AI: NO NEED TO CLEAN YOUR DIRTY DATA

It is digital. But it's thinking is not so binary. Which is why AI doesn't fear or reject your mess and confusion. It embraces imperfection instead.

This changes decades of cleaning dirty data - and failure being a hard stop. When running code. And across entire projects.

In the past, if a field was missing or a format was wrong, the system failed. Sometimes elegantly. Sometimes not. Traditional software can only process the fail cases it has been programmed for. The gravity of real-world complexity pulled it back down to earth and limited its potential.

Generative AI, in contrast, delivers escape velocity.

As the first creative digital technology, it has the cognitive flex to adapt to what it finds. When AI hits a blank field or a conflicting record, it uses context to bridge the gap.

It cannot necessarily fix the gap. If your API fields are blank, the AI cannot be allowed to invent data that cannot be directly inferred. But unlike legacy systems, it can navigate the gap and keep on being useful.

Traditional Code: 'Error. Field Missing. Abort.'

Generative AI: 'The database field is empty, but I see a note in the PDF attachment. Shall I flag for review?'

Gaining escape velocity matters - because no data set is ever truly complete and clean. And instead of trying to fix 20 years of history, we can instead work on data triage, and assess what is feasible right now.

It is liberating when you realise you don't have to coddle AI like legacy software. We finally have a tool that is robust enough to handle the ambiguity of the world, exactly as it is. And whilst data tends to rot over time, digital intelligence is only going to improve with age.

So what's today's in-the-end-at-the-end?

Unlike the perfect dataset, AI is real. It is accessible to all organisations. And it means dirty data no longer stops you taking on that project which will set your organisation free.