Artificial Intelligence without business vision is a waste

What you will read here:

• Technology + business = enhanced results 
• How do you teach the AI tool to seek the high value answer?
• 3 steps to promote alignment and collaboration between business and technology

It doesn't have to be done just because it is possible. This simple phrase is a good guide for the moment we are experiencing with Artificial Intelligence (AI). As I wrote in a  recent article, the incredible possibilities of technology still cause a lot of enthusiasm and create a race for investments in tools and development of spectacular applications, but result in few actions that really generate value for companies. 

This is because AI's ability to accurately answer the most varied business questions sometimes functions as a sort of siren song, an incantation, in which one loses clarity about what needs to be done, losing focus on what should be the real goal. And in a digital context, that goal is to meet the needs of the consumer first. In this lack of clarity, technology often ends up being used to satisfy sterile curiosities and not to generate relevant information for the development of effective solutions that have an impact on business and customers. 

 A good method for technology professionals to know this difference - between mere curiosity and data that can generate value - is a reflection on what is intended to be done with the information found. For example, if the demand is to know what the consumer profile of the business is, with the correct data input and programming, the AI tool will certainly be able to provide this answer, even separating by demographic groups or by any other classification desired. But the following question is the key: what are we going to use this information for?
If there is no clarity in this response, if it does not call for action and there is no continued strategy based on it, then the use of AI is a waste. Other tools that are even simpler and cheaper to apply can respond to this curiosity of the business, if applicable.

Technology + business = enhanced results

Having overcome this barrier of the need to use technology or not, the next issue that technology and development professionals need to understand is that there is no way to exempt ourselves from business, knowledge of business needs and consumer needs. From "taking orders" from the business area, our function has become to enable the construction of value in a multidisciplinary and collaborative manner. This is because it is becoming less and less about prescribing software to execute programmed commands and more and more about creating technologies capable of learning. 

That is, we have to bear in mind that it is not a traditional software, which presents a static response. The results will be fluid and will change over time. In addition, similar situations may present different answers. And all these aspects should be translated directly into AI. And who is able to help us do this translation, to find out what is relevant and teach what the software needs to learn? Business professionals. 

This profile provides insight into what business problems the tool will help solve, what questions make sense for the strategy, and what information should be considered to actually bring results with a positive impact. Therefore, before developing, we need to have a lot of alignment with the business team to discover what kind of learning should be programmed and which paths.

Teaching the software to seek the correct response

I usually compare the time to teach AI software to the training of a new professional who will perform the same task. The logic is the same. When you are training a new person, you do not say what the correct answer is, but teach them how to evaluate which one is the correct one. This is especially valuable in the current context of high volatility and rapid changes. 

To illustrate, let's imagine that an e-commerce store is looking to improve its virtual service. The first thing to do is to understand with the business team how real attendants receive training to respond to consumer problems, which is the step by step process that should be done. In a case of problems with the purchasing scoring program, for example, the attending person first needs to confirm the customer's identity. Then, if the purchase was made, they request the photo of the invoice. After these steps, the credit enters the customer account in points.

Knowing this and other standards that are part of the process of a real service, we know how to train the software to do the same. However, a set of questions will not always generate an expected set of answers. Thus, when the team observes the real service, with some of the numerous possible variables, it gains important insights to
predict ways of solving new questions and problems that arise. 

3 tips for promoting the connection between teams

As already mentioned, for us to have this knowledge, it is necessary to install a mindset of collaboration between technology and business professionals. This means having joint discovery sessions, sprint design to solve problems, as well as having regular conversations to keep up with reality, and changes of direction. 
In order for a technology leadership to be able to establish this environment of collaboration and exchange of experiences for the construction of value, it is necessary to take certain actions:

1 - Establish a business partnership - It is necessary to identify people in the business area who have an innovation profile and interest in solving critical business problems. They will be good allies. These people will bring a broader, more up-to-date view of business problems, what ways they would use to solve them, and what the value of this solution is to the company.
On the other hand, the technology team should offer learning, context, and a basis on the possibilities of AI technology. Thus, business professionals will be more prepared to discuss new solutions, identify opportunities to apply AI and enhance value. 

2 - Don't be an alien when it comes to explaining - In the enthusiasm of explaining the countless possibilities of Artificial Intelligence, it's very common for leader or development professional to get lost in nomenclatures and technical terms that are not very accessible for those who are not part of the area. This, of course, creates a communication barrier. People stop listening.
So, don't start a conversation about (the assessment metric) ROC curve, or False negative, False positive, precision etc. Leave these terms for later when there is greater understanding and maturity on the subject. This will happen during the process of joint work. However, it is worth remembering that, in order to simplify something complex, it is necessary to have a lot of control over the subject. If it is difficult to find the right words or analogies to provide proper understanding and, in fact, to teach, one needs to study more. Dig deeper into the topic.

3 - Promote reflections on value versus cost of the new solution - Here is an idea that we have already presented in the previous article on Artificial Intelligence: to maintain good alignment between business and technology teams, it is important to make a joint reflection on the value of the correct answer versus the cost of the wrong answer. That is, despite the intention of the teams to use AI technology, it is important to always evaluate whether the implementation costs are lower than the gains that could exist with the learning generated by the tool. I would like to take this opportunity to recall the idea that opened this text: it's not just because it's possible that it makes sense. 

So, in order to develop solutions with Artificial Intelligence capable of enhancing the delivery of value to customers and bring impactful results to the company, add knowledge. It is based on collective intelligence that companies gain the ability to identify and take advantage of opportunities and use available technologies to truly innovate and captivate.

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