• Home
  • /
  • CIO Zone
  • /
  • AI and jobs: Congratulations, you’re a manager

AI and jobs: Congratulations, you’re a manager

Anyone using an artificial intelligence assistant is essentially a manager – of models and/or non-human agents rather than people.
Angela de Longchamps
By Angela de Longchamps, Founder and CEO of Inspired Leadership.
Johannesburg, 13 Mar 2024
Angela de Longchamps, founder and CEO of Inspired Leadership.
Angela de Longchamps, founder and CEO of Inspired Leadership.

With the rise of artificial intelligence (AI) and the associated concerns around job security, people working in IT may think their technical skills give them the edge.

But if humans wish to remain in the driving seat, it might be more important to focus on distinctly human capabilities and learn to manage, leverage and delegate to AI.

How can workers remain relevant in a world driven by data processing technologies? How can leaders best respond to the changing workforce needs that result?

The job threat: Evolution, not extinction (for now)

The short answer to the question “How likely is it that AI will replace humans?” is: “Not very, at least not anytime soon.” The slightly longer answer is that while your job might not be taken by a robot, it is quite likely to be taken by a human who knows how to use one.

While AI will affect many jobs (up to two-thirds, according to Goldman Sachs), losses are expected to be offset by new job creation.

In fact, a 2023 study by Upwork found that two-thirds of C-suite leaders plan to increase hiring as a result of generative AI. Recent MIT research suggested that in most organisations, it is still too expensive to replace people with machines.

While your job might not be taken by a robot, it is quite likely to be taken by a human who knows how to use one.

While a substantial amount of manual, repetitive and routine work can be automated, the current trend is that AI is being integrated into existing roles, causing jobs to evolve rather than disappear entirely.

Generative AI (GenAI) and large language models (LLMs) in particular have become widely used as virtual assistants working alongside humans. For example, in software development, AI platforms are used to review, debug and optimise code and even generate entire functions based on natural language descriptions.

At a high level, GenAI adds value by processing vast amounts of unstructured data, allowing people to work more efficiently and focus on higher-order tasks, like complex decision-making and creative problem-solving.

One perspective is that we are moving from a knowledge economy to an allocation economy – that is,we still need humans to assign work to the robots, train and monitor them, and make decisions about their outputs.

What does this mean for our workforce strategies and employees’ development needs?

Five ways people leaders can be AI-GILE:

Understand AI and use it smartly and ethically: Apart from helping to make work more efficient, AI can be leveraged to make better workforce decisions. For example, AI can help flag potential bias in recruitment and performance management processes, and might even help identify employees at risk of disengagement. By learning about AI’s limitations (particularly around ethical data usage) and implementing responsible AI practices, we can maintain the credibility to keep people at the centre of governance.

Lead the talent and skills conversation: If you haven’t already, do your research both internally and externally to learn how different roles and functions are already being enhanced or streamlined by AI tools, and what new needs are emerging. This will enable you to proactively drive future hiring needs and potential role redesign. More broadly, keep an eye on the emergence of new roles in the market.

Recruit for and train adaptive and “meta” skills: Given how many roles will be impacted by future technological advancements, ensure you invest in recruiting and retaining talent with the right attributes and potential to adapt. Apart from complex problem-solving skills and non-traditional thinking styles, train people to develop curiosity, an appetite for lifelong learning and the capabilities to self-manage.

Consider the implications for managing existing employees: For example, when coaching and mentoring developers who are increasingly using AI platforms to optimise code, the focus may shift to learning prompt engineering, problem framing and how to exercise judgement in uncertainty.

Ask: How can we be a learning organisation? Business leaders have a critical role not just in training people to learn new skills and capabilities, but in enabling an organisational culture that supports them as they adapt to constant changes. By truly understanding and then communicating the opportunities that AI presents, inspired leaders can help allay employees’ fears, and encourage managers to do so too.

What can workers do to stay competitive?

Learn how to use GenAI platformsin your job: Learn how AI works, including its limitations and ethical considerations – and then find ways to apply it in your current job. This makes you more valuable and marketable to employers. The more practise you get using GenAI to help you work smarter, the more you can develop the higher-order skills that will help you stay competitive in future. Speaking of “meta” skills…

Learn to think like a manager: Anyone using an AI assistant is essentially a manager – of models and/or non-human agents rather than people. Being able to articulate a clear vision of what you want, translate outcomes into effective instructions (known as prompts), evaluate and refine results as you get them, and know when to get into the detail – these are skills usually associated with getting work done through others, but now they apply to getting work done through AI.

Consciously practising these skills will set you up as a more effective contributor, and potentially as a future team leader. Just don’t neglect the human skills, of course, because regardless of what technology you use, we all still work with, and ultimately for, real people.

Further reading and references: