In 2018, artificial intelligence will be an important topic, as it is the second wave of digital transformation that leading companies are already adopting. If you are a director at one of these companies, don’t let them tell you about it — be a protagonist of this change.
The Gartner research company predicts that, in 2020, 85% of all CIOs (Chief Information Officers) will lead artificial intelligence projects in their organizations. This is due to the fact that, in the last couple of years, there have been spectacular advances in the research on artificial intelligence, which have enabled us to deal with business challenges using this type of technology and were once unimaginable.
Why is this happening now? The costs of the required computer infrastructure have become much lower, we have large volumes of data to train intelligent systems and there have been hefty financial investments made by, for instance, digital giants and venture capital firms. Just to give you an idea, according to the Harvard business Review, companies like Google, Facebook and Microsoft have invested over USD 20B in artificial intelligence during 2016, and venture capitals have funded projects in about USD 5B during that same year.
Artificial intelligence may help with almost any business challenge that organizations may have nowadays. Artificial intelligence enables them to obtain new clients, for instance, by suggesting the best moment to propose a promotion to a person through sentiment analysis. It allows them to improve client satisfaction, anticipating what they will want and consequently serving them. It improves their efficiency, for instance, suggesting solutions to professionals when they must make a decision. It also qualifies them to create new business models using artificial intelligence to serve specific client segments in new ways and with new products.
Thus, we may confirm that artificial intelligence will permeate everything we do, in any business process and in any type of organization. In the next few years, any usual action in a professional setting will be supported or performed by artificial intelligence. Some of our emails will be answered automatically, we will receive suggestions on possible options and the most adequate course of action when making a decision, we will request our vacation from a virtual assistant or even sell to clients that an intelligent system decided to prioritize.
Organizations must adopt artificial intelligence in the short term, as a source of competitive advantage, and in the long term, as a means of survical (if they do not do so, their competitors will). In this stage, however, in order to be successful, one must be open to experiment with artificial intelligence, to make tests and even to fail. We must maintain an attitude that accepts change and inonovation.
Even though there are sectors that, due to their characteristics, are more prone to adopt new technologies such as artificial intelligence, we find that, unlike other past changes, this one is becoming a source of interest in almost any kind of organization. For instance, banks, due to their handling of large volumes of information and working with easily digitizable assets, have steadily adopted artificial intelligence, but other sectors such as telecom, retail and airlines have not been left behind.
Three key factors in the successful adoption of artificial intelligence are: the capacity to imagine how to use this kind of technology in a practical manner with business challenges; having available talent with deep business and technical knowledge in order to execute what has been imagined; and the capacity to integrate this type of new solution into pre-existing and highly complex ecosystems.
Lastly, even though impressive advances have been made in terms of artificial intelligence capacity, there are still major issues to be dealt with. Some of these issues include: anything that has to do with ethics in the adoption of artificial intelligence; the capacity of explaining not only why an intelligence has made a decision, but the reasons that led to that; avoiding biases in decision making, which are introduced essentially during the intelligence training phase; or the capacity of learning with smaller data volumes (from millions to hundreds), like humans do.