Edward Diller

“The answers were in the room.” That line stuck with me from last week’s PSG Equity leadership session in NYC, and it was true. The patterns across CROs were hard to ignore: • AI is reshaping workflows faster than teams can operationalize it • Sales motions are evolving, and execution is not keeping pace • The bar for performance is rising, even as complexity increases What stood out most was the openness. Leaders were transparent about where execution is breaking down and what is actually working. That kind of honesty is what accelerates real change. Progress is not driven by frameworks alone. It happens in rooms where experience is shared, challenged, and pressure-tested. Grateful to be part of that conversation.

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The evolving B2B tech landscape makes competitive displacement the default path for GTM teams. Simultaneously, it adds new layers of organizational risk, internal scrutiny, and emotional complexity for buyers. We created The Definitive Guide for Competitive Displacement to give GTM teams a smarter way forward—rooted in precise intelligence, not spray-and-pray ABM. Inside, you’ll discover: ▪️ Why traditional competitive intelligence leaves Sales, Marketing, CS, and Product flying blind ▪️ The exact data signals that show which accounts are truly ready to switch — and which never will ▪️ A step-by-step campaign framework for turning competitor intelligence into pipeline ▪️ GTM best practices from strategy to execution ▪️ How to fuse data, content, and activation into a single displacement play The switching window opens for buyers when the cost of staying exceeds the risk of moving. This guide helps you locate that window and act before it closes:

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The market for B2B sales tools is full of AI features that look impressive in a demo and don't change Monday morning. The ones that do are boring to describe and transformative to use. They remove a step that used to take 45 minutes. They make sure a trigger doesn't get missed because a rep was busy. They turn "we should probably follow up on that" into a task that already exists in the CRM. That's the version of AI worth paying attention to. We've been building toward this at Firmable for a while. AI agents that monitor the market for buying signals - leadership changes, funding rounds, hiring spikes, etc - and push them into your CRM automatically when they fire. Not a dashboard to check. Not another tab to open. But a task in HubSpot or Salesforce that says: this happened, here's the context, here's who to call. The sales teams that figure out this kind of AI first won't just be more efficient. They'll be having conversations their competitors don't even know they should be having. That gap will be obvious within 12 months. I'd start closing it now. #aisalesintelligence

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The same prompt, asked 11 times, produced five different winning vendors. One test asked ChatGPT and Claude the same question: which revenue platform to buy before onboarding five new SDRs. Gong showed up in all 11 threads though it was recommended in only three. The whole story is the difference. Citation is visibility, and recommendation is revenue. Most brands only track the first one. The winner kept changing because the model kept inferring a different buyer. Gong won when it read the task as coaching new reps, Apollo won when it read the task as prospecting, Salesloft and Clari won together on stack consolidation. HubSpot and Outreach won together on CRM migration. Two threads never settled on one vendor at all. Same prompt, four different assumed jobs, four different results. You can control whether the sources it returns connect your brand to the jobs you actually win. Most teams ignore this and audit their own site. They should audit which comparison pages the model cites. Zapier's roundup content showed up in 70% of source records. A June 2026 study found identical products default to the known brand 100% of the time, until a credible alternative gets even a 0.1-star edge. It breaks the monopoly. And it's not just about one model. Other GEO research has found only 11% of domains get cited across multiple AI engines, meaning visibility on one platform tells you almost nothing about the others. You're optimizing for many, each inferring a different buyer. Stop tracking the mention rate. What's your recommendation rate, and for which job? #AISearch #ProductStrategy

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Your buyers didn't get harder this year. You just trained them to wait for the discount. I've been thinking about why so many sales teams are seeing the same pattern right now. Deals stalling in week 8, prices challenged in week 10, and 30% concessions flying out the door in week 12 of every quarter. Leadership calls it "tougher buyers." I think it's something else. Last quarter I watched a Head of Sales walk into a forecast call and tell his team that buyers had become "impossibly difficult to close at full price." The team nodded. What nobody named in that room: they had personally approved 30%+ discounts in week 12 of the previous five quarters in a row. They didn't get tougher buyers. They got the buyers their pricing strategy created. The first time you discount aggressively at end of quarter, it feels like a one-time exception to hit the number. The second time, the buyer remembers. By the third or fourth time, your buyers have a pattern, and they've learned that holding out works. Procurement teams write this down. "Wait until late December" is a documented buying playbook in many large companies now. The 30% you offer voluntarily in week 12 becomes the floor they negotiate from in week 10 of the next quarter. The discount didn't just train your buyers. It trained your reps too. They build deals expecting the discount in week 12. They forecast it before negotiation even starts. What leadership sees as "flexibility to close" is actually muscle memory of giving up margin every 90 days for so long that nobody remembers what closing without a discount feels like. So every quarter the discount gets bigger, the buyer waits longer, the margin shrinks, leadership says buyers are tougher, and the whole thing repeats. Meanwhile, the teams that don't do this look almost stubborn from the outside. Their quoted price is the price. Deals close mid-month and mid-quarter, built around value instead of a deadline. Those teams aren't winning fewer deals. The buyers who would only sign with a 30% concession self-select out. The buyers who actually need the solution close at full price. The quarter ends quieter. Margin holds. Next quarter starts on level footing. If your team is exhausted negotiating every Q4 with buyers who "won't move on price," look at the last three years of end-of-quarter approvals before blaming the market. The market didn't get harder. The pattern got predictable. 💫 P.S. Meet the newest member of your sales team! Leadhunt Outreach Agent is live now!!! It learns what you sell, finds your ideal target customers, communicates with them the way you would, handles all the replies and books qualified meetings straight into your calendar. If chasing prospects is where your soul goes to die, consider the chasing handled. 💪🏼 ✨ Grab your free access here: https://lnkd.in/encEJxq7

