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Artificial intelligence isn’t just a tool anymore. It’s becoming a teammate. For account executives (AEs), sales engineers (SEs), and frontline managers, AI is reshaping expectations, introducing new skills, and changing how success is measured. Companies that adapt quickly will gain an edge not only in deal execution but also in hiring, onboarding, and coaching.
In this post, we’ll break down the new skills matrix for modern sales roles, show how managers can run AI-enhanced 1:1s, and explain how scorecards and performance reviews evolve in an AI-driven world.
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AI has moved beyond administrative support. Today, it’s embedded in workflows across the sales cycle, writing outbound emails, surfacing insights on deal coverage, preparing tailored executive narratives, and even analyzing pipeline health in real time.
The impact is clear: sales roles aren’t disappearing, but their responsibilities are shifting. Success depends less on manual execution, like updating CRM fields or memorizing product details, and more on higher-order skills such as judgment, prompting, and strategic storytelling.
AI introduces a new baseline of competencies that every sales professional must develop. Let’s explore how each role is evolving.
Traditionally, AEs were measured by volume, calls made, emails sent, and demos booked. With AI now automating much of this activity, the AE’s value lies in how they interpret, refine, and elevate AI output.
An effective AE knows how to craft precise prompts that generate hyper-relevant outreach. They can judge whether AI’s suggestions are on point or need refining, and they excel at turning raw data into executive-level storytelling. For example, instead of spending hours drafting a discovery recap, an AE can ask AI for a first draft, then focus on tailoring it into a narrative that resonates with a CFO or CIO.
SEs have always been the bridge between product and prospect. But with AI able to summarize documentation, generate demo scripts, and handle FAQs, SEs are now freed to play a more strategic role.
The most impactful SEs are those who use AI to model customer scenarios, validate technical feasibility, and guide cross-functional teams with data-backed insights. Imagine an SE prompting AI to simulate a customer’s environment in seconds, then applying their expertise to ensure accuracy and build credibility. Their job isn’t to produce more collateral; it’s to translate AI outputs into persuasive, technically sound solutions.
Sales managers once spent countless hours pulling reports, trying to understand deal health and team performance. AI eliminates that burden by surfacing insights instantly—whether it’s gaps in stakeholder coverage, activity trends, or messaging consistency.
This shift redefines the manager's role. No longer a “data reporter,” the manager becomes a true coach. The skills that matter now include evidence-based coaching, spotting team-wide patterns, and helping reps upskill in areas like prompting, data judgment, and executive storytelling. Picture a manager opening a 1:1 with clear AI evidence of missing executive engagement in a late-stage deal. Instead of asking vague check-in questions, they can focus on deal strategy, roleplay outreach, and provide actionable guidance.
Hiring in sales used to emphasize activity levels and hustle. In the AI era, the qualities that differentiate candidates are adaptability, judgment, and the ability to transform data into compelling stories.
Updated scorecards should include criteria like prompt-writing ability, skill in refining or challenging AI-generated content, and competence in building executive-ready business cases. Some forward-thinking teams are even testing this in interviews by asking candidates to use AI for a quick task (such as drafting a discovery recap) and then evaluating how they refine and present the output. This approach reveals not just technical comfort with AI but also the candidate’s ability to add the human layer of insight that technology can’t replace.
Onboarding can no longer stop at teaching product knowledge and playbooks. AI fluency must become a core part of the first 90 days.
That means new hires should be trained on the company’s AI-enabled CRM and engagement tools, exposed to best practices for when to trust AI versus when to rely on personal expertise, and grounded in the ethical considerations of AI use. Done right, onboarding equips new team members not only to use AI tools, but to use them strategically, ensuring they can deliver impact immediately.
Traditional 1:1s often rely on anecdotal updates from reps, which are subjective, incomplete, and inconsistent. With AI, managers now walk into coaching conversations armed with objective data on account coverage, stakeholder engagement, and messaging quality.
Imagine a manager and AE reviewing an AI dashboard together. The AI flags that, despite multiple meetings, there’s no CFO engagement in a high-value deal. Instead of asking “Did you follow up with Finance?”, the manager can shift immediately into coaching—brainstorming strategies for executive outreach, roleplaying the message, and building confidence in the rep’s approach.
This approach makes 1:1s less about reporting and more about growth. Both manager and rep walk away with clarity, accountability, and next steps rooted in real evidence.
Performance measurement also needs a refresh. Counting emails sent or calls logged is less meaningful when AI automates so much of the busywork. Instead, reviews should focus on how effectively reps use AI to drive outcomes.
For AEs, this means evaluating how consistently they use AI to identify multithreading opportunities or strengthen executive engagement. For SEs, it’s about how they validate and enhance AI-driven demos and solutions. For managers, it’s how well they coach the team using AI evidence. A rep who consistently leverages AI to influence deal strategy and executive buy-in should be rated far higher than one who simply generates more activity without impact.
Of course, adopting AI is one thing—operationalizing it is another. Many leaders struggle to track whether their teams are actually applying AI effectively. That’s where tools like Pod step in.
Pod’s scorecards make AI-assisted coaching concrete and measurable. Managers can see how reps are using AI in their workflows, provide structured feedback, and align coaching conversations with business outcomes. By embedding AI-driven criteria into hiring, onboarding, and performance reviews, Pod ensures AI isn’t just a sidekick—it’s a core driver of sales excellence.
AI doesn’t eliminate sales roles, it redefines them. The winners will be those who embrace AI as a co-pilot, updating their skills matrix, coaching models, and performance frameworks to reflect this new reality.
Sales leaders who act now can build organizations where AI amplifies human impact, creating more strategic, effective, and future-ready teams. Learn more about AI-readiness by booking a demo with Pod today.
