The energy sector faces a unique convergence of pressures: aging infrastructure, renewable integration mandates, regulatory complexity, and rising customer expectations. AI is not optional for utilities that want to remain competitive — but the path from pilot to production is littered with failed initiatives.
Three AI Use Cases Delivering ROI Today
Based on DGT's work with energy clients, three AI applications are consistently delivering measurable returns:
- Predictive asset maintenance: ML models that predict equipment failures 2-4 weeks before they occur, reducing unplanned downtime by 35-45%
- Demand response optimization: AI-driven demand forecasting that improves grid stability and reduces peak load costs by 20-30%
- Customer engagement automation: Intelligent service agents (built on Salesforce Agentforce) that handle 60-70% of customer inquiries without human intervention
The Integration Challenge
The biggest barrier for energy companies is not AI capability — it is integration. Most utilities run a complex mix of SCADA systems, SAP for operations, Salesforce for customer engagement, and dozens of specialized tools. AI models that cannot access data across these systems cannot deliver enterprise-scale value.
DGT's approach uses Celonis process mining to map the actual data flows across these systems, then builds integration architecture that enables AI models to access the right data at the right time. This is not glamorous work, but it is the work that determines whether AI creates value or stays in the lab.
For energy companies, the AI opportunity is not about building better models. It is about building better integration architecture.