3 reasons why smart CEOs start Machine Learning projects right now

While most CEOs struggle in the face of COVID-19, some leverage data and do things differently

T he COVID-19 crisis hits organizations with full force. This black swan imposes a unique leadership challenge to CEOs. Since the outbreak, I have personally experienced the struggles of leaders and employees alike––many of whom were not able to adapt to the radically changing environment.

Some CEOs we worked with in the past six weeks, however, are doing things differently. Besides ensuring the health of their communities, they were able to shape the future of their endangered business by kicking-off or leveling up their Machine Learning (ML) activities. Here’s why.

1. ML activates the company — and customers

A crisis itself enables an entirely new space of opportunity. The urgency to act creates a willingness to listen, reduces inertia, and fosters an openness among employees. Most importantly, it creates a momentum to get things done!

So-called Artificial Intelligence has long been met with resistance among employees in Germany (trust me, I’ve talked to many who think it’s just like Blockchain). Not so any longer — if done right. CEOs can channel the momentum by letting their employees drive ML projects and have them contribute to the future of the business. You won’t believe how empowered and committed employees feel to do so right now.

The CEO of a steel processing company we work with knew his organization will be heavily hit by the consequences COVID-19. Still, he didn’t quit our collaboration but used the momentum to further strengthen his core business. Now, we jointly develop a prototype for a Recommender System (something resembling Amazon’s “Customers who like this also like …”). Our system comes up with suggestions about what his customers need––especially during the crisis. This ensures his sales drop significantly less than expected.

2. ML projects work perfectly from home

Well, just take a look at the picture below. That used to be the office of our Head of Data Science (don’t worry, he’s back and safe). Alessandro is pretty much all around the world––surfing, playing the Hang (if you don’t know what it is, look it up) and coding. Yes, despite travelling sometimes, we never feel he’s gone . This is also due to platforms like GitHub that allow to develop ML models and prototypes in distributed teams.

For Data Scientists and Machine Learning Engineers, working remotely is the (old) normal. In fact, this is what they are used to. Smart CEOs know that ML projects can easily be implemented remotely. On top of that, they are well aware that their organization can grow its virtual collaboration capacities by working closely with those who truly know how to do it. It’s easy: ML projects work also in times of COVID-19!

3. ML is powerful even with little money

One of the biggest myths I have encountered in the past years is that ML is prohibitively expensive. Until today, I am bewildered by how this emerged. Just to be clear: it doesn’t have to be. My observation is that smart CEOs keep costs low by having a prototyping mindset. They only scale if the value is validated.

Sticking with the above example: For the steel processing company, we didn’t do the Recommender System full scale at first. We started small using only a subset of data and tools, but quickly validated with the sales people whether our model’s recommendations are useful––or whether this project is prone to fail. We are fine with killing prototypes!

Plus, from my personal experience, I think about one third of the ML community is truly mission-driven. This group of developers and innovation managers alike believe in the power to change society for the better while creating business value. So whether there is a large budget or not, people are willing to support!