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The secret of deploying Artificial Intelligence in Business

A lot has been said about the impacts and possibilities of using Artificial Intelligence in business. Without getting into technical minors if we are talking about AI or Machine Learning, the fact is that we already know that it helps companies to find new strategies to increase revenue, reduce costs, increase efficiency, reduce risks and improve business smart processes.

After more than 2 years of pitching the market and deploying some artificial intelligence solutions, through our Discovery AI platform, we arrived at a secret element of AI deployment in organizations. The process is complex and there are several critical points, but Innovation is the major point of success or failure. I explain, for innovation I mean as a principle! In this sense, we are talking about innovation as a value that people need to have and also express in behaviors, as well as, an aspect of organizational culture.

For Arian Saddam Hossain, “Artificial Intelligence is composed of various algorithms and business rules modeled to create a computer system to process while it learns from organization's data. The System learns and transforms your data into information for decision making or establishes an intelligent (automatic) process. The more AI processes data, the more it learns!

Adopting an Artificial Intelligence solution is embracing a journey of innovation, a new way to discover how to improve your business with anticipation, acceleration and growth. In short, a journey for the entire life of the organization.

In this sense, the journey of deploying AI, not by chance, is exactly the same as a Design Thinking (DT) process, namely:

1- Define a customer-centric problem

2- Discover what the data has to say about the problem (for us, we still need to know the readiness of the data and its quality for use) and what the processes are in the organization

3- Creativity and Innovation for AI solution of the minimum scope (MVP - Minimum Viable Product)

4- AI training to make all the magic of the solution happen

5- Validation of AI modeling, finding out if it works… or not!

6- If it works, we move on to the scaled solution.

Unlike the DT process, where there is still a lot of empathy phase with research to extract intelligence out of the data, here, the extraction takes place through mathematical modeling. But in the end, the goal is the same, how can we do better and differently?

On another day, we were presenting our AI solution for a Supermarket and asking questions: have you ever thought if in your supermarket you could predict the demand for high-value products?, What would be the impact on your business efficiency and your customer experience management? What if, you could manage all inventory knowing, at your fingertips, which products will expire? Also, you could know the exact price of the promotion to attract the customer and still generate earnings? Wouldn't that be great? There is no doubt that AI can help us to be more efficient and better, but everything gets stuck when the human decides not to take risks and not to innovate.

In the case of the supermarket, I confess, a little surprised, I heard the general director of a large supermarket chain say: “Interesting, but here we do everything empirically!” With that, he said between lines: "I'm not worried about having the pain of innovating to be more efficient, I'd rather keep doing what I've be doing for ever!"

This week we participated in the "Açolab Marathon 2022". There, we heard from several stakeholders: startups, companies, academia, etc. that innovating with technology required a lot of perspiration, what we strongly agree. This was defined as: the human in the process. The market is still dreaming that will be a magic platform, with an AI that will be plugged into systems, that are running in the organization, and … Aha! everything will be different and better.

But that's really not how it works. Until things seem to be as simple as this, we need to: take information architecture and data storage seriously, understand the existing processes very well, understand the pain of the operation and the business, work continuously to make the data ready, and use a lot, a lot of human, creative and innovative mind to solve the problem.

Also, we still won't have the Aha! if we don't have managers capable of sustaining all the pain of this process: sometimes chaotic, sometimes uncertain, sometimes with things going wrong, sometimes with things working out and, above all, saying and betting that this is the direction the company wants to follow. It means, to keep innovating, even if we have to stop doing what we are doing for a while and we have to be patient to adjust everyone to bet together on innovation process.

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