AI: Implementing a successful strategy in your company

What you will read here:

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

As a subject of hype in the market, Artificial Intelligence (AI) has presented an important real growth in companies. To get an idea, an MIT Sloan Management Review study pointed out that 58% of organisations predicted that AI would bring significant changes to their business models by 2023. And a Forbes article from 2019 indicated that 73% of top executives in the United States had the goal of assertively expanding investment in the technology. Similarly, the Trends to Transform your Company in 2020 survey, conducted 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 figures, what is observed is that most of these companies have not had advances in the uses of technology beyond the experimental. This is because, when we think of Artificial Intelligence, we often have a vision related to something spectacular, almost a realisation of the projections of science fiction. And if it is not about creating something like that, something like a smart robot, it somehow loses interest and meaning.
 
This romanticised idea ends up removing the possibility of less grandiose usage, but that really brings value to the companies and their consumers. And the main - and most immediate - value at this time of technology maturity in the market comes from the ability it offers to facilitate the decisions of its customers. That is, it is the reading of tastes, desires, and needs of customers with increasing precision to the point of enabling increasingly personalised offers. 

There is so much information, so many possible options, that the problem to solve is how to reduce this volume, how to reduce the noise and deliver relevance. The consumer has no more time - nor patience - to lose. Someone looking for trainers, for example, does not want to search 30 pages of product offers that have nothing to do with their taste. They prefer to enter an e-commerce store that offers a page with options aligned to their tastes. 

In this context, the differential is filter quality and the ability to unravel patterns and predict desires. That is where AI comes in. Today, this type of use of technology is what
translates into better experiences, better customer relationships, more loyalty and, consequently, a better result for the company. But, as I said before, this use does not cause much hype and companies still have difficulties in understanding Artificial Intelligence as one more tool among others, as a facilitator to generate impact, and not as the impact itself. 

It is time to reverse this logic

I often 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 realise 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 thinking of it as an enhancer of the value to be offered to the customer, which is central to 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 it?" It is time to reverse this and start looking for what your customers' needs are. What are the questions from your customers which, when answered, will generate greater value for them and the company? Only after having this clarity should we go searching for the right data and the application of AI. The starting point defines whether the strategy will have successful results or not.

Think about an airline aiming to improve the supply of seats. If the customers' main questions are about the best time to buy tickets to a sports championship or a show, the use of AI with internal company data will not be sufficient to deliver the appropriate answer. This data would give you information on the best day to buy seat 31B (and your consumer is not interested in it). It will be necessary to seek external data on these events which, in addition to internal flight information and customers' personal preferences, will provide ammunition for the AI tool to harness its full potential to deliver real value to the passenger. 

This vision is the one that will define if the 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 client and move the pointers of the business. 

Guidelines for Implementing a Successful Strategy

To build an effective AI strategy that actually drives companies' results, a few points are critical:

Raise awareness among your people

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 fronts, leaders, product management professionals, and teams designing experiences need to know the capability of Artificial Intelligence to identify and take advantage of opportunities. On the other hand, development professionals need to go beyond technology and discover the real problems of the business.

Break silos

For information to circulate, it will be necessary to break silos, and unite the technology teams with the business fronts so that they can discuss the journey from end to end.

In addition to creating and maintaining alignment of focus and objectives, this exchange of knowledge, with multidisciplinary teams, helps accelerate the operation, eliminating communication noise and unnecessary processes.

Value of the correct response x Cost of the wrong response

Before adopting AI in the company's strategies, always consider both sides of the coin: if the company uses technology to correct the response to a given business question, will the result be higher 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 learning that could generate future gains.

Start small

To begin with, discover a customer problem which could generate a lot of value if resolved. But if the initiative goes wrong, it will not have a negative impact on their experience.

A very interesting example is the application we made of AI tools in the journey of customers at a clinical analysis laboratory. After identifying that a major issue in the journey was the scheduling process, we verified that the problem was in the registration for the tests to be performed. With difficulty understanding the doctor's handwriting, the patient gave up the online registration and contacted the call centre.

To create the solution, we noticed that most tests are correlated. For example, we can assume that those who will undergo a bone densitometry need to perform blood tests to measure calcium levels. The solution considered was the elaboration of a recommendation system which was able to predict possible tests that were linked through the use of Artificial Intelligence. Thus, the patient would only need to type one of the tests prescribed and identify the others among the options offered by the registration tool.

In the first month, we verified that the test group - which received the recommendations - completed 25% more of the registrations than those who did the conventional online scheduling process. The result? With the simple use of technology, the customer gained time and a better experience, and the laboratory gained in speed, savings on structure and call centre personnel and, especially, in user satisfaction.

Build scalability

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

Thinking about 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. On a production line, if machines - or robots - have the ability to recognise and deal with new situations, they can adjust automatically, without the need to be reprogrammed with each change of route. Furthermore, you can have a much better result if robots are learning from all the new information.

To get out of the experimental stage and really reap impactful results, it is time is to bring the consumer to the focus of strategies and prepare its personnel to harness the potential of technology. Together, your company's collective intelligence and AI have great power to generate high value for your customer, your business, and to captivate the market.

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