Talent trends for the AI-native C-suite

Before AI, the most effective executives were functional experts who led a team of specialists: leaders who had mastered a function, built a team, and knew how to scale. That profile still matters today, but with AI amplifying skillsets, the builder-executive is setting the new standard.

We surveyed nearly 175 functional leaders across 100+ companies in our portfolio, and unsurprisingly, 86% were confident AI will meaningfully change how their team operates in the next 12 months. AI is now enabling more fluid, integrated leadership models that elevate both strategic and hands-on capabilities. Hiring for an AI-forward team and culture is rapidly evolving:

1. The interview process is changing: CEOs are diving into understanding AI tools themselves so they can better evaluate candidates’ AI fluency.
2. Team hierarchies and structures are transforming: Teams are now delivering exponentially more output with agentic hybrid teams and more conservative hiring projections. 49% of our portfolio companies say they’re already delivering more without adding headcount.
3. Roles are shifting/blurring together: Product leaders must now understand model performance; finance leaders must model AI-native unit economics; sales leaders must iterate on how AI can support the full sales lifecycle; engineering leaders are now expected to spend more time with customers; marketing leaders are becoming more technical.

In this report, we’ll explore five executive-level trends we’re seeing, along with the roles being augmented and transformed by the AI era. Drawing on insights from functional leaders in our operating network and our trusted advisors at Artisanal Talent, Bessemer’s Talent team examines the defining trend of each function, how that trend is redefining the function’s value in the C-suite, and what it means for CEOs rethinking their organizational design.

Key insights for the CEO hiring in the AI era

• Engineering org design is a competitive variable. The strongest engineering leaders are rethinking ratios, workflow, and structure to match the speed AI makes possible. The highest-value leader is the player-coach builder who is personally in the codebase while making organizational and strategic decisions.
• The CFO is one of your highest-leverage hires in an AI-native model. The best ones are architects of the P&L who sequence capital decisions with AI costs in mind, shape monetization strategy, have the expertise to lead strategically, and build financial frameworks from first principles when the old benchmarks don’t translate.
• Speed is the new product moat. The product leaders getting ahead are compressing roadmaps from quarters to days. Today’s most effective ones are actively engaging with how AI systems perform and evolve, which allows them to move faster, adapt more nimbly, and drive stronger outcomes—keeping pace with how quickly engineering teams are moving.
• In sales, the biggest lever is the infrastructure you build around your sellers. Sales leaders who focus on hiring operational roles as they expand AE headcount, such as a RevOps or GTM engineering, are compressing sales cycles and expanding ACV faster.
• Marketing leaders must rethink narrative and go-to-market as complexity grows in marketing operations. CEOs should look for those who can flex between craft, pipeline ownership, and attribution. The best marketing leaders are multiplying their own output through AI.
• Functional lines are blurring and giving way to new roles. GTM, sales, and customer success are converging around pipeline outcomes; product and engineering are sharing AI system ownership; finance and operations are leveraging AI to gain efficiency and time for strategic ideation. The Forward-Deployed Engineer (FDE) is an example of how technical roles are moving closer to the front lines, becoming more customer-facing.

Five trends of the AI-driven C-suite

1. Engineering: leaders are reimagining team operations to match the speed of shipping

The economics of software development have changed faster than most engineering org charts have. Teams are shipping in hours versus weeks, and code generation is up 180% while code review is up by 30%. The question engineering leaders are now grappling with is whether they’ve actually redesigned the org to take advantage of AI, or whether they’re running a 2022-era team structure in a 2026 product environment.

A strong engineering leader is rethinking team ratios, how work is scoped, how review and quality gates operate, as well as how much of the coordination overhead that used to require mid-level management can now be handled by AI tooling and smaller, more autonomous pods. With 90% of our portfolio’s engineering teams actively deploying AI, the org shape itself is becoming a competitive variable—since it’s not just about who you hire, but how you structure the workflow around the hire.

Applying AI everywhere at once isn’t a hard and fast rule. Some of the engineering leaders getting the clearest results start with a deliberately small team (sometimes even an undersized scrum of 3-4 engineers) and then measure the output that team can hit before scaling the model org-wide. They’re also tracking the cost of tooling and tokens against the output gain, only rolling out the practice further when the ROI is positive. This discipline matters more in some functions than others. Engineering use cases tend to have a more straightforward path to positive ROI than business-enablement teams, where it’s easier to burn through tokens on the wrong use cases and drive up costs without a matching lift in output.

