Jake Dunlap advises companies on revenue operations and go-to-market execution, including sales forecasting, software-stack decisions, AI productivity, and commercial leadership.
Jake Dunlap is most useful for practical revenue operations and go-to-market execution advice.
Best for
A mid-market private equity firm or revenue operations engagement with portfolio companies.
Not for
A consumer packaged-goods brand seeking fixed sponsorship, impressions, or product placement.
Key takeaways
A $1.5M forecast miss two days before a board meeting needs buffer.
Tool sprawl persists because nobody is paid to shrink the stack.
AI saved time matters when turned into customer conversations, pipeline, or output.
Check first: Confirm audience demographics, engagement, and whether they offer consulting or sponsorship work.
Main ideas
Revenue problems usually trace back to who is paid to fix them · One commercial leader should not carry an entire growth thesis · A forecast needs room to be wrong
Ongoing revenue-stack management, sold as a standing function rather than a one-off audit · Shared go-to-market operator bench for a portfolio · AI workflow builds scoped to one of six named outcomes
Jake Dunlap advises on revenue operations and go-to-market execution.
In the LinkedIn posts reviewed here, they focus on practical problems behind revenue plans: commercial leadership gaps in private-equity portfolios, unchecked software spending, fragile sales forecasts, and how to measure AI-driven productivity.
The examples offer concrete ways to spot operating risks and respond before they become larger problems.
Dunlap links operating problems to underlying causes, then offers practical changes.
A forecast that misses by $1.5 million shortly before a board meeting becomes a case for building buffer into a rolling 90-day process.
Tool sprawl becomes an incentives problem: if nobody owns ongoing stack reduction, adding tools remains easier than removing them.
On AI, they argue that time saved matters when a company turns that capacity into more customer conversations, pipeline, or output.
The portfolio-company post extends the same risk-focused approach to leadership continuity, proposing a shared bench of experienced operators rather than starting a search after a key hire leaves.
These posts offer concrete examples and operational recommendations.
Across these posts, Dunlap treats revenue problems as design problems: the question is usually who is accountable for the outcome, where a single point of failure sits, or whether a plan has room to be wrong.
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They favor ongoing ownership over one-time fixes, structural hedges over individual heroics, and capacity gains spent on growth rather than banked as saved minutes.
Two arguments turn on the same mechanism: people are measured on top-line results, so nobody's paycheck depends on containing cost or retiring a tool.
Dunlap states the counter-rule plainly, then admits almost no one follows it.
Revenue problems usually trace back to who is paid to fix them
Dunlap looks past the immediate complaint to ask who benefits from the current arrangement.
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On tool sprawl, they argue the accumulation is rational: the VP of Sales is measured on hitting the number, marketing on sourced pipeline, customer success on retention, and none of them on cost.
Each can request a tool that helps their own metric, so tools pile up.
Teams use roughly 30% of what they already own, Dunlap adds, and the gap widens because platforms ship new capability every quarter and no one's job is to find it.
Their rule: no new tool gets approved without first answering what it eliminates or what AI replaces.
The reason that question goes unasked, they say, is that nobody's compensation depends on the answer.
The proposed fix is a standing function rather than an audit, like the shared operator bench: someone owns the problem continuously, not just once.
In the AI post, Dunlap says role productivity is the outcome executives push back on hardest.
A rule that sounds easy — “what are we eliminating or is AI replacing first?” — fails not because leaders forget it, but because no one is paid to enforce it. The alternative is a retainer-style function that tracks the stack, removes tools that are not earning their place, and keeps pace with a market that outruns a part-time internal owner.
One commercial leader should not carry an entire growth thesis
Dunlap compares a private equity portfolio's dependence on a single VP of Sales or CRO to customer concentration, a risk no firm would leave untested.
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The failure is slow and expensive: four to six months to fill the seat, another year before anyone admits the hire is not working, then the same search again — 18 to 24 months of lost growth, landing just when a raise or exit was planned.
Dunlap sees a structural, repeatable failure mode in every portfolio company, but treats it as an operating decision rather than a portfolio-level risk.
