Firms warned AI spend will fail without orchestration

Enate has warned that many businesses will struggle to realise financial returns from artificial intelligence spending without stronger coordination of how AI tools operate across workflows.

The company's comments follow findings from the PwC 2026 Global CEO Survey. It reported that 81% of UK chief executives are prioritising investment in technology, AI and data. Over the past year, 21% reported revenue growth. The survey also found that only 12% of CEOs said AI had delivered both cost and revenue benefits.

Kit Cox, CTO and Founder of operations platform Enate, said organisations often focus on individual AI tools and overlook how they fit together in day-to-day work.

"AI without orchestration is a puppet without a master", said Kit Cox, CTO and Founder, Enate.

Cox argued that the market conversation around autonomous AI agents has led some leaders to expect full automation of human work. He said that expectation does not match how AI systems operate inside businesses.

Expectations gap

"Businesses are being sold a dream of AI agents that can work completely autonomously and replace human work, saving time and money, but it's simply not true," said Cox.

Cox described a scenario where companies deploy multiple AI tools that each perform a narrow function. He said organisations then struggle to connect those tools to the wider process. That process includes people, hand-offs, approvals, data inputs and reporting.

PwC's survey points to pressure on leadership teams to move quickly. It said 42% of CEOs cited the pace of transformation as their top concern. The concern related to keeping up with technological change, including AI.

Enate framed this pressure as a risk factor for fragmented deployment. Cox said companies can end up with disconnected pilots, overlapping tools and unclear ownership. He said this can limit measurable business impact.

Automation hangover

"Over the last few years, automation enthusiasm has driven companies to invest in shiny AI products and even lay off employees. They were given the false promise that AI could shoulder the workload", said Cox.

The statement reflects a broader debate among technology buyers about how to assess AI spending. Many organisations report rapid experimentation and high levels of tool adoption. Some finance and operations teams still struggle to link that activity to revenue growth, productivity improvements, or lower costs across an end-to-end process.

Cox said businesses should treat AI as one component inside operational design, rather than a standalone layer.

Orchestration focus

"The real value lies in orchestration, bringing workflows together so AI can actually deliver outcomes", said Cox.

He said leaders should start with a clear view of the work that takes place across teams and systems. He said that assessment needs to come before selecting tools and redesigning roles.

"That starts with leaders having a solid understanding of the work that needs to be done. Once the objective is clear, fixing the foundations of the business to deliver becomes the priority," said Cox.

Enate's view places emphasis on governance and process design. It describes orchestration as a layer that coordinates tasks across people and software. It also ties AI activity to structured workflows and reporting.

The company said organisations can scale AI more responsibly when they combine human expertise with AI technology. It contrasted this approach with deployments that treat AI as separate from core operations.

Visibility issues

"If service providers don't know what work is being done, they won't be able to see where the improvements can be made," said Cox.

He said a lack of visibility can result in fragmented AI adoption. He said this can limit efficiency gains and longer-term growth. He also said this fragmentation can create barriers to deploying agentic AI at scale.

"Without that visibility, AI becomes fragmented, limiting its ability to drive efficiency and long-term growth," said Cox.

As CEOs continue to allocate budgets to technology, the gap between experimentation and measurable returns looks set to remain a focus for boards and finance leaders, particularly where AI investment spans multiple departments and systems.