Concerns about artificial intelligence’s disruptive effects on the workplace often dominate discussions about how the emerging technology will impact the labour market.
Much commentary on the topic veers from bleak predictions of the destruction of jobs and outmoding of traditional skills to celebrations of the fortunes on offer to those who can unleash AI to boost performance.
However, for some employers and educators, AI is already helping to smooth out the acquisition of skills, and to improve existing jobs. They say the technology can help organisations assess worker skills, plan for emerging needs and train their staff — boosting corporate productivity and staff career prospects.
“What we’ve found is that one of the best ways to learn about AI is to use AI,” says Jim Swanson, executive vice-president and chief information officer at Johnson & Johnson.
The pharmaceutical company uses an AI-driven process called “skills inference” to assess and plan across its workforce, in ways that would not be possible manually. “It’s proving to be an important asset in helping us understand and enhance our workforce capabilities,” Swanson says.
DHL, the international delivery company, uses AI to compare the skills staff have and those needed in open positions. Through its “career marketplace”, staff can be directed to the right training, to advance their careers more effectively, and managers can be supported to fill empty positions.
This use of AI encourages internal hiring, which is less expensive and quicker than external hiring, explains DHL’s Ralph Wiechers, executive vice-president for human resources. It also means candidates are more likely to be a good fit.
AI has further applications in identifying and creating training materials for new skills quickly — ideal when business needs are evolving rapidly. “For an organisation to be adaptive . . . to get the right skills, it needs to be automated, compared to in the past where you could prescribe a training pattern that would remain stable,” Wiechers says.
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Many companies using AI in their workforce management infer skills using data generated from across the organisation — for example, existing job titles, the work staff do, activity on technology, and supervisor reports.
At J&J, a dedicated team developed a company-specific skills taxonomy with 41 “future-ready” skills, such as data management or process automation. It then trained AI to identify where these skills existed in the organisation, based on workers’ previous experience, roles and current positions. Workforce management systems, updated by employers and managers, create a data set to train AI models to assess skills and evaluate them on a proficiency level from zero (no skill detected) to five (thought leadership).
In addition, AI tailors recommendations for learning and development, too, suggesting to users the courses they should take to further their careers with the company. Mapping the organisation’s skills in this way “helps our leaders make informed decisions about hiring, retention, and talent movement”, says Swanson.
Our learners . . . don’t just want to read or watch training materials; they want to be an active participant
Other organisations are using AI to improve training itself — through simulations, or by giving more people access to personalised feedback.
At Bank of America, employees can use AI to practise difficult conversations — discussing sensitive issues with clients, for example. By trying out approaches with a simulation, staff can “practise real-world interactions in a totally safe environment”, says Michael Wynn, senior vice-president for innovation and learning technology.
“It gives them the opportunity to build some confidence, test out their skills . . . that traditional methods don’t allow them to do,” Wynn says. Managers can see where staff are improving faster by responding to the feedback the AI gives them, and also where staff struggle — suggesting areas educators need to focus on.
“One thing that really helped us navigate through the labyrinth of technology was understanding that our learners don’t want to learn the same way,” Wynn adds. “They don’t just want to read or watch training materials; they want to be an active participant.”
Nick van der Meulen, an MIT scientist who focuses on supporting organisations with technological change, says AI automation allows employers to assess more skills, potentially with greater accuracy than existing approaches.
“You can give people insight into how their skills stack up . . . you can say this is the level you need to be for a specific role, and this is how you can get there,” says van der Meulen. “You cannot do that over 80 skills through active testing, it would be too costly.”
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But, while the technology has “tremendous promise”, van der Meulen is also aware of its limits — and the fact that developing the infrastructure requires work.
Similar warnings from others in the field underline the idea that, despite the hype, turning assessments and decisions to artificial intelligence can still be fraught. Skills assessments are only as good as the data they are trained on, and human input is crucial for a system to work.
“You need to have a definition [of skills] that’s easy to understand and useful for an algorithm,” says van der Meulen. He concedes AI may not be “100 per cent accurate”, and problems can arise, for example, if employees “don’t go through the effort of making sure their digital footprint is complete”.
That means, in most cases, it should be recognised as a rough assessment of skills that staff and managers can correct and add to, rather than something definitive.
High-stakes evaluation and growth decisions are best suited to remain under human supervision
To overcome this problem, J&J allows staff to edit their skills history and add information — goals, interests, certifications — that may not be automatically in the data sets, to ensure that the AI has as much information to draw on as possible.
These limitations mean caution is still advised when using the technology, says Nimmi Patel, head of skills, talent and diversity at Tech UK, the British trade body. “AI can process large amounts of data very fast. But algorithm evaluation as it exists today could struggle to understand the nuances of individual growth and development trajectories.”
She believes “high-stakes evaluation and growth decisions are best suited to remain under human supervision” through a hybrid approach.
At J&J, Swanson stresses that AI skills assessments are not used in day-to-day performance management. At both J&J and DHL, participation is optional. But early figures show that AI platforms have been popular at both organisations. “It’s about understanding the big picture of our organisation’s skills and helping people know exactly where they should focus their learning,” says Swanson.
