Advancing AI-Resilient Early-Career Pathways Initiative

Advancing AI-Resilient Early-Career Pathways Initiative is sponsored by Jobs for the Future (JFF). This initiative aims to identify, pilot, and learn from innovative models that support early-career workers in an AI-transformed labor market.

Advancing AI-Resilient Early-Career Pathways Advancing AI-Resilient Early-Career Pathways An open call for early-stage pilot projects that help early-career workers build skills, access quality jobs, and navigate an AI-transformed labor market Artificial intelligence (AI) is fundamentally changing how people enter the workforce and advance in their careers.

While AI can create more productive and meaningful work, it also risks widening economic divides by displacing jobs and closing off entry points to stable careers, particularly for early-career workers who disproportionately hold entry-level, routine-task roles most vulnerable to automation.

We are in a critical window for intervention to ensure early-career workers have the skills, experience, and networks that shape career trajectories across a lifetime. Jobs for the Future (JFF) is launching the Advancing AI-Resilient Early-Career Pathways Initiative to identify, pilot, and learn from innovative models that support early-career workers in an AI-transformed labor market and help ensure AI leads to quality jobs for all.

Watch the Informational Webinar Download the Webinar Slides RFP released : February 6, 2026 Proposals due : February 25, 2026 (11:59 p. m. PST) Awardee notifications : March 11, 2026 Required in-person convening : March 25, 2026 (Stanford University) Grant period : April 1, 2026–November 30, 2026 Travel costs for the in-person convening will be funded.

JFF is seeking pilot projects that address one or more of three interconnected intervention areas that together reflect critical leverage points for strengthening early-career pathways. Reimagining Work-Based Learning In an AI-transformed labor market, employers are increasingly valuing workers with experience and expertise while reducing entry-level roles that allow early-career workers to build skills and gain workplace experience.

At the same time, other avenues for building experience through work-based learning opportunities remain difficult to scale, are unevenly distributed across sectors, and often are inaccessible to workers who lack professional networks or have been historically excluded from opportunity.

We’re hearing strong demand from the workforce and education field for a wide array of creative models—even beyond the tried-and-true successes of apprenticeships and paid internships—that give jobseekers, employers, and the learn-to-work ecosystem alike more opportunities to structure work-based learning experiences that meaningfully prepare workers for the jobs of the future.

How might we design new or reimagined work-based learning models that enable early-career workers to build experience and demonstrate value in AI-integrated workplaces?

What a Pilot Might Look Like (Select Examples) Create new approaches to work-based learning, such as models of varying lengths of time, in which learners engage at several different points in their career arc, or that diversely structure employer/postsecondary partnerships Redesign apprenticeships, internships, or project-based opportunities to reflect AI-augmented workplaces, including skills passports or other verifiable, competency-based ways for early-career workers to demonstrate skills beyond traditional credentials Develop work-based learning models tailored to small, medium-sized, or public-sector employers Leverage AI tools or platforms to expand access to work-based learning experiences that have market value Employer Incentives and Business Value Employers are critical to providing early-career pathways, yet many are reducing investments in entry-level roles amid economic uncertainty and rapid technological change.

Without clear incentives, evidence, or infrastructure, AI adoption may further reduce employer investment in early-career talent. What incentives, tools, or models can help employers—from small businesses to large private or public sector organizations—adopt and sustain practices that improve early-career job quality and access in an AI-driven economy?

What a Pilot Might Look Like (Select Examples) Test employer-facing tools that demonstrate the ROI of investing in early-career talent in AI-enabled environments Design shared or intermediary-led models that reduce costs or risks for employers to hire and retain early-career talent Pilot financial, operational, or reputational incentives to retain and advance early-career talent Explore models that support entrepreneurial, self-directed, or venture-based pathways, where individuals create value through new products, services, or AI-enabled business models, while still building durable skills and pathways to longer-term economic security Articulation and Assessment of Durable Skills While many durable skills frameworks already exist, AI is changing what skills are most in-demand and what proficiency looks like in practice.

Emerging evidence suggests that employers increasingly value more specific, role- and context-dependent expressions of durable skills, creating a need for greater clarity and precision about what these skills look like in practice in AI-integrated workplaces. How might we better understand, surface, and communicate what durable skills look like in an AI-enabled labor market in a way that leads to positive employment outcomes?

What a Pilot Might Look Like (Select Examples) Explore new ways to assess durable skills application in AI-integrated work or learning environments Test AI-enabled approaches that surface signals of durable skills proficiency from work artifacts or interactions Pilot partnerships with employers that translate AI-era durable skills into hiring or advancement decisions Build durable skills development and credentialing into training programs, specifically for AI-augmented roles Redefine the frontier of durable skills in roles that involve human/AI collaboration Eligible applicants may include, but are not limited to: Workforce development organizations Postsecondary education institutions Intermediaries or nonprofit organizations JFF recognizes that innovation often emerges from new combinations of partners and perspectives.

Applicants may apply as a single organization or as part of a partnership. Partnerships across sectors are strongly encouraged, particularly when they strengthen feasibility, relevance to workers and employers, or pathways to scale.

Estimated award size : $50,000 to $100,000 per grant; award size will vary depending on the winning pilot project's maturity Number of awards : We anticipate awarding three to six grants Timeline for the grant : April 2026–November 2026 (eight months) Peer learning community : As part of this cohort, grantees will engage in a peer learning community throughout the grant period, with structured, ongoing opportunities to exchange learned knowledge, troubleshoot challenges in real time, and collaboratively strengthen approaches.

Submit your proposal by February 25, 2026 For questions about the application process, contact AdvanceAIearlycareers@jff. org .