ServiceNow CFO: Three questions every AI investment must answer

ServiceNow's President and Chief Financial Officer presented a framework for making enterprise AI investment decisions in an increasingly pressured landscape. With trillions of dollars being spent globally on AI initiatives and competitive advantages shifting rapidly, enterprise leaders face constant requests to fund multiple promising projects, all backed by credible data and internal advocates. Yet capital remains finite, forcing explicit trade-offs. The CFO frames this as a challenge that mirrors difficult past periods but at a new intensity, requiring both agility and discipline.

The framework rests on three questions. First: does the investment deepen competitive moat—the hardest-to-replicate aspect of a business? The CFO stressed that when AI can produce functional code in minutes, feature advantages become matchable by competitors in weeks, raising the bar for what justifies funding. Pairing AI with proprietary data, deep domain expertise, and hard-won systems creates defensible advantage; JPMorgan Chase's in-house LLM Suite connected to the firm's own data exemplifies this. ServiceNow itself has leveraged 20+ years of helping customers execute more than 100 billion workflows, yielding deep domain expertise, proprietary data, and a large embedded customer base. The CFO acknowledged that even organizations with a strong build-it-ourselves culture must remain open to acquisitions that bring critical capabilities faster—a shift many companies are making, exemplified by ServiceNow's own recent $7.75 billion acquisition of Armis, positioned as a bet that closing the gap between asset visibility and cyber risk mattered more than half a dozen other competing initiatives.

Second: are we funding a real customer need? The CFO emphasized that customer voice, often absent from investment rooms, should drive decisions. One concrete example was feedback from enterprise customers struggling with fragmented AI efforts—multiple parallel initiatives with no central visibility or governance. This insight led directly to an investment in the AI Control Tower, a central hub for managing AI across the enterprise. The CFO flagged that expensive investment mistakes often stem from disconnects between customer needs and the innovations a company chooses to fund; if you cannot trace a direct line from customer insight to investment decision, it is a red flag.

Third: are customers adopting what we built and deriving measurable business value? The CFO argued that investment decisions do not end when initiatives are greenlit or even when customers are onboarded; execution is the true measure. Teams closest to customers post-sale serve as early-warning systems for adoption friction and workflow breakdowns. At enterprise scale, even a single point of revenue retention is worth hundreds of millions of dollars. According to ServiceNow's Enterprise AI Maturity Index, 59% of organizations are using agentic AI, but only 9% have made significant progress in creating autonomous, multistep AI workflows—meaning companies are paying for capabilities they have not yet unlocked and therefore not seeing hoped-for value. When customers do not see value, churn follows, and wasted capital that should fund competitive innovations instead simply vanishes from the balance sheet. The CFO concluded that success lies in staying agile without becoming reactive, ensuring decisions are aligned across the business, grounded in customer need, and tied to real value creation rather than experimentation alone.