Revenue operations in healthtech is quietly becoming AI operations.
Watch what healthtech companies are asking of their revenue teams lately and you'll notice the job changing underneath them. It is no longer enough to keep the CRM clean and the dashboards current. The new expectation is that someone in the revenue engine can look at a repetitive workflow, decide whether AI belongs in it, build the automation, measure whether it worked, and document it so the gain survives turnover.
That is not a data analyst. It is not a CRM administrator. It is a new discipline sitting between the two, and after twenty years in healthcare revenue work, I think it is the most consequential seat in the go-to-market org.
The job has four layers, and most teams only staff two.
Every revenue operation I have seen rests on the same foundation. First, trustworthy systems: pipeline data that is deduplicated, enriched, and monitored, so nobody argues about whose number is right. Second, automation: the handoffs between sales, marketing, and account management that should never depend on someone remembering to send an email. Third, reporting leadership can actually run the business with: funnel conversion, source performance, forecast confidence, and early signals of friction. Fourth, documentation, the least glamorous layer and the one that determines whether the other three survive a departure.
Most companies staff the first and third layers and hope the second and fourth take care of themselves. They don't. Manual workarounds accumulate, tribal knowledge concentrates in two people, and the operation gets slower every quarter while headcount grows.
AI changes the economics of layers two and four specifically. Lead routing, account research, meeting prep, CRM maintenance, follow-up support: this is repetitive, high-volume work that agents now handle well when someone wires them into the systems where the work actually happens. And the wiring is the job. Companies are done paying for AI curiosity. The question that matters is "is it working?" and not just "is it running?"
I learned this the long way.
My first healthcare job was at a physical therapy clinic in Abilene, reconciling accounts receivable and filing claims with CPT and ICD-9 codes. I launched their online claims filing. Nobody called that revenue operations in 2001, but that is what it was: cleaning the data, fixing the workflow, and making the money flow visible.
From there, a third-party administrator, where I ran the RFP process and repriced claims against negotiated discounts. Then at Cigna, the work finally got a name. As Director of Sales Operations for National Accounts, I owned sales operations for large accounts based in the the West half of the US: comp plans, quota setting, forecasting, reporting, and process optimization. When leadership asked whether something was improving, my job was to define the question, pull the right data, and return an answer they could take to quarterly investor reports. You learn precision fast when your number gets read by Wall Street.
At Alight, I grew channel partners’ business during COVID and managed the operational integration of our partner management software with Salesforce. This taught me a lesson every operations person eventually learns: the CRM is only as good as the hygiene, the definitions, and the adoption behind it. When you’re managing data for relationships ranging in size from 2 people to over a million, process optimization is essential.
Years on both sides of the benefit consultant relationship taught me something most revenue dashboards miss: consultant relationships have a health status long before they have a revenue outcome. A consultant health score, built from engagement and pipeline signals, is one of the smartest metrics a healthtech revenue team can run. Almost nobody builds it.
What building looks like now.
During my time at Gadoci Consulting, I have helped teams automate internal workflows with Zapier, n8n, and APIs, design AI-assisted processes, and turn tribal knowledge into documentation and SOPs that non-technical teams actually use.
We level every AI opportunity on a simple framework. Level 1 is individual productivity: a rep or account manager working faster with tools they already have. Level 2 is workflow automation: routing, meeting prep, CRM maintenance, automated reporting. Level 3 is custom applications: agents and integrations built for a specific business need. Most companies chase Level 3 headlines while their Level 2 problems quietly cost them hours every day. The durable gains almost always start at Level 2.
The other discipline that matters is discovery: establish the manual baseline before you automate anything, separate the AI problems from the process problems, and treat ROI as an outcome rather than an opening claim. According to McKinsey's 2025 survey, roughly 88% of organizations now use AI somewhere, but only about a third have scaled it. The gap is not the technology. It is the operational work of wiring AI into how revenue actually gets made, measuring whether it helped, and writing down what works.
The person this takes.
Healthtech is operationally complex in ways generic RevOps experience does not prepare you for. Benefit consultants, health plans, compliance realities, and long enterprise cycles all leave fingerprints on the data. The people who will do this new job well are the ones who know the industry's workflows from the inside, have owned a number, and can build.
If you run a revenue team in healthcare technology, look at your org chart and ask who owns layers two and four. If the answer is "nobody, exactly," that is the hire to make next.
Melissa Prude has spent 20+ years across the healthcare delivery continuum, from claims and sales operations at Cigna to partnerships at Alight and Included Health, and now AI and workspace automation consulting with Gadoci Consulting. She is based in Dallas.