How a data scientist can help keep AI practical

There is always an art of the possible. Possibilities exists in every discipline. Sure AI can do wonders to your processes or products. But how do you decide on the limits at any point in time? I see this as a discussion between a business user and a data scientist. Is there a path towards Nirvana - Nirvana being your desired end state with AI as an enabler? Sure is. That path should be a happy union of guiding your people and processes towards end state; and making it possible in stages through appropriate use of data science. The data scientist needs to keep the path real. Keeping the path real entails not just coming up with models In phases that work and are not over engineered; but also guiding the business users on prepping the process and people towards good data. The data scientist should also help find ways for impacted people to better understand, appraciate, help mould and accept the new reality of data driven decisions. It is imperative that a data scientist takes more steps towards understanding humans and processes than a business user takes towards understanding data science. In fact, I would even consider the argument that a business user should never have the need to understand data science. And that might just be your path towards practical AI. #abhayPracticalAI #artificialintelligence #ai #machinelearning

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