___________________________________________________________________________________________________ Procurement
that raw capability translates into value everywhere, including in high-volume transactional work that looks nothing like those problems. The smarter approach uses frontier intelligence selectively, where judgement earns its cost, alongside a governed, auditable, ERP-native AI system that handles the routine, high-frequency bulk of transactions. Each layer does what it was built for, on a common substrate that logs and traces every decision the same way. for thirty seconds before responding is not cheaper simply because it reaches the same answer. The correct unit isn’ t cost per token; it’ s fully-loaded cost per complete transaction, measured at your volume peak. In practice, disciplined context caching alone can reduce consumption on a high-volume inference workload by roughly two-thirds, with no change to output quality. That saving comes from engineering rather than from model choice.
A competitive edge
A new, better model will appear every few months, but when frontier-grade intelligence becomes abundant and nearly free, it stops being a real differentiator for anyone. What remains scarce is everything that intelligence must plug into: clean, reconciled, permissioned procurement data, deep integration with the systems of record where transactions settle; an audit trail that satisfies a regulator and human oversight positioned where it changes outcomes.
Supply chain teams that have built their processes around governance, traceability, and ERP-native integration won’ t need to chase the latest leaderboard. For them, it will be as simple as plugging the better model in and carrying on, business as usual. ■
Two-layer architecture that works
None of this means frontier-level reasoning has no place in supply chain and
procurement. It’ s genuinely valuable for
judgement-heavy tasks such as spotting
spend patterns across fragmented category data, shaping sourcing strategy, assessing risk across complex multi-tiered supplier relationships, and negotiating where the counterparty is adaptive and the optimal move isn’ t obvious. The mistake is assuming
Gopinath‘ GP’ Polavarapu www. jaggaer. com
Gopinath‘ GP’ Polavarapu is a seasoned technology executive and AI strategist who has spent over 20 years turning emerging technologies into billion-dollar growth stories. As Chief Data & AI Officer at JAGGAER, he leads the enterprise-wide AI vision for one of the world’ s largest Source-to-Pay platforms, driving innovation through agentic, predictive, and generative AI. scw-mag. com 17