Why perfect is the enemy of valuable and may be killing your AI Transformation
It is such an exciting time to have the opportunity to re-imagine the future.
Right now we are at the very start of a transformation as we usher in a new General Purpose Technology that we’re all aware will change most of what we know. Over the last 2 years I’ve been working in this space to understand the impacts and benefits of the AI Age, while also trying to understand what the new normal could be. At The Field Institute we’ve put together what we think might be a next best seller - the 7 Habits of Highly Effective Cyborgs. What will the habits of successful AI natives be?
In the same context we have been working with companies and dealing with numerous questions about AI - all very interesting and valuable, and most importantly, constantly edging ourselves and our clients forward on the road of discovery.
One misconception we encounter a lot, which prompted this post is, “We can embark on an effective AI implementation because our data is not ready”.
While your organization is cleaning data, your competitors are already gaining AI-driven insights from their 'messy' information.
Here's a thought: If Google had waited for the internet to be "clean" and perfectly structured, we'd still be using phone books.
Think about today's AI models. They weren't trained on pristine datasets – they learned from the internet, arguably the messiest dataset in history. Yet they're transforming industries daily.
The thing I’ve learnt is that the GenAI solutions available today are able to fathom insights and discover patterns we didn’t even have the questions to ask. It is in the interacting and the delving with human curiosity and insight that these patterns emerge. This is the springboard you need to be on - NOW!
A quick success story: A manufacturer stopped waiting to clean decades of production data and instead let AI analyze their unstructured maintenance logs. Within weeks, they found patterns that cut downtime by 23%. The data wasn't perfect, but the value was real.
The solution? Stop thinking sequentially and start working in parallel:
- Begin extracting insights from your existing "messy" data
- Let AI help identify what actually needs cleaning
- Improve data quality where it matters most
- Scale what creates proven value
Remember:
- Old Way: Clean → Structure → Analyze → Act
- New Way: Analyze → Discover → Enhance → Scale
The perfect time to start your AI journey isn't when your data is perfect. It's now.
What's holding your organization back from starting? Share your thoughts below.
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