Most companies approach this question the wrong way around.
They start with the technology — which tools to evaluate, which vendors to talk to, which pilots to run — and work backwards hoping to find a use case. The result is a collection of tools that enthusiasts adopt, everyone else ignores, and nobody can demonstrate has moved a metric that matters.
An actual AI strategy starts somewhere different. Here’s what that looks like.
Start with the output gap, not the tool
Before you look at a single piece of technology, answer three questions.
First: what does your revenue team currently produce, and where is the gap between that and what a well-designed team at your stage should produce? Specific numbers — pipeline generated per rep per month, proposal turnaround time, percentage of rep time on direct selling activity, conversion rate at each stage. Not impressions. Not sentiment. Numbers.
Second: where in the commercial workflow is that gap being created? Pre-call preparation? Proposal generation? Pipeline hygiene? Each has a different cause and a different solution.
Third: which of those causes are addressable through workflow redesign and AI — and which require something else entirely, like better management, better messaging, or a more honest conversation about which market you’re actually in?
Only once you’ve answered those three questions does the technology conversation become useful. At that point you’re not evaluating AI tools in the abstract. You’re identifying specific interventions for specific problems. The tool selection follows naturally. The strategy drives it, not the other way around.
Where AI actually creates leverage in a B2B revenue motion
Not everywhere. The returns are concentrated in a small number of high-value applications. In most scaling B2B technology companies, those are:
Pre-call research and preparation. A rep who walks into a discovery call with genuine account intelligence converts at a higher rate than one who doesn’t. AI can now generate that briefing in minutes. The leverage is real, immediate, and measurable within weeks.
Proposal and follow-up generation. The gap between a good sales conversation and a compelling written proposal is where deals die quietly. Most reps aren’t strong writers, most proposals are generic, and most arrive too late. AI-assisted proposal generation — built on good templates and trained on your best examples — cuts turnaround from days to hours and improves quality simultaneously. This is consistently the highest-impact intervention available.
Pipeline data and reporting. Most pipeline reviews are based on data that is partially wrong, manually entered, and already out of date. AI-assisted CRM hygiene produces more accurate data with less rep time. Better data produces better management decisions. The benefit compounds over quarters rather than weeks, but it’s significant when it arrives.
What an AI strategy is not
A list of approved tools. A usage policy. A transformation roadmap with eighteen workstreams and a two-year horizon. A vendor recommendation dressed up as independent advice.
If your AI strategy document is longer than two pages, it’s not a strategy. It’s a project plan pretending to be one — and it will produce the same outcome as every other project plan that tried to predict the future eighteen months out in a market moving this fast.
What it is
A clear answer to three questions: where is the output gap, which workflow changes close it, and how will you know it’s working?
Everything else is detail. The strategy fits on one page. If it doesn’t, start again.
The timing reality
There is a window here that is real but not permanent. The companies redesigning their commercial workflows now will compound an efficiency advantage that becomes increasingly difficult for later movers to close. Not because the technology will be unavailable — it will be everywhere — but because two years of running AI-assisted commercial processes builds pattern recognition, workflow discipline and management capability that can’t be replicated quickly.
The question isn’t whether to have an AI strategy for revenue productivity. It’s whether you do it now, while the advantage is still available, or later, when catching up is the best outcome on offer.
Still here? Good. You might be exactly my kind of client.