“AI has clearly boosted engineering productivity, but those gains only matter once you factor in the full cost of tooling and tokens. Leaders can’t just evangelize AI. We’re responsible for making sure each use case delivers a positive, measurable ROI.” — Jessica Popp, Bessemer Operating Advisor.

Engineering leadership is evolving toward a more integrated model, where the most effective leaders combine strategic oversight with hands-on engagement. The emerging archetype—the player-coach builder, or “Super IC”—is deeply involved in the codebase, leveraging AI tools, shipping alongside their team, and shaping the architectural and organizational decisions that accelerate overall team velocity.

As business becomes more agentic, engineering leaders will need to orchestrate multiple AI agents running in parallel. This shift reflects a durable expansion of the role, where blending execution and leadership is becoming a lasting advantage. The leader who can navigate both people-scaling and this transition simultaneously will pull ahead. As Farhan Thawar, Shopify’s VP & Head of Engineering, puts it: “If you don’t figure out how to harness the agents in 2026, you’ll be behind.”

For CEOs hiring an engineering leader

• Treat the ROI of AI investments as a leadership metric, not an IT line item. Ask candidates how they’d measure the return on tooling and token spend against team output, and how they’d know when to scale a workflow versus limit it. The best engineering leaders should be rigorous about proving these new economics before expanding them.
• The Super-IC model creates an opportunity to raise the bar for engineering leadership. The strongest leaders balance deep technical engagement with architectural vision, thoughtful hiring, and a culture that enables teams to move quickly. Candidates who bring both dimensions are rare and incredibly valuable, as they help unlock sustained growth and scale across the organization.

2. Finance: the AI-native CFO will rebuild financial models from first principles

The CFO at early- and growth-stage companies directs a company's financial strategy, managing financial risks, and overseeing budgeting, forecasting, and reporting to ensure long-term stability and growth. The AI-native CFO is the architect of the P&L, partnering with the CEO to shape the company’s financial architecture and growth levers, such as monetization design, compute cost modeling, and capital sequencing.

Finance leaders are becoming both more strategic and tactical, as they’re responsible for operationalizing AI within their companies, while also looking for opportunities to get ahead faster. This adds pressure to the CFO, making their AI fluency all the more critical. Looking forward, the best CFOs will know how to leverage their teams and tools to strategically operate the business. They’ll see market opportunities and monetization levers before the board asks. They’ll decide how to sequence capital so that talent acquisition is appropriately allocated to drive revenue growth.

In an AI-driven environment, the CFO’s purview is to model and forecast how AI usage drives productivity gains and impacts the P&L. However, AI adoption is still early among financial leaders. From our recent study across 113 of our portfolio companies, only 24% of finance leaders were actively deploying AI, with a majority citing data quality and system fragmentation, along with security and compliance concerns, as core blockers.

But this gap is a hiring opportunity. The AI-native CFO builds the finance tech stack and organizational culture that compresses the time from question to decision. This neo-CFO also translates strategic ambition into a capital allocation sequence. They help determine when to invest in AI infrastructure and how to scenario-plan for regulatory and geopolitical risk. The most valuable early-stage finance leaders build new frameworks from first principles, such as modeling gross margin for an AI product.

For CEOs hiring a CFO

• Hire for the forward-looking mandate. In your next CFO hire, test whether candidates can play the role of the architect of the P&L. Ask them to walk through how they’d model AI compute costs as a percentage of gross margin, or how they’d sequence a hiring plan when revenue visibility is low. The ability to build financial frameworks for conditions without established benchmarks will separate the best from the rest.

3. Product: leaders are establishing market dominance by compressing roadmaps

Speed is the new moat in product, and the leaders who understand that are actively rewriting how the function operates. The traditional Product Manager was built around the roadmap as a planning artifact: aligning stakeholders, managing dependencies, and shipping on a schedule.

That cadence is increasingly misaligned with how AI products actually improve—typically through smaller teams or individuals iterating rapidly on generating code that then leads to PRs, running evaluations, adjusting parameters, deploying, observing output, and then repeating the process. The unit of work is now closer to a day or a week, not a quarter.

