From Steel City to the Chicago Urban League: In Conversation With Karen Freeman-Wilson

On the latest episode of the AI in Chicago Podcast, I had the privilege of speaking with Karen Freeman-Wilson, CEO of the Chicago Urban League. A Harvard-educated attorney, former Indiana Attorney General, and former Mayor of Gary, Indiana, Karen brings a unique perspective to the intersection of AI, workforce development, and economic equity.

Our conversation surfaced a critical insight that every leader thinking about AI's societal impact should absorb: The AI revolution won't wait for communities to catch up—we must build the bridges now.

The Making of a Leader

Karen Freeman-Wilson's story begins in Gary, Indiana—a city whose rise and fall mirrors the broader arc of American industrial history. Gary was built by U.S. Steel in 1906 as a company town, growing to become one of the most prosperous cities in the Midwest. But as steel manufacturing declined, so did Gary's fortunes.

"I grew up watching my city struggle with deindustrialization," Karen recalls. "That experience shaped everything about how I approach economic development today. I saw what happens when an industry leaves and a community isn't prepared for what comes next."

That formative experience drove Karen through Harvard Law School and into public service, where she would become one of the first Black women to serve as a state Attorney General in American history.

A Career of Firsts

Karen's career is marked by barrier-breaking achievements:

First Black woman elected Attorney General of Indiana (2000-2003) — She focused on consumer protection and civil rights enforcement, establishing patterns she would carry throughout her career.

Mayor of Gary, Indiana (2012-2019) — She inherited a city in fiscal crisis and led efforts to stabilize finances while pursuing economic development strategies.

CEO of the Chicago Urban League (2020-present) — She now leads one of the nation's oldest and most influential civil rights and urban advocacy organizations.

"Each role taught me something different about systemic change," Karen explains. "As Attorney General, I learned how to use legal frameworks to protect people. As mayor, I learned the limits of what any single city can do alone. Now at the Urban League, I'm focused on regional economic transformation."

The Chicago Urban League: A Century of Service

Founded in 1916, the Chicago Urban League has spent over a century fighting for economic and social justice for Black Chicagoans. The organization provides direct services—job training, housing assistance, youth development—while also advocating for policy change at local, state, and federal levels.

Under Karen's leadership, the Urban League has made AI and technology workforce development a strategic priority. "We cannot afford to have another generation left behind by economic transformation," she states. "When manufacturing left, Black communities were devastated. We will not let that happen with AI."

The AI Imperative

Karen sees AI as both an opportunity and a threat to the communities the Urban League serves. The opportunity lies in the creation of new, well-paying jobs that don't require traditional four-year degrees. The threat is that without intentional intervention, these jobs will flow to communities that already have advantages in education and infrastructure.

"AI companies are creating roles that didn't exist five years ago," Karen observes. "Data annotation, prompt engineering, AI system monitoring—these are real jobs with real salaries, and many of them don't require a computer science degree. But if we don't train people for these roles, they'll go to the same places tech jobs have always gone."

The Urban League's response has been to build training programs that meet people where they are. "We're not trying to turn everyone into a software engineer," Karen explains. "We're identifying the roles that match the skills our community members already have, and building bridges to those opportunities."

The Three-Front Approach

Karen describes the Urban League's AI strategy as operating on three fronts:

Workforce Development: Direct training programs that prepare workers for AI-adjacent roles. These range from basic digital literacy to specialized certifications in data annotation, AI ethics compliance, and technical support.

"We partnered with several tech companies to understand exactly what skills they're hiring for," Karen notes. "Then we built curricula backward from those requirements. Every program we offer leads to a credential that employers actually recognize."

Entrepreneurship Support: Helping Black entrepreneurs leverage AI tools to grow their businesses. This includes both technical training and access to capital.

"Small business owners don't need to become AI experts," Karen observes. "They need to understand how AI can make their operations more efficient. We're teaching them to use AI for marketing, customer service, inventory management—practical applications that directly impact their bottom line."

