Figuring out what artificial intelligence (AI) actually means is an opaque process. Press releases that claim technology CEOs haven’t sufficiently instituted “re-skilling” efforts to support their workforces in the face of AI’s emergence don’t help the cause much for those of us running tech companies. It’s especially unhelpful when so-called experts deliver overly generalized directives for companies to follow when it comes to preparing our team members for the mysterious world that AI will create.
Now’s the time for the experts sitting outside the day-to-day reality that confronts those actually doing work in tech companies to provide actionable ideas that company leaders can actually employ — and that workers can simultaneously invest in—so that re-skilling can evolve in the face of AI’s emergence.
First off, let’s start by creating a simple and common understanding of what AI means. Parse for everyone in the company the idea that AI means enabling machines to do work more effectively than humans, and that machine learning (ML) and deep learning (DL) are more nuanced layers of AI that represent how machines develop more effective algorithms over time. It matters not whether these algorithms live in massive computational cloud installations or client side IOT hardware (or both), so long as they can do things humans shouldn’t be doing — or things we can’t accomplish on our own — in support of company goals.
So what might this look like in the context of re-skilling your team?
It means being explicit about how every team member comes to understand AI and how it might be employed to solve problems for your organization. For example, creating a simple approach to AI that utilizes data to rapidly develop and deploy predictions (which will continue to become cheaper as the insightful book “Prediction Machines” articulates), that in turn allow team members to apply judgements and decisions (based on those predictions) to enable rapid progress towards goals. Revisiting this iterative data > prediction > judgment (decision) loop creates an AI skillset that every team member can adopt and bring to how they do their own work.
Re-skilling your team for an emerging AI workplace also means people need to be simultaneously macro- and micro-minded. Macro-minded means teammates need to work at developing their ability to understand the strategic big picture that the company is building towards. What’s your big vision — what will your company be in 5, 10, or 25 years from now? Once a clear macro mental construct becomes solidified in everyone’s mind, shifting to the micro-minded framing becomes crucial. Breaking down the big idea into micro steps requires that each employee become facile in evaluating data analytics and insights in ways that support the daily march towards the big idea. As a tech leader there’s perhaps nothing more important in the age of AI than supporting your team’s ability to bounce from macro- to micro-mindedness in an effort to overcome every obstacle your company faces on its long-term path.
Finally, avoid generalized statements like those uttered by alarmist consultants. Take any comment you read or hear in a podcast per the vital need to apply AI to your workplace, (and the resultant requirement that your workforce needs to dramatically be re-skilled for a future we can’t imagine), as an opportunity to step into creating simple data, prediction, and judgement/decision loops. Develop these AI loops over and over, and in every department in your company — from engineering to sales. Then, use these basic AI loops to validate that your team is doing the hard work of toggling in unison from a macro-minded view of where you are heading, into the myriad of micro-minded activities that will increase your probability of success over the long haul.
Perhaps we are indeed heading to a future in which AI will replace everything humans might do, but the sooner we invest in really developing simple applications of AI within our workplaces and daily lives, the better we will get at understanding where the line can exist between the machines and us pesky humans. Taking an applied approach to how we develop within the company also brings hope that we’ll be more effective at steering machines towards human enhancing efforts, but that we might also steer our machines towards both unbiased and ethically appropriate ends.
Originally published on Medium on July 15, 2019. This Substack version is maintained as the canonical archive.