Does AI in Business Central Actually Work? An Honest 2026 Review
Only 13% of companies running AI in their ERP report measurable EBITDA impact. After dozens of deployments, here is the honest answer to the question every operations director is asking — and the three use cases that deliver every time.
McKinsey found that despite 92% of large companies running AI pilots inside their ERP or adjacent systems, only 13% report a measurable impact on EBITDA. When an operations director asks "does AI actually work in Business Central?", they are asking exactly the right question — and they deserve an honest answer rather than another vendor pitch.
Where AI Fails in ERP (And Why)
AI fails in ERP for a predictable reason: it gets applied to the wrong problems. Adding a large language model on top of a bad master data model does not fix the master data — it produces confident-sounding wrong answers at scale. Automating a broken approval workflow with AI does not fix the workflow — it accelerates the broken process. The failure mode is almost always the same: AI used as a veneer over unresolved data or process debt that existed long before the AI project started.
The Three Implementations That Deliver Every Time
After dozens of AI implementations on Business Central — across retail, distribution, manufacturing, and professional services — three use cases produce measurable results consistently, regardless of industry or BC customisation depth.
1. Late Payment Prediction (Accounts Receivable)
BC holds customer payment history, invoice ageing, and payment terms going back years. A model trained on this data predicts, at invoice creation, which customers are likely to pay late and by how many days. Finance teams using this approach see a 15–25% reduction in average days sales outstanding within two billing cycles. The value comes from acting on the prediction at the right moment: before the invoice is overdue, not after the collections call has already failed.
2. Dynamic Inventory Replenishment
Standard BC replenishment is rule-based: reorder when stock drops below a minimum quantity. AI replenishment incorporates seasonality, supplier lead time variance, and historical demand patterns to set dynamic reorder points that adjust with business conditions. Businesses using dynamic AI replenishment typically see 10–20% lower safety stock with the same or better service levels. For businesses managing 1,000+ active SKUs, the freed working capital is significant — not a rounding error.
3. Plain-English Analytics
"What was our best-selling product category last quarter in the UAE, and how does it compare to the quarter before?" used to require a trained Power BI developer or a half-hour of Excel exports. With a natural language layer over the BC data warehouse — which is what Aurora BI provides — it is a seven-second question. The value is not that the answer is new; BC already has the data. The value is removing the friction between the question and the decision-maker who needs to act on it.
The pattern across all three: the AI is not replacing a human, and it is not being asked to do something clever. It is applied to a specific, bounded problem where BC already has clean data and where the current process has a measurable inefficiency. That is where AI in ERP delivers ROI. Everything else is a pilot that becomes a slide deck.