Across the country, apprenticeships are increasingly seen as a strategy for building durable career pathways by combining paid work experience with structured learning and industry-recognized credentials. Apprenticeships are now expanding beyond the trades into sectors such as education, healthcare, and information technology. As apprenticeships expand, state leaders, funders, and workforce policymakers have an opportunity to focus on scaling programs that deliver reliable value for workers and employers alike.
This is where evidence becomes essential.
Strong apprenticeship systems depend on timely, reliable information that helps leaders track participant progress, demonstrate results, and improve programs in real time.
Many apprenticeship programs operate across complex systems with varied data sources and reporting needs, which underscores the importance of clear information to understand what is working and where refinement can add value.
When data are aligned with program goals and used intentionally, leaders can move beyond compliance to strengthen implementation, target support, and improve outcomes. They can also make more defensible decisions about which programs to expand, which partnerships to strengthen, and where additional investment is most likely to improve apprenticeship completion and career advancement.
As apprenticeship programs expand into new sectors, leaders need evidence to answer practical questions: What do high-quality apprenticeships look like in practice? What conditions support successful scale? Which system capacities, partnerships, and investments are most closely associated with better outcomes for participants and employers?
Those questions shape Mathematica’s apprenticeship work, which focuses on generating evidence about how apprenticeship systems perform in practice. This evidence can reveal where participant experience, completion, or advancement may be lagging. For example, participation, completion, and advancement data can help leaders see how programs are expanding and how well they are working. This matters in a period of rapid growth and heightened expectations for results.
As interest in apprenticeship continues to rise, success should not be measured solely by the number of programs launched or participants enrolled. Those indicators matter, but they don’t tell the whole story. Lasting progress also depends on quality, participant experience, completion, and whether apprenticeships create meaningful pathways to advancement for workers while helping employers meet talent needs.
Evidence helps bring that fuller picture into focus. It can show where programs are succeeding, where barriers remain, and what strategies are most likely to strengthen results over time. It can also help leaders decide where to direct technical assistance, how to define quality across sectors, and which investments are most likely to support sustainable growth. And it can help leaders distinguish between growth that is merely visible and growth that is sustainable and effective.
Mathematica works with federal, state, and foundation leaders to generate evidence they can use to strengthen and expand high-quality apprenticeship systems. As apprenticeship continues to evolve, leaders will need clear insight into what works, for whom, and under what conditions. They will also need practical tools to learn from implementation, adapt to new contexts, and make informed choices about where to invest.
The central question is no longer whether apprenticeship programs will grow but whether that growth will produce lasting value for workers, employers, and the systems that support them.
