“ That lets teams spot supplier stress, liquidity constraints and demand shifts early enough to protect both the supply chain and the cash position.”
The report describes spend analysis as part of AI’ s“ autonomising” capability – where AI can perform well-defined tasks with limited human oversight, freeing up teams to focus on resilience planning and predictive decision-making.
John Roberts, Senior Director of North America Procurement at NTT DATA, adds:“ As we automate tasks such as invoice processing and spend analysis, it’ s not just about cutting costs; it’ s about unlocking our team’ s capacity to build resilient supply chains and use data for true risk detection.”
The data quality paradox However, spend analysis automation faces a significant hurdle: data quality. Leaders point to poor data standardisation, siloed ownership and complex integrations as key obstacles to scaling AI effectively.
Yet, the report suggests AI can be part of the solution. Rather than waiting for a perfect“ single source of truth,” procurement teams should use AI to progressively improve the data they already have.
“ You don’ t need a perfect‘ single source of truth’ to start,” says Andrew.“ Use AI to continuously classify, clean and reconcile what you already have. Over time, you get a virtuous cycle: better data, better models, better decisions on cash and supplier risk.”
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