Can you walk us through your process for evaluating AI investments against core IT needs? What criteria do you use to determine when AI innovation is worth the potential trade-off?

We use a framework to evaluate IT investments, which applies to AI as well. We consider the operational impact of AI adoption, security posture, governance complexity, talent readiness, and time to value.

@Hari, your COE practice seems to build on the standard process owner model, with AI integrated as an additional tool. Does your COE evaluate whether AI is truly the right solution for a given opportunity, or if technologies like RPA or automation might be more suitable? How does your governance process help clarify which initiatives should leverage AI versus other approaches?.

We have a two-stage gate process for AI technology. First, an AI governance committee—comprising legal, security, data, IT, and business stakeholders—evaluates new technologies. Each area applies its criteria to identify potential issues early. Once approved, the initiative enters the regular project pipeline, where it is prioritized alongside other projects. Despite some shadow IT challenges, this mechanism provides structured evaluation.

As a lean manufacturing company, our approach centers on value creation for farmers, customers, and dealers. We established an AI factory—a small group of COE members from order-to-cash and digital e-commerce. This team evaluates automation opportunities, using a staged project methodology. Initiatives are assessed for their contribution to value, and only those meeting criteria are pursued. This process helps prioritize ERP and digital initiatives and leverages existing tools like SAP and ServiceNow. The AI factory has matured over a year and effectively filters requests, focusing on major priorities.