S&P Global’s upstream energy portfolio includes 16 products providing intelligence about oil and gas exploration. Customers using those products sometimes had to move between applications with different interfaces to complete their work. When the company set out to modernize the portfolio, it could have developed another application to bring those functions together.
Instead, it built a flexible platform designed to support different workflows using AI agents. S&P Global introduced it to a handful of customers, and the early response was encouraging. But Firdaus Bhathena, the company’s executive vice president and chief technology and transformation officer, was careful about what that success meant.
What mattered was the quality and speed of customer feedback, which helped S&P Global prepare the platform for wider adoption. The experience illustrated a larger argument Bhathena made during Newsweek’s latest "AI Impact Forum" webinar: Faster, cheaper work can create value, but lasting competitive advantage depends on what businesses can accomplish for their customers. Ranjit Tinaikar for the Nov. 4 conversation, “What Enterprise AI Looks Like When It Works,” examining how companies can turn advances in AI into meaningful results.
S&P Global has accumulated decades of proprietary and public information that customers use to make financial and other business decisions. That remains valuable in an AI-driven market, Bhathena said, but the company’s advantage also depends on the methods it applies to that information and the relationships it has built over time. Those relationships give S&P Global an opportunity to rethink how clients receive and use information as AI changes their work.
Making the most of that opportunity also requires discipline about where to invest. Bhathena recalled an early period of AI experimentation that he called the “dabbling phase,” when S&P Global had hundreds of projects underway. Encouraging employees to explore the technology had value, but the company eventually needed to concentrate on initiatives capable of making a meaningful difference.
Productivity improvements can free resources for other investments, Bhathena argued. But competitors will eventually adopt many of the same capabilities, limiting how long efficiency alone can distinguish one business from another. He also questioned how companies measure their progress: “AI adoption is an input metric, it’s not an output metric,” he said.
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