AI adoption is moving quickly — but too many mid-market firms remain stuck in testing mode.
To uncover what it really takes to move from experimentation to execution, Wipfli spoke with innovators including Jacob Ollman (Moltin AI at Schneider), Dr. Michael Simon Bodner (AWC Technology) and Todd Adams (Alloya Corporate Federal Credit Union).
They’ve each lived through the messy middle of AI adoption. From cleaning data to winning cultural buy-in, their lessons form a practical checklist for mid-market leaders who want to move with confidence.
1. Clean your data before you launch
“You can’t lie to AI and expect it to tell you the truth.” — Dr. Michael Simon Bodner
AI will amplify whatever data it’s given. That means messy silos, duplicate records or unstructured notes will multiply into messy outputs. Start by:
- Consolidating data sources into a single source of truth.
- Establishing data governance policies that define who owns what.
- Creating an “AI readiness” audit to flag gaps before rollout.
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