Their proposed hedge is a shared bench of senior go-to-market operators deployed when a gap opens.
This is not the Value Creation team, they stress, but hands-on people in the business who provide continuity through each phase of growth.
When someone leaves, capacity is reassigned rather than rebuilt from zero.
Fifteen portfolio companies, fifteen VPs of Sales or CROs, fifteen growth theses each resting on one person executing well and not walking out the door. Instead of hiring one CRO and one VP of RevOps per company, the portfolio builds a bench it can deploy as soon as a gap opens, rather than four to six months later.
Dunlap describes a CRO who learned two days before a board meeting that the quarter would miss by $1.5M, and whose team spent a night rebuilding the forecast from scratch.
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Their diagnosis is not an unrealistic forecast but a process with no buffer: every number runs in a straight line, with no allowance for a slow week, a product delay, or reps out on leave.
Nobody finds out until panic is the only option.
Dunlap calls this a design failure, not laziness or deceit, directing the fix away from blame and toward structure.
They recommend replacing month-to-month forecasts with a rolling 90-day model and spending at least 20% of the current month fixing the next two, so a team tracking behind plan learns it on day one instead of 48 hours before a board meeting.
The 90-day window buys three months to run plays and shift budget. A forecast with no buffer, as Dunlap puts it, is an educated guess with your name on it.
AI earns its keep through what the saved hours produce
Dunlap pushes back on the demand that every AI workflow prove an attributable revenue number, noting that no one asked Salesforce to justify itself before replacing Excel or NetSuite before replacing spreadsheets.
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They tie each workflow to one of six outcomes: pipeline, sales cycle, average selling price, win rate, net revenue retention, and role productivity.
Executives readily accept the first five.
Role productivity is where the questioning starts, but Dunlap argues the value is real: if an account executive spends eight hours a week on research, CRM updates, call prep, follow-up, and admin, and AI cuts that to three, those five hours have economic value.
The company must then answer what it does with the recovered time: more customer conversations, more accounts managed, more pipeline, more deal advancement, or avoiding the headcount growth that would otherwise come with scaling.
That, Dunlap argues, is the real opportunity: changing how much output a company gets from each person it employs.
Cutting administrative work from eight hours to three leaves five hours that count only if they become conversations, coverage, pipeline, or avoided hires. Dunlap focuses the ROI question on how the time is redeployed, not how much is saved.
Explains why a single revenue leader can be a portfolio-level risk and how a shared operator bench reduces that exposure.
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Dunlap compares a private equity portfolio’s commercial leadership to customer concentration: 15 companies with 15 VPs of Sales or CROs, each growth thesis dependent on one person executing well and staying.
The post traces the hidden timeline: 4 to 6 months to fill the seat, another year before a miss is admitted, then a new search and ramp—adding up to 18 to 24 months of lost growth.
The alternative is a shared bench of senior go-to-market operators deployed when a gap opens.
These are not a Value Creation team, but hands-on operators who create continuity and avoid starting each search from zero.
For brands planning a campaign
For a private equity portfolio operations or revenue operations engagement, make the case for a shared commercial-operator bench instead of per-company searches. Scope the work around continuity, deployment speed, and stress-testing reliance on a single person.
Explains the incentive problem behind stack bloat and why the fix is an ongoing function, not a one-time audit.
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Dunlap argues that revenue and RevOps leaders are compensated on top-line metrics—the sales number, pipeline sourced, retention—not EBITDA.
Each new tool can help an individual hit their number, while no one owns the cost.
The post says teams use about 30% of what platforms already do, a gap that grows as vendors ship new capabilities every quarter.
The proposed answer is a standing function: someone tasked with tracking the stack, removing tools that are not earning their place, and keeping pace with the market faster than an internal hire could alongside a day job.
For brands planning a campaign
For a consulting or revenue operations brief, use this to sell an ongoing stack-management retainer, not a one-off audit. Scope it around tool elimination, feature adoption, and deciding what to replace or retire first.
Shows how a forecast with no buffer creates last-minute crises and offers a concrete fix: a rolling 90-day model.