Shipping speed is the highest-correlated factor to business success, and the companies pulling ahead are compressing timelines of product roadmaps.

In practice, the AI-native product leader who drives market leadership does two things: (1) leads builders who leverage agentic systems to accelerate experimentation and (2) discovers new opportunities not just to prioritize customer needs, but also build for the “third horizon” at a faster clip. Product leaders who build for that longer vision position a business to expand its TAM, deepen customer engagement, and build a platform to serve broader audiences.

The lines between product and engineering are blurring, and both teams are becoming equally accountable for product performance, system quality, and shipping speeds.

For CEOs hiring a product leader

The evaluative question for CEOs searching for a product leader has now become: “Is the AI getting better, and how do we know?”

• Turn the interview into a working session. Ask candidates to engage with a real product decision you’re facing (ideally one that involves a trade-off between shipping speed and AI output quality). Their rationale for how work should be organized across teams for optimal efficiency will shed more light on their working style than any pre-prepared example or tooling they’ve used.

4. Sales: leaders are technical “quarterbacks,” coordinating systems building in the sales process to maximize revenue per AE

Founders and CEOs are often the company’s first salespeople, and when it’s time to hire a leader, that means shifting from a founder-led sales process to a scalable sales organization. No matter the stage or scale, sales is responsible for how the organization’s infrastructure supports selling to customers across geographies, industries, and product lines. That includes architecting sales processes and creating playbooks to improve efficiency and increase revenue per Account Executive (AE). Leveraging the right technology in the pre-sales process optimizes target audiences, lead qualification, process optimization, and allows sellers to accomplish more with less overhead.

Pre-sales, marketing, and customer success are converging. The organizational separation between pipeline generation and revenue retention is thinning, and the sales leader who thrives builds toward unified revenue outcomes.

“Before AI, you could get by with hiring a sales leader who could 'sell' and assume you'd figure out the rest later. If you're going to succeed, it's a must to hire a sales leader who can sell and is hands-on with AI in GTM, not simply a spectator managing others using AI. It's important for building a successful GTM organization.” — Tony Rodoni, Bessemer Operating Advisor

That’s why the highest-leverage additions to a modern sales org are strong sellers who are technical: revenue operations, solutions engineers, and go-to-market (GTM) engineers who instrument the funnel, build the demo environment, and connect product telemetry to pipeline signal. AI hasn’t made great sellers less valuable, but rather it has become the infrastructure necessary to win deals faster than the competition.

Early stage CEOs who hire a RevOps or GTM Engineer early, as they build their AE team, are compressing sales cycles and expanding ACV faster. Growth stage CEOs who hire experienced Presidents are well-equipped to handle the rapid scaling that AI companies are experiencing. Hiring an executive who has led teams at scale, brings a track record of closing the largest enterprise accounts, and has overseen a revenue remit in the hundreds of millions (if not billions) is now critical to maintaining any unprecedented growth.

As sales orgs become more technical, this has direct implications for who a CEO hires as sales leadership. AI-native sales leaders are now technical quarterbacks, leveraging the expertise of RevOps, GTM engineering, and agentic solutions to unify and maximize outcomes from human AEs. That way, human sellers can focus on relationship building while providing strategic and consultative input to prospects as they aim to convert larger ACVs at higher rates.

The demand for great sellers at high-growth companies hasn’t changed, but AI is playing a powerful role in augmenting skills, building faster processes, and helping sales organizations set new expectations—making the performance of the top 10% the new norm.

Fieldguide VP of Sales Brent Kasper shares how he uses Claude Code to turn reps into their own sales coaches:

“We wanted our reps to have that coaching layer available constantly, not just during forecast calls. So we built it. The tool scores deals, surfaces where reps are strong and where they're weak, and forces honest conversations about what's real and what's wishful thinking—the goal isn't to replace sales management. It's to raise the floor.”

