Michael James Attea

Research from Ars Technica confirms Yext Research findings: AI search doesn’t just rely on a handful of “top” sources -- it cites a wide, long-tail network of websites, listings, and directories. That’s exactly what our AI Citations, User Locations & Query Context study found: 86% of AI citations come from brand-managed or verifiable sources, spanning tens of thousands of unique domains. The takeaway: visibility in AI isn’t about being popular -- it’s about being structured, consistent, and everywhere the machine looks for truth. Links to both research articles in comments 👇

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Now this is an eval! OpenAI has released GDPval (great name). It includes real tasks, with real inputs and outputs, across 44 occupations covering $3 trillion in annual US wages. All of the tasks were created by experts in their respective domains. The best model right now, Claude Opus 4.1 (good for OpenAI being transparent about this), does 47% of the tasks as well or better than a human (so, roughly $1.5 trillion worth of tasks). And even when the models weren't 100% successful, they still accelerated work, doing in minutes what took humans 7-9 hours. They're going to continue improving and expanding the eval. Also, they're using what they're learning from it to improve model training (I'm assuming this includes RL), and have already seen an impact. Buckle up.

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📊 Longitudinal studies got you stressed? You're not alone! Sample aggregation is shaking things up, but we've got the lowdown on keeping your data consistent and your insights razor-sharp. Learn strategies to maintain quality over time, even as the sample landscape evolves beneath your feet: https://bit.ly/4lP5mLW #questmindshare #b2b #consumerdata #onlineresearch

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Wisconsin Databricks User Group Meetup: LakeBase: Operational OLTP Database Virtual Registration: https://lnkd.in/dAyV2R-F In-Person Registration: https://lnkd.in/d_23VBzd Tyler Faulkner Xorbix - Software and Data Engineer II Topics: LakeBase: Operational OLTP Database Getting LakeBase Up and Running: Walk through spinning up a fully managed Postgres instance on Databricks—show the one-click setup, workspace integration, and how you can jump straight into Databricks SQL (and pgAdmin) for instant queries. Unity Catalog ↔ LakeBase Sync: Demonstrate two-way synchronization—pull Delta Lake tables into Postgres with optional indexes, then push updated feature or model data back into Unity Catalog for consistent, governed analytics. Deep Integration with Databricks Apps: Highlight how LakeBase plugs directly into Databricks Apps to power fast, secure operational applications—feature serving, state management, and real-time insights all under one roof. Upcoming Features & Q&A: Discuss upcoming features such as database branching for safe schema experiments, and autoscaling compute. Then, open the floor for questions on operationalizing LakeBase in your stack. Use Case Presentation: Fred Heller Hitachi Solutions America - Senior Director Data Science & AI Data to Decisions – Business value through Databricks Data Intelligence Transform data into outcomes with the power of unified analytics and AI in today’s data-rich and insight-poor environment, organizations struggle to turn fragmented data into actionable business outcomes. This talk explores how Databricks empowers enterprises to solve domain-specific problems by unifying data engineering, analytics, and AI on a single platform. We will highlight real-world use cases across industries. Agenda: 5:30PM to 5:35PM - Introduction 5:35PM to 6:00PM - Use Case Presentation and Q/A 6:00PM to 6:30PM - LakeBase Presentation 6:30PM to 7:00PM - Questions Networking

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Unlock powerful insights with DSS Professional's Intelligent Analysis. Metadata tools for demographic and directional data, including gender and age. 'In Area Number' analysis with custom duration settings for strategic planning. Download & learn more: http://tiny.cc/dahuadss

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Data science should do more than explain what happened. It should shape what happens next. At Zimmerman, we turn complexity into clarity by embedding data science into every stage of marketing, so insights flow directly into action and results. 🔗 Read the article to see how we put data science to work. #MarketingData #CustomerInsights #MarketingEffectiveness #ModernMarketing #GrowthFocused #ZimmermanAdvertising https://lnkd.in/e5ySJ72T

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What do Fred Gardi and Ryan O'Neil have to say about the optimization landscape? Revisit their conversation covering perspectives on growth and adoption of decision science, advocating for optimization within broader AI strategies, and the Nextmv Hexaly integration. Read the post → https://hubs.la/Q03qrkp90  #decisionscience #DecisionOps #hexaly #datascience #heuristics #orms

