Everyone Is Talking About AI. Not Enough People Are Talking About Data.

Everyone is talking about what AI will do for their business. Very few people are talking about what their data infrastructure needs to do first.

I spend a lot of time in conversations with IT leaders, data engineers, and CTOs who are either deep into AI projects or trying to figure out why the ones they started haven’t delivered. And when you get past the surface-level answers, the problem is almost always the same: the data wasn’t ready.

That is not a failure of ambition. It is a failure of infrastructure.

The part of AI that nobody puts in the roadmap

AI models are only as smart as the data you feed them. That sounds obvious when you say it out loud. But in practice, most enterprise organizations have a significant gap between the data they think their AI is using and the data it is actually using.

Here is what that gap looks like in the real world. You have operational data sitting in store systems, ERP platforms, manufacturing nodes, partner databases, and legacy applications scattered across your environment. Some of it is syncing to a central system on a schedule. Some of it has not moved in hours. Some of it is moving inconsistently because the network connection at a remote location dropped last night and nobody noticed.

Your AI is running on that data. It is not running on a clean, current, unified view of your business. It is running on whatever made it into your data warehouse before the last job ran.

For a model that answers questions, that might be a tolerable problem. For an AI agent that takes action, it is not.

Why the urgency is increasing

The AI conversation has moved quickly from “what could we do with AI” to “how do we get AI into production.” And the gap between those two places is largely a data infrastructure gap.

IDC projects that by 2027, 80% of agentic AI use cases will require real-time, contextual, and widely accessible data (IDC FutureScape 2026). And yet Gartner predicts that over 40% of agentic AI projects will be canceled by end of 2027 due to escalating costs, unclear business value, or inadequate risk controls (Gartner, June 2025).

Those two numbers belong together. The demand for real-time data is growing. The projects that skip that foundation are getting canceled. The gap between them is exactly where data infrastructure sits.

The cost of getting this wrong scales with what the AI is doing. A model that gives a flawed answer based on outdated data is a problem. An agent that takes an automated action based on outdated data is a much larger one.

What SymmetricDS actually does in this picture

SymmetricDS is a data replication platform. It has been solving the problem of keeping databases synchronized across distributed and edge environments for nearly 20 years. That includes retail store systems syncing with headquarters, manufacturing plants syncing with enterprise systems, financial services firms syncing across geographic locations, and cloud and on-premises systems running in parallel.

Most modern CDC and replication tools are built for cloud-to-cloud pipelines. They do a good job moving data from a SaaS application into a data lake. What they are not designed for is the distributed, edge, and multi-tier environment that most enterprises actually run on, including environments where nodes go offline, where network connections are unreliable, and where data needs to move bidirectionally with conflict resolution.

That is exactly the environment where AI needs real-time, synchronized data the most. We are not a new entrant positioning around AI. We are infrastructure that already runs in thousands of production environments, now directly in the critical path of what every AI initiative depends on.

The question worth asking before your next AI project

Before your organization commits to the next AI project, ask one question: where is the data for this initiative actually coming from, and how current is it?

If the answer involves a scheduled sync job or a replication tool that was not designed for your environment, you have identified the risk before it becomes the reason the project underdelivers.

Data first, then AI. The infrastructure that keeps data moving is the infrastructure that makes AI work.

Jumpmind builds SymmetricDS, an enterprise data replication platform for distributed and edge environments. If you are evaluating your data infrastructure ahead of an AI initiative, we would be glad to have that conversation. Contact our team or learn more about SymmetricDS.