So, today I got into a conversation about how AI and legacy systems intersect, particularly in industries like banking and government that still rely on older tech infrastructures. There’s a real challenge here: the pool of people trained in these older technologies is shrinking, which makes those who do have the skills almost irreplaceable. But the work itself? It’s often just about keeping things running—far from innovative. This is where generative AI could step in, potentially automating some of these repetitive maintenance tasks and even bridging the gap between old and new systems.
But here’s the tricky part: as much as AI can take on, there’s a painful reality. High-level experts are often stuck managing these niche, outdated problems, instead of pushing forward with more strategic initiatives. And while AI can’t yet replace the nuanced judgment humans bring, it can operate at incredibly low levels—working through tedious, repetitive processes that don’t need human oversight.
This is where things get interesting. If we can properly train AI to handle these routine tasks, it leaves room for higher-level strategy and broader thinking. The goal isn’t just efficiency—it’s freeing up people to focus on real innovation. There’s a long way to go, and the transition isn’t painless, but there’s an opportunity here for AI to shift the focus from patching up old systems to pushing into new frontiers.
What are those new frontiers? We are quickly moving towards automating transition processes. Generative AI not only learns the task but also how to automate the transition processes to learn the solution (aka building a model). Practically speaking, AI develops a plan of action, which might be about resolving inconsistencies in translating data between tables or learning how to process unstructured information from scarce examples and few expert comments. For all organizations, the explainability of machine learning was another frontier. However, generative agents will record their conversations regarding the problems they are solving. Large language models will act as judges, finding the best plan or selecting the most appropriate tool. In the past, meeting minutes and email trails were often a source of crucial knowledge. In Generative AI, those internal dialogues are inherent features that do not necessitate costly and time-consuming project documentation efforts.
If your organization is grappling with this balance between legacy system maintenance and future-focused innovation, consulting services that specialize in AI integration and strategic automation can help. Whether it’s dentifying which processes to automate or guiding the transition, expert advice can make all the difference in shaping a smoother, smarter way forward.
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