolved.
How to position AI expertise you're building
For senior professionals who are not deeply technical but who are building genuine AI fluency in their function, the positioning question is important: how do you communicate real AI capability without overclaiming expertise you don't have? The candidates who handle this best in our 2024 and 2025 searches present specific, concrete examples of AI-enabled decisions or outcomes they have driven, rather than general statements about "AI experience."
A CFO who can say "I led the implementation of an AI-assisted financial close process that reduced our close cycle from 8 days to 4 days, and I worked directly with the AI vendor on the model configuration and validation" is telling a credible and specific story. A CFO who says "I have experience with AI in finance" is saying very little. The specificity is the credential; the general claim is not.
This approach also has SEO implications for career positioning: the specific examples you carry — the vendor names, the specific use cases, the measurable outcomes — are exactly what recruiters and hiring managers are searching for when they look for AI-capable senior finance or operations executives. Building a specific, quantified inventory of your AI-enabled contributions is more valuable than any generic "AI strategy" training certification.
Which AI roles survive the hype cycle
Every technology wave produces a surge of roles in its wake that are real in the short term and contract when the hype rationalizes. AI is following this pattern in the senior executive market. The roles we expect to be structurally durable: positions where AI model evaluation, procurement, and governance is a genuine full-time function at scale (Chief AI Officer at very large companies with complex AI deployment); technical roles requiring deep ML engineering or research background; and hybrid roles in regulated industries (healthcare, financial services, legal) where someone must specifically own the intersection of AI capability and regulatory compliance. The roles most likely to rationalize: standalone "AI Strategy" roles that don't have operational accountability, AI advisory roles at companies that haven't yet deployed anything at scale, and broad "innovation" functions that were rebranded as "AI" without substantive change in mandate. For the current compensation picture, see our VP Engineering report which covers AI-native compensation in detail.