Youth Development: Exposing young people to AI careers early, before they make decisions about education and career paths.

"If a young person has never seen anyone who looks like them working in AI, they won't imagine themselves in that field," Karen states. "We're bringing AI professionals into our youth programs, taking students to visit tech companies, and creating internship pathways. Representation isn't just about fairness—it's about expanding what young people believe is possible for their lives."

Breaking the Credential Barrier

One of Karen's most persistent themes is the need to break down credential barriers that exclude qualified workers from AI jobs.

"Too many employers are asking for four-year degrees for roles that don't actually require them," Karen argues. "They're using credentials as a proxy for capability, and in the process, they're screening out talented people who took non-traditional paths."

The Urban League is working with employers to develop alternative assessment methods—skills-based hiring practices that evaluate what candidates can actually do rather than where they went to school.

"We've had tremendous success with employers who are willing to try this approach," Karen reports. "They're finding that candidates from our programs often outperform traditional hires because they're hungry, they're focused, and they've been trained specifically for the roles they're filling."

Beyond Training: The Policy Dimension

While workforce development is the Urban League's primary focus, Karen emphasizes that training alone isn't sufficient. Systemic change requires policy advocacy.

"Individual training helps individual people, and that matters," Karen acknowledges. "But if we want to transform outcomes at scale, we need policy changes that create more opportunities and remove structural barriers."

The Urban League advocates for policies including:

Procurement requirements: Requiring that government AI contracts include workforce diversity commitments and local hiring provisions.

Tax incentives: Creating incentives for AI companies that locate in underserved communities and hire locally.

Educational investment: Increasing funding for community colleges and vocational programs that offer AI-related training.

Digital infrastructure: Ensuring that all communities have the broadband access and computing resources needed to participate in the digital economy.

"Policy creates the conditions for individual success," Karen summarizes. "We need both—programs that help people right now, and policies that change the system for the future."

The 2026 Roadmap

Looking ahead, Karen outlines an ambitious agenda for the coming year:

Scaling AI training programs: The Urban League aims to triple the number of participants in its AI workforce programs by the end of 2026.

Corporate partnerships: Karen is pursuing agreements with major employers to create direct hiring pipelines from Urban League programs.

Regional coordination: Working with other Urban League affiliates and community organizations to share best practices and coordinate advocacy.

Policy victories: Pushing for state and local legislation that advances the workforce development agenda.

"We're past the point of pilots and experiments," Karen states. "We know what works. Now we need to do it at scale."

Key Takeaways for Leaders

Don't wait for the crisis: Communities that prepare for economic transitions before they happen fare far better than those that react after the fact. AI workforce development should be a priority now, not after job displacement accelerates.

Meet people where they are: Effective training programs start with the skills people already have and build bridges to new opportunities. Not everyone needs to become a programmer.

Challenge credential inflation: Employers should evaluate whether degree requirements are actually necessary or whether they're just convenient screening tools that exclude qualified candidates.

Combine programs with policy: Individual training helps individuals, but systemic change requires policy advocacy. Organizations should pursue both strategies simultaneously.

Invest in youth exposure: Career paths are shaped early. Young people need to see AI careers as possibilities before they make educational decisions.

Build coalitions: No single organization can drive economic transformation alone. Partnerships across sectors—nonprofit, corporate, government, educational—are essential.

The Stakes

As our conversation concluded, Karen returned to the lesson she learned growing up in Gary: economic transitions are unforgiving to communities that aren't prepared.

"The AI revolution is happening whether we're ready or not," Karen states. "The only question is whether we'll be active participants in shaping it, or passive recipients of whatever the market decides to give us. I know which side the Chicago Urban League is on."

Listen to the full conversation on the AI in Chicago Podcast to hear Karen's perspective on Gary's path forward, her experience as Indiana's first Black woman Attorney General, and why Chicago's Black community has unique advantages in the AI economy.

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