Enterprise sellers have never generated more activity. Their win rates have not moved. That paradox is the central finding from a two-year experiment in applying artificial intelligence to complex B2B sales. Karl Pinto, who built one of PagerDuty’s top-ranked enterprise teams, argues that most companies are aiming AI at the one part of the sales process that was never the problem.
Pinto spent nearly two decades in enterprise software across Dell, Salesforce, and PagerDuty, most recently as Regional Enterprise Sales Director for the Northeast at PagerDuty. In that role, he built and led one of the company’s top-ranked global enterprise teams. The team closed roughly seven out of every ten opportunities it qualified, a win rate well above the enterprise software norm. He also personally ran the largest deal of its kind in the company’s history, a seven-figure agreement with one of the largest banks in the United States.
His observations are not the usual vendor hype. He is not skeptical of AI. He is skeptical of what sales leaders are doing with it. “Speed was never the bottleneck in a complex deal,” he says. “You can send a hundred more emails and run a dozen more calls and still lose, because the thing that decides the deal happens somewhere those activities never reach. We bought a faster car. The traffic is on a road the car never drives.”
The bottleneck was never throughput
In Pinto’s experience, enterprise deals are not won or lost on volume. They turn on two things that resist automation: whether the seller has qualified the opportunity honestly, and whether the seller has earned access to the person who actually controls the budget. “Most pipelines are fiction,” he says. “It looks real in the system because someone logged a meeting and set a close date. Whether it is real depends on questions a dashboard cannot answer. Does this account have a problem worth paying to solve, and are we in front of the person who signs for it?”
He describes a pattern he has watched repeat inside hypergrowth sales organizations: teams generate enormous activity against accounts that were never going to buy, then act surprised when the forecast slips. “Activity is comfortable. It feels like progress,” he says. “Qualification is uncomfortable, because half the time the honest answer is that the deal is not real and you have to walk away from it. AI made the comfortable part frictionless and left the uncomfortable part exactly as hard as it always was.”
This is the quiet trap of the current AI moment. Most sales tools are designed to make actions easier, not to make decisions better. A seller can now draft a cold email, generate meeting notes, and build a territory plan in minutes. But a bad account remains a bad account, an unreachable economic buyer remains unreachable, and a forecaster with no evidence of urgency remains just as unreliable. The friction that once forced people to slow down and think has been removed. The quality of what they are thinking about has not changed.
Pointing the technology at the wrong layer
The problem, Pinto argues, is where teams have deployed the technology. Most have aimed it at the throughput layer: writing more messages, booking more meetings, producing more first-touch volume. Few have aimed it at what he calls the diagnostic layer, the inspection work that determines whether any of that volume converts. “Point it at the wrong layer and all you do is manufacture bad pipeline faster,” he says. “Your reps are busier, your CRM is fuller, and your win rate is identical. You have automated the noise.”
Throughput is easier to automate. It is measurable. It sits in tools designed for automation. Diagnostic work is messier. It involves judgment, context, and the willingness to tell someone that a deal should not move forward. It also happens to be the only layer that changes outcomes. In a complex sale, a rep can write a thousand personalized emails and still lose to a competitor who has a single trusted conversation with the economic buyer.
Pinto is not alone in this observation. Sales operations teams across the industry have been comparing activity metrics to conversion metrics for years. The pattern is consistent: organizations that increase outreach volume without changing qualification standards see no improvement in win rates. They see a higher marketing-qualified lead count, a higher meeting count, and a higher number of opportunities that stall. The pipeline looks larger. The conversion rate tells the real story.
The diagnostic layer is harder to build for, which is part of why it gets skipped. It means using AI to pressure-test a deal rather than populate it: surfacing which opportunities have a validated champion, which have stalled on a single contact, which carry a close date nobody has justified, which have never once touched someone with budget authority. “That is the work that moves a number,” Pinto says. “It is just less photogenic than a tool that writes your emails for you.”
What discipline looks like underneath the tooling
Pinto’s own approach treats qualification as an operating system rather than a reporting formality. He runs his teams on MEDDPICC, the enterprise qualification methodology, but insists the acronym is not the point. “Half the companies that say they run MEDDPICC are running it as a form somebody fills in after the deal is already decided,” he says. “That is theater. The discipline is inspecting the behavior, not the field. Did the rep actually meet the economic buyer, or did they type a name into a box?”
