Ashley Rogers

As an avid data geek, I love Cellebrite’s Industry Trends Survey. It’s by far the most comprehensive research on trends in digital investigations and digital forensics. With year-over-year data, it allows us to identify long-term shifts, validate our instincts, and spark meaningful discussions with customers and prospects. According to Cellebrite's 2025 Trends Survey, 98% of prosecutors say digital evidence is critical for securing convictions. As someone who believes that 100% agreement is rare in real life, that number is remarkable. To add on that - over half now consider digital evidence more important than DNA. That reflects a massive shift in modern investigations over the last 15 years: today, evidence is everywhere — a blend of physical and digital artifacts. Our mobile phones act as time capsules — capturing our diaries, locations, and timelines all in one place. They store our conversations, movements, habits, and behavioral patterns — often more reliably than our own memories. For investigators, it’s a goldmine. But with more data comes more complexity. A single case can involve multiple devices, encrypted apps (with the number of apps growing exponentially), cloud storage, and terabytes of raw data. This is where AI makes the difference — especially in today’s environment of fragmented communication platforms, multiple languages, and diverse media (text, voice, video). Modern digital forensics platforms use artificial intelligence to surface the most relevant evidence faster — spotting connections, detecting patterns, flagging anomalies, and reducing review time. Speed matters. So does depth. The volume of data is growing, and digging deeper takes time. But with the right tools, investigators don’t just keep up — they stay ahead. And having those tools isn’t enough. Your team needs the training to fully leverage your tech stack and unlock its true potential. In today’s world, the ability to investigate someone’s mobile “time capsule” effectively is essential to keeping our communities safer — and the landscape is constantly evolving. To stay current with emerging trends, I highly recommend downloading Cellebrite’s Industry Trends Survey. (Link in first comment.)

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It’s funny (and a bit ironic) how my last post on using AI within GTM teams turned into a magnet for a flood of sales pitches. I appreciate the enthusiasm and the creative use of LinkedIn Navigator, but let’s be real—many of these messages scream "copy-paste" and completely miss the mark. 🚩 Sales 101: Understand the problem before you pitch the solution. If you didn’t even read the full post, how are you solving anything? Just a friendly reminder to tailor your outreach—authenticity wins. lol 😆

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In every GTM organization, there are two functions that quietly shape the trajectory of the field: Enablement and Revenue Operations. Too often, these teams operate in parallel — aligned in intent but not always in execution. When that happens, sellers feel the friction. But when Enablement and RevOps come together with one strategy, one rhythm, and one measure of success, the impact is transformational. Together we can: 📈 Ground programs in real data and insights 🗺️ Streamline processes so sellers spend time selling, not navigating complexity ⚒️ Build change that sticks because it’s both strategic and practical I just had the chance to spend two days with my peers last week in RevOps reflecting on what to continue, stop, and transform — and it reinforced one truth: collaboration isn’t optional, it’s the engine of seller success. Curious — for those of you leading GTM functions: 👉 How do you ensure cohesion between RevOps, Enablement, and Sales Leadership in your org? 👉 Where have you seen the biggest unlocks when these functions work as one? Thanks to Brandon Deer Erika Wall Alexandru Bold Richard Silva Alexandru Filipov Razvan Petria Sonia Pintea Andreea Băloi for the engaging conversation, thoughtful debate and alignment on how our org can continue driving UiPath Act II forward, faster! #RevenueOperations #Enablement #SalesLeadership #GTM

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There’s a space I’ve always found fascinating—the space between knowing who your customer is… and knowing when they’re actually ready to buy. Most teams define their ICP. Fewer define their MQL criteria. Even fewer build the connective tissue between the two. Here’s the reality: An MQL isn’t just a lead that’s engaged. And it’s not just a lead that matches your ICP. It’s both. ✅ They fit your target customer profile. ✅ They’ve demonstrated intent through meaningful behaviors. MQLs sit at the intersection of fit and timing—and that’s what makes them so powerful when done right. In my latest post, I explore: 🔹 How to connect your ICP to MQL definitions 🔹 What real engagement signals look 🔹 How to operationalize MQLs without overwhelming sales 🔹 Why collaboration between marketing and sales isn’t optional—it’s essential If you're building go-to-market systems, leading a demand team, or just trying to turn “leads” into real revenue, this one's for you. 📖 https://lnkd.in/ggGgZb_G Would love to hear how you're evolving your own MQL definitions—especially in B2B where buying signals can be so nuanced.

