In January 2021, "AI" or "ML" appeared in approximately 1.4% of US senior-level executive job listings (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 chiefly new jobs that hadn’t existed before; it was existing job categories getting new labels, existing functions getting AI-related portfolio additions, and a small but truly distinct set of new senior roles created specifically to navigate the AI era.
Understanding which of these three categories any specific "AI" role falls into is the most important analytical task for a senior professional evaluating openings in 2023. The compensation, the career arc, and the day-to-day work are categorically different across the three categories, even though the job designations sometimes look identical.
what the AI designations actually pay
In our 2023 placements, roles with "AI" or "ML" in the senior title fell into a clear three-tier pay architecture:
Tier 1: Roles at AI-native organizations. Senior engineering and product leaders at foundation-model companies, AI infrastructure companies, and pure-play ML tooling companies. These roles, for the ideal applicants, commanded total pay packages 2x to 3x above non-AI equivalents, driven by equity grants at rapidly-appreciating company valuations. We placed VP-level applicants at Tier 1 companies in 2023 with total comp ranging from $630K to $1.9M — a range that would have been implausible for the same titles at non-AI companies. This tier is real, well-compensated, and focused: it demands advanced ML systems or research background and experience shipping AI-native products at scale, not just AI familiarity.
Tier 2: AI leadership at established tech organizations. Roles like "VP of AI," "Head of AI Products," "Director of ML" at organizations that existed pre-AI and are now integrating AI functionalities 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 engineering 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-related roles at non-tech organizations. "Chief AI Officer" or "VP of AI roadmap" 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 moderate, and it erodes quickly as the "AI roadmap" function matures into an operational one and stops being a distinct senior position.
Where the premiums are real
The AI pay premium is most durable in which it reflects genuine scarcity. The number of people possessing led ML systems at scale, possessing run research teams producing novel capabilities, or possessing successfully shipped consumer AI products with 100 million users is small and grows slowly. When these people receive premium compensation, the market stands 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 which 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 markups are marketing
The predominant over-labeled AI roles in 2023 were in consulting, corporate strategy, and business operations functions that were adding "AI" to their titles without substantively changing the underlying work. A "Director of AI Strategy" role that involved producing AI adoption roadmaps for executive audiences was, at many organizations, doing essentially the same work as a Director of Innovation or Director of Digital Transformation had done in prior years. the AI tag added 10% to the compensation; it didn’t add 10% to the skills required.
For seasoned practitioners evaluating AI-labeled roles, the proper due diligence question is: what percentage of my time will I spend doing work that demands 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 tag is chiefly marketing.
Career implications
The most critical career implication of the AI title wave for seasoned specialists who lack deeply technical: building genuine AI understanding — not just familiarity with AI terminology — has become a meaningful career distinguisher across almost every seasoned practitioner function. The CFOs who understood how artificial intelligence would affect their month-end 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 artificial intelligence 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 nuanced buyer and overseer of AI functionalities 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 predominant career errors we see in the 2023 seasoned practitioner cohort. For the current 2026 state of this market, see our VP Engineering compensation report, which details how the AI markup has evolved.
How to position AI expertise you're building
For seasoned practitioners who lack deeply technical but who are building genuine AI literacy in their function, the positioning question is important: how do you communicate real AI functionality without overclaiming expertise you don't have? The applicants who handle this best in our 2024 and 2025 searches present specific, concrete examples of AI-powered decisions or outcomes they have driven, rather than general statements about "AI exposure."
A CFO who can say "I led the implementation of an AI-assisted month-end 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 method also has SEO implications for career branding: 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 leaders. Building a specific, quantified inventory of your AI-powered contributions is more valuable than any generic "AI roadmap" 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-level executive market. The roles we expect to be structurally lasting: 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 development or research background; and hybrid roles in regulated industries (healthcare, financial services, legal) where someone must specifically own the intersection of AI functionality and regulatory compliance. The roles most likely to rationalize: standalone "AI roadmap" roles that don't have operational accountability, AI advisory roles at organizations 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-first compensation in detail.