Enterprise AI is becoming an operations problem

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AI 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.

That'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.

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.

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