S&P Global announced the completion of its acquisition of Enertel AI Corporation, a Toronto‑based AI firm that builds probabilistic, nodal‑level power price forecasts for North American markets. The transaction closed on March 18, 2026, adding real‑time, AI‑powered forecasting tools to the company’s Energy division.
Enertel AI was founded in 2021 and has developed a suite of models that integrate market prices, load, weather, and fuel costs using graph neural networks. Its short‑term forecasting platform serves physical power traders, utilities, and asset operators, complementing S&P Global’s existing long‑term market intelligence, benchmarks, and historical data.
The acquisition is part of S&P Global’s strategy to deepen its presence in high‑value energy analytics. By combining Enertel AI’s next‑hour and sub‑hour nodal forecasts with the firm’s broader market intelligence, S&P Global can offer clients a full spectrum of insights—from strategic outlooks to day‑ahead price signals—supporting faster, data‑driven decisions in a market that is rapidly evolving due to renewables and grid complexity.
S&P Global said the deal is not expected to have a material impact on its financial results or on the Energy division’s earnings. The company did not disclose the purchase price, and the transaction is being accounted for as a strategic investment rather than a revenue‑generating acquisition.
"The power markets are undergoing unprecedented transformation, and our customers need intelligence that moves at pace," said Dave Ernsberger, President of S&P Global Energy. "Enertel AI was built for this environment. Their proven track record and innovative approach make them a natural and compelling addition to S&P Global Energy. We’re committed to expanding into high‑value areas of the Energy market, and this acquisition is a clear step forward in that strategy.",
Analysts note that the addition of real‑time forecasting capabilities positions S&P Global to capture growing demand for near‑term market data, a segment that has seen accelerated adoption as utilities and traders seek to optimize operations amid volatile renewable output and evolving regulatory frameworks.
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