Almost every big company now runs AI somewhere — McKinsey puts it at 88%. But only 39% can tie the money they’re throwing at AI to EBIT. And, simply put, that’s because most companies have taken a scattergun approach to the problem.

I see the same pattern almost everywhere. Finance has one number for what's driving the business, marketing has another, and the commercial team has a third — and when those numbers disagree, nobody can prove which one is right.

The problem is architectural, and fixing it is what we created Enterprise Decision AI to do. What that looks like in practice changes by industry, so I'll start with the three where it bites hardest: CPG, Retail, and Automotive.

Enterprise Decision AI in CPG, Retail, and Automotive at a Glance

• Enterprise Decision AI is the category of AI built to make better business decisions — it tells you why revenue is moving and what to do about it, not just what happened. 
• In CPG, having one causal AI model puts finance, marketing, and trade on the same footing, so their numbers finally agree.
• In Retail, having one model measuring every channel on equal terms — i.e. Amazon Ads, Walmart Connect, in-store — means they can finally be properly compared.
• In Automotive, a unified Enterprise Decision AI can tell whether a new vehicle launch really added sales or cannibalized from the existing lineup.

What is Enterprise Decision AI?

Enterprise Decision AI is the category of AI built for one purpose: helping organizations make better business decisions. It goes beyond reporting what happened, and beyond predicting what might happen, to tell you why something is happening and exactly what to do about it.

DATA POEM coined the term to describe what POEM365 delivers: a single causal model that unifies insight, forecasting, planning, and optimization into one decision layer, connecting every growth driver across the entire enterprise.

It shows you what's actually driving revenue, and what each decision will cause before you spend on it. POEM365 is trained on 250+ billion transactions and $5 trillion in spend (and counting). Its engine, FOUNT, draws on Judea Pearl's work on causal inference — the math of proving that one thing actually caused another. Every deployment is tuned to a single company's own data, so the whole business can finally, and genuinely, work from the same source of truth.

Generative AI, agentic AI, and Enterprise Decision AI: what's the difference?

Generative AI creates content. Tools like ChatGPT and Copilot draft documents and produce creative work at scale; agentic AI runs repetitive workflows and makes operational calls faster than any team could. Both are genuinely useful, and I'm not arguing against either. What Enterprise Decision AI does that’s different is that it helps you to make business decisions.

The difference lies in the architecture. Generative AI is built on Large Language Models trained on text and media, and agents are usually offshoots from that. POEM365 is a Large Causal Model trained on time-series business data — revenue, spend, pricing, distribution, and market signals. One generates. The other decides.

Why correlation isn't enough

That distinction matters because neither generative nor agentic AI solves the measurement problem: both work by spotting correlations. An agentic system that notices revenue rose when you spent more on a channel will tell you to spend more on that channel. It can't tell you whether the spend caused the lift, or whether something else you never measured drove both. Correlation, not causation.

Agents are part of how POEM365 works, not a substitute for the model underneath it. Four of them — Insights, Forecasting, Planning, and Optimization — run on top of the same causal model, which is why their answers agree instead of contradicting each other. For the decisions that matter — what to fund, when to launch, how to price — that gap between correlation and cause is the difference between a smart move and an expensive one.

MIT Sloan notes that cause-and-effect questions demand more accuracy than lower-stakes work like ad copy — enough that, with a general-purpose LLM, getting it right generally requires a human in the loop. Clearing that bar is the whole point of Enterprise Decision AI — giving you causal answers you can actually act on, instead of wasting time reverse-engineering the numbers to work out if they're even right. 

Enterprise Decision AI use cases in CPG

CPG is where I’ve seen this break down a lot. A big consumer-goods company usually runs media through one model, trade promotions through another, eCommerce through a third, and demand planning through a fourth — four models, four owners, four answers.

Each was built to do its own job well, by a different team, often a different vendor, on a different reporting cadence.

Media gets read weekly; trade promotions get reconciled retailer by retailer, often months after the fact; demand planning runs on a forecast horizon of its own. Taken on its own, every one is defensible.

The trouble starts when you try to read them together — because the business doesn't run in four separate pieces, even if its analytics does.

The CFO and the CMO can both be right — and that's the problem

This is an issue I've seen a lot of Fortune 500 companies face: the CFO's revenue forecast says the business grew 4%; the CMO's marketing model says marketing drove 8% growth; and the trade team's promo analysis says promotional spend is down 15%.

But with three separate models, how do you know how they tie together? So, every team trusts its own numbers, and often discounts the rest.

The reason is architectural. Marketing mix modeling, the standard approach, can only weigh one variable at a time. Add trade promotions, pricing, and distribution to the same model and their effects blur together — what statisticians call multicollinearity — so it can't separate them.

Deloitte's Consumer AI Dossier makes a related point: AI can handle planning decisions with clear cause and effect, but it leaves the messier causal questions to people.

