DATA POEM

Gartner just put large quantitative models at #1 on its Top 10 Strategic Technology Trends for 2027. We’ve been building one for 7 years. When the transformer arrived, most of the industry used it to teach machines language. We used it to teach a machine how a business grows. Here’s the distinction that matters. LLMs are built to predict the next word. They’re remarkable at reading, writing and summarizing. But ask one to tell you what happens to sales if you cut price 5% and shift $10M from TV to retail media, and you’re asking a language model to do math it was never designed for. The architecture is the problem. Business decisions don’t live in text. They live in numbers: spend, price, promotions, distribution and sales, all pulling on each other at once, over time. That’s why we built POEM365 our Large Causal Model, trained on time-series data, not language. It learns from more than $5 trillion of spend and outcome data and forecasts new events with 93 to 95% accuracy. The correlation models I grew up with in planning rooms top out around 65-75%. An LQM understands quantities. A Large Causal Model goes one step further: it understands which growth drivers actually cause the outcome, and what happens when you change them. I spent 20 years in those planning rooms watching budgets follow whoever argued hardest. That’s the business problem. Not a lack of dashboards, but a lack of confidence when the big call has to be made. Gartner predicts that by 2030, LQMs will replace LLMs in 25% of strategic forecasting and operational modeling tasks. For everyone who has argued that business decisions deserve their own kind of AI, this is a good day. Most AI in business today helps teams write faster, design faster and build better dashboards. POEM365 exists to help leaders decide with confidence. That’s the mission we work on every day. You can check Gartner's full list here: https://lnkd.in/gFSddBxe

Your AI agent will give you a confident reason for every dollar it moves. The reason is fiction. Ask it why it shifted spend out of brand media and into promotions, and it answers with total conviction. It just can't tell a good-sounding answer from a true one. That's the gap Tapati Bandopadhyay, PhD of Third Eye Advisory names in her new paper, "Beyond the Trust Wall: How Causal Reasoning Embeds AI TRiSM Into Autonomous Enterprise Action." She calls it fast to act, slow to explain. An agent reallocates a marketing budget at 2 a.m., and no one sits with it while it decides. The systems that take action are maturing faster than the systems that can explain the action. Her case makes the cost visible. Promotions looked like the biggest lever at 14.5% of new customers, with brand media at 10.6%. An agent running on standard prediction would have moved the budget into promotions. FOUNT found the other 7.1% of brand media was quietly generating months later, money last-click measurement simply could not see. She turns that into a test every agent should pass: explain the outcome "the way a controller explains a variance." Isolate the baseline. Remove the noise. Attribute each lever, now and later. What's left is the only number an agent is allowed to act on. A causal chain gives a guardian agent a checkable claim to test, so you can ask whether an agent "has earned the right to act on it" before the money moves. Tapati, thank you. The idea that every decision should come with its cause attached is the one DATA POEM was built on, and you've given it a sharp frame. Read the full paper here: https://lnkd.in/gA_ZqF4R #DecisionAI #CausalAI #EnterpriseAI #AIROI

For 20 years I watched growth budgets go to whoever had the most conviction in the room. Last week at Masters of Marketing in Orlando, run by the Association of National Advertisers, Havas Media Network and DATA POEM hosted a private breakfast on a question every CFO and CEO keeps asking: what actually drives growth? I loved having this open conversation with Sharona Sankar-King, Chief Data & Product Officer at Havas Media Network North America, and a small group of senior marketers before the morning keynotes. While the entire industry is focused on LLMs, we spent the morning talking about something different: Large Causal Models, and why they’re what decision-making actually needs. Here’s why. Marketing has never had more data, yet most measurement still tells us what happened alongside growth, not what made it happen. That isn’t a data problem. It’s a math problem and an architecture problem. Most companies explain growth with dozens of separate models, one per team or channel. Their answers never add up. The fix is one Enterprise Growth Model, built on Large Causal Models trained on time-series business data, not text. It traces causation instead of correlation, learns how every growth driver moves the others, and shows what a different decision would do while there’s still time to act. The questions around that table were the right ones: where causal AI earns trust with finance, what’s still hard, and how enterprise marketers can put it to work in 2027. Thank you, Sharona and the Havas team, for co-hosting, and to everyone who joined us. Together with Havas, we’re bringing that model to the brands who need it most. If your growth plan still runs on models that disagree, watch the full conversation here: https://lnkd.in/gJqbxs_C

For years, enterprise data could answer one question: what happened. Never why. Never what to do about it. POEM365 answers all three. It is built on breakthroughs in causal forecasting and causal optimization, the science of finding what actually causes a change, not just what moves alongside it. Causes before effects. Always. The data era was about recording the past. The decision era is about changing what comes next. Our investors backed that shift before the market named it. The paradigm is changing.

For twenty years, analytics only got more detailed about the past. More data, better dashboards, still describing what already happened. POEM365 is not an LLM or a predictive model. It is a large causal model, built on FOUNT, our Large Causal Architecture, and trained on billions of transactions. It maps cause and effect across your entire business to show what happened, why it happened, and what to do next. It is not a better dashboard. It is the decision layer that comes after it, where you see as one, act as one, and grow as one. The paradigm is changing.

Accuracy is everything when you are forecasting your company's future. The M5 is the world's benchmark for it. Google, Amazon and Salesforce all compete there. The winning entry used more than 200 models. POEM365 uses one. It beat them all: 36% average superiority across standard benchmarks, and 90%+ accuracy for its Fortune 500 clients. One causal model, outperforming hundreds. See what that accuracy looks like on your numbers. The paradigm is changing.