AI advisory & strategy implementation

A crop protection manufacturer wanted to move from a traditionally run business to an AI-enabled one, without betting on a single tool. Pricing, finance and credit ran on tribal knowledge and manual lookups: RFP responses took days, e-commerce and prepaid reconciliations were done by hand at month-end, and credit reviews varied from one analyst to the next. The need was an enterprise AI strategy, the data and governance foundations to support it, and measurable value early.

Magoya treated this as a business transformation first and a technology program second. Discovery brought business and IT leadership to the same table to score four candidate use cases on value, data readiness, human-in-the-loop complexity and ERP integration risk, and to prioritize one quick win with a clear outcome and ROI.

Before building anything, Magoya set the foundations the whole program would rest on: a governed data layer so AI never touches the production ERP directly, a private-tenant architecture so sensitive pricing and customer data never leaves the company’s own cloud, and a simple rule, deterministic at the core and AI only at the edges, so every number stays reproducible and auditable while AI handles the messy human language around it.

From there, phased releases took the quick win to production and started the next use cases in parallel, each one reusing the same foundations. Adoption was built into the operating model, not added at the end: every consequential output requires human approval, confidence thresholds relax only as accuracy is proven, an evaluation suite gates every model or prompt change, and a weekly review keeps business owners in control. The end state is an organization that owns its AI solutions, with IS running them, not a vendor dependency.