AI: Implementing a Successful Strategy in your Company

Artificial Intelligence

What you will read here:

  • The starting point will define the success of the strategy
  • Tips for implementing technology
  • Exit the experimental stage and build scalability


For years, it’s garnered hype in the market, Artificial Intelligence (AI) has shown real growth in companies. To give you an idea, a study by the MIT Sloan Management Review found that 58% of organizations predicted that AI would bring significant changes to their business models by 2023. And a 2019 Forbes article pointed out that 73% of top American executives had a goal of sharply expanding investment in the technology

Despite these striking numbers, what is observed is that most of these companies have not made any progress in using AI beyond the experimental phase . This is because, when you think of Artificial Intelligence, you often have a vision related to something spectacular, almost almost science fiction like. And if it is not to create something along these lines, people usually think something like an intelligent robot. 

This romanticized idea ends up ruling out the possibility of  use, let's say, less grandiose, but that really brings value to companies and their consumers. And the main - and most immediate - value at this time of technology maturity in the market comes from the ability that it offers
to simplify  decisions for customers. In other words, it is the reading customers' tastes, desires and needs with increasing precision to the point of enabling increasingly personalized offers. 

There is so much information, so many possible options, that the problem to solve is how to reduce this volume and how to reduce noise and deliver relevance. The consumer doesn’t have a lot of time - and no patience. Someone looking for a shoe, for example, does not want to search 30 pages of product offers that have nothing to do with their taste. He prefers to enter an e-commerce platform that offers a page with options aligned to their preference. 

In this context, the differentiator is the quality of filters and the ability to unveil patterns and predict preferences. This is where AI comes into the picture . Today, this type of of technology translates into better experiences, better customer relationships, more loyalty and, consequently, a better results. But, as I said before, this use does not cause much fanfare and companies still have difficulties in understanding Artificial Intelligence, as a facilitator to generate impact, and not as the impact itself. 

It's time to reverse the logic

I tend to compare this moment of technology to the beginning of the internet, when it made its debut, everyone was fascinated.. Today, the internet is part of our daily lives in such a natural way that we only realize that it exists when it is missing. The same will happen with AI, soon. 

It is necessary to take the focus off the technology itself and start to think of it as an enhancer of the value it offers. I say this because much of the discussions about Artificial Intelligence within companies still begin with the question: "what data do we have and what technology do we need to work with?" It's time to turn that around and start looking for what your customers' needs are. What are your customers' questions that, when answered, will generate greater value for them and the company? Only after having this clarity, we should start looking for the right data and the application of AI. The starting point defines whether or not the strategy will have successful results.

Let's think about an airline that aims to improve the supply of airline tickets. If the main questions from customers are about the best time to buy tickets for a sports championship or show, using AI with internal company data will not be enough to deliver the right answer. Instead  you will have, for example, information about the best day to buy the 31B seat (and your consumer is not interested in that). It will be necessary to seek external data on these events, which, added to the internal information on flights and personal preferences of customers, will provide ammunition for the AI tool to take advantage of its full potential to deliver real value to the customer. 

This vision is the one that will define whether companies will be able to leave the experimental stage with this technology and, in fact, use it in practice to generate real positive impact for the customer. 

Guidelines for implementing a successful strategy

In order to build an effective AI strategy that drives results, a few points are essential:
Make your people aware

The first step to be taken should be training. It is necessary to educate everyone in the company about the possibilities of the technology. Business executive, leaders, product management professionals and teams that design the experiences need to know the capacity of Artificial Intelligence to be able to identify and take advantage of opportunities. On the other hand, development professionals need to go beyond technology and know the real problems of the business.

Break molds

For information to circulate, the need will be to break molds, unite technology teams with those in thefrontlines  so that, together, they discuss the journey from end to end.In addition to creating and maintaining alignment of focus and objectives, this exchange of knowledge, this multidisciplinary team, helps to speed up the operation, eliminating unnecessary communication and processes. 

Value of the correct answer x Cost of the wrong answer

Before adopting AI as part of a company's strategies, always consider both sides of the coin: if the company uses technology to get the answer to a certain business question, will the result be greater than if you used conventional methods? In this equation, on the one hand, you must to consider the costs of implementing the technology and, on the other, the lessons that can generate future gains.

Start small

To begin with, discover a client's pain that, if solved, can generate much value. But if the initiative goes wrong, it will not have  huge negative impact.

A very interesting example is an application that we developed  using AI tools in the for  a clinical analysis laboratory. After identifying that a major friction in the their journey was the scheduling process, we verified that the problem was registration of new exams. . With difficulties understanding the doctor's handwriting, the patient gave up the online registration and went to the call center.

To create the solution, we realized that most exams are correlated. For example, let's suppose that whoever is going to do a bone densitometry needs to perform blood tests to measure calcium levels. The solution considered was the preparation of a recommendation system that, with the use of Artificial Intelligence, was able to predict possible exams that were linked. Thus, the patient would only needed to type one of the prescription exams and identify the others among the options offered by the registration tool.

In the first month, we found that the test group - which received the recommendations - completed 25% more registrations than those who did the conventional online scheduling process. The result? With a simple use of technology, the customer gained more time and a better experience, and the laboratory gained more agility, cost-saivngs and user satisfaction.

Build scalability

After successfully applying AI in small initiatives, it's time to take a little more risk and gain scale in other points of the journey and even in other business fronts that generate even more value.

Thinking of greater impact generation, we have uses related to the interpretation of the physical world, with image analysis. In industries, for example, the installation of Artificial Intelligence in the automation of production processes brings great results. If, on a production line, machinesor robots are able to recognize and deal with new situations, they can make adjustments automatically, without the need to be reprogrammed with each route change. In addition, you can get a much better results if the robots are learning from new information.

To get out of the experimental stage and really reap impact results, the time is to bring the consumer to the focus of the strategies and prepare their people to take advantage of the technology's potential. Together, the collective intelligence of your company and AI have great power to generate high value for your client, for your business and to delight the market.