Enterprise AI spending is surging, but nearly half of all initiatives are failing to deliver on their promises. According to a new report released today by Coastal (a Salesforce and Snowflake consultancy) in partnership with Oxford Economics, 46% of organizations say their AI projects haven't met expectations—even as 74% are increasing budgets.
The disconnect is stark. While 84% of business and technology leaders believe AI makes them more competitive, the operational reality tells a different story: most companies have learned how to launch AI, but far fewer know how to run it at scale.
The problem isn't technology. It's operations.
"Enterprise AI has reached a turning point," said Eric Berridge, CEO of Coastal. "Over the past two years, the focus has been on proving that AI can work. Now the challenge is whether organizations can actually operate it at scale."
The 2026 AI Operations Report surveyed 800 U.S. business and technology leaders across industries—all with at least one AI initiative actively in production. What they found reveals a pattern that should alarm every CIO, CTO, and CFO making AI investment decisions.
The Gap Between Investment and Impact
Only 26% of organizations begin AI initiatives with a clearly defined business problem. That's not a typo. Three-quarters of enterprise AI projects start with solutions looking for problems, rather than the reverse.
This "AI-first" approach is exactly backwards. The organizations seeing results are the ones treating AI as a means to solve specific business problems—not as an end in itself.
The report identifies four critical failure points where most AI initiatives stall:
1. Data Isn't Production-Ready
70% of organizations report data access or quality issues during AI setup. But here's the real problem: 73% encounter the same issues while running AI in production.
This isn't a "set it and forget it" problem. AI requires continuous data management—clean pipelines, governed access, and ongoing quality monitoring. Most teams budget for launch, not for operations.
For CIOs and CTOs: If your data strategy ends at deployment, your AI strategy will too. Budget for data operations as a permanent line item, not a project expense.
For CFOs and business leaders: Every AI business case should include ongoing data management costs. If the ROI calculation assumes zero operational overhead after launch, it's wrong.
2. Adoption Gaps Limit Value
77% of organizations say employees are eager to use AI. Yet 73% struggle with actual adoption due to lack of trust, poor workflow fit, or unclear outputs.
The enthusiasm is there. The execution isn't.
AI that doesn't fit how people actually work will be bypassed, no matter how sophisticated the model. The best technical architecture in the world can't overcome bad change management.
For technical leaders: Design AI for human workflows, not theoretical efficiency. Involve end users early and often. Test integration points before scale.
For business leaders: Adoption is a P&L issue, not just an IT issue. If your sales team won't use the AI forecasting tool, you've spent money on shelfware.
3. Ownership Is Unclear
Only one in six organizations has a dedicated AI or transformation team. That means 83% of companies are running AI initiatives without clear operational ownership.
When nobody owns the outcome, nobody drives the result. AI becomes an orphaned project that drifts until it fails.
The organizations getting results treat AI as an ongoing operating function—with dedicated teams, defined roadmaps, and continuous management built in from the start.
For CIOs and CTOs: Create a center of excellence or transformation office with explicit P&L accountability. If AI is strategic, it needs permanent organizational support.
For CFOs and COOs: AI operational costs don't end at deployment. Budget for ongoing ownership—either internal teams or external partners who can manage at scale.
4. AI Behaves Like Operations, Not Software
Here's what most teams miss: AI doesn't behave like a system you deploy and move on from. It requires continuous tuning, monitoring, and governance—more like managing a supply chain than deploying an ERP system.
Model drift happens. Data quality degrades. Business requirements change. Organizations that treat AI like traditional software deployments are setting themselves up for failure.
For technical leaders: Build operational dashboards from day one. Track model performance, data quality, and user adoption in real-time. Plan for retraining and versioning as part of the lifecycle.
For business leaders: Every AI investment needs a run cost, not just a build cost. If your business case shows zero operational expenses post-launch, challenge it.
What Separates Success from Failure
The report concludes that organizations seeing results aren't distinguished by the technology they use, but by how they operate it. Four practices separate winners from the rest:
- Treat data as a continuous requirement, not a one-time setup task
- Design AI for how people actually work, not theoretical efficiency
- Define the problem before selecting the solution, not the reverse
- Assign clear ownership for performance in production, not just deployment
These aren't technical challenges. They're operational and organizational—which is why so many technical teams struggle with them.
The Bottom Line for Leaders
If you're a CIO or CTO: The next two years will separate organizations that can operate AI from those that can only launch it. Build for operations, not just deployment. Budget for continuous management, not one-time projects.
If you're a CFO or business leader: AI ROI doesn't materialize automatically. It requires ongoing investment in data, adoption, and governance. If your AI business cases show zero operational costs, they're fiction.
If you're making AI investment decisions: Start with the business problem, not the technology. Ensure clear ownership. Design for human workflows. And budget for the operational reality that AI is never "done."
Enterprise AI isn't stalling because the technology doesn't work. It's stalling because most organizations aren't built to run it. The winners will be those who recognize that AI is an operating function, not a deployment project.
Continue Reading
- Why 80% of AI Projects Never Make It to Production
- The Hidden Costs of Running AI at Scale
- How to Build an AI Center of Excellence That Actually Works
Source: Coastal 2026 AI Operations Report (survey of 800 U.S. business and technology leaders conducted with Oxford Economics)
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