RSM executive says change management will decide enterprise AI success

By AI, Created 5:55 PM UTC, May 20, 2026, /AGP/ – Robbie Beyer of RSM told the CAIO Connect Podcast at TechEx in San Jose that enterprise AI wins depend on governance, employee adoption, and change management more than the technology itself. He said companies that treat AI as a core business transformation tool, not a trend, are more likely to turn investment into measurable results.

Why it matters: - Enterprise AI projects can fail even when the tools work well if employees do not adopt them. - Beyer said the biggest payoff comes when companies connect AI to clear business outcomes, including cost savings, revenue growth, risk reduction, and faster time to market. - Mid-market companies need practical guidance because they often have growth ambitions without the internal structure of larger enterprises.

What happened: - At TechEx in San Jose, the CAIO Connect Podcast hosted Sanjay Puri and Robbie Beyer, who leads data and AI advisory at RSM. - The conversation focused on how enterprises can turn AI spending into business value. - Beyer said success depends on leadership, governance, and strong change management. - Beyer said RSM helps mid-market companies build AI strategies, governance systems, and implementation plans.

The details: - Beyer said many companies fail on AI adoption because employees do not use the technology effectively. - He said mid-market firms face a different reality than large enterprises and often need hands-on help to define strategy, manage data, and deploy AI safely. - Beyer said both mid-market companies and Fortune 500 firms want to become AI-enabled, but their maturity levels differ sharply. - He said demand for AI is rising across industries, including family-owned businesses and large corporations. - Executives are moving faster because they worry about falling behind competitors. - Beyer cited use cases including warehouse automation, inventory control, and marketing content creation. - He said leaders should treat AI as a core business transformation tool rather than a passing trend. - Beyer introduced a framework he called “run, protect, grow.” - Under “run,” companies use AI to improve back-office efficiency in tasks such as data entry and financial processing. - Under “protect,” companies use AI to reduce risk through governance and data controls. - Under “grow,” companies use AI to improve customer targeting, prevent churn, and optimize marketing. - Beyer said the framework helps executives focus on outcomes instead of vendor hype and unclear ROI. - Beyer said AI vendors including OpenAI and Anthropic are investing in consulting partnerships. - He said enterprise data is often locked inside organizations, which limits model value unless AI is integrated into business systems. - He said consulting firms like RSM bridge that gap by combining engineers, strategists, governance experts, and industry specialists. - Beyer described those teams as “forward-deployed engineers” who work inside client organizations to implement AI. - He said companies often underestimate how hard it is for employees to trust and use AI tools. - Beyer gave an example from a state health department that moved from paper-based processes to AI dashboards that predicted health risks. - He said the system improved outcomes, but staff still needed training and support to adapt. - He said adoption fails when organizations ignore the human side of transformation. - Beyer said AI agents are one of the most important enterprise opportunities right now. - He said companies are seeing strong ROI from agentic AI, but they need governance before scaling it. - He warned that companies must manage data access, customer interaction, and reputational risk carefully. - Beyer said he prefers human-generated data, supports human-in-the-loop systems, and believes agentic AI is still “underhyped.”

Between the lines: - The message from Beyer was not that AI tools lack value. It was that organizations often lack the operating model needed to use them well. - The emphasis on governance and change management reflects a shift in enterprise AI from experimentation to implementation. - Vendor partnerships and “forward-deployed” support suggest AI deployment is becoming a services-heavy business, not just a software purchase.

What’s next: - Enterprises that want AI returns will likely need stronger training, governance, and cross-functional execution. - As agentic AI expands, companies will likely face more pressure to set rules for data access, oversight, and customer-facing uses. - Mid-market firms may continue to rely on outside advisors as they move from AI pilots to scaled deployment.

The bottom line: - Beyer’s core message was simple: AI success depends less on the model and more on people, process, and trust.