Nine in 10 C-suite leaders in the technology, media and telecommunications (TMT) sector say they are satisfied with their AI investments. Yet fewer than one in three organisations have a framework capable of measuring what those investments have actually delivered.
That contradiction sits at the heart of The Value Edge, a new report from Economist Enterprise, supported by HCLTech. The study surveyed 202 C-suite executives across the US, UK, Ireland, Belgium, the Netherlands and Luxembourg between December 2025 and February 2026, split evenly between telecommunications, media and entertainment (TME) companies and technology and semiconductor firms.
The findings suggest that AI enthusiasm is increasingly running ahead of evidence. More than half of organisations report high AI investment but limited measurable returns in commercial and revenue management, the business function executives themselves identify as AI’s biggest growth opportunity. Despite widespread deployment, relatively few organisations have established ways to assess AI’s commercial impact.
Philippe Verlinde, Chief Digital and Information Officer at Barco, illustrated the challenge using GitHub Copilot licences issued to hundreds of engineers. Holding a licence, he told researchers, “doesn’t say whether they’re using it the way it was intended.”
The same disconnect is visible in governance. While 87% of organisations report agentic AI already operating at scale, only 17% say governance actively shapes how those systems function. Pallavi Mahajan, Chief Technology and AI Officer at Nokia, warned that introducing AI without fixing underlying processes simply automates existing inefficiencies. “If you just put AI as a layer on top of something that is broken and disconnected, you’re not fixing the problem,” she said.
People, rather than technology, remain another obstacle. Forty percent of telecommunications, media and entertainment organisations identify organisational culture as a barrier to AI adoption, compared with 26% of technology and semiconductor companies. Deepika Adusumilli, former Chief Data and AI Officer at BT Group, argued that telecommunications “has not had the people transformation to be ready for technology transformation.”
The urgency to demonstrate AI’s value is growing as competitive boundaries across the sector begin to blur. According to the report, convergence between telecom, media and technology companies is now a stronger driver of AI investment than competition from traditional rivals. 35% of respondents cited industry convergence as a primary investment driver, compared with 21% who pointed to existing competitors. Hyperscalers are expanding into telecom infrastructure, media companies are monetising content archives as AI assets, and semiconductor firms are moving deeper into industries such as automotive and healthcare.
That shift is also reshaping partnership strategies. Rather than converging around a single model, organisations are spreading investments across joint ventures, managed services and other collaboration approaches, with responses across eight partnership models clustering between 30% and 39%. Benji Coetzee, Chief Strategy and Growth Officer at KPN, summed up the trade-offs succinctly. “Either build, buy or partner, you can’t have your cake and eat it,” she said.
The convergence is beginning to redefine business models as well. KPN is developing a dedicated subscription for AI agents, anticipating a future in which software agents, rather than people, generate much of the traffic flowing across telecom networks. Barco, meanwhile, has shifted parts of its cinema and pathology businesses from hardware sales towards consumption-based pricing, charging customers based on cinema screenings or tissue samples processed instead of the number of devices sold.
Taken together, the examples point to a broader contest emerging across the TMT industry and it is the control of the “last mile”, where AI-powered services interact directly with customers and where long-term commercial relationships are won or lost. Companies that continue to treat AI primarily as an efficiency tool risk losing that position to competitors redesigning products, pricing models and customer engagement around AI from the outset.
For much of the past three years, enterprise AI conversations have centred on productivity gains and workforce disruption. The report suggests the conversation is entering a different phase. Boards are no longer asking whether organisations are investing in AI. They are increasingly asking whether those investments can be measured, defended and translated into durable business value.