The rapid growth in Artificial Intelligence (AI) capabilities has made attracting and retaining top AI talent one of the most vital ways for a company to gain a competitive advantage. However, investing in AI talent doesn’t come without challenges. The speed of the technology’s growth, headline-grabbing pay packages, and an intense battle for top talent have made it difficult for companies to establish a clear, replicable compensation philosophy that encompasses all types of AI talent (Foundational, Applied, and Translators).
For Chief People Officers (CPOs), the challenge is clear: how do you maintain a disciplined compensation strategy that accommodates different types of AI talent while allowing large pay packages for select individuals? The answer may lie in segmentation: understanding that not all roles that touch AI warrant differentiated treatment. Boards and executive teams should help empower management to articulate which roles, if any, truly justify differentiated pay practices based on an assessment of value to the business. In doing so, CPOs can take the mystery out of paying for emerging AI roles, ensuring pay programs support the company’s long-term compensation philosophy rather than chasing buzzy pay packages.
Compensation and culture are intertwined within a company’s compensation philosophy, which encodes company values: what it rewards, who it elevates, and what behaviors it signals are worth paying for. Most of the time, the two quietly reinforce each other without friction. However, in moments of deliberate change, such as a strategic pivot, a new CEO, or an emerging AI talent war, one begins to drive the other.
A CPO who wants to shift culture toward greater performance differentiation can begin by redesigning the compensation plan. Conversely, an organization that restructures pay in response to market pressure without examining the cultural signals being sent may find that the plan changes faster than the values it is meant to reflect. Compensation decisions do not occur in isolation. Exceptional packages send signals across engineering teams, executive leadership, and the broader workforce. Without a clear narrative, these decisions can erode engagement and trust. CPOs play a central role in shaping that narrative.
While there are as many cultures as companies, there are several red flags to watch for, including:
CPOs who have experience with a broad range of talent issues and previous “talent wars” are well-positioned to help boards and managers steer around these issues by remaining committed to a talent strategy that supports business priorities rather than market noise. To ensure business alignment, CPOs should consider three types of talent when designing an AI talent strategy.
There are three broad categories of AI talent in the marketplace. Most companies need talent from only a subset of the three groups:
These are the deep technical experts who create proprietary intellectual property or a durable platform advantage. Accordingly, these roles are most likely to require exceptional pay to attract and retain talent. This talent is scarce and highly mobile, often motivated as much by impact and opportunity as compensation.
This bracket includes engineers, data scientists, and product leaders who focus on deploying AI within the business. While this talent market is competitive, it is significantly deeper than the foundational talent group. For many organizations, these roles sit within competitive yet structured pay programs like those for other engineers.
This group includes senior leaders who combine technical fluency with business judgment. Their value lies in orchestration, governance, and strategic prioritization — not coding. Any premiums for these roles should reflect enterprise scope and accountability and truly above-and-beyond performance, not simply proximity to AI.
As organizations evaluate how their talent fits into the roles above, it is critical to anchor talent decisions in the broader enterprise strategy. If executives or management believe a role deserves exceptional pay, CPOs should encourage them to articulate exactly how and why this role will contribute to outsized growth and develop ways to measure that impact.
Using these categories can help CPOs size compensation relative to economic contribution: a mission-critical AI architect shaping the company’s core platform may justify differentiated pay, while a technically impressive individual contributor working on incremental AI use cases may not. A common misclassification also deserves attention: a senior engineer with an impressive résumé deploying off-the-shelf models on internal tooling may look like Foundational talent, but is almost certainly Applied, and should be compensated accordingly.
A mid-size financial services firm is building a proprietary credit risk model it believes will reduce loan default rates by 15–20% — a capability that could represent hundreds of millions of dollars in avoided losses over five years. The company identifies a machine learning researcher with a track record of publishing novel approaches to time-series modeling and recruits her from a top AI lab. Her base salary lands at 2x the engineering band, she receives a $500K sign-on award vesting over two years, and her equity package includes performance-based vesting tied to model deployment milestones. This is a legitimate Foundational hire: the role creates defensible IP, the pay is structured to retain her through delivery, and the premium is sized against a quantifiable business outcome.
A consumer technology company is expanding its use of a large language model (LLM) to power customer service automation. The VP of Engineering advocates for classifying a senior engineer leading this effort as “Foundational,” citing his PhD, his experience at a well-known AI company, and headline pay packages from competitors. Rather than accepting or rejecting that assessment at face value, the CPO walks through the company’s segmentation framework: Does this role create defensible intellectual property? Does it accelerate speed to market or enable a step-change in product quality? The answers make clear that the role involves fine-tuning and integrating commercially available models; important and specialized work, but not proprietary IP creation.
Applying the framework, the company places the engineer in the Applied category, with total compensation at the 90th percentile of the software engineering band. Pay is meaningfully above peers to reflect the specialized skill, but without the outsized equity package that would have created compression and set a precedent for future AI hires. The framework gave the CPO a defensible, transparent rationale to share with the VP of Engineering and the broader leadership team.
The most effective companies generally reserve compensation exceptions, regardless of category, for Foundational talent, especially when the role’s performance is tightly aligned with strategic outcomes. But even in such cases, exceptional pay is no excuse to abandon good governance. Boards and CPOs should resist the temptation to anchor compensation decisions solely on résumés, degrees, proximity to AI, or prior pay levels by asking questions such as:
How compensation premiums are structured matters as much as the amount. Long-term incentives such as milestone-based vesting and performance equity are generally the most effective approach, aligning compensation with sustained impact. In contrast, solely increasing base salaries can lead to compression and other downstream issues. Any sign-on and retention awards granted should be transitional, not structural defaults. To help guide these discussions, CPOs may begin by:
Finally, it is crucial to think in advance about the on- and off-ramps for AI scaling and implementation across different compensation scenarios. Not every AI hire represents a permanent structural role. Some may be specialist-for-hire talent required to build a foundational capability over a defined time horizon. Compensation structures should allow for normalization as both AI talent supply and internal capabilities mature to prevent tying CPOs’ hands far into the future.
The challenges outlined above have reared their head during prior periods of intense talent competition, such as the cybersecurity, digital transformation, and quantitative finance talent wars. Like then, periods of peak demand push organizations to stretch pay aggressively. As supply increases and capabilities diffuse, however, the market recalibrates.
Despite the many reasons for caution, deviation from standard pay practices may be warranted in specific situations, such as when a mission-critical, scarce AI role directly impacts long-term enterprise value. In such cases, acting quickly is warranted when competitive dynamics materially affect the ability to compete or defend market position.
That said, most boards will find that, absent the above rationale, they are best served remembering that AI talent compensation is not exempt from the company’s overall compensation philosophy just because it is cutting-edge. Those who overreact at the height of scarcity often spend years managing compression, morale challenges, and costly legacy structures. Those who spend smartly and remain committed to strategic priorities have a chance to make the most of this pivotal moment and create long-term competitive advantage.
