I recently attended RevOps AF Europe 2026. As expected, AI was a topic in nearly every session. Speakers covered everything from AI governance and pricing models to implementation strategies and use cases. What surprised me, however, was not the amount of discussion around AI, but the lack of truly compelling real-world examples.
The overall impression I left with was that most organizations are still experimenting. They know AI is important. They know they need a strategy. But many are still trying to determine where AI actually creates measurable business value.
The biggest takeaway I brought home had less to do with AI itself and more to do with the foundations required to make AI successful. For years, RevOps professionals have emphasized the importance of documented processes, clear definitions, clean data, and well-defined sales and marketing playbooks. These activities are often viewed as administrative overhead—important, but rarely urgent. AI changes that equation.
One speaker made a comment that stuck with me: “If you can articulate the problem, you can automate it.”
That statement sounds simple, but it gets to the heart of why many AI initiatives struggle. AI is excellent at helping execute a process. It is much less effective at defining a process that does not already exist. If a company cannot clearly define what constitutes a qualified lead, AI cannot reliably qualify leads. If lifecycle stages are inconsistent, AI cannot accurately measure conversion rates. If CRM data is incomplete or unreliable, AI cannot magically generate trustworthy forecasts.
In many ways, AI exposes process weaknesses that organizations have been able to tolerate for years. The technology forces teams to become more precise about definitions, handoffs, ownership, and success criteria. Without that foundation, AI simply accelerates existing confusion.
A related discussion focused on AI governance. One presenter described how their company eventually centralized more than 400 separate AI projects that had emerged across the business. The story reminded me of the challenges many organizations have faced with spreadsheets over the years. When every department creates its own calculations, assumptions, and reporting logic, the organization loses confidence in the data. There is a real risk that AI follows a similar path if companies allow teams to build disconnected solutions without shared standards and governance.
The lesson I took away from the conference is that the organizations that benefit most from AI will not necessarily be those with the biggest budgets or the most advanced tools. They will be the organizations that have taken the time to define their processes, document their playbooks, and establish a shared understanding of how their business operates.
AI may eventually transform how we work, but it does not eliminate the need for process discipline. If anything, it makes process discipline more important than ever.
