Companies are finally untangling their chaotic IT systems for AI

For twenty years, enterprises have solved problems the way most of us do: with whatever's closest at hand. Need to cut costs? Cloud services. Customers on phones? Build an app. Factories need monitoring? Add IoT sensors. Each solution worked. Each one also left behind a mess.

Now those messes are catching up. IT teams are managing not ecosystems but archaeological layers — cloud platforms bolted onto legacy systems, mobile apps that don't talk to data warehouses, IoT sensors feeding into databases that can't sync with anything else. The result feels familiar to anyone who's inherited a codebase: it works, kind of, but nobody fully understands how, and every change breaks something else.

The numbers tell the story. Fewer than half of CIOs (48%) say their current digital initiatives are actually meeting business targets. Operations leaders consistently point to the same culprits: integration complexity and data quality issues. It's not that the individual tools are bad. It's that they were never meant to work together.

The AI problem that forced a reckoning

Then AI arrived, and suddenly the patchwork became unsustainable. Generative AI, machine learning, agentic systems — they all demand something the current architecture can't deliver: massive volumes of clean, coordinated data moving at speed through tightly orchestrated systems. You can't train a useful model on data that's scattered across seventeen incompatible platforms with no way to sync it reliably.

Achim Kraiss, chief product officer of SAP Integration Suite, describes what happens when you try: "A fragmented landscape makes it difficult to see and control end-to-end business processes. Monitoring, troubleshooting, and governance all suffer. Costs go up because of all the complex mappings and multi-application connectivity you have to maintain."

This recognition is shifting how enterprises think about their infrastructure. Rather than layering another tool onto the pile, companies are consolidating — moving toward integrated platforms (often called iPaaS, or integration-platform-as-a-service) that handle data movement, system connectivity, and governance from a single point of view.

It's not glamorous work. It won't make the news. But it's the unglamorous foundation that actually makes AI implementations work. The companies that get this right — that move data cleanly and at scale — will be the ones whose AI systems actually deliver. The ones that don't will have expensive models trained on bad data, running on creaky infrastructure.

The shift is already underway. Organizations are learning that how data moves through a business matters just as much as what you do with it once it arrives.