In a keynote at DTW Ignite 2026, TM Forum CEO Nik Willetts presented findings from an industry research study titled Why Trust and Assurance Are Key to AI Success. The study highlights a gap between perceived trust in AI systems and the availability of supporting evidence. It reports that 72% of communication service providers (CSPs) believe their AI systems are trustworthy, while 14% are able to provide concrete evidence to support that assessment.
The findings are based on a TM Forum survey of 130 AI decision-makers from global operators, conducted in partnership with IBM’s Institute for Business Value. The results point to challenges in the industry’s broader transition toward more autonomous operational models, where trust across networks, IT systems, data, and service ecosystems is considered important, but is not always backed by operational verification needed for scale.
In response, TM Forum has introduced the “Race to 2030” initiative, which outlines a framework intended to support CSPs in developing more AI-integrated operations. This includes a focus on autonomous networks, adaptable IT architectures, and AI systems designed for reliability. The study notes that without evidence-based assurance, some organisations may find it difficult to demonstrate the security of their networks, the robustness of IT systems, or the consistency of AI-driven decisions.
The regulatory environment is also developing. National authorities are introducing AI-related safety regulations, with Europe taking a leading role. The EU AI Act includes requirements for high-risk AI systems to meet audit and governance standards by August 2026. A proposed Cloud and AI Development Act is also expected to introduce enforceable sovereignty assurance requirements for public sector procurement.
The study suggests that forthcoming regulations may influence organisational readiness by increasing focus on AI risk management and operational assurance. DTW Ignite 2026 also included sessions such as the Trustworthy AI and Data Summit, which featured demonstrations, Catalyst projects, and workshops focused on AI adoption and interoperability.
Overall, the discussion centres on moving from early-stage AI initiatives and governance planning toward systems that can demonstrate operational assurance, auditability, and scalability in real-world deployment.
