Having been bandied about indiscriminately, the expression “artificial intelligence” has become something of a catch-all. In marketing rhetoric, it could refer to anything from a simple chatbot to the fantasy notion of software that could someday replace humans in everything they do.
To make matters even worse, the term “AI” is often associated with bombastic expressions like “new industrial revolution,” “new paradigm,” “transformation,” etc. Of course AI (incidentally, it would be more accurate to say “AIs,” given their great variety) marks a break with the past; the problem is that these pompous terms make it difficult to understand what it all means. That’s quite irritating, especially when the term is supposed to be used for professional purposes.
As things currently stand, a “strong AI”—in other words, a universal AI capable of imitating, or even surpassing, human intelligence—is scientifically and technically out of reach. At the same time, AIs are already capable of carrying out certain tasks much more efficiently than we can, precisely because they don’t function the way we do. I will discuss three of those differences here, differences that make AIs especially relevant in guiding change in businesses.
An AI is capable of absorbing quantities of data that are inaccessible to human beings.
These days, data is the fuel of artificial intelligence. Data is what allows the models to improve over time, and it is at the heart of the in-depth learning (machine learning) that allows machines to render increasingly suitable services in a broader range of situations.
However, in a business context, data (much like IT systems) is everywhere. A digital business is one in which (nearly) everything can be measured, from the company’s performance indicators to the messages posted on social media to the actions performed by employees at their work stations.
That is what gave rise to the concept of “Big Data.” Except that until now, that data was at worst ignored or at best piled up in data repositories, like mineral ore that no one knew how to exploit. AI is a game-changer, turning that lead into gold. Self-learning AI models are now capable of making sense of data.
Data is the fuel of artificial intelligence.
An AI can work on a project continuously, for an indeterminate period of time, and without additional cost.
Whereas, for a given project, a human team will join forces for a given period of time (a few days, a few weeks, perhaps even a few months) before moving on to something else, an AI can keep concentrating on a project indefinitely, ensuring continuity, while freeing up managers’ time.
For example, in the field of change management in the context of a transformation project, the company might launch a training program, perhaps repeated several times and, ideally, complemented by individualized employee coaching over the course of several months. No matter the good intentions and the resources invested by the company, however, these initiatives are inherently temporary in nature. The trainings can’t be repeated indefinitely; person-to-person coaching can’t go on forever.
On the contrary, an artificial intelligence solution, such as the one we’re implementing with InsideBoard, can guide employees through their own change process, with no time limit. By continuously measuring user adoption of a new tool and the actions performed by each individual over the course of a work day and in the long term (which would be impossible for humans), AI can collect bulk data that allows it to diagram the personal development of each individual and offer them personalized guidance.
AI guides each employee through his/her own change process, continuously and with no time limit.
AI can see things that humans never would.
This point is a consequence and prolongation of the last two. Not only does AI collect an enormous quantity of data about a given individual over an unlimited period of time, but it also collects data about many individuals, which allows it to establish statistical models, comparisons, and correlations.
Moreover, it can compare information originating in different systems (software, databases, etc.), and draw relationships among these disparate sources.
Thus, to return to the previous example, AI can compare an employee’s journey to those of his or her team members or a team’s journey to that of another team, or even make comparisons among several sites or in several countries. It can also analyze employees’ interactions within the communities that they form inside the company. All of this will serve to highlight the particular patterns in employees’ development profiles—patterns that would otherwise go unnoticed. It thus becomes possible to improve the employee experience by offering each employee personalized action recommendations that are well-suited to their individual journey.
If we want to put artificial intelligence to work on behalf of the company, first we must understand the three characteristics that we’ve just gone over. Indeed, if artificial intelligence is useful at all, it’s because it is different from human intelligence. We here at InsideBoard never lose sight of that premise as we focus on the use of AI to promote individual change.
If artificial intelligence is useful at all, it’s because it is different from human intelligence.
In taking this approach, AI contributes to the success of companies’ transformation projects by meeting individuals where they are and motivating them to improve themselves. It leaves plenty of room for human creativity to imagine the range of activities offered to employees to support their growth.
Finally, when artificial intelligence is applied to change management initiatives, all it does is remind us, albeit in a new way, of the well-known principles of educators and psychologists: I can help you change because I am different from you, which allows me to better understand you and to increase your own desire to change.