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GenAI is a tool, not the entire toolbox. Why GenAI still requires a ‘manual lift’ 👇 Gener8 interconnected datasets provide a uniquely comprehensive view of user journeys, allowing us to pinpoint AI's growing role in the path to purchase. How are people really using Generative AI for complex shopping missions? We analysed a recent computer hardware purchase journey that provides a fascinating snapshot. It involved a male millennial buying a multi-part AMD hardware bundle, and his path shows exactly where GenAI fits... and where it still falls short. Here's the journey: 📌 1: Classic Discovery (Google) The user started with broad searches on Google. This was the typical high-level exploration, jumping between retailer and aggregator sites. 📌 2: GenAI as a ‘Simplifier’ (Perplexity) Hitting friction, he turned to GenAI. He used a specific query: "find me a motherboard bundle in the uk using the AMD Ryzen 5 9600x". This is a key shift, using AI as an intelligent assistant to cut through the noise and get a specific, curated answer. 📌 3: The ‘Manual Lift’ (The Gap) This is where it gets interesting. The user tried to use AI for the next step optimisation, asking it to "search partpicker list for cheapest deals". But this didn't end the journey. He still had to do the manual legwork himself, visiting Currys, Argos, Idealo, and PCPartPicker before finally deciding on Amazon. 📌 4: The Transaction (Amazon) The user completed his multi-item purchase on Amazon, buying his processor, motherboard, RAM, and cooler all in one go, resulting in a single eReceipt. This journey shows a ‘hybrid’ reality. GenAI didn't replace the path to purchase, it became a new, powerful step within it. While GenAI was brilliant at defining the shopping basket, it couldn't (or wasn't trusted to) complete the optimisation mission. The user still relied on their own research for the final price hunt. We're seeing GenAI used not as a one-stop solution, but as a specialised tool to solve a specific problem, a powerful ‘assistant’ that sits alongside search and traditional web browsing. What are your thoughts? Are you using GenAI as a tool alongside search? #Perplexity #GenerativeAI #GenAI #PathtoPurchase

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8 Claude Workflows for ABM in 2026 Claude changes how AI shows up in B2B GTM. Now it's engineering context into reusable Skills and autonomous Cowork workflows. Here are the 8 Claude workflows that will 10x your ABM 1. Account research at scale Cowork aggregates firmographics, tech stack, hiring, news, financials, and buying signals. Claude Cowork + Clay + Chrome + Notion 2. Tiered content personalization Tier 1: Bespoke, account-specific → Tier 2: Industry-level relevance → Tier 3: Programmatic nurture Claude Cowork + HubSpot + Notion 3. Self-updating stakeholder mapping Feed CRM + LinkedIn data via Clay. Cowork continuously updates buying roles, engagement levels, coverage gaps,and flags missing Buyers. Claude Cowork + HubSpot CRM + Clay 4. Account-specific messaging at scale The Claude Skill adapts your value prop using account context, recent news, proof points, and persona language. Stack: Claude Skills + Claude Cowork + HubSpot 5. Intent signal translation Cross-references with news, hiring, initiatives, and recommends next-best-action within 24 hours. 6sense/ Bombora export → Notion → Claude Cowork 6. Automated sales → CS handoff briefs Claude scans transcripts, extracts commitments, maps stakeholders, flags risks, generates a clean CS brief. Gong/ Fathom.ai (or integrate with HubSpot) transcript export → Google Drive → Claude + Cowork → HubSpot 7. Competitive positioning by account Claude Cowork generates account-specific positioning based on competitors in play, stated priorities, and champion intel. Claude Cowork + Notion + Chrome (competitor research) + Perplexity Pro 8. Dynamic account plans For Tier 1 accounts, Cowork builds and refreshes structured plans: goals, stakeholders, 90-day actions, resources. Claude Cowork + Notion + HubSpot CRM How the system works: Claude Skills --> Reusable knowledge (playbooks, messaging rules, research frameworks). Claude Projects --> Persistent account workspaces. Claude Cowork --> Autonomous execution. --- Want the full Claude ABM Playbook? (8 prompt templates, SKILL.md example, 4-week implementation roadmap) Comment "CLAUDE" and I'll send it over. #ABM

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Exciting insights came out of #QlikConnect this week! Our AI Council is driving responsible #AI adoption with expert guidance from leaders like Nina Schick, Dr. Rumman Chowdhury, Kelly Forbes, and Dr. Michael Bronstein. Get their thoughts on the latest AI innovations like agentic AI 👇

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The Quiet Shift in Healthcare Data Strategy from 'Demographics' to 'Behavior' Hook/Opening You're still buying "cardiologists in New York" lists. Healthcare organizations winning in 2026 buy "cardiologists who've adopted AI triage tools, prefer short-form video over webinars, and engage most during Q1 budget cycles." The difference? One costs $500/list and gets 2% response rates. The other delivers 1.5-1.7x conversion lift through behavioral segmentation + ABM. ​ Three seismic shifts making demographic lists obsolete: $50B Rural Health Funding = New buyer behaviors. States allocating $200M+ each need data showing which rural hospitals prioritize value-based care vs tech modernization. Demographics can't answer this. ​ FDA's Transparent AI Mandate = Clinicians now demand explainability. Behavioral data reveals which CMOs engage with "clinician-in-loop" vs "autonomous" messaging. ​ Multi-stakeholder Buying Committees = CIOs want interoperability proof. CMOs want clinical outcomes. CFOs want ROI calculators. One-size-fits-all lists fail all three. ​ The 2026 reality: Healthcare marketing isn't "who you are" (demographics). It's "what you do" (behavior) + "how you decide" (buying signals). Static lists die. Dynamic behavioral profiles win. When did you last analyze prescribing patterns, tech adoption signals, or engagement channel preferences in your provider data? #LakeB2B #Ampliz #HealthcareMarketing #HealthcareData #ABM #ProviderSegmentation #HealthTech

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Cloud data platform Snowflake's principal data strategist Jennifer Daniell Belissent, PhD tells Verdict that asking how to win the AI Space Race is the wrong question. 'Coopetition' and an open source approach is the way forward, says Belissent. #ai #airegulation #opensource #coopetition https://lnkd.in/eeHqhzNE

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