- Key insight: Firms are using AI adoption to justify large-scale workforce exits.
- What's at stake: Institutional knowledge loss, culture erosion and competitive agility are all at risk.
- Forward look: Expect mandatory AI training and role redesigns to proliferate industry-wide.
Source: Bullets generated by AI with editorial review
When Accenture CEO Julie Sweet recently announced AI-related layoffs, she unwittingly kicked off a debate about the merits of firing AI skeptics.
"We are exiting, on a compressed timeline, people where reskilling based on our experience is not a viable path for the skills we need," Sweet said in the company's fourth-quarter earnings call. "And we're continuously identifying areas of how we operate Accenture to drive more efficiencies, including through AI in order to create more investment capacity."
The consulting firm's headcount dropped by 11,000 in the past three months. Some of this headcount reduction was due to attrition, so it's hard to say exactly how many people were laid off because the company thought they couldn't be reskilled. Accenture declined a request for an interview.
Similarly, Mumbai-based technology company Tata Consultancy Services announced in July that it will be laying off 2% of its workforce, or more than 12,000 employees, to become more AI-focused.
"We have been calling out new technologies, particularly AI and operating model changes," TCS CEO K Krithivasan told
This week, TCS said it will offer severance packages of up to two years' salary to long-serving employees whose skills no longer align with the company's needs.
The news that companies are kicking AI laggards out the door (or "exiting" them, which seems to be the euphemism du jour) has elicited reactions ranging from approval to shock, especially in the financial industry.
"This mandatory top-down approach to AI usage and skills within a bank would be a pretty disastrous approach," Gilles Ubaghs, strategic advisor for commercial banking and payments at Datos Insights, told American Banker. "Critical to the successful implementation and any hope of gaining ROI on what can be sizable investment into AI technologies is staff buy-in and support."
A report that MIT published in August found that 95% of AI proofs of concept provide no return on investment, and this was in many cases due to a lack of employee engagement, he said.
"This sort of 'use it or we lose you' approach seems like it would only entrench opposition among critical staff and will only hinder take-up and any efficiency gains," Ubaghs said.
Consequences
Debra Andrews, CEO of the consultancy Marketri, also said AI-related layoffs could have unintended consequences.
"I definitely think it can backfire," Andrews told American Banker. "There's no doubt that any time a company does layoffs, even if it's being transparent, saying we're doing layoffs because we're implementing AI is going to erode the culture. It's going to breed mistrust throughout the organization. As leaders and companies, you've partially accepted responsibility for people you manage, for their career paths."
Not planning ahead for how the workforce can evolve with AI "is not so much a failure of the employee, it's a failure of the people that are managing those people and the leadership not offering those resources," Andrews said.
Companies should have planned for the advances of AI a year ago, "even if they didn't think it could quite be where it is today," she said. "You could run scenario analysis and imagine, what if it was able to do this? How might my department need to evolve? How might my people need to be trained? And so when you go and lay off a bunch of people, and it takes people by surprise, you are eroding the trust of your organization. And quite frankly, the people who are retained probably will start to question whether it's a place that they want to continue to be employed."
Andrews also challenged the idea that some people can't be retrained.
"It's hard to imagine that 11,000 people are untrainable and not able to be skilled, because AI technology is not that hard to learn," she said. "It is very intuitive and with some prompting basics, people can learn to do this. It is curious to say that a certain subset of your employees cannot be skilled in AI."
Retraining existing staff is more cost-effective than large-scale turnover, said Fawad Bajwa, managing director and global AI practice leader at the consulting firm Russell Reynolds.
"Hiring a bunch of people with AI expertise is expensive and time-consuming," Bajwa told American Banker. Teaching employees to use new tools will generally be faster and less expensive.
Bajwa is seeing companies hire AI experts for leadership roles, such as chief AI officers, chief data and analytics officers and chief transformation officers, "who are taking a holistic view of what the company does today, what it needs to be tomorrow," Bajwa said. "But on the flip side, if someone in marketing doesn't use AI tools, it's better for you to educate the workforce in terms of how to use these tools. Redoing your entire marketing department will be way too expensive and time-consuming."
Dan Latimore, chief research officer at The Financial Revolutionist, sees AI-related layoffs as a shot across the bow.
"This is an indicator that white-collar workers need to make sure that they've got AI skills that will help them do their job," Latimore told American Banker. "Accenture typically has great training programs; even workers at other firms who don't yet have that focus on AI will benefit from learning those skills on their own time."
Latimore also said he suspects that the phrase "people for whom reskilling isn't a viable path" encompasses a lot more than just a lack of AI competence.
"This is a way of shedding workers that has a unifying message behind it," Latimore said.
Better approaches
When Andrews read that
The best AI deployments are those that are driven by business users to solve the actual problems they have in their daily work, and which use their knowledge of where the challenges and pain points are to implement something that's effective, Ubaghs said.
"Management should be driving a bottom-up approach, where they support and empower staff to use the tech to the best capacity, as opposed to a top-down mandate driven from central teams who are removed from the business," Ubaghs said. "Giving people space to dissent is important."
There's also an argument to be made that experienced bank talent is only going to become more valuable, he said.
"Where AI is having the most disruption is in areas like coding and customer service, where we are already seeing a major drop-off in entry-level employment roles," Ubaghs said. "Banks that cut off experienced staff arbitrarily and early are liable to lose critical knowledge sets that will be harder to replace with AI than many now suppose.
"AI needs carrots not sticks, and managers need to show, not tell, staff how AI can make them more efficient," he said.
