The speed of business today leaves no room for hesitation. Decisions that once took weeks now happen in hours, and competitive advantage depends on how quickly companies can turn information into action. Yet many organizations are discovering that AI alone can’t deliver that speed. It only amplifies the quality of the data and systems beneath it.
A decade ago, “predictive analytics” was the phrase on every CIO’s lips. Businesses poured resources into data collection and dashboards, convinced they could forecast the future. But the real lesson from that era wasn’t about algorithms. It was about readiness. Without clean, connected, and timely data, predictive tools couldn’t deliver meaningful results, and with AI, it’s starting to feel like history might be repeating itself.
That lesson was a key message in a recent episode of the Future of ERP podcast, where I spoke with Gary Hayes, Chief Architect - Solution Enablement at Cognizant, and Alex Pierroutsakos, Industry Executive Advisor at SAP. Both shared how the fastest-moving organizations today are those that already understand this shift. They no longer see ERP as a back-office ledger but as a decision platform that connects operations, finance, and customer data in one intelligent system.
From recording data to realizing value
Across industries, ERP systems are shifting from recording what happened to anticipating what happens next. In the past, most enterprise energy was spent on manual data entry and reconciliation, building the foundation for insight rather than using it. Now, with AI and automation integrated into cloud ERP, companies can invert that model.
Instead of waiting for reports, employees can interact with systems that analyze data in real time, surface anomalies automatically, and suggest next steps. This transformation isn’t just about efficiency. It’s about keeping pace. When competitors can sense and respond to market changes instantly, reaction time becomes the new measure of success.
Utilities: From Reactive to Predictive Operations
Nowhere is that shift more visible than in utilities. For decades, these organizations operated on long maintenance and billing cycles, constrained by siloed operational data. The introduction of smart grids and connected assets flooded them with information, but it took AI to make that data actionable.
Hayes describes it this way: “Utilities have always been great at managing transactions. The next step is learning how to turn those transactions into insight, how to look at the data and use it to improve the process itself.”
By combining real-time sensor data with historical patterns, utilities are predicting equipment failures before they occur, optimizing crew dispatch, and resolving meter issues automatically. “We used to manage systems stop by stop,” Hayes says. “Now, AI helps us see the whole flow, and that changes how fast we can respond.”
What used to be a reactive industry is now operating on foresight. And as regulatory demands and renewable integration increase complexity, the ability to anticipate rather than react is becoming the competitive edge.
Chemicals and manufacturing: optimizing in real time
In manufacturing, the gains are equally striking. Pierroutsakos points to a chemical producer using machine learning to adjust process variables mid-run.
“When you’re running a continuous process, conditions change constantly,” he explains. “In the past, operators had to react after the fact. Now AI analyzes those shifts in real time, recommends adjustments, and optimizes the yield before the batch is even finished.”
The same principle applies to logistics and discrete manufacturing. By combining IoT data with ERP analytics, companies are synchronizing maintenance, production, and supply decisions that once lived in separate silos. Cloud ERP makes this possible by giving AI continuous access to live operational data, eliminating the lag that once separated insight from action.
Customer operations: accelerating the back office
AI’s impact isn’t limited to factories or control rooms. In customer-facing processes, it’s speeding up everything from order fulfillment to complaint management.
“Every industry deals with customer complaints,” Pierroutsakos says. “AI helps automate and expedite that process so people can focus on solving issues, not sorting tickets.”
Leading utilities are reimagining their exception case management workflows with AI as a digital workmate—not a tool that sits on the side, but an embedded teammate that works shoulder-to-shoulder with supervisors, agents, and back-office teams.
The result is faster response times, fewer billing errors, and more consistent experiences. As Hayes puts it, “The intelligence that once lived in spreadsheets is now built into the system itself.”
Innovation at speed
Even with these advances, the hardest part isn’t deploying AI. It’s preparing for it. The transition from legacy systems to cloud ERP is as much a cultural challenge as a technical one. Businesses must identify the high-value use cases, modernize the data behind them, and move quickly from pilot to scale.
The pace of change is only increasing. Those who delay modernization risk being left behind by faster, more data-ready competitors. The next evolution of ERP isn’t about transactions; it’s about transformation, where decisions happen in real time and readiness determines who keeps up.