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Workforce Innovation Fund (WIF) Technical Assistance

For the Workforce Innovation Fund (WIF) Technical Assistance project, Maher & Maher partnered with Jobs for the Future (JFF) to provide programmatic technical assistance (TA) to Workforce Innovation Fund and Pay for Success grantees.  In 2012, the U.S. Department of Labor awarded $146.9 million in Workforce Innovation Fund (WIF) competitive 4-year grants to 26 grantees across the nation.  The primary purpose of these grants is to support innovative approaches to the design and delivery of employment and training services that generate long-term improvements in the performance of the public workforce system, both in terms of employment and training outcomes, and cost-effectiveness.  In addition, the secondary purpose of the WIF grants is to test the effectiveness of these strategies through a national evaluation model, wherein the grantees are receiving assistance to develop consistent and measurable evaluation standards that can ultimately be used to compare the effectiveness of the strategies being implemented.

The Pay for Success (PFS) model is a new way of financing social services to help governments target limited dollars to achieve a positive, measurable outcome. The PFS model offers a financing solution for preventative social services, which are often the first services to get cut in austere budget times, even though in many cases they lead to long term benefits to the workforce system, such as reduced programmatic costs, more efficient public spending, and better social outcomes.

With such a diverse set of WIF grants, Maher & Maher approached this challenge by implementing a comprehensive technical assistance program that focuses on Open Innovation and peer to peer learning networks.  First introduced at the University of California, Berkeley  by Dr. Henry Chesbrough, Open Innovation as being “a more distributed, more participatory, more decentralized approach to innovation, based on the observed fact that useful knowledge today is widely distributed, and no company (or organization), no matter how capable or how big, could innovate effectively on its own.  Yet, at the same time, there is a critical role for an overarching architecture that connects these seemingly disparate activities together.”

Workforce Innovation Fund Community

Our Open Innovation TA Model is group-focused problem solving based on dialogue and coaching. The more “open-source” problem-solving and sharing model allows grantees to overcome issues more quickly and adopt proven solutions faster. This, in turn, accelerates workforce innovation and the widespread adoption of models that work.  In this project, the Maher & Maher & JFF TA team combined traditional forms of grantee support, such as professional coaching assistance, onsite meetings, and grantee conferences, with state-of-the-art virtual technologies that extend the TA team’s capacity and foster innovation and collaboration to produce a more robust and cost-effective TA solution. For instance, our Open Innovation Plan includes:

  1. Launch of a Virtual Innovation Hub for knowledge sharing across grantees and with the larger workforce system.
  2. Production of highly interactive virtual events featuring video panels of well-recognized subject matter experts, followed by virtual break-out sessions for small groups of grantees to share challenges and possible solutions.
  3. Annual in-person training events focused on peer-to-peer seeker/solver brainstorm sessions and one-on-one consultations with subject matter experts.
  4. Development of action-oriented policy toolkits focused on the phases of change management.

Overall, the Maher/JFF team supports the WIF project’s aim to improve the performance of the country’s workforce system by first determining what works, then building a body of evidence-based practices within the field of workforce development.  The outcomes generated through these WIF investments will consequently play a significant role in determining the future course of publicly-funded employment and training programs.