For decades, enterprise leadership ran on a predictable rhythm: review last quarter’s dashboards, debate in a boardroom, lean on executive intuition, roll out the initiative. By 2026, that rhythm cannot keep pace with cross-border market shifts, supply chain volatility, and the sheer number of decisions a modern business has to make every day.

Decision intelligence (DI) is the response to that gap. Rather than another passive reporting layer, DI combines data science, causal modelling, and increasingly, agentic automation, to answer one operational question in real time: what is the right move right now?

Gartner projects that by 2027, half of all business decisions will be augmented or automated by AI agents for decision intelligence, a forecast it has repeated across its 2025 and 2026 data and analytics predictions. That is not a distant trend. The world’s largest companies are already running decision intelligence inside their core operations, and the systems below show what that actually looks like in production, not in a slide deck.

Moving Beyond Legacy BI

Traditional business intelligence is a rearview mirror. It tells you what happened. It does not tell you why, and it does not tell you what to do next. A dashboard that refreshes overnight is already describing yesterday by the time anyone opens it.

Decision intelligence systems are built to look forward instead of back. By combining causal modelling with real-time data, they aim to explain why something happened, not just that it happened, and to recommend the next action rather than leaving a human to infer it. That distinction, symptom versus cause, is the entire premise of the category.

What Brands Are Actually Running

Here is how some of the world’s largest companies are using decision intelligence systems today, with sourcing for each.

Walmart runs a network of AI models across its supply chain, including a multi-horizon recurrent neural network built in-house to forecast demand across different planning windows. Indira Uppuluri, SVP of Supply Chain Technology at Walmart, has described the company’s approach as “agentic AI for decision-making, optimization and proactive problem solving,” used to reroute inventory automatically when a port closes or a region sees a sudden demand spike. (Source)

Amazon runs one of the most aggressive dynamic pricing systems in retail, repricing an estimated 2.5 million listings a day, roughly once every ten minutes per product, based on demand, competitor pricing, and inventory position. This figure has been independently tracked by pricing researchers since at least 2018 and is widely cited across e-commerce analytics. (Source)

Netflix uses causal inference and treatment-effect modelling, not just collaborative filtering, to decide what each subscriber sees and when. The company has reported that roughly 75 to 80 percent of what people watch comes directly from its recommendation system rather than active search. (Source)

Mastercard’s fraud product is literally named Decision Intelligence. It evaluates more than 500 data points per transaction and, as of the company’s own published figures, scores and safely approves roughly 143 billion transactions a year in well under 50 milliseconds. The newer Decision Intelligence Pro layer adds generative AI to map relationships between entities around a transaction, with Mastercard reporting fraud-detection improvements of 20 percent on average and as high as 300 percent in some cases during initial modelling. (Source)

Visa runs a comparable system at network scale. Visa Advanced Authorization analyses more than 15 billion VisaNet transactions to train its risk models and returns a real-time score in about 20 milliseconds. Visa has publicly stated it helped block more than $40 billion in fraud in FY23 alone, and that its newer VAAI Score has cut false declines by 85 percent. (Source 1, Source 2)

JPMorgan Chase uses graph-based entity resolution, mapping relationships between accounts, devices, and behaviour rather than scoring transactions in isolation, to surface fraud and money-laundering patterns that rule-based systems miss. Independent reporting on the bank’s graph infrastructure puts the protected scope at more than 60 million households. (Source)

Unilever has partnered with Aera Technology, a dedicated decision intelligence platform vendor, to build what the company has called an “end-to-end, self-driving supply chain.” Aera’s Decision Cloud continuously monitors material flows and logistics constraints and recommends or automatically executes rerouting and allocation decisions when disruptions hit. (Source)

Starbucks’ Deep Brew platform blends loyalty, weather, time-of-day, and inventory data to personalise offers and manage stock. A widely-cited “30% ROI” figure traces back to a single low-authority blog post and has since been repeated, with wildly inconsistent secondary numbers (some sources claim 270% ROI, others $2.5B in attributed revenue) across SEO content sites with no link back to anything Starbucks has published. Treat any specific Deep Brew ROI figure as unverified until Starbucks or a primary financial filing confirms it. (Source)

Procter & Gamble built an AI-driven supply chain visibility system, in partnership with KNIME and phData, to unify data across 5,000 products and 22,000 components. The result, by the company’s own account, cut response time on supply chain queries from over two hours to near-immediate, and consolidated daily regional planning meetings into a single global one. (Source)

Coca-Cola ran an AI pilot that sent retail managers specific, time-stamped restock alerts via WhatsApp rather than generic inventory warnings. In an initial three-country test, this lifted sales 7 to 8 percent versus stores not using the tool, and improved demand-forecast accuracy from roughly 70 to 90 percent, according to the company’s CIO, Neeraj Tolmare. A later Fortune report on the same program described a wider rollout across roughly 1,000 outlets, with month-to-month sales gains of 5 to 20 percent. (Source)

HSBC, working with Google Cloud, screens over 1.2 billion transactions a month for signs of financial crime, replacing a rules-based system that generated a high volume of false positives requiring manual review.

Nike acquired Celect, an MIT-spinout demand-sensing platform, in 2019, to predict hyperlocal demand and allocate inventory across its omnichannel network. Then-COO Eric Sprunk put the goal directly: “We have to anticipate demand. We don’t have six months to do it. We have 30 minutes.” (Source)

Canadian Tire partnered with Bloomreach, using its AI-driven Discovery search and merchandising product, across its retail banners. The retailer reports conversion increases of 20 percent or more across each of its Atmosphere, Mark’s, and SportChek brands following the rollout. (Source)

A Decision Intelligence System Built On Memory

We built Alfred, a decision intelligence system with one core difference. It remembers your organisation history.

Alfred connects the tech stack your functions run on and the rest of your stack as it expands, into one continuously updated memory of how your business actually works. It tells you what changed, why it changed, and what to do next, in plain language, without waiting on an analyst or a weekly report.

Contact us and schedule a demo.