Would you trust AI to maximize your professional potential?
Get promoted fast. Be recognized for what you do. Understand your team. Become a better leader. Scale your business. Retain your best employees. All of those sound like goals that employees, managers, and executives have for their professional careers, right? Depending on where you stand on yours right now, it’s highly likely you wish for a couple of those things for yourself.
Up until today, the only way to achieve them was for you to stay quiet and work hard in the hopes that it will all click. But what if there was a different, more technological way to do it? Would you use it? That’s precisely the question that stems from new platforms that use AI to help you get to those goals.
Unlocking new skills through algorithms
One of the most notorious platforms for doing this is called Yva, which markets itself as a “real-time, secure and ethical predictive people analytics platform.” The platform is based on a pre-trained artificial neural network that looks into digital interactions, such as when you answer an email to your boss or assign a project on Slack to your outsourced software developers. After analyzing those instances, Yva provides tailor-made recommendations and insights on how you can improve your performance.
The suggestions coming from Yva help employees, managers, and execs in different ways. From recommendations on how to become more productive and identifying potential leaders among the workforce to boost employee retention, the platform promises to use the data it collects towards better business for everyone involved.
Yva is far from being the only platform of this type. There’s Socos, which uses machine learning to improve educational outcomes and workplace development. There’s also Cogito and a similar platform called Butterfly, both of which use AI to understand the emotional states of employees and managers to prompt recommendations to improve their daily routines.
All of them work on the same basis. Artificial Intelligence can gather data from you in a non-intrusive way while you go about your everyday tasks. After analyzing that data, the platform sends a targeted message to help you shift your focus, pay attention to specific behaviors that might impact you negatively, or provide new paths of action. After some time of working with them, these platforms expect you to form new routines that integrate the suggestions by force of habit.
Put it in that way, it all sounds Pavlovian. However, the developers working with these platforms expect the users to actually get involved in the process and accept AI’s ability to bring out their full potential, thus developing new skills over time. It’s not that you’ll have to study, as these AI-based tools look to identify and boost the most wanted skills in today’s market, soft abilities like emotional intelligence, social skills, creativity, or strategic thinking.
Since AI will be able to replace certain hard skills (like building an app or doing all the accounting aspects of a business), these platforms aim at people to prepare them for a future that will be eager for capabilities that are more inherently human. As Vivienne Ming, co-founder of Socos, puts it “let’s take the best of what machines can do and stop wasting human beings’ time doing those things. Then let’s work to lift people up, and let them explore. Let’s massively extend the creative class.”
A challenging implementation
Maximizing the workforce’s potential feels like those deals that no one would object to. Employees can increase their skill set, feel more motivated, and invest more in their work. Managers can become stronger leaders, able to better understand their teams and bring out the best off colleagues. Executives can reap the benefits of a happy workforce, improving top and bottom lines and retaining the best talent they have.
Yet, there’s something a little unsettling about using AI to do so – privacy. Though you wouldn’t have to go through quarterly employee evaluations with forms and interviews for your bosses to know where you stand, giving these platforms the permission to monitor everything you do and how you do it feels a little troubling. Even when these tools can work with anonymized data, they perform better when you feed them more information.
So the basic trade is, for you to become more productive, you have to let these platforms into everything you do in your work. Since all of these companies are privately owned and the legal framework is still fuzzy about AI’s implementation in the real world, it’s easy to feel somewhat shaken up about what those companies will do with that information.
David Yang, Yva’s creator, says that one in five people are against sharing their data with the platform. However, as he sees it, Yva and other tools like it can be a better choice than HR employees, since the algorithms would have the biases HR might have and that often influence certain decisions. Which brings us to the other problem – the training of these algorithms.
Since most of them rely on machine learning to work, these tools need a training phase and constant retraining afterward. If those instances aren’t properly implemented, the human biases of their creators can be directly transferred to the evaluations of said platforms. In that way, you could end up being evaluated differently because of your age, sex, or ethnicity, even when all the other factors of evaluation remain the same.
It’s not that these AI tools can’t work, it’s just that we need to be careful about their implementation. It’s highly likely that these solutions will cause some friction in certain people (and even in specific industries) and the training stage needs to be uncontaminated to ensure no bias makes its way into the algorithm.
But if we can all work on those things consciously, maybe the future imagined by Yang is possible: a day when smaller companies can rely on these AI-powered people analytics platforms to maximize their potential and boost the “more human” skills. In the meantime, those platforms are already here and it’s up to you, me, and everyone else to figure out how are we going to use them and where will we draw the line.
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