Workplace Data That Dont Augment Human Performance Will Be Used To Automate It

Use it or lose it. JL

Michael Schrage reports in Harvard Business Review:

Anybody and everybody who wants to succeed in tomorrow’s 21st Century organizations will have to commit to levels of self-monitoring, self-surveillance, and self-quantification that makes Orwell read like Pollyanna. The reason isn’t post-industrial intrusiveness or invasiveness but an imperative for professional self-preservation and self-improvement.Workplace data that don’t cost-effectively augment human performance will likely be used to automate it.
Counting calories and steps may be terrific for improving personal health. But what counts most in promoting professional success? Quantifying the “business self” is an essential precursor for enterprise networks that empower people to manage their strengths and weaknesses better, faster, and cheaper.
The personal productivity future is clear: Anybody and everybody who wants to succeed in tomorrow’s 21st Century organizations will have to commit to levels of self-monitoring, self-surveillance, and self-quantification that makes Orwell read like Pollyanna. The reason isn’t post-industrial intrusiveness or invasiveness but an imperative for professional self-preservation and self-improvement.
Don’t think Big Brother, think Big Data-Driven Coach. The best chance knowledge workers have to cost-effectively compete with smart(er) machines is by embracing technologies to become smarter and more influential themselves. The typical American office worker reportedly processes well over 5,000 megabytes a day. That number is rising. Failure to convert rising data tides into greater personal productivity is an invitation to unemployment. This will hold as true for Indian and Chinese knowledge workers as their OECD counterparts. This trend is as global as it gets.
In other words, workplace data that don’t cost-effectively augment human performance will likely be used to automate it. For world-class organizations, tomorrow’s “quantified self” hurtles beyond constant self-assessment to relentless self-improvement. If you’re not getting measurably better, you’re going to go away.
Where self-tracking/monitoring tools, not unlike magnification mirrors, are used primarily to let people literally see themselves in new light and higher resolution, these technologies will also recommend the best and most realistic options for improving performance.
Call it the enterprise Amazon-ification or Netflix-ification of quantified self-help. Much the way Amazon suggests books to read and Netflix recommends videos to binge watch, data-driven digital firms will aggregate, synthesize, and customize explicit recommendations designed to make their people productive and effective. More sophisticated “recommenders” will proffer advice to stimulate creativity and collaboration. Innovative leaders will algorithmically invest in optimizing human performance, as well as process efficiencies.
By passively monitoring chats, emails, memos, and presentations, for example, enterprise recommenders “know” individual communications patterns and styles. So as managers draft critical project reviews, the software — like an “emotional autocorrect” — could suggest word choices to make those criticisms affectively effective. Managers are, of course, free to accept or reject those data-driven edits, but the recommender will, of course, monitor their choices and the concomitant results. That’s how recommenders learn.
Depending upon their level of personalization, recommenders can prompt introverts to use enterprise social media more effectively and encourage overly-aggressive communicators to throttle back. Specific memos to read, colleagues to invite, and meetings to skip could all fall within the algorithmic purview of these data-driven advisory regimes.
Consequently, getting — and staying — on the career fast track will require the humility and self-discipline to follow the best advice of the smartest machines you can find. Professional success may be contingent upon trusting these technologies as much — or more — than one’s colleagues.
The managerial and organizational implications are, of course, enormous. They’re anticipated in the growing “people analytics” movement that calls for leadership at all levels to rigorously define — and measure — what excellence should mean. The critical difference now clearly emerging is that, increasingly, the best dispensers of advice and counsel may be the machines themselves and not the ostensibly human leadership.
Indeed, one of the real risks — and opportunities — for this next-generation of enterprise recommenders is that top management will increasingly depend upon these technologies to understand their (very) human colleagues. The line between reliance and dependence becomes vanishingly small. That said, what should managers do when the recommenders they rely on do a measurably better job than their own human intuitions and insights?
That’s going to be the challenge haunting top managers in tomorrow’s digital enterprises. Sometimes, it’s not just what you do that counts, but how you count what you do.