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New tools don’t always promote efficiency.
Software doesn’t always end up being the productivity panacea that it promises to be. As its victims know all too well, “death by PowerPoint,” the poor use of the presentation software, sucks the life and energy out of far too many meetings. And audit after enterprise audit reveals spreadsheets rife with errors and macro miscalculations. Email and chat facilitate similar dysfunction; inbox overload demonstrably hurts managerial performance and morale. No surprises here — this is sadly a global reality that we’re all too familiar with.
So what makes artificial intelligence/machine learning (AI/ML) champions confident that their technologies will be immune to comparably counterproductive outcomes? They shouldn’t be so sure. Digital empowerment all too frequently leads to organizational mismanagement and abuse. The enterprise history of personal productivity tools offers plenty of unhappy litanies of unintended consequences. For too many managers, the technology’s costs often rival its benefits.
It’s precisely because machine learning and artificial intelligence platforms are supposed to be “smart” that they pose uniquely challenging organizational risks. They are likelier to inspire false and/or misplaced confidence in their findings; to amplify or further entrench data-based biases; and to reinforce — or even exacerbate — the very human flaws of the people who deploy them.
Via Miloš Bajčetić
