Cognism

Teams waiting for the “right moment.” The perfect strategy deck. The fully built SOP. Total clarity before action. And what happens instead? You end up optimising in theory, debating edge cases, and seeking certainty that only real-world execution can provide. You don’t get clarity before you move. You get clarity because you moved. Real insight doesn’t come from another internal workshop. As Alex Kusters said, 'It comes when the rubber meets the road': - When prospects respond (or don’t). - When conversion rates tell you what’s working. - When messaging lands in the real world. Calculated risk isn’t recklessness. It’s disciplined experimentation. And in a market that’s moving as fast as ours, across data, AI, compliance, and GTM strategy, execution speed is often the competitive advantage. Listen to the full podcast episode here: https://lnkd.in/enmguMVv

AI-ready marketing isn’t about using ChatGPT more. It’s about redesigning how work gets done. After speaking to Alex Bacon and Nicole Leffer, one thing is clear. There’s a huge maturity gap between teams using AI and teams building capability with AI. Here’s what “AI-ready marketing” actually looks like: 1️⃣ Workflow-first thinking AI-ready teams start by breaking their work into clear, defined steps, from research and briefing through to distribution and reporting. They then embed AI into specific parts of that process, using it to support execution rather than replace strategic thinking. 2️⃣ One tool, one standard Instead of fragmented experimentation, mature teams align around a shared platform and agreed use cases. They invest in shared training and common language so everyone is building capability in the same direction, rather than creating isolated pockets of AI usage. 3️⃣ From chats to systems Rather than prompting back and forth until something “looks right,” teams turn successful prompts into templates and structured workflows. Over time, this creates repeatable systems that drive consistency and scale, not just one-off outputs. 4️⃣ Human accountability AI may accelerate the work, but humans remain responsible for the decisions and the final output. Strong teams build review, judgement, and ownership into the workflow, ensuring quality and brand standards are never outsourced. 5️⃣ Data-ready foundations AI performance depends on the quality of the inputs. That means connected systems, clean CRM data, clear definitions, and measurable goals. The aim isn’t more data - it’s better context that allows AI to produce meaningful, reliable results. Interested in learning more? We’ve broken this down further in our latest piece: https://lnkd.in/eTRgX-Xt

Does your outbound approach earn the right to interrupt? In the latest episode of RevOps Review, Jordan Crawford talks about the Permissionless Value Prop (PVP) - a simple but uncomfortable idea: 👉 If you had perfect information, what would you say? 👉 What message would be so useful that it earns attention, even without permission? The best messages don’t just describe your product. They connect internal + external data to deliver something independently valuable. The pool cleaner example in this clip is a perfect illustration. Instead of pitching, we used aggregated market data to tell someone: “Here’s what 56 businesses in your zip code charge. Here’s the price band with the highest conversion and lowest churn. You’re currently outside it.” If your outbound relies on generic value props, you’re competing on copy. If your outbound relies on unique insight, you’re competing on information. One earns the conversation. The question is: What do you know, or could you know, that would make your message independently useful? Check out the full episode here: https://lnkd.in/eMuB-xRs Jeff Ignacio

AI in sales is no longer experimental. G2’s AI Sales Intelligence Report reveals that 60% of B2B sales teams now use AI in their process. But adoption isn’t the advantage. The real differentiator is the quality of the data behind it. As Mick Loizou Michail, VP of Marketing at Cognism, puts it: “Outdated, incomplete, or ungoverned data doesn’t just limit AI performance - it actively becomes a liability.” AI doesn’t improve bad data. It scales whatever you feed it. If your data is accurate, structured, and up to date, AI sharpens prioritisation, improves timing, and reduces wasted effort. If it’s not, you don’t just get weaker outputs, you introduce risk. Because AI is nothing without the data. And if AI systems rely on your data for enrichment, scoring, summarisation, or automation, governance becomes part of the equation too. AI readiness starts with data readiness. If you’re investing in AI-driven prospecting, the G2 AI Sales Intelligence Report is worth your time. 👉 Read it here: https://lnkd.in/eyNcGDDS

