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I step into scaling fintech and software revenue organizations when forecast discipline…
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Joshua Adragna posted thisThe real signal is not that people want your playbook... It’s that they want the output without formally valuing the operator who created it. That is where directors, new VPs, and revenue leaders need to be careful. Because a real playbook is not a template. It is context, timing, judgment, repetition, adoption, pressure, and trust. You can hand someone the document. But without the operator who built it, most teams just end up with a prettier Google Doc.
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Joshua Adragna posted thisThere’s a weird signal that shows up when your revenue system is actually working. People stop asking normal questions. They start trying to get the intel & playbook. -Not the philosophy. -Not the high-level recap. -The actual system. After one acquisition, I had people from the acquiring company poking around. -How did you train the team? -How did reps adopt the motion? -How did you keep deals moving? -How did you get everyone saying the same thing? On the surface, it sounded collaborative. But I knew what it was. They wanted the thing. And that’s a lesson a lot of operators learn too late. If your work is valuable enough for people to quietly extract, it is valuable enough to be owned, protected, and compensated. A real high performing playbook is not a Google Doc. It is years of reps, deals, failures, pattern recognition, pressure, and judgment compressed into a system other people can actually run. And the funny part is, giving it away usually does not work anyway. A playbook without the operator who built it is just a document. The value is not only in the steps. It is in knowing when to press, when to pull back, where the deal is lying, which rep needs confidence, who's distorting the story, and which signal actually matters -- That part does not copy and paste.
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Joshua Adragna posted thisA lot of startup traction is not built to survive real diligence. It is built to survive the conversation. I’ve seen this a bunch. A company gets a little momentum and the packaging starts. Big logo slides, inflated pipeline, selective metrics, loose expansion story, and activity dressed up like inevitability. It sounds good in the room. But that is not the same as building a business serious investors, buyers, or operators trust. Lots of founders optimize for pitch optics when they should be optimizing for buyer confidence. These have never been the same thing. The companies that get exposed later usually are not the ones with no story. They are the ones with a polished story stacked on top of fragile commercial truth. And that gap is getting harder to hide. Compressed markets expose what was always there. Weak pipeline, soft retention, vague expansion logic, and momentum that depended more on narrative than operating truth. A lot of companies did not build for diligence. They built for the room. The irony is that markets like this create real opportunity for the teams willing to face what is true. To get honest, tighten the business, and bring in people who know how to operate under this kind of pressure. Some founding teams will do that and get stronger. Others will keep preserving the story a little too long, and that is where a lot of unnecessary value gets lost.
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Joshua Adragna posted thisMost early-stage founders do not have a sales problem. They have a commercial clarity problem they are trying to hire around. I've seen it happen over and over. They hire a VP Sales before the business is clear enough to scale. The market does not understand the product fast enough. The value prop keeps changing. Deals are driven by founder force, not repeatable motion. Pipeline is full of hope. The motion is not actually there yet. So the new hire walks into ambiguity, gets buried in noise, and 6 months later everyone calls it a talent miss. It usually wasn’t. A lot of early sales hires fail because they were hired to scale something that was not clear enough to scale. Hiring does not create clarity, but does expose whether you had it.
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Joshua Adragna posted thisCompressed markets changes what founders should actually work on. > brand. > noise. > inflated pipeline theater. If funding or acquisition is even on the table in the next 12-24 months, your job is to reduce buyer risk, now. -Clean the revenue story. -Get honest about churn. -Fix sales-to-CS handoff. -Cut founder-led selling. -Make pricing stick. -Build forecast discipline. Know exactly where margins break. The majority wait until the process starts, which is amateur hour -- by then the buyer smells stress. Investor(s) knows you’re scrambling. Stronger companies aren’t perfect but they’re prepared. Prepared gets paid. Unclear gets punished.
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Joshua Adragna posted thisPlenty of founders still think prepping for funding or acquisition means tightening the deck. Wrong. In this market nobody buys the deck first. They buy whether the biz holds up under real scrutiny. -Messy pipeline. -Founder-carried revenue. -Weak retention. -No clean reporting. -Pricing that doesn’t hold. -Forecasts built on hope. That’s where deals get discounted. Investors slow down. Acquirers walk. Story matters less now while proof matters more. The companies still winning aren’t the sexiest, they’re the cleanest. Optimizing for buyer confidence > optimizing for pitch optics.
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Joshua Adragna posted thisAI isn’t fixing bad go-to-market. But it is scaling it. If your pipeline is garbage, AI gives you more garbage faster. When your messaging is off, AI helps you spray it wider. If your funnel is broken, AI just fills it with noise. That’s why some teams are “seeing activity go up” and still missing numbers. AI didn’t fail... It just exposed that the system underneath it never worked. Good operators use AI to compress time. Everyone else uses it to hide problems a little longer.
