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How to implement AI in an organization


Before applying technology, companies should ask themselves why they want to do it (Illustrative image Infobae)
Before applying technology, companies should ask themselves why they want to do it (Illustrative image Infobae)

“Alexander, we want to start with AI in the company,” the owner of a large organization tells me during our first consulting meeting. I nod and, to myself, I begin to realize that I am facing another victim of the AI ​​epidemic in which it seems to be mandatory to have something to do with AI, even without first having a fundamental articulation with the plan of action.

The next question is even more revealing: “When do you think we can have something?” The client is confusing a mere tool with an end. He is demanding to do anything quickly with the AI ​​label, with the sole objective of getting this label in the eyes of competitors, customers, investors and the workforce itself.

From then on my options are limited. If I want to meet the demands I have to work isolated from the organization’s core teams and create a completely lateral product.

When I finish my consulting, the product I designed lacks maintenance, integration and support to become a miserable file in no time.

This is not an isolated story. It is an example of a common pattern caused by the frenzy of this AI revolution that, due to its speed, does not allow a careful breakdown of all its aspects.

Artificial intelligence should be a means and not an end in itself for companies EFE/ Luis Eduardo Noriega A
Artificial intelligence should be a means and not an end in itself for companies EFE/ Luis Eduardo Noriega A

My first antidote to this epidemic of frenetic implementation is to outline the why in one sentence. Why does an organization have to take the step towards intelligent automation? It is very likely that the answer is living inside the customer since he saw the competition announce their brand new service. It’s totally normal. Answering this question out loud forces that notion to refloat from the subconscious to the conscious field. In that flash of consciousness the true transformation begins.

From that moment on, we must look for the best place within the organization through which the AI ​​will enter, the optimal leverage point, which is defined as the process within the organization that has the best relationship between potential benefit and the implementation effort. And here we must stop at a key point: an AI project today involves cultural transformation. Therefore, costs do not only involve technical costs but also human ones. AI is not just another technology in an organization’s arsenal. If treated this way the results are generally delays, frustration and financial loss. Raising morale for a second AI project becomes more difficult and the organization falls further and further behind. When evaluating efforts, what we call the seven vectors in comprehensive training to implement AI must be taken into account: Ethics, Expectation Management, HR, Culture, Security and Privacy, Costs and Implementation.

This model implies a constant tension between the vectors. Something similar to the “short sheet” idea, only with seven people pulling and pulling.

At the intersection of the possible fields of each of these areas is where an AI project can thrive over time.

It is important to note that within these seven areas the technical solution itself is not found, that is, the one that solves the problem in question within a developer’s laptop in a demonstration. The seven vectors come into play the day an organization decides to take that technical solution to the territory, to the street, to production and intends to keep it there, delivering value every day, improving, expanding and growing.

When a leader within an organization is aware of these seven points, another myth disappears, that of thinking that AI can be bought, in other words, that an external agent can go to raise the organization to a new level of efficiency and Productivity powered by intelligent automation. I want to be emphatic at this point, there is no Nobel Prize in mathematics or AI priest who can achieve this without the entire team getting on the boat of transformation.

Old paradigms must be released and for that the presence of internal change leadership that has the respect, trust, and closeness of the teams is necessary.

This is the leadership profile that I think is linked to the training of professionals, with the aim of carrying out strategic management of artificial intelligence and business automation, so that all organizations implement AI projects that add real and sustained value over time.



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