Enterprise-wide upskilling programs are dominated by artificial intelligence. In the new corporate arms race, companies are striving to equip their people with the skills they need to exploit this emerging … Continue reading The missing link in AI upskilling
Enterprise-wide upskilling programs are dominated by artificial intelligence.
In the new corporate arms race, companies are striving to equip their people with the skills they need to exploit this emerging technology, and hopefully reap the rewards in terms of productivity and efficiency.
Typical upskilling programs cover digital skills such as prompt engineering and agent building. The smarter ones also raise awareness of the risks of using AI such as hallucinations, bias and data security. And the more progressive ones round out the curriculum with related human skills – the most obvious being critical thinking.
In this dynamic environment, a resurgence in our appreciation of soft skills is also motivating us to double down on our unique human talents to do what the robot can’t – which is increasingly debatable as the technology advances; or won’t – which is probably because we haven’t asked it to.
Despite the flurry of activity, I maintain our strategic connection between digital skills and human skills remains tenuous, if present at all. While the former are somewhat straightforward – as the technology evolves, so do the skills to use it – the latter are not so self-evident. In the universe of human endeavour, what should we develop?
It reminds me of an employee facing a deadline to submit their individual development plan, so they choose Advanced PowerPoint. Or the CXO who decrees EQ training because, you know… it feels right.
The disconnect exposes a missing link in AI upskilling, and I propose that link is our way of working.
Prima facie, the impact of AI on work is two-fold:
- It automates high volume, onerous, time-consuming processes; and
- It accelerates the speed of output.
For example, an insurance claims assessor offloads 80% of his incoming cases to a robot, enabling him to focus his expertise on the most complex ones. Across the floor, an instructional designer uses a chatbot to produce generic content in a matter of minutes, saving her several days or even weeks of grind.
While these outcomes on their own may be reward enough for some in terms of productivity and efficiency, the greater reward isn’t gained by doing the same job faster, but by doing it better. And that can be achieved by recognising the following principles.
1. AI changes the nature of the role.
When the insurance claims assessor shifts his attention from simple claims to complex ones, he transforms his role from administrative officer to trusted adviser. In practice he stops rubber stamping widgets, and starts conducting deep analysis (critical thinking), referencing policies and precedents (technical research), and making executive decisions (risk management).
Similarly, when the instructional designer uses a chatbot to generate content, she transforms her role from producer to production manager. In practice she stops creating the content herself, and starts directing the chatbot on what to create (strategic thinking), quality assuring the output (critical thinking), and socialising it with the project’s stakeholders (communication).
2. AI provides an opportunity to expand the scope of tasks.
When significant proportions of their workloads are alleviated, capacity is freed up for our people to engage in other tasks that are within their remit, but they’ve historically neglected or deprioritised due to competing deliverables.
For example, the insurance claims assessor finds he has more scope to conduct site inspections, interview witnesses, and negotiate costs; while the instructional designer can engage in deeper analysis upfront to ensure her solution will meet the business need, generate multiple prototypes that evolve via regular consultation, and evaluate the outcomes of the project beyond the happy sheet.
3. AI provides an opportunity to pursue strategic imperatives.
The offloading of previous burden not only frees up capacity for tasks, which I maintain are supported by competencies, but also for strategic imperatives mandated by the business, which I maintain are supported by transferable skills.
For example, the insurance claims assessor may start up a CX initiative to resolve the pain points in the customer journey, or a process improvement initiative to simplify the assessment procedure; while the instructional designer might redeploy her efforts to innovating her learning experiences, or investing in deeper relationships in the business.
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The implication of these principles on AI upskilling is that beyond digital skills, our changing way of working demands specific human skills that aren’t developed by chance or whim, but by intention.
By connecting digital skills and human skills via the change, we understand the paradox that the skills our people need to use AI most effectively include skills that aren’t about using it. We enable the robot to do what it’s good at, which in turn enables us to do what we’re good at, hence we work together to improve performance.
