In February 2026, Equifax told investors it expected to cut $75 million in costs through AI over three years. Five months later, the company has doubled that number.

Announcing its second-quarter results on July 21, Equifax said its AI-driven cost reduction target for 2026 to 2028 now stands at $150 million. The reason for the revision is not a new strategy. It is what the company is already seeing from the AI it has already deployed.

“The pace of adoption is ramping very quickly and delivering big productivity lifts in every corner of Equifax,” said CEO Mark Begor during the earnings call.

What makes this report worth examining is the specificity of where the gains are appearing. Equifax has rolled out AI and agentic tools across product development, technology, IT operations, cybersecurity, software development, cloud cost optimization, and support functions including human resources, legal, and finance.

In call centers, conversational AI has improved customer authentication rates and fulfillment accuracy. In back-office operations, AI-assisted workflows have cut the time required to handle disputes. In software development, where AI's benefits generate the most skepticism from senior leaders, Equifax says the results are already material.

The company emphasized during the earnings call that it considers itself to be at the beginning of this process, not the end. Begor said he is confident there is significantly more opportunity to grow revenue and reduce costs as AI and agentic capabilities become fully embedded across the business. Equifax added 39 new global AI patents in the first half of 2026 alone, bringing its total portfolio to more than 750 issued or pending patents.

The broader significance of this story is what it suggests about the gap between AI expectations and AI results. For two years, research surveys have consistently found that most companies believe AI has not yet delivered the ROI they expected. Equifax, which has deployed AI systematically across every major function, is reporting the opposite: results arriving faster than forecast, in amounts large enough to force a mid-year revision of its own targets.

The common thread between companies reporting strong AI results is not a particular platform. It is breadth of deployment and operational discipline. AI deployed in one corner of a business produces limited returns. AI deployed systematically, with clear metrics, across every major function, produces something different.

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