Lately, a lot of transformation stories focus on things like moving to the cloud or "modernising our data platform” by implementing specific technologies.
These are often good, necessary moves. But if that's the whole plan, we’re missing something. New tech can make processes easier and speed things up. But this doesn’t automatically make our decision systems smarter.
Tool-first transformation usually looks like this: teams can deploy models faster, but decision quality doesn’t improve. Dashboards are pretty, but they aren't focused on the metrics that actually matter.
Some algorithm problems are caused by old technology. But many others come from unclear objectives, messy definitions, poor quality data, or processes that have been built on top of one another. A shiny new platform won't fix any of that. Instead, it helps us do the same thing at scale, faster, and seemingly better.
To avoid this trap, we need answers to a few core questions:
Tech is on the list, but it’s only part of it. If we can't answer the other questions, adding more tech will only hide the real problems, or take focus (and energy and budget) away from what we really need to fix.
Tech is still important. It helps execute a sound strategy reliably: clearer decision lineage, consistent deployment, better monitoring, and improved testing capability.
So we need to regularly improve our platforms. Move to the cloud if it makes sense. Buy new or updated tools if ours are outdated, unsupported, or creating risk.
But we do this without confusing a tool rollout with decision improvement. More effective decisions need a broader view. Not necessarily difficult, but different to the tech-first or tech-only approach.
Disclaimer: The info in this article is not legal advice. It may not be relevant to your circumstances. It was written for specific contexts within banks and insurers, may not apply to other contexts, and may not be relevant to other types of organisations.