AI adoption is starting to translate into real-world returns. But as efforts accelerate, many organizations are running into the same problem: systems that are too expensive, too slow, and can’t scale.

Among companies with disconnected data environments, 67% cited data storage, movement, and duplication as the largest recurring AI cost, according to a recent survey of over 1,200 technology leaders by Economist Enterprise. For those with a unified data architecture, that number drops to just over half.

Now is the time to build the future-proof foundation for AI. But database migrations are expensive and a major source of frustration. The deeper organizations envelop themselves around legacy architecture, the harder it will be to get out. Open and AI-ready databases give companies more flexibility and control over how their data is used, and empower developers to quickly, securely, and efficiently reorient the business around AI.

“The art is distributing speed without distributing chaos,” Jose Manuel Silva, Vice President for Technology and Chief Digital Officer at Natura, said in the report.

This blog will go into the three considerations for enterprise infrastructure that can help speed-up AI innovation, minimize costs, and deliver AI agents that actually work.

For 60% of companies, it takes up to 12 months to get AI workloads into production, according to the Economist Enterprise survey. Developers want to move at the speed of AI, but underlying infrastructure is stuck at an analog pace.