For CEOs hiring a sales leader

• The best sales leaders for the AI era are strategic systems builders. This is someone who can sequence RevOps early when adding headcount, instrument the funnel before scaling it, and build the technical infrastructure that turns good sellers into exceptional ones. Beware of candidates who want to scale AE headcount before hiring operations roles like a GTM Engineer.
• CEOs should be able to test technical fluency in the interview process. Test candidates on how they would structure their team around pipeline efficiency and effectiveness, as well as customer onboarding and support. A sales leader who can’t become an AI power user and model that for their team is less credible. To attract top talent, a CEO must also be able to clearly articulate market position, sales motion, and AI strategy.
• A logo on a resume is a tiebreaker, not a proxy for skill. A sales leader who clawed their way to market leadership in a competitive environment is likely a more durable systems builder than one who rode a rocketship. Prioritize candidates who hit 200% of quota at the third-best company over those who were adequate at a recognizable one. If you have two candidates who both check all boxes, the logo should only be the tiebreaker.

5. Marketing: leaders are owning technical execution more than ever as AI expands the arbitrage of their skills

There’s a compounding dynamic playing out in marketing leadership right now that CEOs must pay attention to. A technically fluent marketing leader with AI can now build solutions that close knowledge gaps in the funnel and new channels, moving more efficiently from insight to decision to execution to measurement, ultimately connecting GTM programming and campaigns to pipeline creation. They’re building systems that help an organization execute faster and at scale. That’s the AI-native marketer’s arbitrage: systems building on top of traditional campaign management.

This arbitrage plays out in four key unlocks:

1. Intelligence and monitoring

AI now allows leaders to track every performance indicator across the funnel continuously, rather than periodically, by instantly surfacing trends, outliers, and opportunities. The CMOs taking advantage are building executive-level intelligence reports—that is, AI-powered dashboards across the acquisition funnel that include channel and campaign performance.

"One of the hardest parts of being a CMO in a complex organization is that there's so much to track, and it's easy to miss details that matter. My first move in a new org would be to build an AI-powered intelligence report that watches every marketing metric, from the acquisition funnel to channel and campaign performance, and sends a daily summary of trends, outliers, and areas that need attention." — Kim Caldbeck, Bessemer Operating Advisor

2. Workflow automation

Beyond monitoring, the highest-impact move is identifying the workflows that consume the most manual time across the marketing org and automating them. The marketing leaders becoming more efficient are personally running AI-assisted campaigns, building lead scoring models, experimenting with emerging channels, and designing automated workflows end-to-end.

3. Personalization at scale

What used to require high-touch account-based marketing resourcing, typically reserved for larger accounts, can now be extended to a much broader segment of the target audience through AI-driven personalization.

4. Precision and timing

Scale alone isn’t enough. A personalized campaign still underperforms with the wrong message or at the wrong moment. The most effective AI marketing leaders are using AI to sharpen precision: tailoring messaging to what a specific account actually needs to hear and sequencing touchpoints based on real-time interaction data instead of a generic nurture cadence.

"Speed and analysis get a lot of attention, but the bigger unlock is personalization and precision. AI allows marketers to tailor their message and timing of every touchpoint based on real signals from that account or similar ones. These are the kinds of judgment calls that used to require hours of manual analysis and can now happen almost instantly." — Allyson Letteri, Bessemer Operating Advisor

Channel distribution is evolving to reward leaders who have technical compatibility with new AI algorithms. Discovery is no longer mediated exclusively by traditional search and SEO. AI-native search, including Perplexity, AI Overviews, and agent-driven queries, is reshaping how audiences find products and evaluate brands. The marketing leaders who optimize their brands for Generative Engine Optimization (GEO) are harnessing the power of branded search in an increasingly AI-curated world and are developing a durable advantage.

As technical capabilities become table stakes for marketing functions, CEOs are increasingly considering “non-traditional” marketers who come up through product, operations, or other technical roles.

For CEOs hiring a CMO

• Ask finalists to do a product demo on something they personally built via AI—a campaign, a scoring model, an automated workflow—and probe them for where they made the technical decisions versus where they delegated them. The hire you want has senior experience managing teams, and the aptitude and appetite to upskill continuously with every new model push as velocity becomes a core competency. Or, give them a brief, some data, and have them vibe code a discrete solution during the interview process.

High-demand roles being augmented by AI

Beyond the traditional C-suite, AI is also creating a high demand in other roles. Below are three roles to consider when building an organization led by AI-native executives.

