New research suggests that while many firms are stuck in AI pilot mode, those pushing ahead account for 74% of all AI-driven returns. The PwC study notes that those leaders may soon pull further away, if the others fail to learn lessons from their success.
As results continue to underwhelm on the technology, many investors are suddenly looking at substantial AI spending, and wondering when the returns advertised will start to materialise. When IBM polled an international cohort of CEOs, for example, the researchers found that progress to realise that was slow. In 2024, two-thirds of leaders said they expected to move beyond the piloting phase of AI changes, but a year later, 60% were still stuck in the nascent period of experimenting.
It’s an experience that is so common-place, PwC has narrativised it at the beginning of its latest research into the apparently limitless potential of AI.
Introducing the paper, the firm sets a scene: “In conference rooms from New York to Singapore, the same moment occurs again and again. Someone pulls up a slide with a tidy grid of AI pilots-chatbots here, decision engines there-and the room nods along. Then the questions begin. Which of these pilots are increasing revenue? Which are driving costs down? How many decisions have been made better, faster, safer? The silence that often follows reflects an uncomfortable reality: for many companies, all that AI activity isn’t producing measurable returns.”
Source: PwC’s AI performance study
It’s an interesting picture the firm paints, given that PwC declared in 2023 that Generative AI was “so powerful and easy to use” it had already “reached a tipping point” – and was poised to “change business models and revolutionise how work gets done”. The firm also suggested that it “may soon reinvent entire industries” – and while that amorphous “soon” might be considered vague enough to suggest it is still possible for the prediction to be right, it seems fair to say this latest study is walking back a little of that earlier optimism. As the hype dies down around the feted technology, and attention turns to the huge sums it has cost to deliver not much of anything, many studies are now pivoting from the things AI could do, to finding the reasons it isn’t doing them.
PwC’s study definitely fits into this trend. Surveying 1,217 companies around the world, it found that “value is currently concentrated in a small cohort” – with 20% of its research group capturing 74% of what they contend are “AI-driven returns”. And as firms look to move beyond piloting, to scaling and profiting from AI tools, PwC suggests learning from the best practices of these leaders could hold the key.
Best practices
Joe Atkinson, global chief AI officer at PwC said, “Many companies are busy rolling out AI pilots, but only a minority are converting that activity into measurable financial returns. The leaders stand out because they point AI at growth, not just cost reduction, and back that ambition with the foundations that make AI scalable and reliable.”
PwC’s research suggests that top performers use AI as a catalyst for growth and business reinvention, particularly by pursuing new revenue opportunities created as industries converge. They are more than twice as likely to report AI improves their ability to reinvent their business model; and two to three times as likely as others to say they use AI to identify and pursue growth opportunities arising from industry convergence, such as collaborating with partners outside their core sector.
Source: PwC’s AI performance study
Getting to that point, of course, has proved easier said than done. First and foremost, these firms are more committed to spending money to make money, PwC finds – with 55% saying their organisation sufficiently funds AI goals, compared to 34% of everyone else; and 68% saying they can reallocated financial and human resources to make the most of AI opportunities, compared to 51% of the rest. But organisational restructuring and discipline are also key – with 1.5x as many AI leaders ensuring their organisation provides dedicated infrastructure to support AI experimentation, and the same gap being present in organisational reviews for scaling AI initiatives.
In an admission which is a far cry from its 2023 musings, PwC suggests, “putting that formula into place requires deliberate, sustained effort”, and importantly admits “it won’t be easy, not with the myriad priorities calling for executives’ attention.” But it still maintains that companies that want to catch up to the leaders can do it – though if they wait, “the advantage that the AI leaders already enjoy will only grow, because these companies are learning fast, redeploying solutions faster, and safely automating decisions.”
Even in that context, however, these findings might be taken with a pinch of salt. A mounting amount of research also suggests many bosses over-estimate the impact AI is having, or have no understanding of how they would actually measure such returns – so letting a portion of PwC’s respondents assure us that they are seeing an above average set of “AI-driven returns” is probably worth interrogating further, before others commit the resources needed to following in their footsteps. But even taking their statements at face value, and assume they are a select group making AI work, the results importantly still might not translate beyond their specific circumstances.
To that end, last year, a paper from MIT found that fewer than one-in-ten firms had seen positive financial impacts from implementing AI. That study did show that that portion of firms were doing very well indeed – Aditya Challapally, the MIT researcher who led the study, telling Fortune that some large companies and younger start-ups are “excelling” with AI because “they pick one pain point, execute well, and partner smartly with companies who use their tools.” That has led some start-ups led by young founders to see revenue “jump from zero to $20 million in a year”. But rather than concluding other companies should attempt to replicate these policies across every other organisation, that may suggest that the ‘AI revolution’ simply does not make sense for larger, established players in a market.
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