Investment on developing AI&ML models – timelines & diminishing return

 

One of the most popular questions that I often get asked by the stakeholders is about the timelines required for a ML model to finish development. I will try to address the subtlety of this topic in this writeup.

AI development is a unique scenario where you are expected to deliver an innovation. It is a special case where the resource required is uncertain. And hence it is sometimes very difficult to understand when & where to “STOP”.

When I talk to businesses one of the questions, what I stress about the most, is for them to define what an “MVP” solution is to them. That is with what minimum accuracy or maximum error rate the AI solution would still be useful for their business.

If you are investing on AI use cases one of the concepts, I would recommend you understand is – AI resourcing & diminishing return. Please look at the graph below –

 

            So, what I suggest to the AI investors are if you haven’t reached an MVP by the point of maximum return, “STOP”. For example – By the end of PoMR if the model is still with an error rate of 30%, and that is something that does not work for your business, may be AI cannot solve this for you. Maybe it needs a completely different approach to solve this. Whatever is the case, deploying more resource is not the solution.

            Driving from my experience with all the AI&ML use cases I have worked for almost a decade now, a general thumb rule which I recommend is – The accuracy or error rate, that you get at the end of 3 months is your Point of maximum return. You should reach an MVP by then. Beyond that it should be fine tuning or customizing to specific business needs. By then if it it’s still miles apart from your business objective, may be its time to pull the plug.

            This is again an opinion piece, and these has been my experience. Will be glad to hear how the journey has been for you.

Competition in the context of the new world order

 Definition of competition has changed. And Elon Musk realized this long back. If you analyze any competitive business now you will somehow find one or few of the big tech 4 – (Facebook, Amazon, Microsoft, Google) involved in some way or the other. And what Elon knew was, his strongest competition will come from Google and Ubers of the world with their self-driving technology, rather than from GM or Ford.

And we know big tech 4 – (Facebook, Amazon, Microsoft, Google) strengths.

· Deep funds available to do experimental innovation. Significantly less pressure to go profitable.

· Army of engineers and scientists.

· Vast computer infra. So vast that a company can rent their unused infra and it can become one of world’s biggest business (read AWS)

So the big question is how do their competitors stay relevant and significant. They should be using what they have gathered over years of being in business i.e. tricks of trade or domain business knowledge. It can be years of building cars, manufacturing things or making software products.

So the strategy can be –

· Avoid direct competition with the tech giants. If the product is too generic in nature they will build it faster and better with their deep resources.

· Integrate data science closely with business products. Analytics should be out of the box and intuitive. If required collaborate with the tech giants’ offerings but never give away domain expertise.

· Enable data science teams with domain knowledge. Celebrate people who are domain experts and make them part of the data science team.