"AI is no longer a question of 'if' or 'when'. It's already here. Embedded in pilots, demos, and proofs of concept across nearly every major enterprise. But there's a catch: most of those AI projects go nowhere. In fact, the percentage of companies scrapping a majority of their AI initiatives jumped from 17% to 42% this year, according to S&P Global Market Intelligence. While the technology is real, the operating model isn't."
"At ServiceNow, we've led AI through shared leadership-not from the top down. The collaboration between technology and business functions can take different forms, but the goal remains the same: make AI deliver measurable business outcomes and avoid siloed innovation. To make this a reality, we've built a pact between the CIO and COO that treats AI as a business system and experience layer, with shared outcomes and measurable results. We've already realized more than $355 million in annual value from productivity and time savings."
"Too many companies get caught up in experimenting with the latest large language model before identifying where it can solve real business problems. Start with three enterprise use cases that directly impact your P&L. Then set public, CFO-approved targets like faster cycle time, higher deflection rate, and lower cost-to-serve. At ServiceNow, we identified key use cases that drive the most value for employees and customers, starting with help desks. ServiceNow runs a fully autonomous IT service desk, with 90% of employee requests handled by AI."
AI is widely embedded in pilots and proofs-of-concept, but many initiatives fail due to lacking an operating model. Shared leadership between technology and business, specifically a CIO–COO pact that treats AI as a business system and experience layer, produces shared outcomes and measurable results. ServiceNow realized more than $355 million in annual value from productivity and time savings by applying this model. Organizations should select three enterprise use cases that directly impact P&L, set CFO-approved targets such as faster cycle time and lower cost-to-serve, and prioritize deployment over experimentation to scale AI across IT, HR, finance, sales, and customer support.
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