Last night, scrolling LinkedIn, I saw another RevOps colleague from my past moving into an Internal AI role.
It made me smile, partly because I’ve found myself making much the same move, but also because I suspect we’re going to see a lot more of it.
RevOps is one of those jobs that everyone seems to define differently. CRM? Data and reporting? Sales process? The people everyone calls when the numbers don’t line up?
I was once told that RevOps people are “the plumbers of the building”: when the processes are working, nobody thinks about them; when something breaks or needs to change, suddenly everyone needs the plumber. Annoyingly, it’s a pretty good description. Good process should be almost invisible.
At its best, RevOps is about making the commercial side of a business work better: improving the processes, systems, data and handoffs that help a company win, retain and grow revenue.
Sometimes that means changing a process or improving reporting. Sometimes it means cleaner data, better automation or introducing new technology. Often it means discovering that the thing you’ve been asked to fix isn’t actually the problem.
I’ve spent years doing this kind of work, from global sales process at PayPal to building a RevOps function from scratch in a much smaller business. The most interesting part has always been working out what actually needs to be fixed or built in the first place. You need to understand how the business really works before you can decide what to change.
Now AI has arrived and given RevOps a much bigger toolkit.
Most companies have already found some of the obvious uses: customer-service bots, help drafting emails, meeting summaries and general productivity. The bigger opportunity is working out where AI can improve the commercial operation itself.
Lead scoring and enrichment, for example, used to mean stitching together several tools, integrations and workflows, and even then there were things you simply couldn’t do particularly well. Now a new lead can be enriched, segmented and scored as it comes in, with relevant context and suggested talking points generated for the person following it up.
Or take an annual account review. A rep might previously have spent hours pulling information from different systems, checking it, collating it and turning it into something presentable before they had even started thinking about the actual customer conversation. Now much of that can happen in seconds: reliable data pulled together into a consistent branded output, with trends surfaced and useful talking points suggested, while the rep adds the judgement and customer knowledge that actually make the review valuable.
There are hundreds of opportunities like these, but you still need someone who knows where to look for them. Someone with enough commercial understanding to recognise what is worth improving, enough process thinking to redesign the work rather than just automate a bad process, and enough technical curiosity to understand what has suddenly become possible.
RevOps people have been exercising those muscles for years.
There are plenty of other routes into Internal AI, of course, but I think Revenue Operations is a particularly natural one. The tools have changed dramatically, while much of the underlying work of finding opportunities, connecting systems and data, and helping commercial teams perform better is very familiar.
I’m looking forward to seeing what my former colleague builds in his new role, and what others coming from the same background do with the opportunity.
If your business is thinking about its own Internal AI opportunities, it may be worth asking your RevOps team what they can already see and what they would put on an AI roadmap.
And if you don’t have that capability internally, or the roadmap is still a blank page, I’d love to hear what you’re thinking about.