Why trust is the multiplier in scaling AI across IT operations

While 2025 started with high expectations for AI and fears of job loss, businesses have realized that extracting meaningful value from AI requires significant human effort. With this reality check, organizations pushed through their learning curve and now, in IT operations, AI is effectively everywhere. 

We recently surveyed 1000+ IT professionals in collaboration with ITSM.tools to sense the State of AI in IT for 2026 and close to 98% of organizations agree that they are already using AI or running pilots. The question is no longer whether organizations are adopting AI, but whether they’re building the trust necessary to scale it successfully.

The report shows that 62% of IT professionals now trust AI more than they did a year ago. When we as IT leaders trust AI enough to embed it into several facets of IT operations, including incident resolutions, workflow orchestration, knowledge management and analytics, our AI outcomes become more visible and real.

We went a little deeper into what drives the AI trust=success and the data revealed four clear patterns.

1. Trust builds when AI consistently delivers measurable ROI

AI earns trust when it proves its value. We found that 82% of IT professionals say their organization has realized value from their AI initiatives so far and 67% of IT professionals now report positive ROI from AI investments.

This shows that measurable impact directly correlates with increased trust. Operationally, IT organizations cite that AI’s biggest impact is seen in:

• Data analysis (70%)
• Automation and workflow automation (49%)
• Knowledge management (37%)

As AI becomes a dependable part of day-to-day IT operations, trust becomes a natural consequence for businesses implementing AI. As IT leaders, the onus lies on us to also build ROI measurement frameworks that prove AI’s impact to secure further investment and organizational buy-in.

2. AI maturity levels create greater AI stability

Apart from overall adoption, we observed that maturity levels in AI are rising, with 43% of organizations saying that they’ve now embedded AI in more than 3 service teams.
Moreover, 64%of them now say they are equipped with the tools, skills and governance needed to scale AI, showing more organizational readiness towards AI.

With strong foundations in place, organizations develop trust through consistency and repeatability rather than one-off experimentation.

3. Responsible autonomy increases AI trust

Organizations that are seeing rising AI trust are not those that fully hand over control to AI, but by implementing it with clear boundaries. Gartner indicates that only 15% of IT leaders are considering deploying fully autonomous AI.

Our survey voices a similar sentiment with:

• 36% of organizations retain human final decision-making. 
• 22% allow limited autonomous decisions in specific scenarios
• And, only 16% fully delegate operational IT decisions to AI.

Taking this phased approach gives organizations time to validate outcomes before expanding AI’s role in IT operations, creating a controlled environment where trust grows alongside reliability.

4. IT leadership-led AI programs strengthen team alignment

Another interesting correlation we spotted while looking at what makes AI projects succeed in organizations, the source of where the push to incorporate AI comes from mattered. Trust and alignment among teams increased when they are championed by IT leadership rather than emerging from isolated teams. 

The study revealed that 54% of AI initiatives were initiated by IT leadership, making it the dominant origin of AI investment. McKinsey’s research reinforces this pattern, stating that the organizations leading AI adoption are three times more likely than their peers to say senior leadership clearly owns their AI initiatives.

Strong ownership with IT creates better governance, clearer communication and more effective rollout strategies, which are all essential components of trust and success in AI systems.

Bottom-up AI projects often lack the cross-functional alignment and organizational governance necessary to scale beyond departmental use cases, struggling to gain enterprise-wide traction and undermining AI confidence.

Scaling AI requires scaling trust in AI systems

The implications for CIOs and IT leaders looking to scale AI are clear: trust in AI is not the result of blind optimism. It is the outcome of seeing real value, building stronger foundations, improving operational execution and backing initiatives with leadership accountability.

IT leadership now has quantitative proof that strategic AI deployment delivers measurable returns that can transform IT from a cost center into an enterprise intelligence hub. 

This article is published as part of the Foundry Expert Contributor Network.
Want to join?