navigating change in the world of work.

It becomes more tempting every day to offload our work to machines (and when I say 'machines' here, I really mean code). Machine learning is becoming ever more powerful, and we are on track towards a future in which machines will do most of the tasks we currently consider to be employable.

It will begin with the repetitive tasks - in fact, it already has. Jobs that perform a few specific functions and require a high volume of repetition are being replaced by machines who can do the same work, faster, and more consistently. This is happening today, and we've seen many companies replace their front-line and warehouse workers with digital counterparts. Next affected will be the jobs that have more variance in responsibility and require data. In terms of job replacement, when you boil it down, machine learning is essentially just automated human decision-making; the algorithms use large volumes of historical data to learn how humans respond to different situations, and make the most similar decision (not necessarily the best decision, but we'll tackle that later). It follows, then, that as we collect more data about ourselves, our companies, and our world, we will hand off more components of our roles to machines. After all, isn't it always better to have something done quicker AND more accurately? 

Maybe not. When we give machines too much autonomy over humans, we remove the compassion from the equation. Some workplaces are turning to machines for personnel decisions, allowing them to automatically terminate underperforming employees without taking into consideration the whole context of why the person might be performing slower than usual. It's so important that we keep humans in the loop in decisions like these - machines are great at processing large volumes of data in short periods of time, and we should continue to use them for that, but the final decision should always be brought by a human. And when machines make biased decisions, underrepresented people are disproportionately affected. If we don't correct the human bias in the data, or provide opportunities for people to notice when a biased decision has been made, the machine will just further the human biases of the past. 

So what does this mean for us?

At this point, machine integration is an inevitability. In a survey of 1201 Canadian women, Randstad found that 30% of working Canadian women are worried about losing their jobs to technology over the next 5-10 years, yet 54% of women are not currently doing anything to protect their careers from being negatively impacted by technology. 65% think employers should help them find another job in the organization when their job is replaced by automation (Randstad Canada, 2019). We can't stop the growing ubiquity of machine learning, we can only work on ourselves and reshape our approach to work. We'll need people in strategic places, and we'll need to have people whose jobs are specifically to ensure that decisions are made with the right context taken into account. Another way we can do this is by refining our soft skills. Machines are great at those repeatable, consistent tasks, but they're not as great at the things that make us human. According to Randstad, 44% of Canadian women identify empathy as the top skill they bring to the workplace. Here's what some of you had to say during #RandstadTechTalks:

If we'd had this conversation five years ago, everyone would have been talking about the importance of learning to code. I'm a developer myself, and a proponent of computer science education, but it has become clear that not even developers are 'safe' from role automation. It's the more metaphysical skills that professionals of the future will use to pull ahead of their machine counterparts - like Amy and RC shared in the Tweets above, compassion, empathy, intuition, and creativity are skills in the human wheelhouse that are harder for machines to develop. We're already seeing workplaces prioritize these in the hiring process, and it will only become more important as machine learning becomes more sophisticated.

. . .

Where do we stand today?

We are in a period of immense change. Technology is advancing faster than ever before, equally stirring up excitement and concern about the AI-powered future. Companies are recognizing the importance of diversity and inclusion, and are working towards fostering healthier, more flexible workplaces. We as innovators are in a position to influence the evolution of the workforce.

Key takeaways:

• Companies are going global, and tech companies expand internationally faster than any other industry. More than half of roles will be fully remote by 2025.
• Diversity is not just important, but imperative. The companies who embrace diversity and foster inclusive workplaces will be the ones who pull ahead in the globalization race.
• ML is advanced, but it's not a silver bullet - stop and think before you use an 'AI' solution. Beware the products that offer to solve all your problems with machine learning, and consider the following: How does it introduce room for bias? Where will the final decision be made? By whom?

The future of work has already begun.

did you miss part 1 of this article? it covers how diversity will shape the future of work and is a must-read!

sage franch

Here is the new bio:  Sage Franch is a developer and tech entrepreneur based in Montreal. As founder and CTO of Crescendo, Sage builds technology that helps organizations foster diversity and inclusion through storytelling. She is a mom, a musician, and a writer of both fiction and non-fiction alike - read her work on trendytechie.ca.​​​​