In 2026 organizations will be expecting to see a return on investment from the GenAI tools they’ve invested in recent years, especially as they look at building use cases for implementing more advanced Agentic AI tools.
Although customer experience (CX) has been one sector to show clear progress, with GenAI already delivering practical gains through agent assistance, knowledge surfacing and workflow support, not all experiences have been positive, and outside of CX enterprises have struggled to see a return.
A widely cited report from Massachusetts Institute of Technology (MIT) suggested that as many as 95 percent of generative AI projects across industries failed to deliver meaningful return on investment.
The hype was unavoidable. Senior leaders experimented with early access to ChatGPT, boards asked for immediate action, and technology teams were pushed to “do something with AI” as quickly as possible. But by late 2025, the mood had shifted.
Pilots launched quickly, demonstrations looked promising, yet many initiatives quietly stalled or were cancelled.
According to Martin Taylor, Deputy CEO and Co‑Founder of Content Guru, the issue was discipline.
“The problem with a lot of generative AI projects was not the technology. It was that organizations never baselined what they were starting from, so they couldn’t demonstrate the delta.”
“Where a lot of AI has gone wrong is that [teams] were not baselining the cost that they were starting with and therefore they weren't able to demonstrate the savings or efficiencies that they had gained from introducing the AI.” Taylor said.
Why ROI was Hard to Prove with GenAI
There was no lack of ambition. What was often missing was the operational groundwork needed to measure and sustain value. Without a clear understanding of existing costs, organizations had no credible way to measure savings or efficiency gains.
The result was a surge of loosely defined pilots that produced impressive demonstrations but little financial evidence. When those pilots concluded, CFOs had little reason to approve broader rollouts.
The discipline required to prove value begins long before deployment, Taylor said.
“We're very much all about baselining the use cases—firstly identifying the use cases, and then which ones are going to be suitable, preferably high volume, not too complex ones are best.”
Organizations need a clear operational picture before any automation begins.
“And then you've got a good idea of the cost to serve now. You've got stats like average handle time, first contact resolution to fall back on.”
Once that baseline exists, results become measurable.
“Once you've got a very clear vision of the starting point, then like any good science experiment at school, you can see what the actual results and conclusions are.”
Why Agentic AI Starts From a Stronger Foundation
Agentic AI is emerging into a different enterprise climate. Budgets face greater scrutiny and expectations are more grounded. And organizations are returning to AI adoption with the benefit of hindsight, Taylor noted.
“When we're looking now at agentic AI, there's a more thoughtful approach because organizations are learning from where they went wrong with GenAI.”
There’s a more thoughtful approach to how projects are scoped. Use cases are more operational and closely aligned with measurable business processes. Enterprises are prioritising high-volume, low-complexity tasks where outcomes can be measured quickly and credibly.
What agentic AI changes is the complexity of what can be contained. Customer journeys that previously required multiple interactions can now be managed automatically, with intelligent handover to human agents when judgement, empathy, or regulatory oversight is required.
Clear metrics sit at the centre of these initiatives. Measures such as average handle time, first contact resolution, containment rates, and cost-to-serve are defined before deployment rather than retrofitted afterward.
“Customer expectations have continued to rise. That started in the pandemic era, where people were at home a lot more, they were not able to conduct face-to-face commerce, and organizations invested in better experiences online, better customer experience, better contact center investment,” Taylor said.
Consumers quickly adapted to those improvements, creating a growing delivery gap between customer expectations and what organizations were realistically able to deliver.