2 weeks Ago By Nisha Talagala, Contributor
The AI wave has come on faster than most (including me) expected. Over the last decade, companies have slowly but surely started deploying AI. Return on Investment (ROI) was beginning to turn positive.
Then ChatGPT arrived, and suddenly AI was everywhere. If a company thought AI was critical to its future before, it has now acquired a new sense of urgency far beyond what it previously had. That said, just because something sounds urgent (or is, in fact urgent) does not make it any easier for an organization.
Some factors (such as the release of more powerful tools) make adoption easier, but the urgency does not translate into a similar ease of implementability. In particular, if an organization does not have a solid data practice or data governance policy - can it do AI? This is a challenging question. On one hand, the easy answer is data has to come first.
AI relies on data, and bad data leads to bad AI or worse, legal and other problems if bad data is used to make problematic AIs. On the other hand, at the pace at which AI is moving, can an organization really say no AI at all till they have achieved acceptable data practice and data governance across every aspect of their business? What would the opportunity cost be? And when would the data strategy be acceptable enough? As AI changes, will the needs of the data strategy also change? As in many cases, the easy answers are all insufficient (at least in my view). Good data practice cannot be ignored, and AI cannot wait.
So what is to be done? Some ideas follow Start with the problem. This is good practice regardless of where your data strategy and data readiness are at. AI that delivers good ROI starts with a focus on the problem, not the AI.
The questions you should ask yourself are: A few things that are critical to get right The pace of AI evolution is fierce. There is unfortunately not much opportunity to wait till all dependencies are fully met. However, organizations that do not have their data practices in good shape will lose out on AI in the long run, and organizations that have solid data practices will have an advantage with AI.
If possible, thread the needle and get both done..