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Dunlap recounts a CRO who discovered the quarter would miss by $1.5M two days before a board meeting; the team rebuilt the forecast overnight.
The problem was not necessarily an unrealistic forecast, but a process with no room to be wrong: every number ran in a straight line, with no allowance for a slow week, a product delay, or reps out on leave.
Dunlap recommends spending at least 20% of the current month fixing the next two months and moving to a rolling 90-day model, so a team tracking behind plan finds out on day one of the month rather than 48 hours before a board meeting.
For brands planning a campaign
For a revenue operations or leadership development engagement, use this post to assess forecast design and cadence. Scope the work around building a rolling 90-day model, setting buffer assumptions, and establishing a monthly operating rhythm that surfaces misses earlier.
Frames the AI ROI debate around six outcomes and explains why role productivity belongs in the business case, even when revenue attribution is hard.
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Dunlap argues companies never demanded a perfectly attributable revenue number before adopting Salesforce over Excel or NetSuite over spreadsheets, but with AI they ask every use case to prove ROI.
They connect AI use cases to six outcomes—pipeline, sales cycle, ASP, win rate, NRR, and role productivity—and note that executives readily accept the first five but question role productivity.
The post gives a concrete example: if an account executive spends eight hours a week on research, CRM updates, call prep, follow-up, and admin, and AI cuts that to three, those five hours have economic value.
The real question is what the company does with the capacity—more customer conversations, more accounts, more pipeline, more deal advancement, or avoided headcount.
That is the AI ROI conversation worth having.
For brands planning a campaign
For a revenue operations or AI productivity engagement, tie each AI use case to one of the six outcomes, capture role-productivity gains, and define how freed capacity becomes pipeline, coverage, or avoided headcount.
Dunlap argues that private equity firms stress-test many portfolio risks but treat a single VP of Sales or CRO hire as an operating decision rather than a concentration risk.
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They say a bad hire can take four to six months to fill, another year to diagnose, and then require the same search again—costing 18 to 24 months of lost growth. The proposed hedge is a shared bench of senior go-to-market operators the portfolio can deploy when a gap opens, instead of starting a fresh search from zero.
Run the portfolio math: count the companies and the commercial leaders each growth thesis depends on, then compare that concentration with customer concentration.
The shared-bench model requires a portfolio large enough to support common capacity. Ask whether a single company or small fund has enough scale to make it work.
For brands planning a campaign
For a private equity portfolio operations or leadership development engagement, position the shared bench as a way to hedge risk, not just speed up staffing. Lead with the cost of the search-and-replace cycle, not the cost of the hire.
Tool sprawl persists because no one is paid to shrink the stack
Dunlap says tool consolidation fails because revenue and RevOps leaders are compensated on top-line outcomes, not EBITDA or cost reduction.
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Each leader can request a tool that helps their own number, so tools accumulate.
They add that teams use roughly 30% of what they already own, and the gap grows as platforms ship new capability every quarter.
The fix is a standing function—someone whose ongoing job is to track the stack, cut tools that are not earning their place, and keep pace with the market—not a one-time audit.
Trace the incentive behind each tool request instead of debating the tool itself. Before approving it, ask what it eliminates or replaces.
Dunlap is not anti-tool: no new tool should be approved without answering what it eliminates or what AI replaces first.
For brands planning a campaign
For a revenue operations or sales consulting engagement, sell ongoing stack governance tied to measurable tool elimination and capability adoption. A one-time audit will not fix a problem rooted in compensation and ownership.
Forecasts need a buffer and a rolling 90-day window
Dunlap describes a CRO who learned two days before a board meeting that the quarter would miss by $1.5M.
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They say the cause was not an unrealistic forecast but a process with zero buffer: every number was a straight line, with no room for a slow week, a product delay, or reps on leave. They recommend moving from month-to-month forecasting to a rolling 90-day model and spending at least 20% of current-month time fixing the next two months, so problems surface on day one rather than 48 hours before a board meeting.
Shift from a single-month forecast to a rolling 90-day model and build in room for known variability, such as slow weeks, product delays, and leave.
