Why A New Model Is Necessary

A new workforce readiness model for the age of AI is essential because automation, shrinking skill lifespans, and widening equity gaps demand lifelong, agile learning systems that blend technical and human capabilities.

The Learning and Development Initiative

Artificial intelligence is transforming nearly every occupation, making traditional education models too slow and static to keep pace. Workers need continuous reskilling in both digital and human skills to stay employable as automation reshapes industries. Without intentional frameworks, AI adoption risks deepening inequality and leaving many without access to opportunity. A modern workforce readiness model aligns education, employers, and policy to promote lifelong learning, ethical AI use, and regional economic resilience.To learn how the NJIT's LDI Workforce Readiness Model can help support you and your organization, please contact Dr. Michael Edmondson, Associate Provost. Below are some of the primary reasons why a new workforce readiness model in the age of AI is necessary.

1. Automation is reshaping roles at scale

Response: AI is changing task mixes across most occupations, so workers need continuous upskilling—not one-time training.

Citation: World Economic Forum, Future of Jobs Report 2023 — Download PDF

2. Skills expire faster than legacy models can keep up

Response: Rapid tech change shortens skill “half-lives,” requiring lifelong, modular learning pathways.

Citation: OECD, Artificial Intelligence and the Future of Skills — Project overview

3. Employers report historic talent gaps

Response: A majority of employers struggle to find the right mix of digital and human skills—tightening the urgency for new readiness models.

Citation: ManpowerGroup, Global Talent Shortage 2025 — View data

4. Human skills are the differentiator alongside AI

Response: As automation grows, capabilities like problem-solving, collaboration, and self-leadership drive employability and wages.

Citation: McKinsey, Defining the skills citizens will need — Download PDF

5. Equity gaps can widen without intentional design

Response: Access and inclusion lag in highly digital jobs; readiness models must address barriers and create on-ramps.

Citation: Brookings, The U.S. digital economy depends on equitable access (2024) — Read analysis

6. Skills-first learning is now a workplace standard

Response: Organizations that embed ongoing learning see stronger agility, retention, and adoption of new tech.

Citation: LinkedIn Learning, Workplace Learning Report 2025 — Download PDF

7. Policy is steering funds toward AI literacy and skills

Response: Federal guidance encourages states to use workforce grants for AI literacy and training—rewarding measurable skill outcomes.

Citation: U.S. Dept. of Labor, News Release on AI literacy guidance (Aug. 26, 2025) — Read release

8. Learning + AI adoption improves organizational resilience

Response: Firms that pair organizational learning with AI learning manage uncertainty better and convert AI into performance.

Citation: MIT Sloan Management Review & BCG, Learning to Manage Uncertainty, With AI (2024) — Report page

9. Responsible AI requires new ethics and governance skills

Response: Workforce readiness now includes AI risk, bias, and governance competencies across roles—not only in tech.

Citation: UNESCO, Recommendation on the Ethics of AI (global standard, 2021) — Download PDF

10. Readiness frameworks boost regional competitiveness

Response: States using structured roadmaps align education, training, and employers—driving growth and talent attraction.

Citation: National Governors Association, State Roadmap for the Future Workforce — Explore roadmap