1. Chief AI Officer

At an early stage, you probably don’t need a Chief AI Officer. At the growth stage, you might, but the role should be customized to your business context. Two AI leadership archetypes are emerging. Head of AI is typically a technical ML/AI leader owning the AI stack (model strategy, evaluation frameworks, and infrastructure) in a hands-on, deeply technical role. The Chief AI Officer, more common at larger companies, owns AI transformation across the organization: a strategic, cross-functional role with a transformation mandate.

For companies early in their journey, the right move is almost always an AI-fluent CTO or VP of Engineering. The CAIO question becomes more relevant at the growth stage, when AI strategy extends beyond engineering into a cross-functional coordination problem.

2. Forward Deployed Engineer (FDE)

The Forward Deployed Engineer is becoming a growth-stage imperative for AI companies selling into complex industries. Pioneered by Palantir, the FDE model is gaining traction at vertical SaaS and AI companies selling into regulated or technically complex industries. FDEs sit at the intersection of solutions engineering, customer success, and product engineering—deploying with customers, solving integration problems, and feeding product requirements back to engineering.

The model also goes beyond its engineering origins. The Field CTO acts as the front-facing leader bridging together sales, customers, and product development. The Field CISO is emerging for AI products handling sensitive data that triggers security reviews too complex for a traditional sales engineer. Field General Counsels (GCs) serve an analogous function for legal- and compliance-heavy industries.

3. GTM Engineer

Another name for RevOps, GTM engineering is a title that appeared in the market about five years ago. This role sits at the convergence of pre-sales, marketing, sales, and customer success, all of which are increasingly unified around pipeline and revenue outcomes. Rather than a trendy new title, the GTM Engineer is a structural response to a real shift: outcomes-based org design over traditional functional silos. About 45% of people in this role work at agencies or as consultants first, due to a common pattern of companies outsourcing GTM engineering to test ROI before committing to a permanent hire.

Scoping the job description for the AI-era executive

Across all executive functions, standout candidates are skewing technical with their new AI chops, but that’s not without applying hard-won industry wisdom to their decision-making. It’s time for CEOs to break the assumptions on what a “great” functional executive looks like, so they can reach an emerging talent pool that can lead in environments that are more experimental and less siloed than traditional organizations.

The exciting thing for CEOs today is that they have the freedom to design a C-Suite with a leader-outcome fit right for their business and industry context, rather than hiring roles that have led functions of the past.

"The most important AI decisions CEOs make won't be about model selection—they'll be about operating model design. As AI blurs the boundaries between functions, competitive advantage will increasingly come from aligning executive teams around shared customer and business outcomes rather than optimizing individual departments." — Barak Turovsky, Bessemer Operating Advisor

For CEOs beginning to scope their next AI-native Executive hire, consider following these next steps:

Step 1:

Focus on one area of the business at a time, and answer these five critical questions to set your own expectations before penning the job description:

1. Goals: What outcomes do I want to see from the organization in the next 12-24 months?
2. Problem-solving: What business obstacles do I want this leader to overcome? What is the next business milestone that matters?
3. Methodology: What new systems or ways of thinking and building do I want this executive to drive?
4. Measurement: What KPIs do I think this executive should own and be responsible for at the board level?
5. Personality and values alignment: What are the qualities and capabilities I want them to possess? And what are the organizational values I want this leader to embody?

Bonus tip: Don’t know what to write? Answer these questions live in a conversation with an investor or trusted partner, and record the call. Turn the audio into a transcript and leverage AI to draft answers you can then edit before proceeding to Step 2.

Step 2:

Have Claude read the entirety of this Atlas report plus your answers to each question shared above. Then, share this prompt.

Prompt for Claude:

I’m a CEO looking to hire an AI-native executive to lead [insert function]. With the research and answers I provided here, help me draft an initial job description and talent acquisition plan to begin the hiring process.

If you’re a Bessemer portfolio CEO hiring for your next AI-native executive, reach out to the Talent team to partner with you on this journey.

This report was authored by the talent team at Bessemer Venture Partners in collaboration with specialists at Artisanal Talent. Additionally, thank you to Operating Advisors and other experts who contributed their perspectives on this report. If you’re navigating an executive search in any of the functions covered in this report, the talent teams at Bessemer Venture Partners and Artisanal Talent are available to consult.