AI checklist: How mid-market leaders are scaling AI responsibly
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.
2. Build readiness beyond IT
“We didn’t just hand out Copilot licenses. We paused, upgraded our data environment and started with a small pilot group.” — Todd Adams
AI readiness means more than technical infrastructure. It requires governance, risk planning and leadership buy-in. Consider:
- Assessing infrastructure and security controls.
- Budgeting an “innovation fund” to de-risk pilots.
- Aligning executives on how AI fits into strategy.
3. Train teams — and win cultural buy-in
“We bring middle managers in for two weeks of training on generative AI, machine learning and agentic AI.” — Jacob Ollman
Resistance often comes from fear or misunderstanding. Leaders must demystify AI and position it as a partner. You can:
- Create training that explains what AI can (and can’t) do.
- Frame AI as a coworker that removes drudgery, not jobs.
- Identify “pollinators” who can spread adoption across teams.
4. Start with wins that make work better
“Meeting notes that used to take an hour are now summarized instantly. It saves time and makes jobs more meaningful.” — Todd Adams
Quick, visible use cases build momentum. Start small with:
- Automated meeting transcription and action items.
- Contract compliance checks and updates.
- Reducing manual data entry in legacy systems.
5. Protect your IP and rethink security
“This thing can crawl over every document ever written in your company. There’s stuff no human would find, but the AI will.” — Dr. Michael Simon Bodner
The same power that makes AI valuable can create risks. Safeguard your organization by:
- Avoiding public models for sensitive data.
- Using enterprise AI tools with built-in role permissions.
- Updating cybersecurity frameworks to cover AI’s reach.
6. Redesign processes, don’t just automate
“Simply adding AI to today’s workflows doesn’t work. You have to rethink how processes should be designed with AI in mind.” — Jacob Ollman
AI isn’t a bolt-on. Instead of automating the past, use it to reimagine the future. Steps include:
- Mapping processes to see where AI could eliminate friction.
- Testing new workflows with small groups before scaling.
- Redefining roles around higher-value work, not repetitive tasks.
7. Empower champions and create new roles
“We created a department focused on data management and AI — it can’t just be a side hustle.” — Todd Adams
Sustainable adoption requires dedicated focus. Build internal capacity by:
- Naming AI champions within each function.
- Creating new roles like “information engineers” to bridge tech and business.
- Giving champions time, budget and visibility to share learnings.
8. Act now, not later
“AI will never be dumber than it is today.” — Dr. Michael Simon Bodner
Waiting only widens the gap between leaders and laggards. To move forward:
- Pick one small use case to start testing today.
- Build feedback loops to measure impact and refine.
- Share results widely to build organizational confidence.
The bottom line
AI is no longer optional. These lessons from mid-market innovators show that scaling responsibly requires more than technology — it requires data discipline, cultural adaptation and intentional leadership.
Use this checklist to guide your next steps, and explore how Wipfli can help you get up and running with AI by checking out our AI services page.
Dig deeper
Watch our “AI in the real world” webinar on demand or see these additional AI resources:
Automation vs. augmentation — making the right AI move
