From Pilots to Performance

Based on findings from The State of AI in the Mid-Market, Kaufman Rossin’s first annual research report on AI adoption across U.S. mid-market companies.

Mid-market companies are past the "should we try AI?" conversation. Eighty-three percent are already testing or actively building with it. The harder question is what’s next: how do you turn scattered experiments into something the whole organization can build on?

The answer usually isn’t more tools. Kaufman Rossin’s research shows the biggest barriers to scaling are internal: cybersecurity and data privacy concerns (43%), a shortage of AI talent or skills (42%), and legacy system integration challenges (41%). The tools exist. The operating environment often doesn’t.

That’s exactly what our four-pillar AI Execution Framework is designed to address.

The Gap Between Adoption and Integration

Our survey found that 52% of companies are testing AI, 31% are actively building with it, and only 2% have reached the Operator stage, where AI isn’t a project but a permanent part of how the business works. Most organizations have real momentum. What they lack is the operating foundation to sustain it.

The four-pillar framework exists to assist in closing that gap.

Pillar 1: Use Cases and Pilots

Start with the work, not the technology.

The most successful mid-market AI programs begin with a specific, high-volume task, not a platform decision. The top use cases from our survey all share one quality: they’re text-heavy and repetitive, where AI delivers immediate gains without a full systems overhaul.

• Meeting notes and transcription (77%)
• Data analysis and insight generation (73%)
• Document processing (67%)
• Marketing and content creation (65%)
• Knowledge search and summarization (64%)

Resist the temptation to chase transformative use cases too early. Start with wins your team can experience within weeks, then document what works. That’s the bridge from "a few people use AI well" to "we have a repeatable process anyone can follow."

Pillar 2: Data and Platform Strategy

AI is only as effective as the data it can reach.

Most mid-market companies have moved faster on tool adoption than on the infrastructure underneath. Only 16% have reached a fully governed and integrated data state. Thirty-eight percent still operate with siloed data that doesn’t connect across departments.

Think of it as an iceberg. The tools employees use every day are the visible tip. The data warehouse, integration layer, APIs, and governance controls are what sit beneath the surface, and that’s where the real investment needs to go. You don’t need a full enterprise transformation to start, just targeted progress on the data that supports your highest-impact use cases.

Pillar 3: Governance Framework

Guardrails don’t slow AI down. They make it safe to move faster.

Sixty-four percent of companies have an acceptable-use policy, and 57% require human-in-the-loop review. That’s a start. But 8% have no formal governance at all, and only 26% have a prompt library or best-practice guide in place.

At a minimum, every organization using AI typically needs four things:

• An acceptable-use policy
• A defined list of approved tools
• Clear rules about what data can and cannot be shared with AI systems
• A human-in-the-loop review process for any outputs that leave the organization

Governance also needs clear ownership across business, technology, and risk, not just in IT or the C-suite. Rules provide clarity. Clarity creates confidence. Confidence accelerates adoption.

Pillar 4: People and Culture

Tools don’t change organizations. People do.

This is the most underestimated pillar. Sixty-eight percent of companies run awareness sessions, and 59% have AI champions in place. But structured training drops off sharply: only 27% offer internal upskilling courses, and AI talent shortage remains the second-biggest barrier to scaling.

Awareness creates interest. Champions create momentum. Neither is enough without practical training in prompting, output validation, and workflow integration. The broader goal isn’t one enthusiastic champion per department. It’s institutional capability, skills and processes that persist regardless of who is in the room.

The Pillars Work Together

Governance without training creates bottlenecks. Use cases without data strategy create dead ends. People without governance create risk. The pillars reinforce each other, and the companies pulling ahead are the ones treating AI as organizational transformation, not a technology project.

They’re not waiting for perfect conditions. They’re choosing one high-value use case, building the right foundation around it, and using that win to justify the next investment.

Ready to build your roadmap? Contact us to connect with Kaufman Rossin’s AI Enablement team.

We work with mid-market companies to assess readiness, define strategy, and build the governance, training, and infrastructure needed to scale AI with confidence.