The distinction produces very different behaviors. A rep operating from the field will refuse to enter an opportunity without naming the buyer, articulating the pain, and identifying the champion. A rep operating from the form will enter the same data after a discovery call and never revisit it. The tooling is identical. The discipline is different. That discipline, not the acronym, is what correlates with win rates.
Pinto’s team disqualified aggressively and refused to advance a deal until it had tested its access to real authority. “Executive access is a gate, not a nice-to-have,” he says. “If we could not get to the person who owned the budget, we did not have a deal. We had hope. AI can help me find that person and prepare for the conversation. It cannot have the conversation for me.”
In practice, that means a rep must do more than place a call to the C-suite. The rep must know what the executive cares about, how the executive measures success, and why the current solution is failing. AI can help with all of that. It can surface the executive’s public statements, key initiatives, and organizational priorities. It can draft an email that references those priorities. It can coach a rep on likely objections. But it cannot establish trust. It cannot read the room. It cannot adapt in real time to an executive’s skepticism.
Using AI to inspect the pipeline
He sees the same gate as the right place to point the technology. Used well, AI can tell a manager which deals in a forecast have never reached an economic buyer, the exact signal most teams discover far too late. “Imagine inspecting an entire pipeline for that one question every morning, instead of finding out at the end of the quarter,” he says. “That is a real use of the tool. It is just not the one most people bought it for.”
The potential goes beyond a single question. AI can parse call transcripts, meeting notes, and email exchanges to determine who was actually present in a conversation. It can flag opportunities where the only contact is a mid-level manager who has no budget authority. It can identify a seven-figure deal that has not changed stage in ninety days. It can track whether a champion has been validated or simply labeled as one. None of this is complicated technology. It is existing AI capability pointed at a neglected problem.
The contrast with current usage is stark. Most AI investment in sales has gone to generation, not inspection. Teams spend money on models that write better emails, score leads, and suggest next best actions. Those tools have value, but they are aimed at increasing volume. The diagnostic layer would instead decrease volume by removing deals that cannot win. Fewer opportunities, higher conversion, more predictable revenue. That is a harder pitch because it runs against the instinct to fill a pipeline with anything that moves.
Sales leaders also worry that disqualifying deals hurts morale. The opposite is true in Pinto’s experience. Reps want to work on deals that can close. Wasting weeks on a fictional opportunity is demoralizing. A manager who uses AI to show the team where time is actually being spent gives reps a reason to trust the forecast and the strategy. It also frees them to invest more in the opportunities that matter.
Faster is not the same as better
Pinto is not skeptical of AI in sales. He is skeptical of using it to do more of what was already not working. The teams pulling ahead, he says, are the ones putting AI underneath a qualification discipline rather than on top of an activity quota. “The winners will not be the teams that sent the most emails,” he says. “They will be the teams that knew which deals were real the earliest and spent their time only on those. That has always been the game. The tooling just raised the stakes on getting it right.”
Part of the reason this insight matters is the economics of AI. As the cost of activity approaches zero, the volume of irrelevant activity will explode. A rep with unlimited AI-generated messaging can contact more accounts in a week than a previous generation’s rep contacted in a year. But buyers are not any closer to signing. They are more likely to ignore the noise. The cost of bad pipeline, meanwhile, is not zero. It consumes time, energy, and management attention. It corrupts forecasts. It drives good reps to quit. Automating noise at scale only makes the problem louder.
The enterprise software industry has been here before. CRM systems were supposed to give sales leaders visibility. They gave them data entry. Sales engagement platforms were supposed to multiply productivity. They multiplied email volume. AI is now supposed to make everything faster. It will, unless leaders choose to make it smarter. The pattern is not mysterious. Every technology wave amplifies whatever process it touches. If the process is built on discipline and honesty, the technology is an accelerant. If the process is built on theater and hope, the technology is a magnifier of waste.
Pinto’s closing point lands as a warning more than a forecast. As AI drives the cost of activity toward zero, the teams that mistook activity for progress will produce more of it than ever, and convert none of it. “Faster is not better,” Pinto says. “It is just faster. Better is knowing what to walk away from, and that is still a human decision.”