Three practical ways to add AI value to your existing tech stack

I’ve worked with some smart people recently using AI to breathe new life into their existing tech stacks. I’ve seen just as many others stuck at the first hurdle, wondering where the hell to start.

So I thought I’d share three practical ways I’m seeing tech teams add real value with AI — without needing to rip everything out and start again.

If you’re looking at your tech stack and thinking “Where do we even begin?”, maybe these will spark some ideas. 👇

🛠️ Use AI to make configuration easier

Low-code platforms promised simplicity — then overwhelmed users with complexity. Configuring them now often takes certifications and near-developer skills.

Smart tech teams are enhancing their low-code UIs by adding a natural language layer that translates user intent into config metadata. It’s no harder to build than your next low-code UI enhancement, and it extends your configurability power beyond power users, out to a wider audience.

Natural language becomes your primary config interface, with the point-and-click UI as your fallback, instead of a bottleneck.

🚀 Use AI to speed up onboarding

Whether you’re selling B2B software or rolling out a new platform at your company, onboarding is often scary and a barrier to starting the project at all. The buying decision is heavily influenced by how easily a team can migrate onto one platform versus another.

Smart folks are using AI to improve every step of that onboarding journey to create a major competitive advantage. They use AI to analyse source data, map it to the target structure, generate transformation logic, and draft help docs and test cases from stakeholder interviews. They’re turning what used to take months into something faster and more repeatable.

🧹 Use AI to improve your legacy data mess

One of the biggest blockers I hear is: “We can’t do anything with AI, our data’s a mess.” But that’s exactly when to apply some AI.

Clever teams are using LLMs to assess the structure, completeness, and consistency of their data — identifying skews, gaps, duplicates, and anomalies. Then they generate a step-by-step technical remediation plan: SQL/Python scripts, transformation logic, deduplication steps, and validation tests.

It’s not magic, and it won’t fix years of data sprawl overnight. But it turns a mountain into a series of manageable hills, and shifts the conversation from “we’re stuck” to “we’re making progress.”

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These are just a few success stories I’ve seen. I’d love to hear how you’re applying AI to legacy tech.