Most deals don't die. They wait. They wait early, for value nobody proved and a signer nobody met. They wait late, for approvals and paper built for some other deal. The process that closed your first hundred deals was not built for your next hundred.
I start with the reports you already run and walk your recent deals with the people who ran them. Then I fix the delays that cost you the most, with clearer process first and AI agents where they take work out.
The Baseline: four weeks, $15K fixed for one segment.
Glenn Vander Laan. Business value selling at Quest Software, a founding member of US deal strategy at Splunk, then go-to-market systems and AI at Okta and Klaviyo. More on the background
For software and AI companies whose growth plans are moving faster than their deals.
Growth targets raised, a new deal desk or RevOps team, enterprise buyers arriving early, and no playbook yet for how sales, finance and legal work a deal together.
SMB to mid-market, mid-market to enterprise, or the US to international, with new paper, currencies and entities. Or pricing moving to usage, or an acquisition that brings a second price book.
Approval steps added after one bad deal that no one has the authority to take out, ten tools for one deal with AI already paid for but unused, and cost to close rising faster than bookings.
Your numbers first. It starts from the pipeline and deal reports you already run: time in each stage, slipped deals, deals lost to no decision, and late discounting. Those become the before.
I then walk a sample of won and lost deals with the sellers, managers and deal desk who ran them. Was value proven, a champion found, the economic buyer confirmed, the buying process mapped? Then approvals, deal structures and the handoff to billing.
What you get is a ranked list: the gaps that cost the most, sized in days and dollars against your own numbers, each tied to the measure a fix should move. Where it applies, the readout also shows the AI already inside your tools that could take a step out. One segment, in about four weeks.
The readout shows where the process slows deals, not who. Where a deal desk lead is in place, the work is built with them, and they see the readout before the sponsor does. And it is yours to act on, with or without me.
A company moving upmarket · its first 40 enterprise deals over the last four quarters · next year's plan counts on $12M of enterprise bookings closing in about 100 days. On average they are taking 171. Even the fastest 25% take 112, so the plan needs these fixes and a reset.
| Average days in each stage | Fastest 25% | All enterprise | Gap |
|---|---|---|---|
| Discovery | 18 | 22 | +4 |
| Value case | 24 | 41 | +17 |
| Proposal | 22 | 30 | +8 |
| Approvals | 13 | 29 | +16 |
| Legal and procurement | 28 | 41 | +13 |
| Booking | 7 | 8 | +1 |
| Total | 112 | 171 | +59 |
How it is sized: days compare enterprise deals with and without the gap. Every comparison stays inside the enterprise segment, so a bigger deal taking longer is not counted as a gap. Deals can carry more than one gap, so the days and dollars overlap. Together, about $8.4M of the $12M plan sits in deals with at least one gap. Medians run lower than averages (158 days for enterprise) because a few long deals pull the average up. Dollars are next year's planned bookings in deals that share the gap, a measure of exposure, not proof the gap alone causes the slip. Figures here are made up for a fictional company, to show the format.
Built on the tools and process the team already runs, and handed to the team when it is done. What the work delivers:
Exit criteria for each stage, so a deal does not advance until the value and the buyer are confirmed.
Discount bands and an approval matrix agreed with finance, with routine approvals routed automatically and in parallel.
Order forms, pre-approved fallback clauses, standard structures for commits and ramps, and bookings billing can use without rework.
Then agents take work out of it, starting with the AI already inside the tools you own. Early, drafting the value case and flagging a missing champion or economic buyer. Late, routing routine approvals and flagging non-standard clauses. Fewer screens for sellers, and each one measured before and after.
For finance: revenue recognition questions flagged to your team before signature, and approval controls documented with an audit trail. Faster approvals come from clearer rules, not looser controls.
The fixes, delivered and measured. Scope and fee are agreed from the readout. The same measures keep running and are reviewed with you each quarter, adjusted for deal mix, so you can see what moved.
Deal strategy and a regular review of the largest deals, run against MEDDPICC, the standard checklist for qualifying a deal, so problems surface before a deal reaches the desk. One to three days a week, more around quarter end. It supports a deal desk lead already in place, or fills the seat until you hire for it.
Larger build work, such as systems implementation, runs through specialists named before the work starts.
A founding member of US deal strategy at Splunk, on deals from $500K to $20M in total contract value, while annual revenue grew from about $450M to $1.8B.
Built the program behind a move from perpetual to term licenses. In year one, the share of bookings on the new model ran twelve points above plan, a year ahead of target.
Approvals ran in single file, each approver waiting on the one before. I redesigned them to run in parallel with the same approvers, a two-month system change, measured in the CRM.
From in-house roles at Splunk, Okta and Klaviyo.
The front of the deal. Fifteen years at Quest Software, as a technical specialist, in business value selling and running professional services sales. Then deal strategy at Splunk: working inside each strategic deal's process, finding the gaps, and writing the customer's specific needs into the contract so the deal could close.
I was in the room as each of those companies took on new segments, new pricing models and bigger deals.
The back of the deal. At Splunk, the program behind the move from perpetual to term licensing. At Okta, business process automation across go-to-market, including one of the company's first production AI uses. At Klaviyo, go-to-market systems. Along the way: booking policies, deal structure guidebooks, CPQ and contract lifecycle implementations, and territory assignment automated across 400-plus reps.
AI in the deal process. For more than three years I have been building AI into business processes, starting with an early production AI model in mid-2023, then agents built hands-on and a seat on a company-wide AI Council. I think about AI at both ends of the deal. On the front end, it helps sellers prove value and reach the right people sooner. On the back end, it takes the routine work out of approvals, paper and booking. How I think about it, and what has and has not worked, is in my newsletter AI in GTM Operations.
I lead every engagement. Senior operators join when an engagement needs more depth, and I name them before the work starts.
With captions.
About a year of closed deals in one segment, won, lost and no decision, from the reports you already run: stage history, discounts, approvals and contract redlines. Ideally 30 or more closed deals, with stage history going back about six quarters, because a deal that closed early in that year may have started two quarters before. Whether a deal had a value case or met the economic buyer comes from the deal walkthroughs: a few hours of interviews with sellers, managers and the deal desk, plus an executive sponsor who reviews the readout.
Analytics partners build them first: a quick point-in-time read for the Baseline, or ongoing revenue intelligence if you want it to keep running. Their work is scoped and priced separately, and using them is your choice.
More than one segment, or a larger sample of deals. The fee is fixed before the work starts.
Work is performed under NDA and a data-handling agreement. Call recordings are optional, and notes from the deal walkthroughs work too.
No. The readout sizes what your team carries, in hours and days, using your own numbers. It is the case for headcount, tooling or rule changes.
It is a deal velocity project. The process gets fixed first, then AI agents take work out of it, starting with the AI already built into the tools you pay for.
A fixed-fee read on where a portfolio company's deals wait, sized against its plan, in four weeks. Start with one company, then compare across the portfolio.
How long did each one take, and where did it wait? Then bring next year's growth plan, and we will see whether your deals can keep up with it. That is the free first conversation. If there is something there, the Baseline uses a year of your deals to size it.
Or email
[email protected]
LinkedIn profile · AI in GTM Operations, the newsletter