There’s a lot of noise in the industry right now about getting your data in order before launching into AI. While there’s truth to that, we don’t believe it tells the full story and, may even be a blocker for many organisations getting started on their AI journey.
Yes, AI initiatives that rely heavily on deep learning or predictive analytics absolutely need clean, structured data. The old adage ‘garbage in, garbage out’ applies here with full force. If your goal is to build models that forecast, classify, or optimise, then foundational data hygiene is non-negotiable.
But AI is a broad umbrella term. Not every use case demands a pristine data lake or a perfectly architected data warehouse. In fact, there are plenty of opportunities to start experimenting with AI that don’t require a robust data foundation at all.
Think about generative AI interfaces layered on top of operational systems. Or AI-enabled process automation that streamlines workflows without needing to crunch historical datasets. These are real, impactful applications that can deliver value today – even if your data is still a work in progress.
The key is to approach AI with curiosity and an experimental mindset. Start small. Learn fast. Let your early wins build momentum. And yes, continue improving your data in parallel – but don’t let perfection be the enemy of progress.
AI isn’t a destination reserved for the data elite. It’s a journey, and every organisation – regardless of where they are on the data maturity curve – has a place to start.
Let’s stop waiting for perfect data and start unlocking benefit where it makes sense to do so.
