Tom Wallington

Sales Leaders: 5 win rate insights you’ve never seen before. For example, engage the CFO and your win rate jumps from 12% to 35%. In this fast-paced **15-minute session**, we'll unpack 5 **new** evidence-based insights pulled from real sales opportunities. They'll focus on lead source, sales stages, deal size, cycle length, and stakeholder impact. This is the first time these stats have been revealed. No theory. No fluff. Just the insights your revenue team needs to start closing more deals. Walk away with at least two tactics you can implement this quarter. ⚠️ You only need 15 minutes to fully benefit from the data and advice. Lock it in your calendar. Can't make the date? Don't worry. Register and we'll send you the recording. 🔗 Register here: 📅 UK June 17th | 3PM BST https://lnkd.in/eNptGBzF 📅 USA June 17th | 1 PM ET / 12 PM CT / 10 AM PT https://lnkd.in/ep4bMYQB #winrate #CRO #revenueIntelligence

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🚨 Bad sales data is quietly destroying SDR performance. If your SDRs are: • Busy all day but booking weak meetings • Spending hours cleaning lists • Chasing prospects that go nowhere • Burning out faster than expected This isn’t a coaching problem. It’s a data problem. We just published Part 2 of our series: 👉 The Hidden Cost of Bad Sales Data on SDR Performance Inside, we break down: • How much SDR time is wasted on bad data • Why volume-based prospecting backfires • The pipeline damage leaders don’t see • What top teams do differently 📖 Read it here: https://lnkd.in/ei9QuRZ3 Question for sales leaders: How much of your SDR team’s time do you think is spent on non-selling work? #SalesLeadership #SDR #B2BSales #RevOps #SalesIntelligence #AIinSales #FACIntelligence

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Most outbound teams fail by choosing between strategy and automation instead of combining both. Learn how modern GTM teams use buyer signals, AI workflows, verified B2B data, and human judgment to scale pipeline in 2026.

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$2M ARR per rep. That number should make your current quota model uncomfortable. Most revenue teams are tinkering at the edges. Better sequences. Faster follow-up. Another enablement deck. Meanwhile AI-first GTM teams are rebuilding the entire selling motion from scratch. 🏗️ The gap between those two approaches is not a tools gap. It is an architecture gap. Owner.com's CRO is not squeezing more out of reps. The whole system is designed differently. Tighter handoffs. AI handling the heavy lift between human touchpoints. Every workflow built around output not activity. That is exactly what ALPN Digital builds. GTM infrastructure that makes elite per-rep performance the default, not the exception. 🎯 If your best rep hit $2M ARR next year, would your current GTM stack support it or collapse under it? #aifirst #alpndigital #revops #gtmstrategy #salesarchitecture

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Most sales leaders chase one thing: more customer data. But even with digital leads, sellers still enter conversations with limited context. More data isn’t the fix if it doesn’t show up at the right moment, in a usable way. The edge comes from conversation control: discovering what the customer values as you speak, and adapting instantly. In this clip, Arun Subramanian explains: ▪️ Why sellers don’t get 20 insights, they get 3–4 ▪️ Why enablement must support live discovery ▪️ Why the best teams win without perfect information That’s the gap Sharpsell.ai closes with real-time, guided selling workflows. ETAuto | Automotive Research Association of India (ARAI) #SalesEnablement #B2BSales #RetailSales #CustomerExperience #GTM #Automotive

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Gong Labs analysed 7.1 million sales opportunities finding only 5% of AI deployments drive commercial impact. Average growth slowed to 16% whilst 96% of revenue leaders expect teams using AI by next year. The problem is AI theatre versus revenue reality. Teams deploy AI everywhere but commercial impact remains elusive. Gong's State of Revenue AI 2026 report surveyed 3,048 global revenue leaders plus 7.1 million opportunities from 3,613 companies. Finding: traditional growth engine losing steam, sales velocity plateaued, productivity becoming primary growth lever. Critical insight: domain expertise separates winners from losers. "Teams leveraging AI solutions with revenue domain expertise grew more in 2025 and reported more commercial impact from their investments." Not generic AI but revenue-specific intelligence. These teams "utilise AI as input in decision-making processes and seeing tangible results." Shane Evans (CRO, Gong): "We're living through one of the most profound transformations in business history. The AI Economy, where intelligence, not information, powers productivity, innovation, and growth. Leaders who thrive will redesign organisations around AI, not as tool, but as strategic capability." The 95% failure rate exposes implementation crisis. Companies embedding "automation and intelligence into every layer of revenue engine" succeed. Rest stuck with pilots and experiments. Measurement finally arriving: "Organisations with high AI utilisation within deals drive productivity, reporting significantly higher win rates and revenue per sales rep." What this means: When 95% of AI deployments fail to drive commercial impact whilst 96% of leaders expect adoption, massive disappointment looms. Mid-market teams buying generic AI tools for revenue problems will join the 95% failure category. Domain expertise requirement means revenue teams need revenue AI, not ChatGPT. The shift from information to intelligence economy rewards specificity not capability. Your next step: Calculate your actual commercial impact from AI, not usage metrics. If can't show win rate improvement or revenue per rep increase, you're in the 95%. Prioritise domain-specific solutions over general tools. Embed AI into revenue workflows not alongside them. The companies treating AI as strategic capability versus tool will capture the growth others miss. 1,100+ mid-market AI leaders stay ahead with weekly plain-speaking playbooks, case studies, use cases and need to know info, curated by experts. Sign up to our weekly brief and be the first to receive the report. (Link under our company name). Udi Ledergor, Lana O'Leary, Maximilian Thiel, Michael Mooney, Sam Levis, John Keenan, Sam Donaghy, Nate Vogel, Matthew Flotard, Kate Morris, Gray Fuller, Lane Stilson, Julian Alvarez, Rhonda Hughes

