Nowadays, we hear the buzzwords like Data Science, Artificial Intelligence (AI), Cognitive (a less scary way of saying AI) over and over, I think that’s very clear.
What is not entirely clear is that when we start the AI projects, the actual requirements remain just as unclear as some of these these “buzzwords”. Let me explain. I had a client in the healthcare industry recently who came up with this requirement:I want to use
Big Data to take the complexity away from the processes our Doctor’s have to deal with every day.
My client was also tired of big consulting firms saying things like “our most prestigious product X can so easily take care of this”. My client’s frustration was that she felt no one was listening to her and everyone was trying to sell her a technology.
This is a very common mistake when we try to solve a business problem by thinking of a “solution” straightaway and without giving it any thought of what it really means.
Cognitive (AI) Requirements are ambiguous in nature
We all know what cognitive solutions can be capable of, but it is also important to bear in mind that AI solutions are hard to build since the nature of their business requirements are often not fully understood. Think about it for a second, how can you possibly provide the best solution to a vague requirement like I stated above without completely breaking it down? What is crucial is that you take the journey with your client and demystify the unknowns first.
Artificial Intelligence and Unknown Unknowns
There are so many unknowns when it comes to AI projects and the issue is that most of these unknowns will remain unknown if a proper methodology is not used to de-code them.
So, how can Design Thinking help?
Design Thinking is based upon 3 simple principles, Observe, Reflect and Make.
These principles will guide you to:
1) Learn the real pain-points of your users by continuously “getting in their heads”
2) See the problem from the user perspective and not yours, and not the CIO’s!
3) Ideate absurdly, yes, free them from the status-quo and help them come up with brilliant ideas.
4) Build prototypes in just a couple of days which would not have been possible otherwise
Using Design Thinking, we managed to quickly (just about 5 days) understand the complex nature of my client’s big ambition of “I want to use Big Data to take the complexity away from the processes our Doctor’s deal with every day” by building the following:
This may seem a lot but you can get the entire thing done in a week. Oh, I almost forgot, my client was delightfully surprised by the entire program and realized that big data solutions can indeed help resolve very complex problems in healthcare
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