Amir Kalantari

I hear it from customers every day - enterprises  that started their journey with AI by “experimenting” have evolved to a more holistic agentic approach that is driving real business value. Google Cloud's new ROI of GenAI study proves that AI is not about dabbling—it's about strategically deploying autonomous AI agents at scale. The data is clear: 📊 74% of executives report achieving ROI within the first year. 🤖 39% of executives have already deployed more than 10 agents across their enterprise. 📈 And among those seeing productivity gains, 39% have seen productivity at least double. And the impact of AI agents extends far beyond simple automation. In marketing, organizations are seeing 32% quicker content editing and 46% faster content creation. In security operations, AI agents are providing 70% reduction in breach risk and 50% faster mean time to respond to threats. Thank you Megan Poinski for covering the results in depth in Forbes, highlighting how companies are leveraging AI. You can read more there or download the study at: https://lnkd.in/gUYPwYuV https://lnkd.in/gfY3hG6Y #AI #GenerativeAI #GoogleCloud

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Perhaps the second most interesting data platform trend this year is around pricing models. 1. Fabric, once boasting a predictable bill, has now introduced some on-demand elements in addition to still needing a Fabric subscription. For clarity: I am not critizing the value this could bring to Fabric customers, but the loss of the “one predictable bill” selling point I think is significant, specially if that continues to become more of the norm. 2. Snowflake’s Gen 1, Gen 2, and data volume based pricing is also creating a lot more “variety” which in itself is not bad, but decreases the clarity of the otherwise clear pricing that I’ve publicly praised them for. 3. If you’ve been a Databricks customer for a while, chances are pricing still remains one of most complex things about Databricks. This is mainly fueled by legacy compute and the concept of “Serverless”. But in my interactions with newer cusfomers, generally compute pricing is clear and becomes less of a stumbling element. Fun times!

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🚀 Massive news for Industrial AI! 🚀 I'm truly excited to share that Cognite and Databricks are partnering to accelerate value and transform industrial operations with AI! 🥁 "By integrating the Databricks Data Intelligence Platform, including products like Agent Bricks, and the Cognite Industrial AI and Data Platform, we're empowering innovation without compromise,” - Andy Kofoid, President, Global Field Operations at Databricks. Imagine: 💡 Unlocking vast, complex industrial data with unprecedented speed and scale. 💡 Building, deploying, and monitoring high-value AI applications directly on a unified Lakehouse architecture. 💡 Empowering engineers and data scientists to move from data to tangible business outcomes faster than ever before. This partnership is all about driving real-world impact – from optimizing predictive maintenance and energy management to enhancing operational efficiency across the board. We’re fueling the next wave of industrial innovation. ➡️ More information here: https://lnkd.in/d2s62JA6 #IndustrialAI #Databricks #Cognite #DataIntelligence #Lakehouse #Partnership #DigitalTransformation #AI #Manufacturing #Energy

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Pendo's autumn release is here - and its a big one! This week we announced 𝐏𝐞𝐧𝐝𝐨 𝐀𝐠𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 the new way to measure and optimize the performance of your AI agents I recently had the chance to sit down with Todd Olson and talk through what this means for customers. Proud of the team for all the hard work in making this release happen, you can read the full announcement here: https://lnkd.in/dcG56jPp

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Salesforce’s Data Cloud may be the most under-discovered data tech yet. It’s not a data lake. It’s an entirely new layer in the stack, built for one purpose: activating your data. Data teams are finally spending more time on activation - executing the recommended decision in downstream systems. That’s what turns data from a cost center into a revenue accelerant. But in the old data stack, activation meant painful integrations between the lake and Slack, CRM, ERP, and every other business system. Most data teams don’t have the bandwidth or skills for that. We use Data Cloud to bring together hundreds of streams into a single data model, and activate it natively across marketing, sales, success, and analytics. Build once. Reuse everywhere. It complements, not replaces, your data lake and operational systems. If your company runs on Salesforce, Data Cloud could be your shortcut to ROI. More on Data Cloud from our engineering leader Muralidhar Krishnaprasad: https://lnkd.in/eQV3vUeb Data teams - what tools are helping you get past the last-mile activation problem?

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💡 85% of AI projects fail—not because of weak models, but because of bad data. Before jumping into AI, ask yourself: 1️⃣ Is your data accessible across teams? 2️⃣ Is it trustworthy and governed? 3️⃣ Is it relevant to real business outcomes? 🚫 Without strong data foundations, even the best AI becomes noise. 📥 I’ve shared a practical framework to evaluate your org’s AI readiness. #AIReady #DataStrategy #AIGovernance #DigitalTransformation #EnterpriseAI #AILeadership #AIAdoption #DataQuality

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