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🤖 AI Sales Chatbots Are Quietly Rewriting Revenue Teams Sales teams don’t lose deals because of weak products. They lose them because follow-ups are slow, leads go cold, and conversations stop too early. That’s where AI sales chatbots are stepping in. From inbound qualification to outbound follow-ups and pipeline nurturing, tools like: Conversica, Cresta, RoundView, BotUp, Landbot, Data Semantics, Kindly, Sanuker, Chatfuel, Ringy, Cliengo, Hyly.AI, BotBuilders, WotNot, Chatbot, Alan AI , Chatchamp powered by trbo, Instabot.io, Floatbot.AI, SMOC.AI, Yosh.AI, Konverse AI, 247chatapp, Forethought, Nurture.AI, Smartsupp, Certainly, Sonar, Hachly, and Hao at Chatsimple undefined are helping teams respond instantly, qualify better, and sell at scale—without burning out reps. What’s changing: • AI never misses a lead • Conversations run 24/7 • Qualification happens before humans step in • Sales teams focus on closing, not chasing The real shift isn’t automation. It’s timing, relevance, and consistency at scale. 👇 Want to see which AI sales chatbots fit your use case and budget? Explore a curated comparison with features, pricing, and best-fit recommendations: 👉 Visit the landing page: https://lnkd.in/guPPDm_5 Foundevo Foundevo Newsletter: Scale your startup with the latest in AI, tech, funding insights, and growth hacks. 👉 Subscribe: https://lnkd.in/greRFqEU #AISales #SalesAutomation #AIChatbots #RevenueTech #SaaS #SalesTech #B2B #Founders #GrowthTech

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The premise behind intent data made sense: track what buyers are researching and reach out at the right time. The reality? A firehose of signals with no context. Accounts that "surge" and go cold. Reps chasing intent scores they don't trust. We broke down why the model is failing and what's starting to replace it. Spoiler: it's not more signals. It's understanding the pain behind them. Read the full piece → https://hubs.la/Q046cJzF0

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Today’s threats mix cyber, disruption, and misinformation, and they can hit any organization, not just high-profile targets. In this article, Tracy Reinhold (#Everbridge CSO) breaks down what this means and how teams need to rethink preparedness. Big takeaway: how you communicate in a crisis matters just as much as how you respond. Worth a quick read. https://lnkd.in/gjzyB3iF

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𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐨𝐮𝐭𝐛𝐨𝐮𝐧𝐝? Pause. ⚠️ More reps won’t fix weak targeting. More sequences won’t fix stale data. More volume won’t fix broken workflows. Outbound doesn’t fail at execution. It fails at fundamentals. This checklist? 𝐈𝐭’𝐬 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞...𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐜𝐡𝐚𝐨𝐬. 😅 Modern GTM teams fix the data layer first - clarity, context, enrichment, systems, measurement. 𝐓𝐡𝐚𝐭’𝐬 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐰𝐡𝐚𝐭 𝐑𝐞𝐝𝐫𝐨𝐛 𝐢𝐬 𝐛𝐮𝐢𝐥𝐭 𝐟𝐨𝐫. 🔎 Know more: https://lnkd.in/ecDsApBB 📅 See it live: https://lnkd.in/epBJxZrS #OutboundSales #GTMExecution #B2BSaaS #SalesOps #RevOps #PipelineGrowth #Prospecting #LeadGen #SalesTech #GTMStrategy #RevenueTeams #OutboundFramework #SalesLeadership #ModernGTM #Redrob

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