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. 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. Enterprises are right to be sceptical about what GenAI is actually achieving for their businesses, David Tepper, co-founder and CEO of Seattle-based start-up Pay-i 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 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.