This podcast traces the shift already underway in utility supply chains, from linear, tactical processes to connected, AI-native operating models. You'll get a practical roadmap for moving past sequential handoffs, toward a system where information flows automatically across engineering, planning, and procurement.
Early adopters are already capturing 50-80% efficiency gains on routine sourcing through AI-assisted tools. You'll hear how integrating design data with demand planning helps you spot project deferrals the moment they happen, unlocking affordability levers before emergency procurement premiums start eating into your budget.
The conversation also digs into why fragmented data foundations are so risky, since they let information degrade at every handoff. Transformer lead times now stretch past three years, and demand is outpacing forecasts. Put those two forces together, and reactive planning starts threatening both your operational reliability and your regulatory cost discipline.
If you lead procurement or supply chain strategy at a utility, this session speaks directly to you. It walks through what it takes to move from procurement as a transactional vehicle to procurement as a co-planning platform, and shows how a clean data foundation lets agentic AI orchestrate your entire supply chain, not just automate individual tasks.
What's Inside:
- Practical approaches for managing transformer lead times that now exceed three years
- How to capture 50-80% efficiency gains using AI-assisted procurement tools
- Ways to connect design software directly into procurement demand planning
- The shift from reactive firefighting to predictive supply chain orchestration
Bring AI-native thinking to your utility's supply chain function.
Listen to the GEP podcast now.
FAQs
If your utility is still running a linear model, information degrades at every handoff between engineering, procurement, and construction. That approach worked when lead times were stable and demand was predictable, but neither holds anymore. Transformer lead times now stretch past three years, and electricity demand is outpacing forecasts as data centers expand and EV adoption accelerates.
Your data foundation is the real constraint on AI adoption, not the technology itself. Most utilities are carrying decades of bespoke configurations and manual workarounds inside that foundation. You're left with a choice: build a clean data foundation that lets intelligent agents act with precision, or replicate legacy customizations that preserve old problems and produce confident-sounding errors instead.
A connected model doubles as a cost management tool, since it lets you source ahead of the market instead of paying premium prices for emergency procurement. Once your planning horizon is long enough, and infrastructure project requirements flow automatically into procurement, the line between operational reliability and regulatory cost discipline all but disappears.