Vikash Kumar

Over the last 15 years, I have watched 100+ data companies emerge, scale and sometimes disappear. The next big wave in B2B data is going to be a transformational pivot and not an incremental improvement. Amit Vasudev and I have had a few recent discussions on this topic. Here’s how we see the evolution happen and what comes next : 1. Data 1.0 : Legacy Data Vendors   Most existing data vendors fall into this category. Data is captured in a snapshot of time and rapidly decays. Venbdors often license data from each other or run the scraping infrastructure themselves. Data is delivered as rows in spreadsheets or via APIs Problem: Data is static, lacks continuity and context. Vendors increasingly face continuous downward pricing pressures and regulatory risks Moat: Technical difficulty and Risk tolerance 2. Data 2.0 : Data Orchestration Rise of Clay and the DataOS category marks a significant shift in the data landscape Orchestration platforms often do not own the data, instead they own the user and the workflows By abstracting 50+ vendors behind a single interface and currency, we have seen a permanent shift in market power away from individual data providers towards orchestration vendors This layer is also making data programmable through the integration of signals, workflows, enrichment and AI inferencing    Problem: Complexity and Rapid commoditization. A red ocean with margins stacked on upstream vendors that have zero platform loyalty Moat : Short term stickiness via data contracts, single currency and UX. Not sure about moat in the long run. 3. Data 3.0 Personalized Data Layer This is where the real shift is happening The future of data is not new fields, but better decisions driven by contextual reasoning. The next wave of data vendors will : - Deliver personalized data via API or MCP server - Operate as continuous real time data streams - Rely on AI inferred synthetic data signals - Generate data through AI research, micro surveys, social mining, targeted crawls and LLMs Data 3.0 will increasingly be "generated" and not collected Problem : This model is harder to build. Synthetic data introduces inaccuracy and uncertainty if not done well Moat : Large engaged audience. Research data flywheel with proprietary data insights. Synthetic data algorithms and heuristics How Zeer is contributing to Data 3.0: - Discovering continuous real time demand inside accounts - Uncovering true (and variable) ICP and buyer personas in target accounts - Building infrastructure to deploy AI research agents and targeted micro surveys to produce personalized data at scale Closing: Data 3.0 is already here and it is a transformational shift from before As the market power realigns, many legacy data vendors will struggle to adapt Next generation of billion dollar data companies will be built by those who collect, generate and deliver personalized insights and not rows of records WDYT? Please share your thoughts below or reach out directly.

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From the front line of delivering Agentic systems Forget the pilot and the launch party. The real game of agentic AI starts when it goes live. In 2026, a lot of Agentic systems are shifting from isolated pilot programs to full enterprise-scale deployment. Being in the middle of this shift, I see these areas to focus on 1. Token Discipline. This is the key to control cost for Agentic systems. Treating tokens like a precious currency and implementing real-time metrics for every agent request. Without this, costs spiral and the ROI vanishes. Tokens are the measure of Cost Of Goods Sold (for those of you in finance LOL). 2. Think agent Observatory, not dashboards. Develop or integrate with systems that evaluate agent accuracy, detect subtle failures, and trigger automated playbooks is top of mind. I see observability as the smoke detector for customer trust. Without one we are flying blind, and the first one who will notice that our system doesn’t deliver value is…our customer. 3. Institutionalize Lineage. This often overlooked. Every data source, prompt version, model iteration, reasoning result and evidence should be traceable. Version everything. And in all this, my observation is also that the definition of the secret sauce, our intellectual property, and competitive advantage, has shifted from traditional AI models a few years ago to the agentic reasoning prompts and the data layers and knowledge graphs powering these agents. Interesting times! #AIStrategy #AgenticAI #LiveAgents 2026

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AI initiatives live and die in the gap between PowerPoint promises and actual GTM execution. Why are we still watching fancy AI demos without seeing any impact? Three common failure points I see in my conversations: 1. Teams launch (so many) tools that nobody actually uses 2. New AI toys aren't integrated into existing workflows 3. Someone bought the shiny new thing before figuring out what problem they're solving The pattern is painfully familiar: leadership team sees a demo, gets excited, drops cash, then six months later asks why they're not seeing ROI. Meanwhile the core sales enablement isn't even dialed in yet. Here's the reality check: Sustainable AI success requires the boring fundamentals. Map the process first. Find the actual friction. Then apply the right solution - which might be AI, or might just be better process design. Demos are easy. Implementation is hard. Adoption is everything. What AI tools are actually delivering value in your GTM stack?

