New report: We’re adopting AI faster than we trust it. Here’s what the data shows.

Key takeaways

• More organizations are adopting AI faster than ever, but fewer trust it to make release decisions on its own.
• The confidence gap between executives and practitioners is striking: 81% of CEOs trust AI-driven delivery, compared to just 50% of QA Directors who feel the same.
• This isn’t about AI underperforming. It’s about organizations catching up to what production-scale AI actually demands.

Quality Transformation Report: Major findings in 2026

We surveyed 2,501 IT decision-makers, QA professionals, and business leaders across six countries for our second annual Quality Transformation Report. Respondents came from organizations with 150-plus employees across manufacturing, energy and utilities, retail, financial services, and the public sector.

One of the major findings: confidence in AI agents making release decisions dropped from 48% in 2025 to 34% in 2026. That’s a 14-point decline in a single year. Meanwhile, 68% of organizations have implemented AI across some or all delivery workflows, and 36% say AI agent implementation across the SDLC is a top IT priority for 2026.

This means organizations are doubling down, but with less confidence than they had 12 months ago. That gap is what happens when pilots move into production and teams hit governance gaps they didn’t see coming: visibility problems, accountability questions, and the realization that autonomous decision-making at scale requires oversight models most organizations haven’t built yet.

Trust in AI is declining even as adoption grows

In pilot mode, the stakes are contained. You can monitor closely, intervene when needed, and treat the whole thing as an experiment. But when AI agents move into production and start making decisions about what ships, when, and to whom, the requirements change. You need visibility into what the AI is doing (and why), you need audit trails, and you need intervention points. You also need governance frameworks that most organizations are still building.

We see this pattern repeatedly at Tricentis. Teams that are excited about AI in controlled environments hit friction when they try to operationalize it across the delivery pipeline. The technology performs. The oversight infrastructure doesn’t keep pace.

That’s what the trust decline reflects. Organizations aren’t losing confidence in AI’s capabilities. They’re realizing they don’t have the governance systems to trust it with high-stakes decisions. The gap between what AI can do and what organizations can confidently let it do is widening, and the data is capturing that in real time.

Not everyone is prepared to govern AI agents

While 82% of organizations feel at least somewhat prepared to govern AI agents, only 35% feel fully prepared. That 47-point gap between partial readiness and full confidence is where the trust erosion shows up most clearly.

Organizations have moved past the “AI will solve everything” phase, and they’re in the “how do we actually govern this?” phase. This means progress, not retreat, but it doesn’t come without operational challenges.

53% of organizations manage between 6 and 10 separate AI or automation tools. One-third say this tool sprawl creates operational complexity that blocks continuous quality at scale. When you’re juggling that many tools across testing, automation, performance, and quality intelligence, maintaining visibility into what AI is doing becomes genuinely difficult. Fragmented systems create blind spots, and blind spots erode trust, particularly when the stakes involve production releases that affect customers, revenue, and reputation.

Then there’s the gap between executives and practitioners, and it’s substantial: 81% of CEOs have high trust in AI-driven delivery systems. Only 50% of QA Directors share that confidence.

This isn’t just a difference of opinion. It’s a visibility problem. Executives see strategic progress: faster delivery, competitive advantage, innovation at scale. Practitioners see operational friction: governance gaps, tool sprawl, accountability questions, and the daily challenge of validating what AI is doing. Both views are accurate. The gap between them is where risk accumulates.

The pattern shows up across multiple dimensions. 93% of C-level leaders feel confident their testing strategy addresses critical risks, compared to 30% of QA and DevOps leaders who are uncertain or explicitly lack confidence. 42% of C-level leaders believe their teams are fully aligned on code quality and delivery readiness. Only 22% of QA and DevOps leaders agree.

Executives see the destination. Practitioners see the distance remaining. When those perspectives diverge this sharply, it compounds the governance challenge. The people closest to the work lack confidence in the systems, while the people controlling the budget believe everything is on track. That misalignment doesn’t just slow AI adoption. It increases risk.

Organizations are recalibrating, not retreating

That said, organizations aren’t pulling back. They’re adjusting their approach.

36% cite AI agent implementation across the SDLC as a top IT priority for 2026. That means teams are trying to implement AI more deliberately, with the governance and operational maturity they now understand they need.

So what’s blocking progress? The leading barriers to AI readiness are governance problems, not technical problems. 27% cite security concerns as a top barrier, and 22% cite regulatory and compliance requirements. You don’t solve those with better algorithms or faster infrastructure. You solve them with visibility, auditability, and human intervention points built into the platform from the start.

And when organizations do get governance right, the value is measurable. Early adopters report real gains: 37% see enhanced quality and risk detection, 36% see improved accuracy and consistency, and 32% see better test automation coverage.

Organizations are confronting AI governance requirements

Organizations that build oversight into their AI workflows, that maintain visibility into what their agents are doing and why, and that know when to bring human judgment into the loop, realize the benefits of AI without the blind spots. These organization shave put their trust into AI within defined boundaries. That’s the difference between adoption that scales and adoption that stalls.

The trust decline from 48% to 34% isn’t a warning to slow down. It’s a signal that organizations are maturing past the pilot phase and confronting the governance requirements that come with production-scale AI. The ones that build oversight in by design will pull ahead. The ones that treat governance as an afterthought will keep struggling with that gap.

The path forward: governance by design, not as a bolt-on

So what’s the central insight from this? It seems like the problem isn’t whether to use AI agents but it’s whether you can govern them at scale.

The trust decline from 48% to 34% (from our 2025 report to 2026) reflects organizational maturity, not AI failure. Teams are moving from “let’s see what AI can do” to “let’s make sure we can control what AI does.” That’s the natural progression from experimentation to operational readiness, but it requires a different platform approach. Not just automation or test generation, but real visibility into what AI is doing, why it’s making specific recommendations, and when human oversight is necessary.

That means governance, approvals, and human intervention points can’t be retrofitted after the fact. They need to be built into how teams operationalize AI across the SDLC from the start — which is the design principle behind Tricentis AI Workspace.

Because AI agents will keep expanding their role in software delivery. The question is whether you have the infrastructure to govern them at scale, or whether you’re assuming visibility and accountability will sort themselves out. Based on the data, most organizations are still working through this. Trust is declining because they’re learning what governing AI in production actually requires. That doesn’t mean failure — it’s just the work of turning pilots into production systems.

The organizations that close the governance gap now will scale AI with confidence, not just speed. The ones that don’t will keep widening the distance between what their AI can do and what they can trust it to do.

Download the 2026 Quality Transformation Report for detailed findings on AI adoption, governance readiness, organizational risk, and what distinguishes early adopters from the rest.