Dunlap says the problem was not an unrealistic forecast but a process with no room to be wrong. Do not diagnose it solely as sandbagging or unwarranted optimism.
For brands planning a campaign
For a revenue operations engagement, lead with forecast-process redesign and quantify the cost of late detection. Focus the campaign on building a buffer and spotting problems early, not tightening the number.
AI ROI should be argued through productivity and redeployment
Dunlap pushes back on requiring a perfectly attributable revenue number for every AI workflow.
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They say their work connects AI workflows to six outcomes: pipeline, sales cycle, average selling price, win rate, net revenue retention, and role productivity.
The first five are easier for executives to accept; role productivity is where the ROI debate starts.
They argue that if an account executive spends eight hours a week on research, CRM updates, call prep, follow-up, and admin, and AI cuts that to three, the five recovered hours have economic value.
The more important question is what the company does with them: more customer conversations, more accounts, more pipeline, or avoided headcount growth.
Map each AI workflow to one of the six named outcomes, then ask what happens to the recovered capacity rather than stopping at minutes saved.
Dunlap acknowledges that role productivity is the outcome executives question most, so it needs an explicit redeployment story—not an assumption that saved time is self-evidently valuable.
For brands planning a campaign
For an AI or sales-effectiveness engagement, frame ROI around output per employee and capacity redeployment, not just task-level time savings. Ask what the company will do with the recovered hours.
The four posts reviewed do not name a sponsor, client, employer, investor, or business Jake Dunlap owns.
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They describe unnamed commercial work.
In one post, they write that “this is exactly the kind of work we run for clients on an ongoing basis,” describing a standing function that tracks a client’s software stack, retires tools that aren’t earning their place, and keeps pace with new capabilities.
The client is not named; ask for its identity.
Two other posts address private equity firms and their portfolio companies, arguing that a single CRO or VP of Sales is an unhedged risk across a portfolio and that firms could share a bench of senior go-to-market operators instead.
No fund or portfolio company is identified.
Gong and Salesforce appear once as examples of tools companies already own and underuse, not as partners.
The material discloses no sponsorship, affiliate relationship, or investment.
Jake Dunlap's posts cluster around four recurring areas: commercial leadership risk in private equity portfolios, sales technology and tool sprawl, forecast design, and AI productivity.
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Read those first for the operating philosophy behind the examples; the tags below mark those themes and the audiences the work addresses.
For a different angle on the same revenue-operations territory, Justin Rowe explains paid-media measurement and CRM-to-LinkedIn attribution, Hassan Mahmood covers territory, quota, compensation, and AI planning tools, and Matt Green gives renewal and sales-story plays.
Compare them by whether the fix is organization design, measurement plumbing, planning models, or customer conversation assets.
Rowe also writes for B2B revenue leaders about why standard funnel metrics mislead.
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Both explain the gap between visible lead activity and actual buying: Dunlap argues forecasts need a buffer and tools need an owner, while Rowe explains how LinkedIn optimizes toward form fills unless CRM outcomes are sent back. Both use concrete operating detail and practical fixes.
Rowe's work centers on paid-media measurement, attribution, and wiring CRM lifecycle events back to LinkedIn, including product promotion for DemandSense. Dunlap's posts center on organization design, commercial leadership risk, forecast process, and AI productivity rather than ad-platform integration.
Both cover sales planning and revenue operations, and both warn that AI planning tools can produce confident answers from stale or untraceable data.
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Dunlap says a forecast with no buffer is an educated guess; Mahmood says an AI planning tool will answer even when account data is six months old and rep capacity numbers are someone's best guess. Both favor traceability and explicit data limits.
Mahmood's posts focus on territory, quota, headcount, compensation, and forecast in one planning model, and on Voiant Sales Intelligence as a product.
Dunlap's posts focus on portfolio-level commercial leadership risk, tool governance, forecast cadence, and AI ROI framing.
If a planning-suite product matters, ask whether one is available.
Both write operational B2B sales plays with step-by-step instructions and concrete internal discussions.
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Green's posts on champion defense kits and turning customer calls into sales stories share Dunlap's habit of naming the process failure, such as renewal defense or CFO objections, then giving a repeatable fix.
