Develop
Definition
Build the human capabilities required to work well with AI — and to remain capable, adaptive, and critical as AI becomes embedded in the organization. Development is not tool training. It is the investment in human judgment, literacy, and agency that makes AI adoption sustainable.
The Skills Reality
- 39% of workers’ core skills are expected to be outdated by 2030 (WEF, Future of Jobs Report 2025)
- 9 in 10 jobs will require: digital fluency, social influence skills, and creative problem-solving
- By 2026, AI proficiency is as foundational as Excel was in the early 2000s — expected, not optional
- When upskilling is personalized and tied to business goals: 70% completion rate. Generic “watch this video” training: single-digit completion (WEF / Udemy, 2026)
Top skills growing fastest (WEF 2025):
- AI and big data literacy
- Cybersecurity literacy
- Technological literacy
- Creative thinking
- Resilience and adaptability
The Perception Gap
There is a documented gap between how workers assess their own AI readiness and how their managers assess it (WEF / Udemy “AI Perception Gap,” 2026). Entry-level workers frequently rate themselves as competent in critical thinking and AI use — while their managers disagree. This gap is a leadership signal: development needs are not being surfaced through normal feedback channels.
Two Opposite Failure Modes
Under-use (avoidance): People who fear AI, distrust it, or have not been given real practice stay in low-competency positions relative to colleagues who experiment. The gap compounds over time.
Over-trust (outsourcing judgment): People who use AI without critical review gradually stop exercising the judgment muscles that make their work valuable. They become dependent on the tool for decisions that should remain human. The risk increases as AI capability rises.
Both are leadership failures. Development addresses both — by building genuine literacy, not just access.
The Secret Cyborgs Reframe
MIT NANDA found that 90% of employees already use personal AI tools for work daily. Most organizations do not know who their most capable AI users are. A useful question for leaders: “Who in your organization already knows how to use AI well — and are you leveraging that, or waiting for IT to roll something out?”
The best internal AI capability is often already there, informal and underdeveloped.
What Leaders Need to Develop
For individuals: AI literacy, prompting and task design, output verification, critical thinking applied to AI, ethical judgment, data awareness.
For managers: redesigning workflows for human-AI collaboration, redefining roles, coaching people through AI-related anxiety, holding AI outputs to quality standards, developing teams without outsourcing the development to the tool itself.
For the organization: building development that is personalized, role-specific, and tied to real business problems — not generic courses disconnected from daily work.
What to Pay Attention To
- Whether your team’s AI capability is being built deliberately or just assumed to emerge
- Where the perception gap is widest — where people think they are more capable than they are
- Where AI use is eroding the human skills that make the team distinctive
- Whether junior talent is being developed into the capabilities they will need in 3 years
Connections
Protect Govern Future Skills and Metaskills The New Leadership Role Six Strategy AI Leadership Framework
Sources
- WEF - Future of Jobs Report 2025 — 39% skills outdated, top 10 skills
- WEF - The AI Perception Gap — perception gap data, 70% personalized completion rate
- WEF - Skills Outlook
Tags: skills, learning, development, AI literacy, reskilling, future of work