The Maturity Gap Behind the Headlines
McKinsey’s 2026 AI Trust Maturity Survey, based on responses from roughly 500 organizations collected between December 2025 and January 2026, found the average responsible AI maturity score rose from 2.0 to 2.3 year over year. But that improvement masks an uneven picture: only about 30% of organizations reached a maturity level of three or higher in strategy, governance, and the newly tracked category of agentic AI governance and controls. Financial services ranks among the leading sectors overall, with 41% reaching that maturity threshold in agentic AI governance specifically, second only to technology, media, and telecommunications. Investment tracks closely with outcomes: organizations spending 25 million dollars or more on responsible AI report markedly higher maturity and are far more likely to see AI deliver EBIT impact above 5%.
Why Agentic Governance Matters Most for Collections
The gap McKinsey identifies sits precisely where collections operations are most exposed. Nearly two-thirds of respondents (62%) cite security and risk concerns as the top barrier to fully scaling agentic AI, ranking well above regulatory uncertainty or technical limitations. That ordering matters: enterprises aren’t primarily blocked by what the technology can do, they’re blocked by confidence in deploying it safely once it starts taking action rather than just generating a response. For a collections voicebot authorized to schedule payments, update account records, or negotiate a settlement, that distinction between text generation and autonomous action is exactly the risk category the survey is measuring, and financial services organizations report only 38% maturity in agentic-specific governance even while leading in other dimensions.
The Nuance Easy to Miss: Awareness Isn’t the Same as Control
The more revealing finding sits in the space between what organizations say they worry about and what they’ve actually built controls for. Inaccuracy (74%) and cybersecurity (72%) remain the most cited risks, but active mitigation lags behind stated relevance in nearly every category, most sharply for intellectual property infringement and personal privacy. Incident frequency has held steady at around 8% of organizations, yet confidence in incident response has fallen: nearly 60% of organizations that experienced an incident rated their own response as only satisfactory or worse. In other words, more organizations recognize the risks than have actually closed the gap between recognizing them and being able to respond when something goes wrong, a distinction that matters enormously in a regulated collections environment where a delayed or fumbled response to an AI error carries compliance consequences beyond the immediate customer impact.
What This Means for How Collections Should Build
The survey points to a concrete lever most organizations underuse: clear ownership. Organizations with an explicit function accountable for responsible AI, whether a dedicated governance role or an internal audit and ethics team, average a maturity score of 2.6, compared with 1.8 for those without one. Knowledge and training gaps remain the single biggest barrier to implementation, cited by 59% of respondents, up from 51% the year before. For collections teams evaluating how fast to scale an AI deployment, the practical takeaway is that governance structure has to be built alongside the technology rather than retrofitted afterward. Assigning clear ownership for agentic AI oversight, and investing in the training that closes the knowledge gap, is what separates organizations converting AI into measurable financial impact from those stuck managing risk they can see but haven’t yet controlled.