Guides, case studies and research on Enterprise Decision AI, causal modeling and unified growth planning from DATA POEM.
A CMO's playbook for reallocating an existing marketing budget by causal contribution, defending it to the CFO, and holding it in a downturn.
AI is reshaping marketing mix modeling, from open-source Bayesian models to causal structure learning. What's changing, and what it means for your budget.
See how Fortune 500 retailers use AI for demand forecasting, personalization, and supply chain, and where most retail AI investment still falls short of the P&L.
What geo lift and holdout incrementality tests actually measure, where each falls short, and where always-on causal measurement fills the gap.
Predictive analytics tells you what will happen; causal AI tells you what to do about it. The methodology difference, and when each one fits.
See the mechanism behind causal AI's replacement of MMM, MTA, and forecasting models for enterprise revenue decisions, and where each still falls short.
AI agents execute marketing decisions at scale. But can they own them? Why LLM agents recommend but don't decide, and what causal reasoning changes.
A comparison of the top marketing attribution tools 2026, discussing attribution method, incrementality support, best-for use case and pricing signal.
Compare five enterprise AI platforms for 2026: DATA POEM, Palantir, SAS, C3.ai, and DataRobot, by approach, deployment, and best-fit buyer.
Unified marketing measurement usually means combining MMM, attribution, and incrementality. But that's still fragmentation. Here's why we say so, and what consolidating everything into one causal model changes.
We define the Large Causal Model, and how it differs from three academic uses of the term and from a standard large language model.
Consolidating data into one warehouse does not stop teams getting different answers. The real fragmentation is in the models. Here is how to fix it.
A step-by-step way to build the AI business case your CFO will fund: the spend table, the objections, and the build-vs-buy call.
Most enterprise AI projects fail for reasons no better model can fix. Here is the architecture problem underneath, and how to solve it.
Business intelligence shows you what happened. Decision AI decides what to do next. Here is the real difference, and why dashboards alone never move growth.
A practical guide to AI for business growth for enterprise leaders — the use cases that move the P&L, why most underdeliver, and what connects them into one decision model.
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