By Adam Mills
AI Editor
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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.
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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.
“By incredibly successful I don’t mean that it suddenly drove a huge amount of revenue out of the gate,” Bhathena said.
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.
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Bhathena joined host Dr. 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.
“It’s really what you do with that data that matters,” he said.
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.
“I’d rather work on three things that are going to deliver significant value than 300 things,” he said.
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.
He offered executives a more demanding question:
“What are you doing for your customers today with AI that you couldn’t do two years ago?” he asked.
Customers are reconsidering longstanding business processes, Bhathena noted, creating opportunities for S&P Global to help redesign their workflows rather than simply accelerate familiar tasks.
One approach involves bringing engineers directly into customer discussions. Known as forward-deployed engineering, the practice places technical specialists alongside clients to understand their problems and develop solutions.
Sales and marketing teams also need a stronger grasp of the technology to participate in those conversations. Bhathena emphasized hands-on training that helps employees understand what AI can accomplish and how it might change the way customers use S&P Global’s products.
Similar changes are taking shape inside the company’s development organization.
Bhathena discussed S&P Global’s work on an agentic product development life cycle, or PDLC, deliberately distinguishing it from the narrower software development life cycle.
As AI helps engineers produce code more quickly, one of the biggest changes has emerged in the role of product managers.
“Have you ever lived in a world where you felt like we’re building things too quickly?” he asked.
Product managers, accustomed to waiting for software to be developed, must now work more closely with engineers as customer feedback arrives and development cycles shorten.
That requires different measures of success. Bhathena warned against evaluating AI initiatives primarily through cost savings, arguing that companies also need to assess product quality, customer needs and whether what they build succeeds in the market.
“People will react to whatever incentive they’re given. If you measure the wrong thing, you’re gonna get the wrong outcome,” Bhathena said.
He urged executives to establish clear objectives and remain willing to abandon experiments that fail to deliver, rather than continuing simply because substantial resources have already been invested.
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The faster pace of development creates another challenge: ensuring that security, compliance and resilience receive attention from the beginning.
Bhathena emphasized that safeguards traditionally regarded as obstacles to rapid progress should be considered essential features of a product, alongside its functionality and analytical capabilities.
“We cannot think of those as things that are added on after the fact,” he said.
Customer expectations are changing accordingly. Businesses increasingly want to understand how their technology providers manage cybersecurity threats, oversee outside vendors and respond when vulnerabilities emerge.
Bhathena described S&P Global’s commitment to what he calls radical transparency. “Can they trust us to be as transparent with them as they should expect? Are we being proactive and direct with them about what we’re dealing with?” he asked.
This openness has limits. Organizations still need to avoid disclosing details that could create additional security risks. But customers should not have to press repeatedly for relevant information, he argued.
Beyond the operational changes, Tinaikar asked what qualities executives would need as AI reshapes knowledge work and challenges established ideas about expertise.
Bhathena’s answer was humility.
“The opposite of humility would be arrogance that would say, we are better at this than everybody else,” he said.
He said leaders must remain willing to question their assumptions, learn new skills and reconsider practices that succeeded in the past. Experience can become a weakness when it prevents executives from recognizing how much their organizations need to change.
He also emphasized the importance of helping younger employees develop the ability to keep learning as the demands of work evolve.
Bhathena returned to the question of competitive advantage in his closing remarks, imagining how customers might eventually explain their choice of one technology partner over another.
“I’m not working with you because you are cheaper or you’re faster or you even have better product. I’m working with you because I trust you more than the others,” he said.
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