AI in HR, put to work in Michigan.
Client engagements from Michigan's manufacturing, healthcare, and public sectors — with real outcomes and honest context.
Reducing time-to-fill without sacrificing fairness
Challenge
A Michigan manufacturer was struggling with a 60+ day time-to-fill for skilled trades roles. Their recruiting team was manually screening hundreds of applications per opening.
Approach
We ran a structured AI readiness assessment, selected a resume screening tool with explainable scoring, and built a bias audit into the pilot before any live decisions were made.
Outcome
Time-to-fill dropped by roughly 30% in the pilot cohort. Recruiter review remained in the loop for every shortlist. The team now runs quarterly bias audits as standard practice.
A Michigan manufacturer reduced time-to-fill by adopting AI in recruiting — while validating fairness and keeping recruiter review in the loop throughout.
Client engagement — Michigan manufacturing sector
Building an AI governance policy from scratch
Challenge
A regional healthcare system had three departments independently piloting AI tools for scheduling, documentation, and HR — with no shared policy, no oversight structure, and growing compliance concerns.
Approach
We facilitated a cross-functional working session with HR, legal, and IT, mapped the existing tool landscape, and drafted a governance policy covering oversight, transparency, and employee data protections.
Outcome
The organization adopted a unified AI governance policy within 90 days. Two of the three pilots were paused pending vendor compliance review. One was expanded with new oversight controls in place.
We didn't know what we didn't know. The governance workshop gave us a shared language and a framework we could actually use.
Client engagement — West Michigan healthcare sector
Training an HR team to evaluate AI vendors
Challenge
A Michigan state agency was receiving vendor pitches for AI-assisted hiring tools and had no internal framework for evaluating claims, assessing bias risk, or understanding what questions to ask.
Approach
We delivered a half-day training on AI in recruiting, followed by a vendor evaluation workshop where the team applied a structured rubric to two active proposals.
Outcome
The team declined one vendor based on insufficient bias documentation and negotiated stronger audit rights with the other. HR leadership now runs all AI vendor evaluations through the same rubric.
We went from feeling overwhelmed by vendor pitches to feeling like we were in control of the process.
Client engagement — Michigan state government
A note on confidentiality
Client names are kept confidential. Sector, location, and outcomes are shared with permission. If you'd like to speak with a past client, we can facilitate an introduction.
Working on something similar?
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