JULY 20219MANUFACTURING TECHNOLOGY INSIGHTSThanks to the ability to put things into context and have common sense, as we mentioned before, human can deal more efficiently with reaction to significant change. Again, machines-AI can help but in no way they can take alone these major decisions, e.g., in crisis management.Nevertheless, when we are talking about processing big data information in a short period of time (usually almost instantly), machines can deal quickly with this. Think about, let's say, the recommender systems, a huge volume of processed data for a simple effect perceived by users!Finally, machines are not emotionally affected when it comes to rational decisions like the ones related, for instance, to stock trading.A myth: we do not need people any more. I believe the reality is that we can "augment" people with AI and allow them to save time / be focused on added value tasks. Humans and machines-AI can complement each other to make incredible achievements, while putting the human at the center!Another myth: AI Tech is enough to succeed - you just need to have the best "algorithms" in the market!This is one of the most common (and most dangerous) myths.If you want an Organization to succeed with AI, the first that is needed is to stay close to the business needs. Get the business cases right. This does not mean to forget tech: seeking scientific excellence, testing, and using cutting edge technology is a must. However, do not go out using a complete techno-push approach.Once you have your business cases, then you might want to use a "3-D" approach: Make sure to have the adequate skills (people). Technical skills (in data science, data engineering, IT architecture and programming, design...) are essential here but soft skills are equally very important. think about training, communication, connection to business,... Governance and Strategy: the Organization needs to evolve. Functions such as data owners, data officers and central data governance bodies are important. AI needs data to be based upon. Infrastructure. Data lakes, namely, to store data in a structured manner and access them easily and cloud technology. A clear must.The most usual myth here is to think that success is reached by having just one or two of the elements above. It could never work. You need them all.ConclusionHumans and machines-AI can work incredibly well together. Success comes with more than AI technology. Let us think of AI as "Augmented Intelligence" and put the human at the center. Let us beware of the myth that wants to throw away the "old" technologies such as operations, research, or computational science and replace it with deep learning
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