How AI Can Redesign High-value Revenue Work

I used AI to redesign how high-value revenue work gets done, specifically for account research, POV creation, and communication.

This work sits closer to revenue than people sometimes realize. It shapes how well the team shows up to customers, how relevant the outreach is, and how consistently the organization frames value. Historically, it has also been too manual, too uneven, and too dependent on individual effort. The best reps do it well. Others do something more generic. This creates a quality problem, not just a productivity problem.

My new workflows are custom GPTs built for specific commercial use cases. These are not generic AI applied broadly, but tools designed around how we think about accounts, how we frame value, and what strong commercial output looks like in our context.

Specificity creates leverage. Generic tools produce generic output.

The clearest example is our account research and outreach preparation workflow. A rep inputs the account, the relevant context, and the objective. The workflow produces a structured account view: what is likely happening in the business, where the priorities and pain points are, what signals matter, and how to frame our value against that specific situation.

AI In Account Research And Outreach Prep

The rep walks away with a real point of view built around the customer, not a recycled template. Work that used to take 10 to 30 minutes now takes closer to three without reducing quality. And quality is more consistent across the team, regardless of who runs it.

AI In Forecasting Workflows

Another example is the Forecast Center, a custom GPT I built to change how leaders prepare for and run forecast conversations. It solves the problem that most forecast calls turn into leaders reading data back to the room instead of driving strategy from it. Forecast Center takes run-the-business data, pipeline inputs, and customer health information and helps leaders turn that into a clear narrative and a defensible point of view before they ever get on the call.

Our internal standard is simple: If it sounds like a GPT summarized a spreadsheet, it is not done. It should sound like a CRO who has done the homework and is ready to drive the business.

Results

What makes both work is not the model. It is the workflow design, the built-in context, and the bar we hold for what good output looks like. The rep and the leader still own the judgment. AI removes the friction from the preparation so they can show up sharper, faster, and more consistently than before.

We are still early, so I caution against overclaiming the downstream revenue impact. But across these workflows and more, we have already improved speed and responsiveness across the organization without sacrificing quality. Teams can get closer to customers faster because they no longer wait for a meeting or a long manual process to research an account, build context, and form a point of view. This has improved preparation, raised the consistency of customer-facing work, and made the revenue organization more effective at engaging accounts.

Why Renewals Need An AI Redesign

Renewals are also in need of a redesign.

Most revenue organizations still treat renewals reactively. Teams get close to the date, check the health score, and scramble to cover the right relationships.

But by the time teams do that work, they are already behind. I would push for redesigning renewal intelligence as a continuous workflow, not a quarterly fire drill. AI is well suited for that because it can surface risk signals, flag coverage gaps, and help leaders form a point of view on where to act before the window closes.

Why The Biggest Opportunity With AI Isn't What You'd Expect

But remember, the biggest opportunity with AI is not fixing individual friction points. It is rethinking the workflow itself.

Early on, I was probably too focused on solving immediate pain, which created value but was more incremental than transformational in some cases.

Had I understood that earlier, I would have spent less time optimizing isolated tasks and more time redesigning the highest-value workflows end to end, linking clearly to GTM priorities and measurable business impact from the start.

That would have helped me avoid fragmented experimentation. This can lead to useful tools that do not add up to real strategic leverage.

The lesson is that AI creates the most value when it connects to how the business generates revenue — not just where people feel pain in the moment.

Where AI Informs Revenue Tasks

I rely on AI most in areas where better synthesis and faster preparation create real leverage, such as account research, POV creation, messaging, communication, and pattern recognition across large amounts of information. It excels at processing inputs, structuring thinking, and improving the quality and consistency of execution across the organization.

Humans come in where real judgment and accountability are required: forecasting, pricing, final pipeline calls, territory decisions, and major decisions regarding customer risk or investment. AI can inform those conversations, but it does not replace leadership judgment.

The reason is straightforward. I want AI helping us think better and move faster, not owning decisions where nuance, context, and accountability matter most. In a revenue organization, speed matters, but judgment is still the differentiator.

Revenue Leaders Must Keep A Close Eye On AI Slop

Sometimes, AI creates more output instead of better output. The clearest example is AI slop: generic outreach, generic messaging, generic points of view, and much activity that looks productive but does not improve commercial quality. In those cases, AI creates noise, not leverage.

I have also seen AI overapplied in areas where judgment, context, and nuance matter more than speed. If the inputs are weak or the standards are low, AI does not fix that. It amplifies it.

The issue is less the technology itself and more how easily people can confuse volume with value. That is the failure mode I watch most closely because it leads to trust erosion.

I address this by setting a clear standard across the organization, leading by example, and scaling the discipline I used to build my own workflows. This means reinforcing that AI should sharpen thinking, not replace it, and that context, iteration, and judgment still matter.

This is what separates revenue teams that gain real leverage from AI from those that just create more noise.

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