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Joshua Adragna posted thisAI can write the dealership email. It usually can’t tell that the real move is pushing on a 2-year pattern of issues, prior service history, and possible warranty coverage. That’s the same problem in go-to-market. A lot of founders are using AI for revenue decisions without feeding it real operator context. So the answer comes back polished, fast, and incomplete. and this is the gap. Operator-led AI won’t just sound smart, it will know what actually matters.
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Joshua Adragna posted thisEarly in my career, I used to think friction meant I needed to communicate better. Later I realized the friction was structural. I’ve sat in rooms where: the board wanted 5x growth pipeline math supported maybe 3x, product gaps were obvious but everyone kept talking about energy And yes, energy matters. In venture-backed companies, momentum can help bridge a gap. But only if everyone is honest that a gap exists in the first place. When I’d say, “The inputs don’t support the target,” the room would go quiet. Not because it was wrong. Because it interrupted the story. Operators who’ve been through it know this dynamic. Narrative can buy time. It cannot replace math. At some point, you stop performing optimism and start defending reality.
Joshua Adragna commented on a post
2h
Before you cut or hire AEs, the math has to be real. If reps spend 60% of their week chasing pipeline, the issue is not headcount yet. It is pipeline quality, win rate, deal size, sales cycle, and payback. Add AEs too early and you are not fixing the gap. You are just adding more quota to a weak system.
Joshua Adragna replied to a comment
2d
Totally agree they’re moving closer together. I just think “closer together” is different than “one role.” Imo, best version is likely technical RevOps / AI builder paired tightly with people who actually understand field signal, buyer behavior, and what moves pipeline.
Joshua Adragna commented on a post
2d
I get the appeal of the GTM Engineer label, but I’m not sold on the “best SDR plus best RevOps person in one body” framing. These are very different skill sets. IMO, the unlock is pairing field-level commercial judgment with technical RevOps/AI execution. One knows what should be built. The other knows how to build it cleanly. Forcing that into one unicorn role feels like another way companies overload one person and call it innovation.
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Joshua Adragna liked thisJoshua Adragna liked thisWe're live on r/legaltech for a H2H AMA hosted by Melia Russell and Alex Denne. I'm going head to head against 3 of the best founders in Legal AI. Good luck out there! Scott Stevenson, CEO of Spellbook Min-Kyu Jung, CEO of Ivo Ross McNairn, CEO of Wordsmith AI https://lnkd.in/eZgAV98B
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Joshua Adragna liked thisJoshua Adragna liked thisHad the JLL Asset Beacon team on the podcast last week (and held a webinar with them this week). AI for Asset Managers: Real Tools, Real Results, Real Implementation. (Good title, right? Not AI). One of the CEOs (two is better than one!) said something that made us pause. "Budget vs. actuals variance analysis takes our asset manager clients two hours per asset, per month." Let that sit. Two hours. Per asset. Per month. If you're running a 40-property portfolio, that's 80 hours a month. 960 hours a year. On one workflow. And it's not strategy work. It's pulling numbers, reconciling them across multiple sources, flagging variances, writing them up, and sending them to ownership. The kind of work that nobody went to business school to do. Here's what AssetBeacon is doing: they've connected their AI agents directly into the general ledger AND the property management system. Same schemas. Same data models. So instead of an asset manager manually pulling numbers and reconciling them, the agent does it in seconds - line-item variances, month-over-month deltas, flagged anomalies - ready for review (in seconds). The two hours don't disappear because AI is magic. They disappear because the agent already knows where the data lives. That's the unlock most commercial real estate folks are missing. It's not "can AI do this task?" It's "is the AI connected to the systems where the data already is?" Full episode on the podcast - link in the comments. Also, if you want to check out the recorded webinar... DM me.
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Joshua Adragna liked thisJoshua Adragna liked thisMost teams that use AI keep detailed documentation. Whether it’s git-committed .md files, or connecting AI tools to Linear and Notion. Part of making agents productive is giving them enough context to do their job properly. But this creates problems. For one, stuff gets out of date quickly. AI can certainly help in the culling and editing of stale docs, but it becomes a full-time job, almost a librarian role, to keep everything up to date. Additionally, a final direction is never the whole story. It’s one thing to know that your app uses Clerk for auth… it’s an entirely different thing to know the company history of auth providers and all the failed integrations along the way. This is our secret sauce around Brief. We keep a living web of all the decisions and documents you’ve made and create connections. That way when an AI agent goes to correct an auth bug, it doesn’t suggest switching to an auth provider to fix the problem that you dumped 6 months ago. This problem compounds as a company gets older. As people come and go from a company, context gets lost; you don’t remember WHY someone decided to do something. We built Brief to be a product that a new employee can ask about the entire company history around certain decisions and get the whole story on day one.
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JRA Consulting
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Big Brother
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SaaS sales playbook
a 70+ page playbook designed to capture all facets of sales in high growth software sales.
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Winning by design
Jacco Vanderkooj
Demonstrating the expertise to master a customer centric approach to sale and sales management. Disciplines include social selling, and online selling through the use of modern sales techniques, command of modern sales tools, generate customized sales content and perform online demonstrations. Leading, influencing millennial sales reps to their max potential.
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