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  • Writer's pictureSahar Cohen

How to cut 50% of the overall effort of AI projects

Many of our prospects raise the same concern about AI: it seems to be complex. With an experience of about one hundred AI projects, we can submit to you that many times this is a misconception, and that the complexity is not where most people think.

Different aspects of AI complexity

When explaining why they think that AI is complex, most people talk about algorithms that and technology. Building new AI capabilities indeed involves (often sophisticated) algorithms and new technologies. But for people who do AI for living, algorithms and technology are daily tools. The real challenges in an AI project are not technical. The real challenges deal with business problems, product definitions and soft organizational aspects.

Not sure that these are good news, since the business, product and organizations are indeed challenging, but the upside is that in many cases the core AI aspects of a project may be accomplished relatively simply and quickly.

What can you do to ease the development of new AI capabilities?

You do not have to be a data scientist to help significantly ease AI projects. If you are a product manager, or a domain expert and get involved in an AI project, your role is crucial. Data scientists need you to help them make sure that they are solving the right business problem.

Here are three important questions on the new AI capabilities that you can answer and can save over 50% of the overall project effort.

1. AI algorithms are most often designed to automate some business process that involves decision-making (the algorithm’s prediction is the basis for decision making). What is the business process in your case?

2. AI algorithms are based on data. What are the available data sources that can making decisions? Can you, as a domain expert use the data in these sources to make good decisions?

3. What would make the AI solution a success? Can you quantify that?

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