This session shares three real stories of modernizing legacy systems that were critical to the business—not just IT—and shows how AI increasingly supports smarter, safer modernization decisions.
The first: a 27‑year‑old platform running 24/7 factory operations, where downtime meant immediate financial impact.
The second: an NPL company operating across seven European countries, struggling to scale, gain transparency, and respond to growing regulatory pressure.
The third: a Swiss financial consulting firm held back by legacy technology and the lack of unified technology governance—raising risk, slowing decisions, and increasing the cost of growth.
In every case, modernization wasn’t a “big bang” rewrite. It was a series of pragmatic, incremental decisions—defining a clear north star, introducing change gradually, preserving backward compatibility, and evolving toward more event‑driven thinking where it made sense. A key theme of the talk is how AI tools supported these steps: helping uncover hidden dependencies, explore architectural trade‑offs, model impact before changes were made, and reduce uncertainty during transition.
I’ll focus on what mattered at the executive level: uncovering hidden value during requirements work, managing expectations when reality hit, balancing speed, cost, and stability—and using AI to shorten decision cycles and de‑risk long‑term investments rather than automate coding.
If you’re accountable for results in a legacy‑heavy organization and need modernization to enable strategy—not disrupt it—this talk is about taking the first realistic step, understanding where AI adds real leverage, and making the investment pay off.