Case Study: Faster Hiring, Better Matches: How M2N’s MPower Uses AI to Power Skills First Talent

Company

M2N is an Indiana based startup focused on advancing workforce equity through technology. At the center of this mission is MPower, an innovative technology that connects talent to employers’ open roles using a skills first framework. Built with AI at its core, MPower is designed to remove long standing barriers in hiring, particularly those tied to credential gatekeeping, while improving transparency, efficiency and outcomes for both candidates and employers.

Overview

MPower is delivering early signals of measurable return on investment across both talent acquisition and workforce outcomes. The platform has grown to over 1,000 members with zero paid acquisition, indicating strong organic adoption and reduced customer acquisition costs. Skills first matching expands candidate pools by up to 19 times compared to traditional degree-based filters, increasing access to qualified talent while reducing time to hire by as much as 50 percent.

Employers using a skills first approach see up to 40 percent improvement in candidate role fit and up to 30 percent reduction in hiring costs due to decreased reliance on third party recruiters and more efficient screening processes. Early indicators also point to stronger retention, with skills aligned hires demonstrating up to 91 percent higher likelihood of staying and advancing within organizations.

In parallel, MPower is generating operational value through automated compliance support. The platform is working to produce audit ready documentation aligned to VEVRAA (Vietnam Era Veterans’ Readjustment Assistance Act) and Section 503 (of the Rehabilitation Act of 1973) requirements as a natural output of hiring activity, reducing administrative burden while strengthening reporting accuracy. Combined with partnerships across national job platforms and workforce organizations, these enhancements position MPower as both a cost saving tool and a scalable infrastructure for equitable hiring.

Industry Context and Business Challenge

The hiring system is not just inefficient; it is fundamentally broken. For candidates, especially those from underrepresented backgrounds, the barriers are clear. Millions of capable individuals are filtered out simply because they do not hold a four-year degree. More than 60% of all workers fall into this category (increasing to over 75 percent of Black and Brown workers), which means traditional hiring systems exclude two-thirds of the American workforce before they even get a chance to be evaluated.

At the same time, employers are struggling. Despite a large available workforce, they continue to face talent shortages, rising hiring costs and low engagement from outdated job postings. Compliance requirements like VEVRAA and Section 503 add another layer of complexity, requiring documented outreach that is both time consuming and difficult to track.

This disconnect creates a clear opportunity. Skills first hiring models reduce time to hire, improve candidate fit, lower costs and increase retention. How to make that shift scalable, equitable and real?

AI Implementation Strategy

M2N approached AI with intention. Before building anything, they asked themselves hard questions.

• Where is hiring failing people?
• Where can AI actually help and where should it not?
• How do we ensure fairness, accuracy and transparency?

Their implementation strategy was grounded in designing for equity from day one. They brought together a diverse team to challenge assumptions and surface blind spots early. From there, they focused on targeted applications of AI that could improve the experience without replacing human judgment.

In MPower, AI is used to power skills-based matching, generate dynamic job postings, surface insights for candidates and employers and personalize engagement across the platform. Every feature is built to prioritize skills over credentials and to make the hiring process more transparent and actionable.

Solution and Implementation

One of the most important decisions M2N made was keeping humans at the center. AI in MPower does not make hiring decisions. It supports them. Every output is positioned as guidance, and they communicate that clearly to users. M2N also built feedback loops so users can question, correct or challenge what the system produces.

As the platform was developed, they focused on three core areas of learning.

• First, they examine how skills, especially durable skills like teamwork and problem solving, connect to real employment outcomes.
• Second, they track job quality and long-term retention to understand what drives success beyond getting hired.
• Third, they study engagement and candidate confidence, particularly for users who have historically been excluded from traditional hiring systems.

These insights allow M2N to refine the platform, improve how skills are captured, validated and matched to opportunities. It is not a straight path. There are moments where assumptions do not hold, but each iteration moves closer to a system that works better for everyone.

Outcomes

The results so far have been encouraging. Without paid acquisition, MPower has grown to over 1,000 members. That level of organic growth proves trust from a community that has historically been underserved by hiring platforms.

They have also established partnerships with national job aggregation platforms that see MPower as a way to meet diversity and compliance goals more effectively. In parallel, community-based workforce organizations are using MPower to extend the impact of their training programs, helping participants translate their skills into real opportunities.

While the initial outcomes are strong, the work is ongoing. Building equitable systems requires continuous iteration and M2N remains focused on improving both the technology and the experience it delivers.

Conclusion

Today’s hiring system leaves too many people behind while failing to meet the needs of employers. It prioritizes credentials over capability and creates inefficiencies that impact everyone involved.

MPower was built to change that. By combining AI driven insights with a skills first approach and intentional human oversight, M2N is creating a hiring experience that is faster, more inclusive and more effective. This is about more than improving hiring. It is about redefining access to opportunity and building a system that works for both sides of the workforce.

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