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Terence Narcisse — EHCEC, Here is a practical tip for your AI business automation operations: Stop letting inbound leads sit in a voicemail queue while you are deep in execution mode. The data is clear: leads contacted within 5 minutes of an inquiry are 21x more likely to convert than those contacted after 30 minutes. For a small team or solo operator, the bottleneck isn't the quality of the service—it's the physical inability to answer the phone every single time. The actionable step? Implement an automated triage layer that captures the lead's intent and details immediately, regardless of whether you are on a call or offline. This ensures no lead is ever "lost" to a busy signal, even when you are focused on high-value work. Lieutenant James Henderson builds this exact infrastructure for small businesses. We handle the phone answering, lead capture, and scheduling so you don't have to choose between being available and being productive. It is a practical, owner-to-owner solution that typically saves roughly $1,400/mo in administrative overhead with a setup time of about two weeks. Learn more about automating your front desk here: https://lnkd.in/euvUEfkF #AIautomation #SmallBusiness #Operations

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As we observe soaring valuations in the AI industry, it's essential to consider that there may be another tech revolution on the horizon. Explore the dynamics behind the dominance of large language models and what might come next in this insightful piece. Read more: https://okt.to/kwDult 𝘗𝘭𝘦𝘢𝘴𝘦 𝘯𝘰𝘵𝘦 𝘵𝘩𝘪𝘴 𝘢𝘳𝘵𝘪𝘤𝘭𝘦 𝘮𝘢𝘺 𝘣𝘦 𝘣𝘦𝘩𝘪𝘯𝘥 𝘢 𝘱𝘢𝘺𝘸𝘢𝘭𝘭

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🚨 Retail didn’t just use AI this holiday season. It quietly handed over the steering wheel. 🚨 Over the past few days, I’ve been reading about how AI reshaped the 2025 US holiday shopping season, especially in electronics & accessories. What stood out wasn’t experimentation. It was impact at scale. A few data points that stopped me mid-scroll 👇 🔹 Customers who used AI-powered search saw up to 38% higher conversion 🔹 Average basket size jumped when AI guided discovery 🔹 $3B in sales driven by AI agents on Black Friday alone 🔹 Retailers with chatbots saw a 9% lift in conversion 🔹 42% of shoppers used AI for gift discovery 🔹 70% reported a better overall shopping experience using GenAI Let that sink in. 🧭 In 2025, Generative AI became the GPS inside a massive shopping mall. Instead of: ❌ wandering endless aisles ❌ comparing 20 tabs ❌ guessing what fits the budget Shoppers simply said: “Here’s what I want. Here’s my budget. Take me there.” And AI did exactly that—cutting through noise, friction, and fatigue. ❌ Traditional search showed options. ✅ AI delivered outcomes. ⚠️ For those still debating whether AI in retail is “just another bubble” — this might be the moment to pause and reflect. Because bubbles don’t usually: • drive billions in incremental revenue • materially improve CX at scale • change how humans make decisions 🚀 For everyone else: We’re just getting started. 💬 Curious to hear from this community: Have you seen AI materially change conversion or discovery in your org? Where do you think the next big unlock is - search, agents, personalization, or ops? 👇 Drop a comment. Let’s compare notes. (If you’re working at the intersection of AI, data science, product, or retail - happy to connect and exchange learnings.) #ArtificialIntelligence #GenerativeAI #AI #RetailTech #DigitalTransformation #DataScience #GenAI #holidaySales

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💥Hot💥New Release🌐from Packt Publishing🌐 "The AI Optimization Playbook: Drive business success with proven AI strategies, best practices, and responsible innovation" See it at https://amzn.to/45CtY4L 𝕋𝕒𝕓𝕝𝕖 𝕆𝕗 ℂ𝕠𝕟𝕥𝕖𝕟𝕥𝕤: 🔷Understanding the Perils of AI Products 🔶Building the Enterprise AI Strategy ♦️Selecting High-Impact AI Projects 🔷Beyond the Build: Gaining Leadership Support for AI Initiatives 🔶Building an AI Proof of Concept and Measuring Your Solution ♦️Beyond Accuracy: A Guide to Defining Metrics for Adoption 🔷From Model to Market: Operationalizing ML Systems 🔶From Metrics to Measurement: Experimentation and Causal Inference ♦️Generative AI in the Enterprise: Unlocking New Opportunities 🔷Understanding GenAI Operations 🔶AI Agents Explained ♦️Introduction to Responsible AI 🔷Implementing RAI Frameworks, Metrics, and Best Practices 🔶Building Trustworthy LLMs and Generative AI ♦️Regulatory and Legal Frameworks for Responsible AI 🔷The Future of AI Optimization: Trends, Vision, and Responsible Implementation

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Most brands are draining revenue because of "experience problems," not product problems. I wrote the CRO Playbook for DTC Live's first printed magazine to fix that. It’s the framework brands need to optimise conversions without the guesswork. No surface-level tips. No "hacks." Just the exact methodology we use to: 1. Identify hidden friction points. 2. Run data-backed A/B tests. 3. Scale AOV and completion rates across different stages in your funnel. The DTC Live team is giving away 750 free copies to UK brands to get this into the hands of the community it was created for. First-come, first-served. If you want the full, in-depth resource, join the waitlist now: https://lnkd.in/eDudUpaG #CRO #DTC #Ecommerce #Growth #MarketingStrategy

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