This may be the most underappreciated issue in talent management. Historically, careers have been relatively predictable:
Entry-level role →
increasing responsibility → management → leadership.
But what happens
when AI automates many of the tasks that employees traditionally performed to
learn their profession? How does a young financial analyst develop judgment if
AI performs much of the initial analysis? How does a consultant learn
problem-solving if AI produces the first draft? How does a manager learn to
manage if organizations eliminate layers of management?
This creates what I
would call a potential experience-development crisis. Organizations will
have to deliberately create new developmental pathways through:
- Internal gigs
- Project-based assignments
- Rotational experiences
- Human mentoring
- Simulations
- AI-assisted practice
- Cross-functional work
- Deliberately designed stretch
experiences
LinkedIn's workplace
learning research already points toward the convergence of learning, leadership
development, career development, internal mobility, and AI adoption.
Organizations with stronger career-development practices appear better
positioned for AI transformation, while AI upskilling increasingly varies by
role rather than following a one-size-fits-all model. Those of us with
backgrounds in learning see this as a major opportunity: Move beyond “Head of
Learning” toward “Architect of Talent Capability and Career Systems.”
