91% of professionals say their firm still falls short on AI - how to fix that

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As many as 91% of employees say their organization is falling short of reaching the value that AI can deliver , according to the 2026 Future of Professionals Report from global content and technology company Thomson Reuters.

The research, based on a global survey of 1,800 professionals from multiple sectors, shows a widening gap between AI ambition and reality, which is becoming an increasingly significant issue, Kirsty Roth, chief operations officer at Thomson Reuters, told ZDNET.

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Eighteen months ago, employees were eager to experiment with AI , and their bosses were keen to support these explorations. Today, it's another story. Professionals are burning through tokens as they use AI , with their bosses concerned about the upward trajectory of their IT bills.

With MIT research suggesting that 95% of AI projects fail to deliver value , it's unsurprising that companies are getting cold feet.

"People are starting to work out [that] these technologies cost lots of money, and they don't necessarily see the value from them yet," said Roth.

"And so the conversation has turned into the classic change management one, which is, 'OK, we've got all this tech and all these tools, but how are we really changing our processes and the way we operate to be more effective?'"

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New emerging technologies, from agentic AI models to deep research tools , will continue to enter the business. As Boomi CEO Steve Lucas told ZDNET recently, the general state of play in AI is hyper-fragmented and hyper-fractured.

"Professionals now recognize a bunch of terms -- frontier models, private models, domain models, open-weight models, agentic frameworks, agentic harnesses, and agentic loops -- that didn't exist a few years ago, never mind a few months ago," he said.

Roth, who considered her firm's research and drew on her personal experiences, suggested businesses and their professionals who deliver value from AI focus on two areas: well-grounded explorations and solid production use cases.

Professionals responding to the research were clear on what their AI tools must do: safeguard confidential data (96%), ground outputs in authoritative content (94%), and produce explainable and defensible reasoning (90%).