The role of “to-be” UX
The role of envisioning and documenting “to-be” state for your application’s UX in an AI implementation is extremely important. At a very...
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Disclaimer: All content posted in my blogs and on my website are my thoughts, and do not represent the opinion of any organization that I work for or have been associated with.
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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....
Each Practical AI project needs these five sub projects
So let’s say you are tasked with an AI implementation. The use case is known. There is a good understanding of people and processes...
It’s all about good data!
It’s simple. Prediction rules learn from past data. If your data is bad, so will your prediction rule be. What then makes data bad? A few...
Managing the fear of unknown
A common hurdle in implementation of AI is not whether AI works as needed, but the fear of unknown. These fears range from “I need to...
Support intuition with intellect for Practical AI
Our evolution as humans has followed a certain path. We were first very instinctive. Just like a primal instinct would suggest, the cave...
The first step towards getting AI right
Where do I start when asked to add AI to my product or process? The first choice you make is an important one. AI can be implemented in...
And then there is “Practical AI”
Look around. Every one talks about AI. AI as a killer, AI as a savior, AI as an enabler. Whatever the expectations from AI may be, how...
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