Today, we’ve launched HubSpot 2-Way Sync, a native, bi-directional integration between Cognism and HubSpot designed to remove friction across revenue teams.  GTM teams told us the same story:  - Manual entry slows reps down.  - Duplicates create chaos.  - Stale CRM data kills productivity.  So we built something better. With 2-Way Sync, reps can see what already exists in HubSpot before exporting, avoid duplicates and wasted credits, and keep records aligned in both systems, all within their prospecting workflow.  Good integrations move data. Better integrations reduce cognitive load and improve coordination across teams. That’s what we set out to build. Find out more: https://lnkd.in/eU55Y3MR  

There’s a big difference between talking about building something… and putting something in someone’s hands. Most RevOps projects stall in the abstract. But in this clip, Stuart Watson (Head of Revenue Operations at Resolve AI) shares something simple but powerful: - You don’t need to be a product leader. - You don’t need production-ready perfection. - You need something tangible. An MVP. A rough model. A prototype, even with synthetic data. Because once an idea is visible, it’s improvable. That’s how an attribution model went from concept to shipped in a week. Not because AI replaced expertise, but because it made the idea clear enough for experts to move fast. And here’s the real point: AI compresses the distance between idea and execution. Listen to the full episode for more! https://lnkd.in/ebMcJXVG Jeff Ignacio

If your board asked you tomorrow whether AI could replace your SDR team, would you be confident in your answer? Because on paper, AI looks compelling. An AI-led email motion converting 1–2% of contacts into meetings can look efficient. Cheap. Scalable. But compare that with a human-led outbound motion converting closer to double-digit percentages, and the economics change fast. What the data from our State of Cold Calling Report 2026 shows isn’t AI vs people. It’s people + AI + high-quality data. AI is brilliant at stripping out friction: - Research - Note-taking - Drafting follow-ups What it doesn’t replace, especially in complex B2B, is judgment: - Navigating multiple stakeholders - Reading risk and timing - Building the trust that actually moves deals forward The highest-performing teams aren’t asking how to remove humans. They’re asking how to make every unit of human effort more valuable. - Fewer manual tasks. - Fewer low-quality touches. - More real conversations that improve qualification, forecasting, and outcomes. 🎥 Watch the full clip below, and for more data, check out the full report: https://lnkd.in/ek-CPNJ9

Most GTM data looks “good” right up until someone has to make a real decision with it. Forecast calls. Territory changes. Headcount planning. AI-driven workflows. That’s usually the moment confidence drops, not because the dashboards disappeared, but because no one’s quite sure whether the numbers would hold up under scrutiny. That’s usually because data quality audits focus on the wrong thing. “Is the field filled?” Instead of “Would we actually stake a decision on this?” This carousel breaks down the practical steps of a GTM data quality audit, from defining what “trustworthy” really means, to checking freshness, accuracy, coverage, and ownership, not just running another hygiene exercise. If you want the full walkthrough (including how to audit without boiling the ocean), we’ve laid it all out in this step-by-step guide: https://lnkd.in/eSa3wjQZ

If your SDR team reduced the number of dials needed to reach a buyer, what would you do with the spare capacity? One of the most overlooked levers in outbound isn’t activity. It’s attempts per connection. Benchmark data from our State of Cold Calling Report shows the average has dropped from 2.9 attempts per connect to 1.55. On the surface, that looks operational. In reality, it’s a commercial lever. Let’s keep the maths simple: • 10 SDRs • $80/day • 20 days/month At 2.9 attempts per connection, that’s ~552 live conversations a month. At 1.55, it jumps to ~1,032. Nearly double the market conversations. No additional headcount. What’s driving that shift? Accurate mobile data, fewer wasted dials, and AI-assisted prioritisation, so reps call based on fit, intent, and recent activity, not static lists. When connection efficiency improves, leadership conversations change: - What one SDR can realistically own - How much market coverage you actually need - Whether you scale headcount, or scale smarter For more, read the report! https://lnkd.in/exMA6vy3

We’re excited to share that Cognism is entering a bold new chapter in its growth journey as we appoint BBD Perfect Storm | a B-Corporation as our lead creative agency. As our platform continues to expand across sales intelligence, enrichment, CRM diagnostics and AI-ready APIs, this partnership will help bring a clearer, more confident and strategically aligned brand story to life. One that reflects our ambition to support mid-market and enterprise teams across Europe. Thank you to BBD Perfect Storm for joining us on this next phase of growth. We look forward to building something meaningful and distinctive together. Read the full announcement here 👉 https://lnkd.in/eeY2jRzC