Green's posts focus on renewal and champion enablement, customer evidence, and sales storytelling.
Dunlap's focus is revenue operations, forecasting, tool consolidation, private-equity portfolio risk, and AI productivity.
Green teaches through sales conversation assets; Dunlap teaches through operating-model and risk arguments.
For a leadership-development or revenue-operations engagement, the audience case rests on one platform figure and strong topic alignment.
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Jake Dunlap’s LinkedIn profile shows 91,553 followers as of 11 October 2026.
Four posts published 29 September–2 October 2026 use the buyer’s own vocabulary: commercial-hire risk across a private equity portfolio, a shared bench of go-to-market operators, tool sprawl and accountability for reducing it, rolling 90-day forecasting, and AI productivity as ROI.
The posts establish what the content is about and who it targets, but not who is reading.
If reaching private equity operating partners, CROs, or RevOps leaders is essential, ask whether Dunlap has audience survey results by job function, seniority, company type, and geography, or recent post-level reach and engagement for comparable topics and formats.
Selected examples: the 29 September post frames one CRO or VP of Sales hire as a portfolio-level risk, not just a single-company operating decision; the 1 October post argues that tool consolidation happens only when someone’s compensation depends on it; and the 30 September post describes rebuilding a forecast around a rolling 90-day model after a quarter nearly missed by $1.5M.
Plan around topical fit and reach until audience composition is verified.
Jake Dunlap's posts describe client work rather than a published sponsorship or media-partnership programme.
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Their work includes an ongoing revenue-stack function for clients, a shared bench of senior go-to-market operators that a portfolio can deploy across companies, and AI workflow builds tied to six named outcomes: pipeline, sales cycle, ASP, win rate, NRR, and role productivity.
For buyers, this means consulting and operating work scoped around a specific gap, not a fixed sponsorship package.
Ask directly about pricing, team composition, reporting expectations, and whether a sponsorship or media partnership is available.
Ongoing revenue-stack management, sold as a standing function rather than a one-off audit
Buyer requirement
Budget for a continuing engagement, not a single review.
The post contrasts a periodic audit with a standing function that tracks the stack, removes tools that are not earning their place, and follows new capabilities as vendors ship them.
It says teams use roughly 30% of what their existing tools can do, and that closing the gap fails when it is assigned part-time to an internal hire alongside their day job.
Shared go-to-market operator bench for a portfolio
Buyer requirement
Commit at portfolio level rather than hiring one person per company.
The proposed model is a bench of senior GTM operators deployed when a gap opens, so a departure does not restart a four-to-six-month search or lead to a year of undetected underperformance.
The buyer should expect to fund standing shared capacity and define how operators are assigned across companies.
AI workflow builds scoped to one of six named outcomes
Buyer requirement
Agree up front which outcome the build targets: pipeline, sales cycle, ASP, win rate, NRR, or role productivity.
Dunlap writes that executives readily accept the first five, while role productivity is where ROI questions start.
Buyers should be ready to say what the freed-up hours will go toward, such as more customer conversations, more accounts managed, or avoided headcount growth.
Strong fit. The campaign's core problem is the one Dunlap's framing addresses head-on, and the deliverables the buyer needs match the work described in their posts.
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Brief
A mid-market private equity firm with 12–15 portfolio companies wants a partner to cut commercial-leadership risk across the portfolio and install a revenue operating cadence. The engagement would run several quarters, with the operating partner as buyer and a mix of portfolio-company CROs and RevOps leads in the room.
Reasons
The brief treats commercial leadership as a portfolio problem, which is exactly Dunlap's argument: 15 companies, 15 VPs of Sales or CROs, each growth thesis resting on one person executing well and staying. They contrast that with customer concentration, which no fund would carry without a stress test.
The buyer's budget shape — a continuing engagement rather than a one-off review — matches how the work is described. The stack post explicitly separates a periodic audit from a standing function that tracks the stack, removes what is not earning its place and keeps pace as vendors ship new capability.
