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AI and RevOps: The Disciplines Behind Predictable Revenue

AI Knowhow: Episode

116

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Most leadership teams want predictable revenue. Far fewer have built the operating discipline required to sustain it.

In this episode of AI Knowhow, Courtney Baker sits down with David DeWolf and Mohan Rao to make RevOps for Clients practical. The focus is how predictable revenue actually shows up in a leader’s week.

The conversation centers on a simple idea: consistency in revenue comes from consistent habits, especially after the deal is closed.

Leading with Signals, Not Instincts

David DeWolf frames the shift leaders need to make: relying on observable signals instead of intuition. Client health, delivery risk, and account momentum all leave data trails. When leaders pay attention to those signals early, teams spend less time reacting and more time preventing problems.

This shift changes the tone of leadership conversations. Meetings become focused on what moved, where risk is emerging, and who owns the next action.

The Operating Rhythms That Matter

Mohan outlines the cadences that create alignment across sales, delivery, and finance. These include weekly portfolio triage, monthly account reviews, and regular revenue controls that reconcile forecast movement.

The value isn’t in the meetings themselves; it’s in the shared visibility and accountability they create across the organization.

What Leaders Actually Do Differently

For leaders looking to start, two rhythms make an immediate difference:

  • A weekly review of the accounts showing the most negative movement
  • A monthly review of the full client portfolio, with clear plans for improvement

These conversations focus attention on execution quality and client experience while there’s still time to act. Research across sales and operations shows that disciplined management systems drive meaningful gains in performance and profitability.  One study found that organizations using a defined sales process outperform peers with up to about a 28 percent increase in sales productivity compared to those without such discipline in their sales approach. For professional services firms, where most revenue comes from existing clients, small improvements in retention and expansion compound quickly.

The firms that perform best are the ones that treat post-sale execution as a core growth lever.

Trust Is the Real Constraint on AI Adoption

In the second half of this episode, Pete Buer sits down with NYU Professor and Thinking With Machines author Dr. Vasant Dhar for a wide-ranging conversation on trust, variability, and leadership judgment in an AI-driven world.

Vasant’s core message is one many leaders intuitively feel but struggle to articulate: the biggest challenge with AI is trust. And trust, he argues, has very little to do with perfection.

How Leaders Should Think About Trust

Vasant frames trust through two simple dimensions: how often a system is wrong, and what happens when it is.

When errors are rare and the consequences are low, trust comes easily. When errors carry serious consequences, trust drops sharply—even if the system performs well most of the time. This is why people are comfortable relying on algorithms for tasks like document review or recommendations, but hesitate when stakes are higher, such as autonomous driving.

For leaders, the implication is clear. Trust in AI isn’t binary. It must be evaluated context by context, based on risk, impact, and tolerance for error.

Why Variability Matters More Than Accuracy

The conversation then turns to large language models and a reality many executives encounter quickly: the same system can produce different outputs from the same prompt.

Vasant explains that variability, not occasional error, is what undermines trust most quickly. If outputs fluctuate wildly, confidence erodes. If results fall within a tight and predictable range, trust grows—even if the system isn’t perfect.

Drawing on his own experience building AI systems for financial valuation, he emphasizes the importance of repeatedly testing outputs to understand variance. Stability, not novelty, is what makes AI usable in serious business contexts.

Why Leadership Matters More Than Technology

One of Vasant’s most important observations is that most organizations already have the data they need to improve decisions. What they lack is leadership willing to define what “better” actually means.

AI doesn’t reveal value automatically. It responds to the questions leaders choose to ask. Without clarity on goals, risk tolerance, and decision criteria, even the most advanced systems will disappoint.

And once insight is surfaced, the harder work begins: changing behavior. Vasant underscores that implementation and change management—not model selection—are where most AI initiatives succeed or fail.

If this conversation resonates and you’re interested in learning more about how to move RevOps beyond the point of sale, we’re hosting an upcoming webinar on the topic on February 25th at 11 AM ET. You can register at knownwell.com/revops.

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Show Notes

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