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In DemandForecast.ai’s 2026 report, AI produced less forecast error than the incumbent in all 15 demand-forecasting deployments with a measured baseline. Reported error reductions ranged from 14% to 56%, averaging 32%. That is a notable result within this selected group—not proof that AI will outperform in every company.
What the 15-of-15 result means
DemandForecast.ai’s first annual Demand Forecasting Accuracy Report, published in October 2026, compared its AI forecast with the forecast each participating company already used. The comparison covered 15 demand-forecasting deployments from 2022 to 2026 that had a measured incumbent baseline. The incumbent could be a statistical, consensus, or manual forecast; all 15 AI forecasts used Pecan’s predictive engine.
The report says the AI forecast had lower error in each comparison, with a company-reported average error reduction of 32% and a range of 14% to 56%. These are relative improvements against each customer’s own baseline, not a single absolute accuracy score applied across all customers. DemandForecast.ai’s October 1, 2026 release summarizes the headline findings.
How the comparisons were measured
DemandForecast.ai says the results came from live deployments on customer data, rather than surveys. Each customer’s result used that customer’s error metric and definition. The report mentions MAPE, weighted MAPE, and customer-defined measures; it also notes revenue-weighted accuracy in discussing metric differences. These measures are not interchangeable, so the 15 results support within-customer comparisons more strongly than a ranking of one customer’s forecast against another’s.
#1 Best Overall
The deployments ranged from a few hundred to more than 100,000 SKUs, with forecast horizons from eight weeks to 36 months. Differences in product mix, intermittent demand, forecast horizon, and level—such as SKU, SKU-location, or category—can affect absolute error. The company therefore emphasizes relative improvement over a universal accuracy figure. Its 2026 report describes the methodology and examples.
Why the result does not establish that AI always wins
The 15 deployments are a selected, non-random sample. They are part of a larger study of more than 40 deployments, but only 15 demand-forecasting deployments had a measured incumbent baseline for this comparison. The report includes deployments that reached a benchmark and reported results; it excludes pilots that stalled before benchmarking and companies that chose not to share outcomes. As a result, 15 out of 15 describes the measured cases in this report, not all AI forecasting projects or the industry as a whole.
Rank #2
DemandForecast.ai says the team that ran each deployment validated the underlying data. The reviewed report does not describe an independent audit or publish the underlying customer-level data. The company-reported average should therefore be read as its average across these 15 selected deployments, not as an independently audited or standardized pooled estimate.
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What the reported business outcomes do—and do not—show
The report also describes operational outcomes, but separates them from forecast error. Across the 15 deployments, DemandForecast.ai reports an average 70% reduction in manual planning effort. At Mars, approximately 75% of forecast volume was described as touchless. Kristen Daihes, Mars Vice President of Global Supply Chain, said the planning team “can already ‘not touch’ ~75% of the volume.”
Rank #3
Other reported outcomes include overstock reductions of up to 50% and sales lifts of 10% to 25% in one fast-fashion deployment. At one Nucor site, the report says the company recovered $4 million to $5 million in annual sales previously lost to stockouts. These are deployment outcomes reported by the vendor, not effects isolated by the forecast-accuracy benchmark. Inventory, labor, and sales results also depend on decisions such as reorder policies, safety-stock rules, and planner behavior.
How to assess a similar AI forecasting claim
A useful evaluation asks whether the comparison is genuinely against the forecast your team would otherwise use, and whether both forecasts face the same conditions. Check these points before treating a vendor’s headline result as relevant to your operation:
- Baseline: Identify whether the incumbent is statistical, consensus, or manually adjusted, and confirm that it is the forecast actually used for decisions.
- Like-for-like periods: Verify that incumbent and AI forecasts are evaluated on the same historical periods and demand data.
- Metric definition: Agree on the error metric and calculation before comparing results. MAPE, weighted MAPE, revenue-weighted accuracy, and customer-defined measures can produce different views.
- Forecast scope: Match the horizon and level of detail—such as SKU, SKU-location, or category—to the decisions your team makes.
- Sample selection: Ask which pilots or deployments are counted, which are excluded, and whether unsuccessful or unreported outcomes are represented.
- Business impact: Track forecast error separately from inventory, service, labor, and sales outcomes, and document the operational changes that could affect those outcomes.
DemandForecast.ai says a comparable benchmark needs two to three years of SKU-level order or shipment history, the incumbent forecast for the same periods, and an agreed error metric. Its report describes starting with a proof of concept using a company’s own historical data; the right test is one that reflects the business’s actual forecast process and evaluation criteria.
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Does this result predict what will happen at your company?
No. It shows that the AI forecast outperformed the incumbent on the error measures used in 15 reported deployments. Because the sample is selected and the metric definitions vary, it does not establish the likely result for a particular company. A company-specific, like-for-like benchmark is needed to determine whether an alternative forecast improves on its current one.
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