Artificial Intelligence Without Business Vision is Waste

Artificial Intelligence

What you will read here:

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


Recently I wrote an article where I discussed the incredible possibilities of this technology still cause a lot of enthusiasm and create a race for investments in tools and development of show applications, but result in few actions that, in fact, generate value for companies. 

This occurs because the ability of AI to answer the most varied business questions precisely sometimes works as a kind of siren song, an enchantment, in which one loses the clarity of what should be done, and one loses the focus on what should be the real goal. And, in a digital context, that goal is to meet consumers' needs. This lack of clarity, technology often ends up being used to satisfy curiosities and not to generate relevant information for the development of effective solutions that bring impact to the business and its customers. 

A good measure for technology professionals to figure out the  difference between mere curiosity and the data that can generate value ]is to reflect on how the information extracted will be used. If you want to know your business’ consume profile, with the correct data input and programming, the AI tool will certainly be able to answer this, even separating, by demographic groups or by other classifications you choose. But the question that follows is the key: what are we going to use this information for? If there is no clear answer to this, and there is no continued strategy t, the use of AI is a waste. In this case, other tools, more simpler and cheaper tools  could help you get to the same answer. 

Technology + business = potential results

After figuring out whether or not to use the technology, the next we, technology and development professionals, need to understand is that there is no way to exempt oneself from business, from the knowledge of business and consumer needs. Our role becomes one where we enable the construction of value in a multi-disciplinary and collaborative way. This is because it is less and less about prescribing software to execute programmed commands and more and more to create technologies capable of learning. 
In other words, we have to keep in mind that this is not  traditional software, which presents static responses. The results will be fluid and will change over time. In addition, similar situations may have different responses. And all of these aspects must be translated directly into AI. And who is able to help us do this translation, to know what is relevant and to teach what the software needs to learn? Business professionals. 

It’s they who are able to provide a view of what business problems the tool can help solve, what questions should be answered, etc. Therefore, before developing, we need to have clear alignment with the business team to know what type of learning should be programmed and by what paths. 

Teaching the software to seek the correct answer

I usually compare the timing of teaching an AI software to training a new professional who will perform the same task. The idea behind it would be the same. When you are training a new person, you do not say what the correct answer is, but you teach how to assess which one is the correct one. This is especially valuable in the current context of high volatility and rapid change. 

To illustrate, let's imagine that an e-commerce company is looking to improve its virtual service. The first thing to do is to understand with the business team as real attendants receive training to respond to consumer pain, what needs to be done  the step by step. In case of problems with the purchasing scoring program, for example, the attendant needs to first confirm the identity of the customer. Then, if the purchase was made, requesting the photo of the invoice. Once these steps are taken, the points credit goes into the customer's account.

Knowing this and other patterns that are part of the real care process, 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 actual service, with some of the countless possible variables, it gains important insights to predict ways of solving new questions and problems that arise.  

3 tips to promote connection between teams

As I mentioned earlier, in order to have this knowledge, it is necessary to install a collaborative mindset between technology and business professionals. This means having joint discovery sessions, sprint design to solve the problems, as well as keeping regular conversations to follow the reality and the changes of direction. 
In order for a technology leader to be able to establish this environment of collaboration and the exchange of experiences to build value, it is necessary to take some actions:

1 - Establish a business partnership - It is necessary to identify people in the business area who have an innovation profile and are interested in solving critical business problems. They will be good allies and supporting partners. These people will bring a broader and updated view of business problems, what ways they would use to solve them and what the value of this solution is to the company.
In contrast, the technology team must provide learning, context and background on the possibilities of AI technology. Thus, business professionals will be better prepared to discuss new solution paths and identify opportunities to apply AI while enhancing value. 

2 - Don't be alien when it comes to explaining - It is very common, in the excitement in explaining the countless possibilities of Artificial Intelligence, that leaders or developers  get lost in nomenclatures and technical terms. This, of course, creates a communication barrier. 
So, nothing to start a conversation about (the evaluation metric) ROC curve, about False negative, False positive, precision… Leave these terms for a second moment, in which there is greater understanding and maturity on the topic. This will happen during the process of working together. However, it is worth remembering that in order to simplify something complex, it is necessary to have a lot of mastery on the subject. If it is difficult to find the right words or analogies to get the proper understanding and, in fact, to teach, you need to study more. Go deeper into the theme.

3 - Promote reflections on value versus cost of the new solution - Here, there is an idea that we already presented in the previous article on Artificial Intelligence: to keep the good alignment between business and technology teams, it is important to make a joint reflection on the value of the right response versus the cost of the wrong response.In other words, despite the teams' intention to use AI technology, it is always important to assess whether the implementation costs are lower than the gains that may exist with the learning generated by the tool. I take this opportunity to recall the idea that opened this text: it is not only because it is possible that it makes sense. 
Thus, to develop solutions with Artificial Intelligence capable of enhancing the delivery of value to customers and bringing impactful results to the company while adding knowledge. It is through collective intelligence that companies gain the ability to identify and take advantage of opportunities and to use available technologies to truly innovate and delight.