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Your sales team closed £8m last quarter. £6m of it will churn before year two. The comp plan rewarded every single dollar. Most PE operators try to fix this by rebuilding everything at once. New sales comp. New quotas. New territories. New definitions of success. Result? Six months of chaos. Political warfare. And the wrong behavior never changes. Here's what works: Add one weighted column to your existing comp structure. Sales Compensation: Before: Close £100k deal = £10k commission Any logo counts the same Sales Compensation. After: Close £100k ICP-fit deal = £10k commission (100% payout) Close £100k non-ICP deal = £7k commission (70% payout) Same comp plan. Same quota. One new multiplier. The same logic applies across functions: Marketing: MQLs from target accounts = full pipeline credit. MQLs from non-ICP = 60% credit. Customer Success: Renewals with product usage above threshold = full bonus. Low-usage renewals = 70% bonus. You didn't replace the system. You tilted it. Watch what happens: Week 1: Reps start asking "Is this our ICP?" before taking the call. Week 3: Marketing pulls budget from channels that attract the wrong fit. Week 6: CS focuses energy on customers who use the product. Nobody commanded the change. The math did. The deal you inherited had incentives that rewarded motion over progress. The deal you're building rewards the behaviour that scales. One weighted column. That's the lever. The best incentive changes just make everyone care about the same thing. #PrivateEquity #SalesOps #IncentiveDesign #ValueCreation #PortCo

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Most teams misunderstand AI outbound calling. They think it is about replacing SDRs. It is not. They think it is about dialing more numbers. It is not. AI outbound calling is about removing latency, inconsistency, and compliance risk from the first layer of sales conversations. In 2026, outbound sales fails long before a human ever speaks to a prospect. Leads go stale. Follow-ups are delayed. Compliance mistakes happen at scale. AI outbound calling exists to solve that exact gap. At its core, AI outbound calling uses AI voice systems to initiate outbound calls, qualify intent, enforce compliance rules, and route conversations to human agents only when it makes sense. This matters because speed-to-lead is no longer a best practice. It is the difference between winning and losing. If you respond in minutes instead of seconds, the deal is often gone. But speed alone is not enough. High-volume outbound environments also carry real regulatory risk. AI-generated voices are treated as artificial or prerecorded voices under the TCPA. That means consent, disclosure, and opt-out handling are non-negotiable. This is why AI outbound calling must be built as infrastructure, not automation. When compliance is embedded into call execution, not handled manually, teams gain predictability. When qualification happens before human involvement, teams gain efficiency. When AI handles repetition, humans focus on judgment and closing. We recently published a clear definition of what AI outbound calling actually is and how it works in practice. No hype. No buzzwords. Just the operational reality. https://lnkd.in/dMrMJ2z7

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Your GTM stack is an expensive mess. AI-native companies figured out why. Quick one. How many tools does the average B2B sales rep use to close a deal? Eight. Eight platforms stitched together with Zapier and prayer. 28% of reps hit quota last year. Down from 44% the year before. 72% of their time goes on non-selling activities. Cold email response rates have fallen 41% since 2019. We spent a decade buying tools to make selling easier. We made it harder. The average B2B org runs 305 applications. 53% of licences sit unused. It's $2 of sales and marketing spend for every $1 of new ARR. That's not a tooling problem. That's architectural debt. And the typical response - swap vendors, retrain the team, hire another SDR - is treating symptoms whilst the disease spreads. AI-native companies figured this out. Their response isn't to buy better suites. It's to build composable systems from scratch. Cursor hits $1B ARR with 300 people - $3.3M per employee. Traditional SaaS median? About $112K. That's not a rounding error. That's a different operating model entirely. I tracked 210 GTM roles across OpenAI, Clay, ElevenLabs, Notion, and Anthropic this month. 70% explicitly require AI building capabilities. Not "familiarity with AI tools." Actual building skills. Verkada automated 80% of their SDR workflows - 120 meetings a month versus the typical 20. Rippling doubled YoY cold email performance. Not by working harder. By building better systems. Most companies are assigning juniors to "experiment with AI tools." Nice UX, lightweight workflow tweaks. But when you're trying to 5x total turnover, getting a junior to explore ChatGPT plugins isn't going to move the needle (harsh but true). The companies making this work are hiring senior folk with commercial acumen and giving them product mandates. Sprint cycles. Release notes. Measured outcomes. I wrote up the full breakdown - the economics, the company implementations, and why the gap between intent and execution is a seniority problem, not a technology problem. Read the full post here →

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