Everyone's Betting on AI. Here's How to Actually Beat the Odds.

Here's a stat that should make you pause. 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. That's a lot of agreement. And when everyone's running in the same direction, few leaders pause to check their own weak spots first. 

In its survey, Gartner also found that 59% of AI pilots never reach production and most stall for organizational reasons. In practice, that usually means a company can't clearly say who has access to its data, how fresh that data is, where it came from, or what its AI agents are allowed to do with it. Those answers are the difference between an AI strategy that pays off and one that sits in demo mode forever. 

AI is an amplifier, not a fixer 

Think of AI like a megaphone. Use it with a clear message and everyone understands it. Use it with interference and everyone only hears sharp screeching noises.

 AI only successfully delivers when the basics are in place: clear decision rights, aligned incentives, and strong management. Skip those, and here's what happens. Bad data becomes confident, hard-to-trace mistakes. Weak change management keeps pilots stuck on the demo stage. Outdated operating models turn into compliance risks that grow with every new use case. 

AI runs on what you feed it. The faster you adopt AI without addressing these issues, the faster the cracks show. 
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Three gaps AI exposes first 

The CFO can't see the spend. Every team buys its own tools and its own tokens. Costs creep up quietly. Finance ends up staring at a scattered bill with no clear line to value. 

The CIO can't see the data. Teams are already building with AI, often in tools IT never signed off on. Sure, it's a short-term productivity win. It's also company data flowing into places with no governance and no way back to the source. That's a security and compliance headache waiting to happen. 

Business users can't trust the output. People build on whatever data they can grab. An agent running on stale numbers will give you the wrong answer fast, and it'll sound completely sure of itself. When AI lives outside a shared foundation, nobody knows what exists, what data it touches, or what it costs. 

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Three deliberate fixes 

The organizations that adopt AI deliberately will achieve greater success than those that choose to move fast, only to keep up with their peers. Here's what that looks like: 

1. Decide based on your needs, not the hype. Put major AI investments in front of a cross-functional team: business, risk, HR, and IT. Each group sees different risks and different gaps. Together, they catch what any one of them would miss. 

2. Don't be tied down to one vendor. Build systems that can swap models, using flexible APIs, abstraction layers, and portable data. On a multi-model platform, admins decide which models are available and teams pick the best one for each job. One size doesn't fit all. And when a better model shows up next quarter (it will), you don't need to rebuild anything. 

3. Treat compute like the limited asset it is. You can't balance performance against cost, sustainability, and operating limits when spend is scattered across dozens of tools. Centralize your models, tooling, and infrastructure so finance can see the whole bill in one place. 

Build a foundation that beats the AI odds 

AI speeds up whatever's underneath it. Outdated data, unclear rules, and hidden costs all grow faster once AI gets involved. With live, governed data underneath, that same speed works in your favor and closes the gap.

Get that right, and you have dramatically improved your odds. AI stops being a reaction to the market. It becomes an advantage you control. 

Source: Gartner, "Your AI Strategy Success Depends on More Than Just Investment," September 16, 2026. CEO survey data from the 2026 Gartner CEO and Senior Business Executive Survey (n = 446). https://www.gartner.com/en/articles/ai-strategy-agreement