The demand for AI fluency is increasingly influencing compensation and performance expectations across go-to-market (GTM) roles in tech. Going forward, hiring managers who continue assessing candidates the way they did in 2023 will likely overpay the wrong profiles.
This blog examines what AI fluency looks like in GTM roles, why standard interview processes often fail to surface it, and how hiring managers can build evaluation methods that produce reliable signal.
Why AI Fluency Has Become a Compensation Driver
AI fluency has emerged as a baseline requirement across many go-to-market titles. These roles are now evaluated on how effectively they leverage AI for tasks such as prospecting, personalization, content creation, and analytics.
This measurement is flowing directly into pay. Hiring managers are increasingly passing over candidates without working familiarity of these tools in favor of those with applicable knowledge of large language models (LLMs) and agents.
Compensation rates for most GTM roles that require this skill are increasing by at least 20% above average salaries. This premium stacks with the technical experience premium that applies to many sales roles. A candidate clearing both bars commands an offer significantly higher than the baseline. Meanwhile, a candidate clearing neither now reflects an outdated profile that produces a longer onboarding ramp and a slower path to productivity.
What GTM AI Fluency Looks Like in Practice
AI fluency in a go-to-market context is not the same as AI fluency in an engineering or product context. The question is not whether a candidate can build with AI but whether they can deploy it to drive revenue outcomes. Each ground-level GTM role expresses fluency through a distinct set of operational applications:
Sales Development Representatives (SDRs)
These reps need fluency for prospecting and prompt engineering. The most in-demand entry-level sellers are skilled at building refined prompts that enable personalized outreach at scale. The emerging Sales Development Analyst title formalizes this expectation, with companies recruiting candidates who arrive already fluent in AI tools for outreach personalization, account research, and pipeline reporting instead of learning these capabilities through a structured development program.
Account Executives (AEs) and Enterprise AEs
AEs express AI fluency by demonstrating technical credibility in buyer conversations as well as the ability to operate in AI-augmented sales environments. Buyers themselves have become more technical, with purchasing decisions increasingly made by IT professionals, data scientists, and engineers who bring specific implementation questions to initial conversations. The seller who can engage credibly with these stakeholders advances deals that sellers limited to relationship-driven approaches cannot.
Beyond buyer-facing interactions, AI fluency also includes operating effectively in environments where deal velocity, pipeline health, activity effectiveness, and forecast accuracy are measured through AI-generated insights rather than manual reporting.
Customer Success Managers (CSMs)
For CSMs, fluency is reflected in how they drive retention, expansion, and account health. Those working on AI deployments need to engage credibly with data science teams, IT leadership, and business stakeholders within the same accounts while using AI tools to surface patterns across their book of business that manual review cannot catch.
Marketing
Roles such as Product Marketing Managers and Demand Generation Managers demonstrate fluency in campaign development and content production. The marketers commanding the strongest offers can articulate where AI accelerates their work and where it falls short, as well as describe specific campaigns where AI changed the production process or output quality.
Senior sellers
Across the GTM functions, an additional competency layer is emerging for senior sellers. As tech sales teams continue to consolidate around leaner structures, individual reps are increasingly responsible for managing multiple AI agents alongside their deal and relationship-building work. The fluency required at this tier includes configuring agentic parameters, interpreting their outputs, identifying when automation produces something that requires human correction, and optimizing performance over time.
Where Standard Interview Processes Fall Short
Most go-to-market interview loops have remained largely unchanged, even as AI tools have become core infrastructure. Hiring managers still ask about quota attainment, pipeline generation, deal sizes, and methodology, often treating AI usage as a nice-to-have signal pulled from a single screening question. The result is an evaluation process that produces almost no useful information about the competency that should be driving compensation decisions.
These are the most common hiring mistakes we see:
1. Confusing tool familiarity with AI fluency
Asking whether a candidate has used a popular AI tool will likely return a simple “yes” and tell you nothing about how that candidate deploys those tools to produce results. Instead, ask what the candidate has built, automated, or changed in their daily work using AI, and how that change has impacted their pipeline, retention, or campaign outcomes.
2. Not asking where and how candidates developed their fluency
Candidates who built their AI capabilities through peer communities, learning networks, and self-directed experimentation tend to be more adaptable than candidates whose exposure came solely from a previous employer’s tech stack. These self-directed builders also bring a learning agility that will continue to be valuable at modern GTM organizations.
3. Assuming strong communication skills reflect execution ability
Candidates who speak confidently about AI are not necessarily the ones who use it most effectively. A polished narrative about how a candidate uses AI to qualify leads proves less than a working sample of the qualification framework deployed in their current role.
Practical AI Fluency Evaluation Methods by Function
Below, we outline the most important signals to look for when interviewing for specific roles.
SDRs and Sales Development Analysts
- How candidates would research and approach a specific target account using the tools available to them
- What their personalization process looks like at scale
- How they balance automation with human judgment in their sequences
- Where they learned to build effective prompts and how their approach has evolved with practice (is their fluency is transferable, or anchored to a previous employer’s tooling?)
Account Executives and Enterprise AEs
- How AI shows up in deal execution
- How they prepare for executive conversations, navigate technical discovery, and synthesize information across long sales cycles
- How they have used signals like pipeline health, deal velocity, and forecasting accuracy to adjust their work
- How they have engaged with technical buying committees and what role AI fluency has played in establishing credibility with stakeholders
Customer Success Managers
- How they identify expansion opportunities, monitor account health signals, and triage technical questions across their book of business
- How they have worked alongside data science and IT stakeholders on AI deployments (can they operate in AI-native customer environments, or is their experience limited to traditional SaaS implementation?)
Product Marketing Managers, Demand Generation Managers, and other marketing roles
- How AI has changed the production process or output quality in a specific campaign they have shipped
- Where AI accelerates their work and where it falls short
- What their methodology is for translating AI-driven product capabilities into customer value
For senior seller searches across any GTM function, add an explicit assessment of agent management capability. Ask candidates how many AI agents they currently work alongside, how they configure those agents, how they interpret agent outputs, and how they have intervened when automation produced something that required correction.
Build a GTM Team Calibrated for the AI Era
Betts Recruiting has spent over 15 years helping tech companies build GTM teams. Through our Recruitment as a Service (RaaS) model, Betts Connect platform, and Comp Engine, we help you secure top GTM talent.
Contact Betts here to discover how we can help you build a go-to-market team that meets the moment.