
Decision AI vs Business Intelligence: why dashboards don't drive growth
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.

How a Fortune 500 brand recovered $130M in growth from the same marketing budget, using POEM365's unified causal model. Same spend, very different result.

I have been to many conferences recently, and the question that keeps coming up is a version of the same thing: “We’ve already spent the budget. How do we get more from it?”
This is a real answer to that question. Not a projection. Not a model output from a demo environment. A Fortune 500 brand. $1.93B in total marketing and growth spend. Every dollar already allocated. Every channel already funded.
POEM365 didn’t add budget. It re-allocated what was already there.
Forecasted revenue: $2.06B. Same total spend. 0% budget variance.
Siloed models optimize each channel in isolation. TV, retail media, trade, pricing — each model maximizes its own ROI. None of them see what the others are doing.
But marketing doesn’t work in isolation.
+11.1% Synergy revenue generated when paid media, retail media, and trade promotion activate together.
Real money sitting in the interactions between channels. Invisible to every siloed model in the stack.
A causal model runs across every driver, every channel, every retailer simultaneously. It sees the interactions. It re-allocates toward the combinations that compound — and away from the ones that cannibalize.
The incremental sales result:
Brand’s own plan forecast: $687M in incremental sales.
POEM365 plan: $809M. +$122M. Same budget.
Not more data. Not more budget. Only unified decisions.
The $130M didn’t come from a bigger budget or a better team. It came from seeing the whole system at once — and optimizing for the interactions, not just the individual channels.
The architecture was the variable. Everything else stayed the same.
Same budget. Different architecture. +$130M.

Founder & CEO
Founder Bharath Gaddam had a clear diagnosis: the problem wasn't data or talent, it was architecture. Correlation-based models were never going to cut it for the complexity of enterprise growth. The industry wasn't under-resourced. It was fundamentally mis-built.
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Watch Data Poem founder Bharath Gaddam at the ARF on moving enterprise growth from fragmented marketing mix models to causal, on-demand Decision AI. 48 minutes.