Encouraging unmanaged creation of AI agents—or "agent sprawl"—results in conflicting outputs and fragmented customer messaging. With different agents accessing different data sources, companies get inconsistent answers to simple questions like company ARR, undermining strategic alignment.
Just as sales reps require training, AI agents need a consistent foundation of knowledge. This new concept of "agent enablement" involves feeding them curated data from calls, CRM, and playbooks to ensure their outputs are accurate and aligned with company strategy.
By centralizing customer data, methodology, and enablement assets into a single AI foundation, companies can ensure every human and agent delivers a consistent message. This turns AI into a powerful tool for aligning the entire organization with corporate strategy, using "propaganda" for good.
For AI initiatives to succeed, RevOps must adopt a product-oriented mindset. This means moving beyond reactively fulfilling requests for dashboards and reports to proactively building and managing systems that solve the core problems of their "customers"—the sales reps and GTM leaders.
A major risk of AI is reps will "outsource human judgment," losing the intuition that defines top performers. The correct mental model is to treat AI as a "thought partner"—a tool to accelerate research and test ideas, while the human remains responsible for strategic decisions.
Successful AI adoption requires leaders to get their hands dirty. The most effective CROs and VPs are personally experimenting and building prototypes. This hands-on approach helps them develop a crucial instinct for how the technology works, what's possible, and how to redesign processes.
Companies are re-architecting operations around maximizing revenue per employee (RPE), driven by a push for efficiency. This metric has become the primary focus for leadership and boards, with AI seen as the key enabler, shifting focus from trends like Product-Led Growth (PLG).
AI's efficiency gains are leading to a significant redesign of GTM teams. Companies are reducing siloed, specialist functions like Sales Engineers (SEs) and value engineers. The trend is toward a more consolidated, "full cycle" account executive role, boosting revenue per employee.
Sales teams often use terms like "champion" inconsistently. Companies can combat this and prevent AI hallucinations by using dedicated AI agents to analyze internal language. These agents build a company-specific dictionary, or "semantic model," to ensure consistent definitions for both humans and AI.
Of all productivity metrics, new hire ramp time sees the fastest improvement from AI. By providing immediate access to complete account histories, playbooks, and strategic context, AI enables new reps to become effective in as little as two months, compared to a traditional six-month cycle.