The enterprise AI revolution is still in its earliest days—and that’s especially true when we focus specifically on generative AI (GenAI). While it’s tempting to adopt a show-me attitude and wait for GenAI to mature to the point where its value becomes self-evident, that would be a mistake.
By Vijay Kotu, Workflow contributor
When it comes to artificial intelligence, business leaders believe the hype. Why else would so many of them be rapidly accelerating their investments in the technology?
When ServiceNow and Oxford Economics surveyed more than 4,400 global executives to understand the state of AI in the enterprise, 81% said they planned to increase their spending next year. In fact, more than one-quarter foresee growth exceeding 10%.
Most executives—about two-thirds—say they believe their organizations are realizing a positive return on these investments, according to our data. But the emphasis here is on “believe,” because only 35% of business leaders we surveyed say they have clear metrics to measure AI’s impact. In other words, many of these executives are operating on faith.
To be sure, this isn’t true of every organization. Our study, the Enterprise AI Maturity Index, identified a cohort of AI Pacesetters who are pulling away from the pack when it comes to putting AI to work. Some 62% of them say they’re confident they know how to measure the ROI of AI, which gives them the confidence to make even bigger bets and push even further ahead.
What’s holding back the rest? The enterprise AI revolution is still in its earliest days—and that’s especially true when we focus specifically on generative AI (GenAI). While it’s tempting to adopt a show-me attitude and wait for GenAI to mature to the point where its value becomes self-evident, that would be a mistake. We are witnessing a major acceleration in GenAI’s powers. Now is the time to think constructively about how we can use it in our businesses.
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Generative AI
Measuring GenAI’s impact on a business should be a holistic process. Discrete data points, such as time savings and rates of self-service, are helpful, but they are not enough for a sustainable approach to GenAI success. Instead, an emerging best practice is to analyze AI metrics as a whole. Financial metrics inform operational metrics, which are together informed by adoption. Working this way gives you the consistent, context-aware results you need to make key decisions about managing your GenAI portfolio.
One way to take a holistic approach is to work with what we call the GenAI value triangle, depicted here. This triangle offers a way to balance different metrics to unlock a GenAI project’s holistic business value, looking at the three factors highlighted above.
If these three areas of measurement are balanced, then the GenAI project in question is probably a good investment for the business. It will be one that delivers a return on investment and positive operational outcomes while embodying characteristics that allow it to be embraced by users. Conversely, if the metrics are out of balance, such as with a GenAI project that has high operational benefits but low adoption, that suggests room for improvement or a suboptimal investment.
The AI revolution for enterprises is just getting started—particularly when it comes to GenAI. As investment in AI solutions accelerates, organizations need a clear picture of the returns. A holistic approach to value is best. With it, we can build a GenAI portfolio that contributes to strategic advantage over the long term.
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Vikay Kotu is ServiceNow’s Chief Analytics Officer