Is Technical Debt a Barrier to AI App Deployment?

Nearly every IT leader today is in the midst of moving the next generation of AI apps from the design phase into deployment, and they are finding that they must grapple with the problems that arise when those apps are dependent on legacy data or infrastructure. And according to the attendees of our CIO Roundtables, there is no one-size-fits-all answer. Each case of legacy dependence must be evaluated separately.

Once IT leaders have evaluated how specific AI projects are impacted by technical debt, they then check the organization’s existing plan for addressing technical debt, possibly choosing to accelerate the parts of that plan that will best help meet the goals of the specific AI project.

Particularly tricky are AI apps that are dependent on resources that are trapped by technical debt, usually because data is stuck in a system with substantial issues. Er There are two common problems.  In some cases, it is not possible to extract the data from the legacy environment in a way that will support the goals and functionality of the AI app. That might require a rebuild or a total scrapping of the app, with a new design needed to replace it. That’s expensive and time-consuming, but there might be no other option. The second scenario occurs when the new app can get at the data, but the data cannot be delivered at the speed necessary to support a real-time or somewhat real-time AI app. Addressing this issue is possible, but the solution will depend on what the particular legacy system can technically support. Again, one size does not fit all.