AI & Revenue Operations
AI in revenue operations: most B2B businesses aren't ready to take advantage of it yet.
The businesses extracting real value from AI in their revenue function already have clean data, honest pipelines, and documented process. AI makes those things faster and better. It doesn't build them from scratch.
What's actually changing
A genuine shift in what's possible. With a catch.
AI tools (Claude included) can now do things in minutes that used to take hours: analyse pipeline data, draft qualification frameworks, build email sequences, summarise call notes, write proposal structures, interpret reporting. The underlying work is the same. The speed and quality of execution has changed materially.
For a RevOps practitioner, this is a real shift. An engagement that used to take three months to execute properly can now cover more ground in the same time. The output is better because there's more capacity for iteration, testing, and refinement, not because AI is doing the thinking.
The catch: AI is a multiplier. If you have good process, clean data, and honest pipelines, it accelerates all of that. If you have chaos, it produces more chaos faster. The businesses that are already struggling to get their CRM to tell the truth don't need an AI layer. They need the foundations first.
The prerequisite
AI tools are only as good as the data and process they operate on. A pipeline that's 50% wishful thinking produces unreliable AI analysis. A CRM with no stage discipline produces inconsistent AI output. The foundations come first.
What I use
Primarily Claude for analytical and documentation work. Various integrations for CRM analysis and reporting. I'm practical about tooling: I use what actually produces better output, not what's generating the most press.
What this isn't
AI replacing revenue leadership or RevOps thinking. The judgment calls (what matters, what to build, what to stop doing) are still the work. AI handles the execution layer around those decisions.
Where it earns its place
Specific uses in a revenue function. Not a feature list.
CRM data analysis
Pattern recognition across win/loss data. Anomaly flagging in pipeline. Categorising deal notes and contact records. Identifying which pipeline stages are breaking down and why. Work that used to require hours of manual analysis now takes an afternoon.
Playbooks and documentation
First drafts of qualification frameworks, objection handling guides, onboarding content, discovery process documentation. AI doesn't know your business; I do. But it dramatically accelerates the iteration from rough thinking to usable document.
Revenue reporting
Interpreting pipeline and revenue data, drafting commentary for board packs, building reporting templates. The analysis still requires judgment about what matters. The drafting and structuring is much faster.
Email and proposal work
Drafting and refining outbound sequences, proposal structures, follow-up messaging. Particularly useful for getting from zero to a strong first draft, and for running multiple versions to test different angles and tones.
Research and context
Market context, prospect research, competitive positioning. Useful for equipping sales teams with the right background before important conversations, without hours of manual research per account.
Call notes and follow-through
Summarising discovery calls, extracting structured insights, generating next-step recommendations. Closes the loop between conversation and CRM in a way that most teams are currently doing badly or not at all.
The honest version
What AI doesn't replace in a revenue function.
The judgment about what actually matters in a specific business. The architectural decisions about how a revenue function should be structured. The ability to look at a pipeline and know, from experience, which deals are real and which are wishful thinking.
The relationship context that makes difficult conversations productive. Telling a founder their pricing is wrong, or that the problem is how they've been selling. AI doesn't do that. That's still the work.
And the ongoing ownership. A revenue function needs someone who's accountable for it: watching the pipeline, catching the problems early, making the calls. That's not an AI job. That's a leadership job. What AI does is free up more time for it to be done well.
Judgment about what matters in your specific situation
Architectural decisions about how to structure the revenue function
The hard conversations with founders and teams
Accountability for the number
Reading what a pipeline is really telling you
Knowing when a deal is real and when it isn't
Building trust with a sales team
Deciding when to stop pursuing a market segment
In practice
How this shows up in what I do.
I use AI tools (Claude primarily) in most engagements now. Not as a shortcut around the thinking, but as an accelerant for the execution layer. A qualification framework that used to take two days to draft and iterate now takes an afternoon. A CRM audit that used to require hours of manual analysis now takes an hour. Pipeline review summaries that used to sit in my notes now get structured and distributed automatically.
The time saved goes back into more conversations, more iterations, better output. Clients get more complete work in the same engagement window. That's the real benefit: not AI doing revenue leadership, but revenue leadership being done with fewer friction points in the execution.
If you want to understand what this looks like in a specific engagement (what gets built, how AI tools get integrated into the revenue function, and what that requires from the business), that starts with Discovery Week. Same as everything else.
For more detail on the specific tools and workflows: How I Actually Use AI in RevOps Work covers the day-to-day reality. And AI Won't Fix a Broken Revenue Function explains exactly why the foundations have to come first.
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The foundations come before the AI layer.
Discovery Week will tell us whether the basics are in place, and what needs fixing before anything else. That's always the starting point.
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