Amid all the excitement about GenAI, hard data on investment returns is in short supply
Almost a year has passed since a seminal Goldman Sachs research paper posed a deliberately provocative question: “GenAI – too much spend, too little benefit?”. There was little evidence, some of Goldman’s analysts pointed out, of organisations worldwide making much of a return on the $1 trillion they had invested in artificial intelligence (AI) tools.
Fast forward to today and there’s still no conclusive proof that the AI phenomenon is not a case of the Emperor’s new clothes. Recent research from KPMG found that enthusiasm among enterprise leaders for AI remained high, but that none were yet able to point to significant returns on investment. A Forrester paper warned that some executives might start cutting back on AI investment given their impatience for tangible returns. A study from Appen suggests AI project deployments may already be slowing.
All of which is music to the ears of David Tepper, co-founder and CEO of Seattle-based start-up Pay-i. Enterprises are right to be sceptical about what GenAI is actually achieving for their businesses, Tepper argues – and they need more scientific methodologies for analysing returns, both ahead of deployments and once new AI projects are up and running.
“C-suite leaders need forecasts of likely returns and reliable proof that they are being achieved,” Tepper says. “That’s how they’ll pinpoint which GenAI business cases and deployments are genuinely creating new value.”
Pay-i, which is today announcing a $4.9 million seed round, is confident it can provide these leaders with the data they need. It offers tools to help businesses measure the cost of new GenAI initiatives, broken down into granular detail; such costs are currently opaque, Tepper argues, because they depend on a broad range of factors ranging from when and how business users make use of GenAI tools to which cloud architecture that business has opted for. In addition, Pay-i’s platform allows businesses to assign specific objectives to AI deployments and then to track the extent to which these objectives are achieved – and what value is realised accordingly.
The idea is to give enterprises a means to evaluate both sides of the balance sheet for any given AI use case – what it costs and what it generates. “Sometimes, even where the AI is doing what you expected, the net figure may still be negative,” Tepper warns. Either way, executives should get a read out on which AI projects to pursue – and which to drop.
Securing this visibility is going to become ever more crucial as enterprises invest more and more in AI, argues Lari Hämäläinen, a senior partner at the consultant McKinsey. “Scaling and managing this responsibly requires two disciplines,” he says. “[You need]
high-fidelity observability of entire GenAI use cases and rigorous focus on the impact of these systems through understanding the unit economics and business KPIs affected.”
Otherwise, businesses are going to start pulling back in larger numbers. An IDC study suggests that while enterprise GenAI investment will top $632 billion by 2028, 72% of CIOs now see measurement and forecasting of returns on investment as their number one blocker.
Hence Pay-i’s conviction that its valuation tools will find high levels of commercial demand – though the business has only just begun to commercialise, having developed its technology with potential customers. “The case for investment in GenAI will often be compelling, but businesses need to understand how to make that case with robust data,” adds Tepper, who founded the company with Doron Holan. Both men had previously spent around two decades at Microsoft.
The company’s seed round is co-led by Fuse Partners and Tola Capital, with participation from Firestreak, Pear VC, Gaia Capital, and angel investors. “Patience for open-ended GenAI spending is wearing thin,” says John Connors, a former CFO at Microsoft and now operating partner at Fuse. He says Pay-i will give “leaders the data-backed clarity to invest with conviction, transforming GenAI from an opaque cost center into a growth engine”.
Shelia Gulati, managing director of Tola Capital, adds: “This transparency enables businesses to control their AI spend and allocate resources optimally.”