The mechanisms are named, so an engagement can be scoped and measured: four to six months to fill a commercial seat, roughly a year before a miss is admitted, 18 to 24 months of lost growth; teams using about 30% of what they already own; and six outcomes — pipeline, sales cycle, ASP, win rate, NRR and role productivity — for AI work.
The proposed alternative is concrete enough to price: a shared bench of senior go-to-market operators deployed into a company when a gap opens, distinct from a Value Creation team because the operators are hands-on inside the business.
When to choose a specialist
Poor fit. The mismatch is audience, format and commercial shape, not the quality of the thinking.
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Brief
A consumer packaged-goods brand wants a fixed sponsorship package: a flat fee, a set number of posts, guaranteed impressions against a defined audience, and product placement inside short video content published to the brand's own channels.
Reasons
The only audience evidence in the material is a LinkedIn follower count — 91,553 as of 11 October 2026. Ask for demographics, job function, seniority, company type, geography, engagement rate and per-post reach before underwriting an impressions guarantee.
Dunlap's posts don't list a sponsorship programme, rate card or inquiry route. The three posts describing commercial work focus on consulting engagements — ongoing stack management, a portfolio operator bench, and AI process builds against named outcomes — rather than paid placements.
Dunlap's content is diagnostic advice for revenue leaders, PE operating partners and CROs: portfolio hire risk, compensation misalignment behind tool sprawl, forecast process design and AI ROI. That is a poor vehicle for consumer product placement.
A company that only wants a one-time tool audit is also a weak fit: the reviewed argument is that a single audit does not fix a problem rooted in who is compensated for what.
A worked campaign example
Brief
The private equity portfolio operations brief above — reduce commercial-leadership risk across 12–15 companies and install a revenue operating cadence.
Campaign angle
Sell a portfolio-level commercial-risk programme in three moves: quantify the exposure, stand up shared capacity, then install the cadence that surfaces misses while there is still time to act. Lead with the cost of the search-and-replace cycle, not the cost of the hire.
Measurement
Map results to the six outcomes named in Dunlap's posts — pipeline, sales cycle, ASP, win rate, NRR and role productivity. Treat role productivity explicitly rather than as a rounding error: translate recovered hours into more customer conversations, more accounts per rep or avoided headcount growth. Executives question this outcome most, so state the redeployment story up front rather than assuming it.
Steps
Diagnostic, weeks 1–3. Run the portfolio math company by company: identify the commercial leader each growth thesis depends on and count how many theses rest on one person. Convert that to exposure using the timeline from Dunlap's post — four to six months to run a search, up to a year before a miss is admitted, then a new search and ramp. Deliverable: a one-page risk register ranking companies by single-person dependency, sized against what customer concentration of the same scale would trigger.
Capacity, months 1–3. Design a shared bench of senior go-to-market operators with assignment rules, a deployment trigger for when a seat opens or is underperforming, and a ramp standard so a gap does not restart a search from zero. Agree with the fund which companies fund shared capacity and how operators move between them.
Cadence, months 2–4. Replace month-to-month forecasting with a rolling 90-day model that carries buffer for slow weeks, product delays and leave. Add the operating rule that at least 20% of the current month's time goes to fixing the next two months, so a team tracking behind finds out on day one rather than 48 hours before a board meeting.
Stack governance, months 3–6 and ongoing. Assign responsibility for tool consolidation: no new tool approved until the request names what it eliminates or what AI replaces, and sets a target for adopting capability the portfolio already pays for beyond the roughly 30% in use.
What would change the decision
Settle pricing, team composition and reporting expectations directly. Shared capacity only works if the portfolio is large enough to keep a bench busy; a single company or a very small fund likely cannot support it. To verify whether the posts reach PE operating partners, ask Dunlap for audience composition data, including a survey or per-post engagement data.
Choose Rowe over Jake Dunlap when the portfolio company's commercial problem is paid demand generation that looks healthy on paper but is not producing qualified pipeline.
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Rowe works the measurement gap directly: LinkedIn optimises toward the events it can see, so a program trained on form fills gets good at finding people likely to fill in a form, not people likely to buy.
Cost per lead falls while lead quality quietly declines.