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Sales cycles are longer. Buyer groups are bigger. Expectations are higher. But too many teams are still throwing content and training at reps—without knowing what’s landing. Here’s the fix: 📍Track enablement that actually boosts deal size 📍Understand what content gets used—and what gets ignored 📍See where your reps really need help A simple guide to measuring what matters → https://okt.to/x4KZaU

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SHOWPAD BOOSTED QUOTA + FORECAST ACCURACY When targets climb and visibility drops… Clarity becomes a competitive advantage. That’s what Vanessa Metcalf and her team at Showpad unlocked with Gong’s Revenue AI OS. Now they can see: • How sellers actually execute new initiatives • Which deals are healthy (and which aren’t) • Where coaching will move the needle • What buyers respond to — in real time The result? Sharper forecasts. Smarter coaching. More reps hitting quota. See how Showpad transformed their revenue engine → https://lnkd.in/gjk9eJEm

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Most CRMs look “full” but hide gaps that quietly kill the pipeline: missing contact data, no conversation context, and zero deal-risk signals. This article identifies the most common yet overlooked blind spots and explains how to address them with AI-driven sales insights automatically. Stop guessing. Start knowing. Learn more- https://primerole.com #RevopsAI #SalesIntelligence #Contactenrichment #PrimeRole

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CSMs aren’t sales reps. In fact, most chose the CS path because it’s *not* sales. And that’s exactly what makes them powerful revenue drivers. Customer success was never about chasing quotas. But today, CSMs are being asked to deliver growth and protect the customer experience. No wonder so many feel caught between a rock and a hard place – between helping *and* selling. Here’s how the best CSMs are turning that tension into their superpower. 👇 https://lnkd.in/eYQ4ZzG2

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🛠️ Most B2B sales teams waste $5k-$10k per rep on disconnected tools that create more chaos than pipeline. B2B teams with integrated sales stacks report 43% more qualified meetings and 38% shorter sales cycles. Yet most cobble together random point solutions that frustrate reps and kill productivity. Over 1,000 vendors compete for your budget with promises of magical results. Choosing wrong means wasted money, poor adoption, and lost opportunities. Choosing right creates systematic advantages that compound quarter after quarter. We analyzed the modern sales tech landscape to identify which tools drive real results, how they integrate, and what you should actually pay. What you'll learn in this article: ✅ The 5 core tool categories that drive pipeline (and how to allocate budget across them) 💰 Real pricing breakdowns and ROI timelines for top platforms in each category 🔗 Integration requirements that turn disconnected tools into force multipliers The difference between a $75k tool investment that 2x's pipeline and one that sits unused comes down to selection criteria and adoption strategy. This blog cuts through vendor propaganda to show you what actually works. 👉 Read the full article: https://lnkd.in/eTT2ahCp #salesstack #toolstack #salestools #aitools #startup #startups #salesdevelopment #salesengagement #startupbusiness #outboundsales #leadgen #leadgeneration #b2b #b2bsales #b2bservices #b2bbusiness #entrepreneur #tech #business #marketing #fintech #growth #sales #smallbusiness

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SDRs miss quota because they’re drowning in repetitive work They spend 70% of their time on tasks AI could handle fast Here’s what most teams get wrong They either automate nothing or try to automate everything The better approach is to split tasks by what’s repetitive vs what requires judgment Teams that use AI well save 2+ hours per rep daily and often see revenue lift The breakdown 👇

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Are your sales leaders spending more time on manual call scoring than coaching? Over the past few months, we spoke with hundreds of sales and presales leaders who run complex demos with lean teams. They record calls and score them manually to improve coaching. It results in a small team spending days manually identifying the right calls, watching lengthy recordings, and updating scorecards. By the time they finish, deals have moved on, and reps barely get any guidance for their next calls. Offload this work to AI scorecards that are based on sales methodologies like MEDDICC, SPIN, or BANT. This saves your team over two days of manual review as they can scan the scores, see which parameters are low, and align coaching. Want to see where you stand? Run a simple check this week and track how many hours each manager spends on scoring versus coaching. #salescoaching #AIscorecards #avoma #conversationintelligence

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Account Executives aren’t overwhelmed by selling. They’re overwhelmed by everything around the selling. Prepping for calls in five different tools. Re-finding context you already had yesterday. Translating real buyer conversations into rigid CRM fields instead of acting on them. None of that shows up in quota attainment dashboards. But it quietly taxes judgment, momentum, and confidence. Most sales tech was built to record work. Not to support the decisions AEs make all day long. That’s the gap we’re obsessed with at Sales Caddy. Structured, role-specific AI that: • Preserves context instead of fragmenting it • Helps you think before the call—not summarize after • Reduces friction without adding another “tool” to manage Top performers don’t win because they hustle harder. They win because they protect their attention. Sales isn’t broken. The workflow around the AE is. If this resonates, I’m happy to share what we’re seeing work. #sales #accountexecutive #b2bsales #salesenablement #revops #aiinsales

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