![]() ![]() ![]() I believe it will remain so for the foreseeable future in fact, this will be the dividing line between narrow AI and general AI/AGI. This does not devalue the importance of what ‘smart machines’ can do for us - quite the contrary! So while they may soon be quantum-charged and become unlimited in their processing capacity, their capability to truly understand (to intuit a context, comprehend tacit information, hear what hasn’t been said explicitly, deal with ambiguities and much more) is still near-zero compared to humans. Isn’t being human very much about those exact things that we cannot easily compute, measure or algorithmically define? Machines are binary, for now- zeros and ones, if-this-then-that. The bottom line? Machines don’t do relationships - and success is all about relationships, trust and understanding. This shift towards valuing, nurturing and measuring human-only skills in the workplace is already palpable, as we discuss the concept of EQ (emotional intelligence) over IQ. I believe that we need to urgently review how we define and measure human ‘performance,’ and go beyond machine-thinking, sometimes also referred to as ‘ computational thinking.’ I propose the introduction of what I call the Key Human Indicators (KHI) to first complement, and eventually substitute the existing KPIs (Key Performance Indicators) that we rely on so much today. No machine can do the work of one extraordinary (wo)man.”Īs machines start to learn and self-improve, our own work objectives and tasks will change dramatically - yet it is very important to note that the end of routine is NOT the end of human work. Riffing off Elbert Hubbard from some 100 years ago (!) - “One machine can do the work of fifty ordinary (wo)men. The call-center makes for the best example here: while it is routine work to reschedule a flight, it is not routine at all to deal with a customer that has had his flight canceled 3 times in a row, and requires some compassion and extra effort that may involve bending the rules. As we look to the next 10 years I believe that many routine tasks will be 60 - 90% automated, with the remaining 10-40% of ‘human tasks’ likely to be assigned to just a few remaining humans. These roles might include simple language translation and voice assistants, basic bookkeeping, simple call-center tasks, or the drudgery of fact-checking in legal discovery work. In routine areas that do not require any human ingenuity, creativity, understanding or intuition, the computers are sure to win. Humans ‘working like robots’ will not be a plausible strategy in the future.Īs smart machines and algorithms begin to excel over and above human capacity, we must consider what uniquely human qualities will be needed in the workforce, and how performance metrics will need to shift to encompass this change. These are the things that are propelling us towards a new era of automation, virtualisation and robotization. Now, consider the wave of workplace game-changers: big data, the cloud, the Internet of Things (IoT), cognitive computing, intelligent assistants and so-called artificial intelligence (AI). Key Performance Indicators (KPI) such as efficiency, speed, accuracy, lead generation and sales performance are widely used to measure success. It seems unimaginable that our current performance metrics would stay the same as the world around us evolves exponentially, but today, we are still measuring human performance in a mechanical way. As our job roles inevitably evolve, what kind of work will humans still need to do? And, how will we measure human performance in a machine-led future? (Check out the free chapter download at The conversation, however, rarely considers the humans in this equation. The current dialogue around the future of work is dominated by digitisation, cognification, automation and what I describe as the ‘10 Megashifts’ in my recent book Technology vs Humanity. ![]()
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