In a recent episode of the "Beside Ourselves" podcast, hosts Giles, Theo and Rutger dive into one of the most pressing questions of the modern workplace: How will AI adoption impact job security and the nature of work itself?

Drawing from an article in The Economist, the discussion quickly centred on two surprising yet heavily invested areas of AI: computer programming and customer service.

The Threat to "Context-Lite" Roles

Why these two? The hosts identified a key commonality: they are often "context-lite." For many programmers and customer service professionals, the job can be transactional—a "ticket muncher" approach where you write code or answer a ticket without needing a deep understanding of the broader business strategy or the why behind the task.

These roles are prime for automation because:

  • They represent a large pool of potential savings for companies.
  • They generate an abundance of operational, verifiable data (code exchange, release processes, customer feedback).
  • The output is easily testable and verifiable.

The Crucial Role of Human Context

However, the conversation emphasised that not all work can be stripped of context. Roles that emphasise a deep, contextual understanding of the business, the market, or the client—such as the hosts' own consulting roles—are deemed more resilient. AI may assist, but it cannot yet replicate the human element of "getting under the skin of the business."

This context-heavy vs context-lite that emphasises a deep, contextual understanding of the business, the market, or the client—such asistinction also affects decisions about outsourcing. Low-involvement tasks that can be clearly briefed with output-driven expectations are easier to outsource and, consequently, easier to automate with AI.

The group also touched on physical jobs, noting that while spatial AI and robotics are advancing, instances like Amazon's automated warehousing still struggle to cope with complexity, demonstrating that human intervention remains superior in certain physical, complex tasks.

Shifting Career Paths and the Rise of Mentorship

Perhaps the most significant long-term impact discussed is the effect on the career ladder. As AI becomes capable of handling the 'legwork' and basic coding that were once the training ground for junior employees, those entry-level roles may cease to exist. This creates a "blocker" for new talent trying to enter a field.

The solution, the hosts argue, falls squarely on senior professionals and business leaders:

  • Shift Expectations: Senior staff, who gain efficiency from AI automating menial tasks, must redirect their saved time to higher-value activities, including active, hands-on mentorship.
  • Invest in Juniors: Companies must stop treating juniors like "robots" delegated to menial tasks. Instead, the focus should be on teaching them how to think like a senior professional, leveraging their natural adaptiveness to new technology.
  • A Faster Track: The time it takes to move from junior to senior will likely speed up, as new professionals become "AI native" and gain qualifications and experience faster than in previous eras.

The Guiding Principle for Effective AI Use

Ultimately, the podcast concluded with a powerful guiding principle for everyone—from students to senior executives—on how to use AI constructively: "Use AI when you start a job, use AI in the process. Never, ever use it at the end of the process. Never take the output, copy and paste it into the job."

AI is an amazing technology for research, brainstorming, and drafting, but using it as a shortcut to cut corners and submit unedited output is not only "cheating" but a disservice to one's own learning and development. The power and agency to use the technology constructively, to create value and enhance effectiveness, ultimately lies with every individual and business leader.