FAQs

The most pressing challenges include commodity price volatility, fragmented data across procurement and finance functions, growing sustainability and compliance requirements, managing supplier relationships at scale, and the slow adoption of agentic AI capabilities. These challenges compound each other. Poor data makes risk management harder, which in turn makes compliance harder, slowing supplier qualification.  

The most effective path is to build a connected planning infrastructure in which procurement data flows directly into financial forecasting, demand planning, and supply chain systems. Establishing a single source of truth for spend, pricing, and supplier performance, and using AI to automate the data connections, will win the long race. 

Early procurement involvement in demand planning and portfolio decisions is equally important. Otherwise, as procurement gets pulled into decisions, it becomes more constrained in its ability to manage cost and risk effectively.

Direct procurement covers the raw materials, ingredients, and packaging that flow into finished products. When it fails, the pain is immediate: stalled production, margin erosion, empty shelves. Indirect procurement runs the business around the product, like agencies, logistics, IT, facilities, etc. Indirect procurement fails quietly. Bloated overhead, drifting vendor contracts, inefficiencies that compound before anyone notices. Same function, very different consequences, and both need AI-driven visibility to stay in control.

Good analytics stops surprises before they become crises. It models price variance before budgets lock, spots supplier risk before it disrupts, and benchmarks costs against live market data. The best CPG teams go further. They pair predictive analytics with agentic AI, so insights don't just inform decisions. They trigger them, automatically, before the window closes.