AI: implementing a successful strategy in your company

Artificial Intelligence (AI) has been a hype theme in the market for years, and has shown real important growth in companies. To give you an idea, a study of the  MIT Sloan Management Review pointed out that 58% of organizations predicted that AI would bring significant changes to their business models by 2023. And a Forbes article from 2019 pointed out that 73% of top American executives had as a goal to expand investment in technology in a forceful way. In the same vein, the research Trends to transform your company in 2020, carried out by CI&T in December, showed that 49% of the leaders of large Brazilian companies stated that AI was indispensable for business growth.

Despite these striking numbers, what is observed is that most of these companies have not made any progress in the uses of technology beyond the experimental. This is because when we think of Artificial Intelligence, many times, we have a vision related to something spectacular, almost a realization of projections of science fiction. And if it is not to create something along these lines, something like an intelligent robot, it is as if the interest is drained and it becomes meaningless.  

This romanticized idea ends up ruling out the possibility of a less grandiose use, if we can call it that, but that really brings value to companies and their consumers. And the main value - and more immediate - in this moment of technology maturity in the market, comes from the capacity that it offers to facilitate the decisions of its customers. In other words, it is the reading of 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 noise to deliver relevance. The consumer has no more time - and no patience - to lose. Someone looking for a shoe, for example, does not want to search 30 pages of product offers that have nothing to do with his/her taste. He/she prefers to enter an e-commerce that offers him/her a page with options aligned to his/her taste. 

In this context, the differential is the quality of the filter and the ability to unveil patterns and predict wishes. And here comes the AI. Currently, this type of use of technology translates into better experiences, better customer relationships, more loyalty and, consequently, a better result for the company. But, as already said, this use does not cause much interest and companies still have difficulties in understanding Artificial Intelligence as another tool among others, 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, which, when it appeared, aroused fascination - and even some apocalyptic predictions. 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 to be offered to the customer, which is central to the strategies. 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 them?" 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 the most value for them and for 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 the strategy will have successful results or not.

Let's think about an airline that aims to improve the supply of ticket seats. If customers' top questions 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. With them, you will have, for example, information on what is the best day to buy the 31B seat (and your consumer is not interested in it). 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 passenger. 

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

Guidelines for implementing a successful strategy

For the construction of an effective AI strategy that, in fact, boosts companies' results, some points are fundamental:

Make your people aware

The first step to be taken must be training. It is necessary to acculture everyone in the company about the possibilities of technology. Business fronts, leaders, product managers 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, developers need to go beyond technology and know the real problems of the business.  

Break silos

So that the information flows, it is necessary to break silos, unite technology teams with business fronts so, together, they discuss the journey from end to end. 
In addition to creating and maintaining alignment of focus and objectives, this exchange of knowledge, and this multidisciplinary team, helps to speed up the operation, eliminating communication noise and unnecessary processes.

Value of the correct answer X cost of the wrong answer

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

Start small

To begin, discover a customer issue that, if solved, can generate a lot of value. But if the initiative goes wrong, it will not negatively impact his/her experience.

A very interesting example is the application that we made of AI tools in the customers' journey of a clinical analysis laboratory. After identifying that major friction in the journey was the scheduling process, we found that the problem was in registering the exams to be done. With difficulties in understanding the doctor's handwriting, the patient gave up the online registration and sought help from the call center. 

To create the solution, we realized that most exams are correlated. For example, let's assume that anyone who is going to have a bone densitometry needs to have blood tests to measure calcium levels. The thought solution was the elaboration 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 need to enter 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. What was the result? With a simple use of technology, the customer saved time and had a better experience; and the laboratory gained in agility, economy with structure and call center people and, mainly, in user satisfaction.

Build scalability 

After successfully applying AI to small initiatives, it's time to take a little more risk and gain scale at other points in the journey and even on other business fronts that generate even more value. 
Thinking of greater impact generation, we have used related to the interpretation of the physical world, with image analysis. The installation of Artificial Intelligence in industries, for example, in the automation of production processes brings great results. In a production line, if machines - or robots - are able to recognize and deal with new situations, they can make adjustments automatically, without the need to be reprogrammed with each change of route. In addition, you can get a much better result if the robots are learning from all the new information. 
To get out of the experimental stage and really reap impact results, it's time 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.

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