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AI and RevOps: Why RevOps is a Critical Component of Commercial Management

AI Knowhow: Episode

115

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Revenue may look healthy on paper, but for many professional services leaders, it still feels fragile.

Forecasts miss. Renewals surprise. Margins erode quietly. And growth feels harder to manage than it used to even when demand is strong.

In this week’s episode of AI Knowhow, host Courtney Baker sits down with David DeWolf and Mohan Rao to unpack why this happens and why the answer lies in extending revenue operations beyond the point of sale. The conversation kicks off a new mini series on RevOps for Clients, reframing revenue management as an always on, end-to-end discipline rather than a pre-sales function.

The episode also features:

  • Pete Buer’s breakdown of new data from The Conference Board showing executives are officially done experimenting with AI and now expect measurable ROI
  • Part one of a conversation with NYU professor and author Dr. Vasant Dhar on how leaders can think with machines to gain lasting competitive edge

Here’s what stood out.

The Experimentation Phase of AI Is Over

In the news segment, Pete Buer highlights new findings from the Conference Board’s C Suite Outlook Survey and the signal is clear. AI has entered its accountability era.

  • 43 percent of executives now rank AI and technology as their top investment priority for 2026
  • 41 percent say measuring AI ROI is their number one AI planning priority
  • Among board members, that number jumps to 98 percent

The takeaway for leaders is unmistakable. Demonstrating ROI from AI is no longer optional. But as Pete points out, this is not just about cost savings. AI impacts revenue creation, organizational effectiveness, and decision quality, making ROI harder to calculate but more important than ever.

The challenge ahead is not whether to measure ROI. It is aligning leadership, boards, and teams around how to measure value in a world where AI reshapes far more than expenses.

RevOps Solved Sales. Then We Stopped Too Soon.

In the roundtable discussion, Courtney, David, and Mohan trace the evolution of revenue operations from the lone wolf days of sales heroics to the highly instrumented sales and marketing machines most companies run today.

SalesOps brought discipline. RevOps extended it across the pipeline.

But then something stalled.

As David DeWolf puts it, sales and marketing represent only the first 20 percent of the revenue lifecycle, yet that is where nearly all the rigor, data, and operational discipline still live.

The remaining 80 percent is driven by existing clients, renewals, expansion, and margin.

That post-sale world, David argues, remains the wild west. Read David’s latest blog post, The Ghost in the Revenue Machine, for more.

Why Post-Sale Revenue Still Runs on Gut Feel

Most leadership teams say they care deeply about net revenue retention. In practice, however, few can answer basic questions with confidence.

  • Which client relationships are truly at risk?
  • Where is expansion most likely to occur, and why?
  • Which accounts feel healthy but actually aren’t?

Instead of applying the same discipline from pre-sales throughout the client lifecycle, organizations rely on subjective health scores, deck-heavy QBRs, and rosy anecdotes from relationship owners.

As Mohan Rao explains, this creates a classic “local optimization” problem. Sales, customer success, and finance each see part of the picture, but no one owns the systemic health of revenue.

True RevOps, he argues, should function like a general practitioner for the business, not a specialist that shows up too late.

The Breakthrough. Measuring What Used to Be Unmeasurable

So, what has changed?

For decades, leaders could not operationalize post-sale revenue because the most important drivers were hard to quantify.

  • Perception of service quality
  • Strength of relationships
  • Degree of strategic alignment

Today, AI changes that equation.

Advances in language models and analytics now make it possible to analyze the vast amount of communications and natural information organizations already generate, including emails, meeting transcripts, notes, and communications, to surface early signals of risk and opportunity.

As David notes, more than 85% of enterprise data lives in unstructured formats. AI finally allows leaders to turn that human nuance into objective, proactive insight.

This is not about replacing relationships. It is about understanding them at scale, in real time, and before problems compound.

Why Leaders Still Overweight New Logos

If the logic is so compelling, why do organizations continue to prioritize new logo growth over retention and expansion?

The panel points to three reasons.

  • New logos are exciting. Retention work feels operational, even though it drives sustainable growth
  • Human relationships are messy, and leaders avoid what is hard to measure
  • Simple does not mean easy. This shift requires real cultural change

As David points out, applying rigor to post-sale revenue will require new habits, new metrics, and in some cases new leadership DNA.

The upside is significant. When commercial operations extend across the full lifecycle, organizations do not just improve revenue predictability. They unlock the profit engine of the business.

Thinking With Machines. Gaining Edge Through AI

The episode closes with part one of Pete Buer’s conversation with Dr. Vasant Dhar, whose new book Thinking With Machines: The Brave New World of AI explores how humans and AI can collaborate more effectively. Dr. Dhar is a Professor at NYU’s Stern School of Business and the Center for Data Science, and one of the world’s leading authorities on prediction, data science, and trust in AI.

One of the most powerful ideas Dr. Dhar shares is what he calls Dhar’s Conjecture: patterns emerge in data before we understand the reasons behind them. He first noticed this when he applied machine learning to large datasets of consumer purchasing behavior while working with A.C. Nielsen.

Machines spotted the trend that there was a surge of buyer activity on Thursdays in the Northeastern US. When he took the finding to a colleague, he found that it was because that was the day of the week coupons were released in circular magazines.

AI excels at surfacing those patterns, whether in consumer behavior, markets, or business operations, giving leaders the chance to act earlier and with better odds.

Drawing on examples from professional sports and finance, Dr. Dhar reinforces a crucial point. Leaders do not need perfect predictions. They need a slight edge, applied consistently and compounded over time.

That edge, he argues, starts with leadership, not technology.

What Comes Next

This episode introduces a core idea. Revenue should be managed as an always-on system, not a moment in time snapshot.

In the next episode of this mini-series, Courtney, David, and Mohan will get practical, exploring what RevOps for Clients looks like in action and how leaders can begin extending RevOps across the entire customer lifecycle. And we’ll bring you part two of Pete Buer’s interview with Dr. Vasant Dhar.

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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