The “business hospice” is how some companies are describing the final destination for corporate operations at risk of being wiped out by artificial intelligence.
Orchestrating a soft landing for people working in such activities is just one of the new challenges facing managers, according to AI ethicist, Lollie Mancey, who introduced me to the term this month.
Her point is that nobody goes into business hoping their legacy will be an enterprise that consists of a handful of executives and some large language models: “Our organisations are made of people; we have organisational culture and we need to protect that.”
The need to “keep humans in the loop” is becoming almost as much of a cliché as the claim that AI is about better jobs, not fewer jobs. But that loop needs managing, often in novel and unfamiliar ways. I’ve added palliative care nurse to the growing list of roles managers will have to acquire, as AI spawns new, improbable-sounding managerial jargon. Here are six more.
Possibility catalyser. We have to “educate our workforce en masse about what is possible” with AI, says Jacky Wright, McKinsey’s chief technology and platform officer, whose job includes rolling out AI tools to the consulting firm. Being a manager is now also about “being a catalyst and a champion” of what could be done differently with the new technology, adds Nitin Mittal, Deloitte’s global AI leader.
Uncertainty mapper. Nobody should underestimate employees’ “fear of being obsolete”. FOBO squats at the other end of the uncertainty spectrum from FOMO (fear of missing out). PwC’s commercial technology and innovation officer Matt Wood, formerly Amazon Web Services AI vice-president, says the constant quest for efficiency “drives a lot of mistrust inside organisations”. It falls to managers to try to soothe both forms of AI-driven panic.
Organisational designer. Teams need to apply the power of AI tools correctly and in the right place. Their managers need first to work out what parts of any task can be automated, “what can be augmented and what do the new processes look like”, says Aditya Bhasin, chief technology and information officer at Bank of America. Next, they need to redesign the work to ensure staff “do a lot more of the ‘why’ and the ‘what’, and let the machine do a lot more of the ‘how’”, according to Harrick Vin, Bhasin’s counterpart at Tata Consultancy Services.
Growth amplifier. Managers need to ask themselves “what skills can [they] accelerate and amplify using this technology today?” says PwC’s Wood.
Yet they will sometimes also need to act as ambition moderators. I heard one executive last year describe the effect of applying generative AI as an instant promotion for all 2,000 of his staff. But in the words of Deloitte’s Mittal, for a consultant to be able to sit down with an important client and “make sure he’s comfortable and feels that you’re credible — that’s years and years of learnt experience and you can’t ‘prompt’ yourself to it”. Managers need to mentor their team members carefully so they develop those skills and do not immediately assume they can do it all because they have the help of a machine.
Ideas evaluator. If the output of the AI-augmented worker is less likely to be lines of code or quantity of PowerPoint slides, the manager’s role becomes more one of “encouraging peer review or quality assurance, than of checking the individual’s work or making sure they have the technical competency”, says BofA’s Bhasin.
Managers may need to re-examine even the basics of workers’ terms and conditions in light of AI. “If you are doing a job in two hours because you’re using AI, are you going to be paid for the same seven hours you would normally be paid for. How are we going to work that out?” asks Mancey.
This forecast reimagining of managerial work comes with some important caveats. Workplace technology expert Danielle Li, a professor at MIT Sloan School of Management, points out that “everybody knows how to talk about culture and change management”, which is why leaders inevitably frame AI in those terms. But she says they need to put in place the foundations for effective use of AI, notably properly organised, high-quality data.
The potential benefits of AI are many. It could be used, for instance, to disseminate expert teachers’ and managers’ insights, helping to meet the challenge of educating the workforce about the opportunities offered.
First, though, managers need to work out how, in Li’s words, “to incentivise, to compensate, to excite people . . . that the nugget of an idea that lives inside you” will be shared with a machine. And, I might add, how to explain that the same machine may be about to send you, your job and the organisation where you work, on a one-way trip to the business hospice.
andrew.hill@ft.com
