Enterprise AI is becoming an operations problem

Just_Super via Getty ImagesAI keeps getting more capable. Using it inside an enterprise isn't necessarily getting any easier.As companies move beyond experiments and put AI into more parts of their businesses, they're meeting a separate set of challenges. The questions are increasingly about which models should handle which tasks, whether the underlying data is good enough, who and what AI systems can access and whether existing governance can keep up.Several developments this week point to the same conclusion: The next phase of enterprise AI may depend less on access to the latest models and more on whether companies can actually manage them.Enterprises aren't simply choosing an AI model anymore. They're using multiple models with different capabilities, costs and risks, which means someone needs to decide which model handles which task and when those decisions should change.Payments and data company Deluxe, for example, has more than 50 AI agents, with a centralized gateway directing requests to different models. The company weighs factors such as quality, risk, speed and cost when deciding which models to use.Related:Gemini 3.8 Live Transforms Conversational AIThat's a quite different challenge from simply choosing an AI provider. As enterprises add more models, model selection itself becomes an ongoing operational function.But managing the models is only part of the problem. AI projects are also running into a familiar enterprise roadblock: poor or fragmented data. In a recent Collibra survey, 72% of AI decision-makers said a poor data foundation was the root cause when enterprise AI initiatives fell short.The operational questions don't stop with data. Companies may have AI governance policies, but their strategies may not account for agentic systems.An EY report released on Tuesday found that nearly six in 10 respondents at organizations using agentic AI thought that no single group oversaw agents after deployment. Nearly half said their governance frameworks hadn't been updated to address agent-specific risks, while four in 10 lacked visibility into all the AI tools on their networks.That's as much an operational gap as a governance one. Companies can't effectively manage AI systems if they don't know what's running or who's responsible for overseeing it.A more powerful model will solve none of these problems. They require companies to make decisions about architecture, data, ownership and oversight as AI becomes embedded in more of the business.The hard part of enterprise AI may no longer be getting access to powerful technology. It's building an organization capable of operating it.Related:Calls for AI slowdown raise new challenges for open-weight modelsAlso this week in AI news:AI Is Now Leading Driver of New Cybersecurity Spending: Seven in 10 CISOs now rank AI as their top priority for new cybersecurity spending, particularly for security automation and identity and access management.Altman, Amodei Talk AI Pacing at Dreamforce: The AI lab leaders used Salesforce’s Dreamforce conference to discuss slowing the pace of frontier AI development as concerns grow that capabilities are advancing faster than safeguards.Gemini 3.8 Live Transforms Conversational AI: The new Google model adds real-time reasoning, tool use and visual processing while maintaining natural voice interactions.Calls for AI Slowdown Raise New Challenges for Open-Weight Models: Calls to slow frontier AI development could create new challenges for open-weight models, potentially leaving enterprises with more responsibility for testing, monitoring and governance.AI Panic Is Giving CIOs a New Trust Problem: Rising anxiety around AI is creating a new challenge for CIOs as they try to build organizational confidence in enterprise deployments without downplaying legitimate concerns about the technology.‘We’re Going to Stop Talking About AI,’ and Other Industrial Predictions: Manufacturing companies expect AI to become less of a standalone initiative and more embedded in everyday operations.Related:AI changes the ROI equation. Here’s how some have found successAI Makes a Mess of the Tech Job Market: AI is reshaping the tech job market, with an analysis of nearly 50,000 engineering job postings finding shifting skill requirements and a growing number of specialized roles.