3 deliberate moves for businesses to maintain trust in AI decisions

In Gartner’s 2026 CEO and Senior Business Executive Survey, 76% of leaders said AI is the technology most likely to disrupt their industry in the next three years. But in that same survey, Gartner also found that 59% of AI pilots fail to reach production, primarily because of organizational constraints.

If everyone’s so sure about AI, why isn’t it paying off?

Amidst the feeling of certainty surrounding AI productivity, who’s stopping to check if AI agents are actually ready? When such an overwhelming majority agrees and puts real time and money into AI, that consensus can override the hard questions about timing, readiness, and what differentiation looks like down the road.

AI Creates a Fundamental Change

The money and urgency poured into tech investments can’t make up for leadership, infrastructure, and systems that were never designed for AI in the first place. Building trust in a business’ AI workflow requires a closer look at its organizational processes and data infrastructure.

Organizational processes  

In the same survey, Gartner also notes that AI pays off when the organization around it changes too. That means redesigning processes, redefining who makes which decisions, aligning incentives, and managing well. Skip those steps, and AI just speeds up and amplifies existing inputs, good or bad.

If an AI agent takes a customer call, who owns the outcome? If a workflow changes, whose goals change with it? When nobody has answers, an AI pilot will stall. That pilot might show exciting new capabilities in a demo, but scaling it into production means changing how hundreds of people work in their day-to-day. A 2025 study from IBM notes that only 16% of AI programs have scaled across companies, despite the number of pilot programs in place.

Data infrastructure

“Garbage in, garbage out” is a common tech adage that describes how, when low-quality or incorrect information enters into a system, unreliable outcomes will result. AI can only deliver trusted answers when the basics are in place:

●      Who within the organization has access to the data?
●      How fresh is the data when it reaches the AI layer?
●      Where did the data come from?
●      What are agents allowed to do with the data?

Most organizations can’t answer all four. In fact, Gartner predicts that through 2026, organizations will drop 60% of AI projects that don’t have AI-ready data.

AI runs on the data it has access to. If AI agents run on stale, outdated numbers, it’ll give answers based on an older version of the business. Without trusted data, employees may revert back to building reports and analysis “off the AI grid.” As a result, AI outside a shared foundation becomes difficult to track: Nobody knows what exists, what data it’s using, or what it costs.

Providing a governed data and AI platform for its customers, Incorta has identified three moves that businesses can make to build and maintain trust in their AI systems:

1. Controlled, visible spend on a centralized platform.
When every team expenses its own tools and tokens, it gets harder to track. And when that spend is scattered, it’s virtually impossible to gauge AI performance against cost, scalability, and operating limits. Centralize AI models, tooling, and infrastructure so the CFO can see the whole bill in one place.

2. Governed data, so teams can build what they need without being a security risk.

IBM’s 2025 Cost of a Data Breach report found that one in five organizations had a breach tied to shadow AI, adding an average of $670,000 to breach costs. Of those that suffered an AI-related breach, 97% didn’t have AI access controls in place. Teams are already building with AI, often in tools IT never signed off on. It can provide a short-term productivity boost, but that’s company data that is shared with unauthorized tools and can create a tremendous business risk.

Data governance gets a bad reputation as the department of “no” that restricts and slows things down. But done right, it’s the reason business teams can say “yes” faster.

3. A system flexible enough to switch models.
Build systems that can swap models, using flexible APIs, abstraction layers, and portable data. On a multi-model platform, admins can decide which models are available, and teams pick the best one for each job. When a better model shows up next quarter, businesses shouldn’t need to waste time rebuilding.

With organizational and data foundation changes in place, a business can understand and rely on its AI responses. It can also look to its data to provide a hidden advantage. Competitors have access to the same models as everyone else — what they don’t have is your data.

Businesses sit on a data goldmine: years of customer conversations, support tickets, contracts, product usage, financial and planning data, and more. Public models know a lot about the world, and nothing about how specific businesses actually run. Grounding AI in governed data gives outputs that teams can actually use, unique insights no other business can copy, and the freedom to build what is needed — all on a governed foundation. With clarity on governed and usable data, visible spend, and lines of ownership, businesses can trust the AI outcomes generated for their business.

This story was produced by Incorta and reviewed and distributed by Stacker.