Only 5.2% of tech leaders have confidence in explaining AI decisions

Fico has announced the results of its 2026 State of Responsible AI study, developed in collaboration with Corinium Global Intelligence. Now in its fifth year, the report tracks how organisations are adopting responsible AI practices, the early returns they’re seeing from deployments, and the remaining barriers to fully unlocking the technology’s full potential.

This year’s report surveyed 1,004 senior technology, risk, and data leaders, surfacing insights about AI and enterprise decision-making. While organisations have poured billions into AI, the study reveals 71.5% of customer-impacting decisions are still made without it. This, coupled with the fact that only 5.2% of respondents are very confident explaining their AI-driven decisions today, signals the need for Responsible AI practices to improve trust and transparency. When asked which Responsible AI practice would make the biggest difference, respondents pointed most often to AI model development standards (71.9%).

Scott Zoldi, Chief Analytics Officer at Fico said “Most organisations can tell you where and what their AI decided. Far fewer can tell you why. This points to a lack of responsible AI standards and techniques, which directly impacts organisations’ willingness to use AI in customer-impacting decisions. As regulators and customers scrutinise how AI is used in decision-making, auditability and explainability are nonnegotiable.”

AI regulation is intensifying across the globe, with policymakers proposing new frameworks that mandate Responsible AI practices. Yet, only 5.2% of tech leaders are very confident explaining their AI-driven decisions to regulators and customers today, and more than a third (37.3%) are neutral or not confident at all. Left unaddressed, this gap could cost leaders the trust of regulators and customers alike.

Leaders recognise the importance of transparency, with most saying responsible practices such as model development standards (71.9%) and interpretable model architectures (67.9%) have the greatest potential impact for AI. They’re also actively investing in Responsible AI practices, with most using bias detection tools (58.8%) and regularly auditing their AI models (53.5%).

While 85.1% say AI has met or exceeded their ROI expectations, most of today’s enterprise decisions depend on legacy processes: humans (33.3%), rules-based systems (21.4%), and standard analytics (16.8%). Bridging that divide requires responsible standards that earn trust for AI and give the technology a seat in decisions that still run on instinct and legacy rules.

Part of the problem is a lack of shared technical infrastructure to orchestrate responsible AI capabilities. Only 8.2% of organisations have a single shared AI deployment platform fully in place, while 71.2% have one partially in place. Almost all respondents (96.4%) believe closer collaboration between AI and technology leadership on a shared platform would unlock additional returns. Organisational education and shared vision is another barrier. Resistance to change (61.4%) and a lack of shared understanding of AI (58.4%) are the two leading barriers to internal AI alignment.

Looking ahead, 63.2% cite workforce skills falling behind the pace of AI adoption as their top five-year risk, while loss of control over AI systems ranks last (10.4%). This suggests leaders are most concerned about their people keeping pace.

Lariza Carrera, Production Director, Corinium Global Intelligence, said “A year ago, there were serious questions about whether AI would deliver returns. Today, the top question facing executives is how to scale the technology enterprise-wide. Increasingly, the answer has less to do with the technology itself and more to do with the people running it.”

The promise of agentic AI is real, but the numbers show most organisations are still easing their way into the technology. Only 2.4% of tech leaders have broadly deployed agentic AI across customer-facing use cases, while 71.5% remain in early exploration or pilot phases. Security concerns (40.1%) and data integration or quality challenges (38.3%) are the most-cited barriers to achieving ROI with agentic AI.

Agents aren’t where leaders expect near-term returns. Over the next 18 months, domain-specific language models (49.5%), Gen AI (48.7%), and interpretable model architectures (46.6%) top the list of technologies expected to drive ROI, as compared to agents (27.5%).

“Agentic AI is still in early experimentation mode, and that’s exactly when the groundwork matters most,” said Rachael Hadaway, VP of AI Product at FICO. “The leaders investing in security and data integration today are building the foundation for autonomy at scale.”