Editorial illustration for 76% of data leaders say trust paradox stalls AI as people lag behind
AI Trust Gap: Why 76% of Data Leaders Stall Adoption
76% of data leaders say trust paradox stalls AI as people lag behind
Why does this matter? A fresh survey of data leaders reveals a striking mismatch: 76 % say the very trust that should enable AI at scale is instead throttling it. Companies are rolling out models, platforms and dashboards faster than they can teach the people who actually handle the data to use them responsibly.
While the technology sprint looks impressive on paper, the human side is lagging, leaving governance gaps and eroding confidence in the outputs. Here’s the thing: employees who already understand the firm’s data, processes and culture are the ones who could bridge that gap—if they’re given the right guidance. But the same respondents admit that training programs haven’t kept pace with deployment schedules, creating a “trust paradox” where the tools are ready but the users aren’t.
The result? Organizations spend more time polishing infrastructure than ensuring the workforce can wield it safely. That’s why many leaders are urging a shift in focus.
Stop chasing infrastructure, fix the people problem.
Stop chasing infrastructure, fix the people problem The trust paradox exists because organizations can deploy AI technology faster than they can train people to use it responsibly. "It's much easier to get your people that know your company and know your data and know your processes to learn AI than it is to bring an AI person in that doesn't know anything about those things and teach them about your company," Thompson said. "And also the AI people are super expensive, just like data scientists are super expensive." Make the CDO an execution function, not an ivory tower Thompson structures Informatica so the CDO reports directly to him as CIO.
Can AI ever scale without trust? The survey shows 76 % of data leaders flag a paradox: technology outpaces human readiness. CDOs now sit at the crossroads of governance, strategy, and workforce readiness, yet their influence alone may not close the gap.
Organizations can roll out models faster than they can teach employees responsible use, a mismatch that stalls production‑level deployments and forces leaders to reconsider whether technology investments alone can deliver business value. “Stop chasing infrastructure, fix the people problem,” a respondent urged, underscoring a shift from hardware to culture. The data suggests many firms remain stuck in pilot mode, despite abundant tools.
Still, it is unclear whether bolstering training programs will translate into measurable trust gains or simply add another layer of complexity. Meanwhile, the evolving CDO role reflects a broader acknowledgment that AI success hinges on more than pipelines; it demands aligned processes and knowledgeable staff. Without clear evidence that people‑centric interventions can overcome the paradox, enterprises risk perpetuating a cycle of experimentation without impact.
A pragmatic path forward remains to be defined.
Further Reading
- AI Adoption Trends 2026: Trust, Data Quality & Governance ... - Informatica
- The AI Data Paradox: High Trust in Models, Low Trust in Data - Data Engineering Podcast
- Could the 'AI trust paradox' be holding your business back? - TechRadar
- The American Trust in AI Paradox: Adoption Outpaces Governance - KPMG
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Common Questions Answered
What is the 'AI trust paradox' described in the Kantar study?
The AI trust paradox is the contradiction between people's optimistic embrace of AI and their simultaneous doubts about trusting it. According to the study, more than half of consumers aged 18-70 use AI at least a few times a week, yet they remain cautious about its broader implications and potential risks.
How are younger consumers different in their approach to AI?
Younger consumers are described as 'AI natives' who expect intelligent, conversational interactions at every touchpoint. They have nearly universal AI integration in their daily lives and view a high level of personalization as a baseline expectation, rather than something exceptional.
What do consumers expect from companies using AI?
Four in five consumers (81%) see meaningful benefits from AI in company products and services, with expectations that businesses will use AI to improve customer service, reduce costs, enable proactive problem solving, and help make better decisions. Among younger consumers, this expectation rises to 95%.