Despite widespread enterprise AI adoption, many organisations are struggling to realise noticeable financial returns due to outdated operating models, fragmented data, and weak governance. This is according to a recent survey by TTEC Digital, The great CX reset: Why outdated operating models are stalling AI’s payoff
The findings reveal a stark disconnect between widespread AI adoption and realised financial returns for many companies, prompting corporate leaders to rethink how they operationalise AI across the enterprise. Surveying 150 CX, contact centre, and IT leaders, the study found that zero respondents achieved cost reductions through AI, while two-thirds reported that operational costs had risen.
The research points to the practice of integrating advanced AI infrastructure into rigid, legacy operating models as the primary reason for stalling return on investment (ROI). Furthermore, only 1% of executives described their current operating model as highly adaptive and built for continuous change.
“Companies can no longer afford to buy into tech that fails to move the bottom line,” said Marcy Riordan, Vice President of Data & Analytics at TTEC Digital. “The great CX reset is about rebuilding on the timeless fundamentals of great service operations, clean data, skilled teams, and clear internal workflows. That’s the foundation that makes AI pay off.”
Three reasons AI is not delivering ROI
The report identifies three core areas where the gap between AI adoption and AI readiness is widest:
- Connected systems, blind workflows: Technology stack complexity remains high, with 60% of organisations using seven or more distinct platforms. While leaders describe their systems as technically connected, only 43% expressed high confidence in clearly accounting for where AI is utilised across the customer journey, leading to ungoverned automation and redundant tools.
- The AI skills void: While 90% of leaders feel generally confident in their ability to deploy AI, not a single respondent (0%) reported having no internal skills gaps. The data indicates that highly confident teams maintain internal ownership over AI strategy and governance while leveraging external partners to accelerate technical delivery.
- The governance stumbling block: Data quality is improving, with only 15% citing unreliable data. However, governance remains a major hurdle: only 29% of organisations use a formal, cross-functional governance process consistently, while 64% apply policies inconsistently across the enterprise.
As organisations move beyond the initial phase of AI experimentation, corporate leaders are fundamentally shifting their focus. The primary objective is no longer simply deploying the latest technologies, but rather proving tangible, measurable business value.
While early initiatives centered on pilot programs, proof-of-concepts, and exploring potential use cases, today’s market demands accountability. Executives are increasingly scrutinising AI investments through the lens of return on investment (ROI), operational efficiency, and long-term capability.