One reason for this may be that companies haven’t connected their gen AI use cases closely enough to their overarching business strategy, focusing instead on adopting gen AI as quickly as possible rather than tying it to areas where they are trying to drive specific outcomes. This is not uncommon when a new technology comes on the scene; many will rush to invest in technology for technology’s sake, rather than considering where and how it fits into the broader business goals.
In doing so, they may be stuck in a proof-of-concept stage because they have not linked their pilots to clear, differentiated business outcomes. Indeed, most respondents note that they are not yet tracking return on investment, with life sciences and health care respondents ahead of the curve and energy, resources and industrials respondents lagging other industries in measurement strategies. Additionally, only 40% or fewer of all executives surveyed believe their organization has a high level of gen AI expertise, with technology, media, and telecommunications respondents reporting the highest expertise levels.
While we’ve explored the benefits of measuring the value of tech investments more broadly, it can be particularly valuable to do so for gen AI, given the technology’s nascence and the need to set benchmarks, understand progress achieved, and determine what proofs of concept, if taken to production and adopted, will return appropriate value to justify the effort involved.
This isn’t necessarily a problem. Rather, this could suggest that leaders still have significant opportunities to gain gen AI expertise, address risks, build data capabilities, and create ROI measurement programs that scale the impact of these initiatives and traditional AI initiatives overall. Leaders can benefit from integrating ways to track these key performance indicators into the solutions themselves, so it is easy to clearly and consistently articulate ROI by design rather than as an afterthought. Each of these areas—across people, data, risks, and outcomes—is important to a fully formed business strategy for effective gen AI implementation. Given the early stages of gen AI, organizations may want to take these steps now.
Even relatively more advanced industries—such as technology, media and telecommunications—still have headroom, as well as potential pitfalls to consider. Given above-average investment levels, they could potentially face even greater pressure to maintain their leadership position.
link