AI Pay-As-You-Go Solar Analytics

The Problem

“Reduce PAYGo solar churn and credit losses”

Organizations face these key challenges:

Limited visibility into real-time system health and customer usage leads to reactive maintenance and high O&M costs

Rule-based credit and collections miss early warning signals, driving higher delinquency, churn, and write-offs

Fragmented data across IoT platforms, mobile money providers, CRM, and field operations prevents scalable, consistent decision-making

Impact When Solved

15-30% reduction in PAR30 through predictive delinquency and churn interventions20-40% fewer truck rolls via remote diagnostics and failure prediction from telemetry5-10% higher net customer growth from smarter approvals, right-sized down payments, and targeted retention

The Shift

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

Artificial Intelligence for Energy Systems

Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.