New research commissioned by TELUS Digital and conducted by Ryan Strategic Advisory reveals that while enterprises are widely deploying artificial intelligence in customer experience (CX), most lack the automated tools to monitor performance at scale and connect AI investments to business outcomes. The global survey of 815 enterprise CX leaders found that the leading approach across every major CX function is human agents assisted by AI, yet only 32% of organizations use AI-powered quality assurance (QA) and coaching tools.
Key Findings from the Survey
The study, titled Enterprise CX AI: 2026 Global Survey, was conducted in the first quarter of 2026 across 12 countries and 19 industry verticals, including financial services, telecommunications, healthcare, retail, and technology. Respondents were asked about their approach to CX across six major functions: technical support, customer retention/winback, customer onboarding, revenue generation/growth, complaint management, and billing/payments.
In every function, human agents assisted by AI ranked as the most-cited approach. For example, 61% of respondents use AI-assisted human agents for technical support and customer retention, 60% for customer onboarding, 58% for revenue generation, 54% for complaint management, and 51% for billing and payments.
Lack of Monitoring Tools
Despite the widespread adoption of AI, only 32% of enterprises currently use AI-powered QA and agent coaching tools. This means two-thirds of organizations lack the automated infrastructure to effectively monitor AI performance and feed insights back into ongoing improvement. Without these tools, organizations struggle to build a credible return on investment (ROI) case or catch early warning signs of underperformance.
Peter Ryan, President and Principal Analyst at Ryan Strategic Advisory, commented: "The data shows a trend that enterprises are more engaged to deploy AI in different operational aspects of customer experience. What our research also shows is that the operational tools and expertise required to manage these investments, such as the QA layer, the coaching infrastructure and the performance measurement, is not keeping pace. Adoption of AI-powered solutions in CX has moved fast but enterprises haven't caught up to optimizing it quite yet. The companies that will get a real return on their AI spend will be the ones that recognize that closing this gap is what turns AI deployment into performance."
Implications for Enterprises
The findings highlight a critical gap between AI deployment and optimization. Enterprises are investing in AI for CX but failing to implement the necessary monitoring and coaching tools to ensure these investments deliver tangible business outcomes. The report suggests that organizations must prioritize closing this gap to maximize the value of their AI initiatives.
