What is Artificial Intelligence?
Despite being a widely mentioned concept, it is convenient to start by specifically defining Artificial Intelligence.
In simple words, it refers to a field of list of belgium consumer email computer science that focuses on the creation of programs, that is, programming algorithms capable of developing behaviors considered "intelligent" , or to a certain extent autonomous, this because they were believed until recently, exclusive to human beings.
Some examples of intelligent tasks involve the detection of patterns and analysis of information , among other things, which allows digitizing and automating processes.
We are talking about a concept that in recent years has had an exponential evolution, but, like many technological advances, it was not born overnight. The advancement of Artificial Intelligence has had several steps before being what it is today:
• Predictive analysis
It was one of the first steps towards artificial intelligence and is still very useful in the field today. It is based on the use of a certain number of variables that, combined with certain results, allow the generation of a model that gives a certain score and that translates into possible situations .
• Intelligent data discovery
The next step was not only prediction, but the use of programs in the automation of the entire Strategic Intelligence cycle : preparation and incorporation of data, as well as predictive analysis of the same together with the identification of patterns and hypotheses.
• Deep learning
Today, it is a field of AI that is responsible for studying and developing systems capable of “learning” from previous experiences . The program itself is designed so that, based on its interaction with variables and results, it can generate data that helps it learn and improve its performance in the future .
• Machine learning
The most advanced form of artificial intelligence is perhaps the ability to make computers carry out actions without requiring explicit programming, because they are able to know and learn from already existing data.
Considerations as a company
Among many things, artificial intelligence allows for the digitalization of processes, the optimization of resources, and the increase in productivity in companies. These have been enough reasons for it to reach the ears of the C-suite of large companies.
However, this type of technology has a price and opportunity cost that must be considered. HBR conducted a survey of 3,000 executives about the success of AI , where 140 case studies were evaluated and parallel studies were used as a basis to find some key points about AI that are worth reviewing:
1. An upward trend, not a generality
While AI technologies are proving their enormous business value, only 20% of respondents said they are using any of them at scale or as a core part of their business. For many companies, it is still in the pilot stages.
2. Profits for those who bet
30% of respondents who said they adopted AI early and are now using it at scale or in their core processes have seen revenue increases, increased market share, or expanded products/services.
There is evidence that those who have embraced this technology are already seeing its impacts on ROI specifically in the area of Big Data and Advanced Analytics.
3. Without the support of leaders, AI has very little chance of success
For digital transformation processes around AI to work properly, they must be accompanied by leadership from top management, capable of bringing about a change in corporate culture throughout the organization.
4. The necessary capabilities are not abundant
The necessary technological and human capital resources are likely not to be found within the company, nor are they easy to obtain.
Even digital giants such as Amazon or Google are seeking support from small companies that, thanks to their level of specialization, facilitate advances in the industry.
5. Business leaders and technology leaders must work together
For AI developments and advancements to be effective, it is necessary to involve IT specialists and everyone involved in processes. But to ensure that resources and advancements are placed where they can achieve the greatest efficiency and value, it is necessary that the responsibility of an AI project remains in the hands of both technical and business leaders.
6. Identify opportunities for success according to needs
Machine learning is not a macro solution, in fact, AI solutions come in many forms. The specific needs of each company must be identified, where an AI solution could pay off in a short time. For example, a development to improve customer service is far from one that can identify credit card fraud.
7. Culture and digital capability come first
The survey cited shows that the areas that lead the AI sector are, precisely, the most digitalized. Therefore, before investing in developments, it is necessary to do so in infrastructure and digital culture .
8. Offensive strategy proves to be effective
Digital transformation requires intrinsic change. According to HBR, a parallel study shows how effective an offensive digital strategy with AI has been for many companies. This is because companies that have adopted this type of strategy prepare themselves in all areas of the company, from creating growth models with new business models and much more robust growth paths.
9. The biggest challenges: People and processes
Beyond investment, educating people and adapting processes are the main challenges for digital transformation. The role of the leader is to determine which tasks should be done by a human and which tasks will be performed by a machine.