Those are exactly the questions POEM365^CPG takes on, running every function through one model. Its Causal Clarity module breaks growth down driver by driver. When marketing and finance are looking at the same data, everything starts to make sense.

Enterprise Decision AI use cases in Retail

Retail has the same problem in a different shape. An omnichannel retailer sells through its own stores, its own eCommerce site, and a growing stack of retail-media platforms — Amazon Ads, Walmart Connect, Instacart Ads. More arrive every year, each chasing a share of the same shopper.

Each of those platforms is two things at once: a place to sell, and the scorekeeper for what selling there achieved. So each one runs its own measurement and reports its own numbers. You get one ROAS from Amazon, another from Walmart, another from in-store — none measured the same way, and no honest way to compare them.

Retail-media budgets only keep climbing, so the stakes riding on those numbers climb too. Every platform grades its own homework.

Why the totals are inflated 

Across the 40+ brands, including Fortune 500, that we run on POEM365 , the pattern is the same. Add up what each platform claims and the retailer's total ROAS looks great — every platform has every reason to look good, and none to admit where its results overlap a rival's.

Put every channel through one model, though, and the picture shifts. A lot of the channels that looked like they were driving sales turn out to be taking credit for purchases that would have happened anyway.

One omnichannel retailer with 2,000+ stores did exactly this. Using Unified Marketing, they measured every channel by what it actually caused, and by doing so they cut excess inventory by 18% and grew same-store sales.

Enterprise Decision AI use cases in Automotive

Automotive is often the place where conventional analytics can fall apart fastest. A big OEM sells 15+ vehicle lines, in all 50 states, across national TV, digital, dealer co-op, and search, with a dealer network sitting between the brand and the buyer.

The buying cycle runs for months at a time, and every combination of line, market, and channel behaves a little differently. So the number of things to measure multiplies fast — faster than any one team can keep up with.

The usual fix is to match that complexity with more models — one per market, line, and channel. Before long, even a mid-size OEM is running dozens of them, each with its own answer about what's working.

Is your new product launch growing the category or stealing from it?

Every automotive CMO asks the same question, and almost no model can answer it: when you launch a new vehicle, is it bringing in new sales, or just pulling them from the rest of your lineup?

Answering that means measuring what the launch actually caused, while accounting for what would have happened to every other line, in every market, anyway. Last-click and multi-touch attribution can't do that. They track how someone reached a purchase; they say nothing about whether the sale was new to the business.

POEM365^Auto answers it by running the whole portfolio through one model at once — every line, every state, every channel. For one Fortune 50 OEM — $50B+ in revenue, 15+ vehicle lines — that single model, running across all 50 states, hit 90%+ planning accuracy. It judged every launch by one thing: whether it grew the business overall.

Growth Planning is the part of POEM365 built for portfolio decisions at this scale.

FAQs

What is an example of Enterprise Decision AI?

Enterprise Decision AI looks like this in Automotive: a Fortune 50 OEM running one causal model across 50 states and 15+ vehicle lines, with 90%+ planning accuracy. In Retail, it's an omnichannel retailer that cut excess inventory 18% across 2,000+ stores by measuring every channel the same way. In both, one model covers the whole commercial portfolio.

How does Enterprise Decision AI differ from predictive AI?

Enterprise Decision AI is different from predictive AI because it runs on causal inference rather than correlation. Predictive AI forecasts what's likely to happen from past patterns; Enterprise Decision AI works out what's actually driving the outcome. So predictive AI gives you the forecast, and Enterprise Decision AI tells you what to change to move it, and by how much.

What is AI-driven decision-making for enterprises?

AI-driven decision-making for enterprises means using AI to answer "What should we do next?", not just "What happened?" In causal terms, it's a model that tests options across the real levers — media spend, pricing, promotions, distribution — and tells you what each one will cause before you commit.

What is the use of Enterprise Decision AI?

Enterprise Decision AI solves the measurement problem most big companies have: every function runs its own model and gets its own answer. It's used for breaking growth down driver by driver (why did revenue perform as it did?), testing decisions before you commit to them (what changes if we shift budget?), and telling new sales from stolen ones (is this launch incremental or cannibalizing?).

The question your current AI can't answer

Across CPG, Retail, and Automotive the symptom changes but the cause holds: every function runs its own model, so every function gets its own answer, and everyone ends up arguing over whose is right.

The decision in front of every leader comes down to one thing: does your whole business run on a single, shared model of cause and effect, or on a scattered pile of different ones that keep producing separate truths? One of those is Enterprise Decision AI. Pretty much everything else is that pile.

DATA POEM runs this across 40 brands including Fortune 500 businesses, and $2 billion in active growth budgets — in CPG, Retail, Automotive, and six other industries. The question every leader keeps asking is "What should we do next?" If the AI you have can't answer that in a way your CFO trusts, POEM365 was built to.