The fix Rowe describes is sending CRM lifecycle events — MQL, SQL, opportunity, closed won — back to the platform so the algorithm can see the deal.
If a portfolio company is spending on LinkedIn and the operating partner cannot answer whether the right accounts are paying attention, that is Rowe's problem, not Dunlap's.
Rowe addresses demand-program measurement, not the leadership single-point-of-failure risk and forecast cadence that define the portfolio's need.
Their own description is that the manual version breaks constantly and gets rebuilt for every client.
Ask how the work will hold up over time.
Their posts also close with a trial offer for a named product, so the engagement looks like a tool plus implementation rather than the fund-level operating role Jake Dunlap describes.
Rowe is a detection layer for demand problems; Dunlap's work targets who owns the number and whether the plan can absorb being wrong.
Choose Mahmood when the portfolio's commercial pain sits in annual sales planning rather than commercial leadership continuity.
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Mahmood describes the October-to-Q3 cycle in concrete terms: territories exported from one tool, pipeline pulled from the CRM, a comp file from finance with rep names that do not match either, six weeks of reconciling files by hand, and a plan locked because the team ran out of time rather than because anyone proved it was right.
The failure mode — a rep given a number their territory cannot support, a quota gap or capacity shortfall discovered in Q3 — is a planning-model problem.
Mahmood's offer is a thinking layer across territory, quota, headcount, comp and forecast, with every number showing its math, sitting on top of platforms like Pigment, Anaplan and Varicent rather than replacing them.
That is the right purchase when the operating partner wants scenario answers in an afternoon and traceable assumptions behind each number.
Mahmood's work is planning software and the consulting behind it, so it competes for a different budget line than a shared operator bench or a revenue operating cadence.
The distinctive stance — refusing to answer when the underlying data cannot support a conclusion — prevents confident wrong recommendations but leaves the decision with the buyer, and it is only as useful as the account and capacity data feeding it.
Adopting it also presupposes that planning platforms are already in place; a portfolio company running planning across spreadsheets has groundwork to do first.
It fixes the model, not the hire, so a fund whose exposure is 15 growth theses resting on 15 individuals still needs Jake Dunlap's side of the work.
Choose Green when the portfolio's risk is retention and expansion rather than new-logo leadership.
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Green's renewal-season argument is specific about who is actually in the room: most champions have defended a budget line about once in their career, while procurement does it every quarter, so vendors are sending someone who went jogging once against a marathon runner.
The remedy is a defense kit sent a month before it is needed — the usage story in three sentences a human can say from memory, one defensible peer benchmark or none at all, and pre-written answers to what it costs all in, what breaks if the line is cut, and what the cheaper alternative is — framed with "No ask here."
Green's second line of work turns five customer interviews into 90-second stories built from characters, circumstances, conflict, choices and consequences, so reps can answer "I'm not sure my CFO will go for this" with a story rather than a case study.
That is the better engagement when net revenue retention is the number the fund is watching.
Green delivers frontline enablement and retention mechanics, not fund-level leadership structure, forecasting or stack governance.
The work assumes portfolio companies have landed customers and usable usage data; where adoption is thin, Green says the defense kit is hard to write, and recommends fixing usage with three months of runway rather than arming a champion.
The pitch also stops short of a portfolio programme — it is built for individual revenue teams, so a fund buyer would have to assemble the wrapper Jake Dunlap's work already describes.
The two are complementary: Green offers depth in retention, while Dunlap's work covers the portfolio-level commercial risk identified first.
This page draws on four LinkedIn posts by Jake Dunlap: commercial leadership as a single point of failure across private-equity portfolios, software-stack sprawl and the compensation gap behind it, forecast design with room to be wrong, and how AI productivity gets converted into growth.
The four posts do not show publication dates; on 11 October 2026, the profile showed 91,553 followers.
Three other creators — Justin Rowe, Hassan Mahmood and Matt Green — appear in the comparison sections.
The posts do not identify books, shows, or businesses, so that part of a name search is not covered, and no sponsorship programme, rate card, audience survey, or per-post engagement data is published in the posts here.
