Is your AI delivering true business growth?

An overwhelming 97% of respondents to RSM’s Middle Market AI Survey 2026 reported satisfaction with the performance of their artificial intelligence solutions in delivering business value. At first glance, that figure indicates AI is succeeding in the middle market. But the details tell a different story.

Only 17% said their organization was using AI to achieve transformation across the enterprise—indicating that most organizations have yet to capitalize on AI’s potential to drive true business acceleration.

The survey also revealed that 78% of companies use productivity metrics, such as process efficiency and time savings, to evaluate AI return on investment. This focus on productivity rather than transformation reflects where most organizations are today on their AI journey.

In retrospect, the survey data only answers half of the questions. AI is a powerful tool for increasing productivity, but companies need to operationalize its use at scale to transform how work gets done. The distinction between productivity gains and enterprise transformation may seem subtle, but it highlights the chasm between where most organizations are on their AI journey and where they want—and need—to be. 

Although organizations are deriving value from AI, many are measuring success based on individual use cases, in which the human remains squarely and consistently in the loop, rather than on broader enterprise transformation. This focus helps explain why many businesses are satisfied with AI even while struggling to operationalize it at scale. 

Why businesses reported that AI is working

The high AI satisfaction rate is likely attributable to organizations evaluating AI based on individual worker productivity. While some organizations may be starting to look beyond that metric, others may not have advanced far enough in their AI journey to consider enterprise AI value. It’s tempting to dismiss the survey satisfaction rate as a case of “You don’t know what you don’t know,” but the reality is more nuanced.

Early AI use cases focused primarily on improving productivity for tasks that included a human at all steps in the loop. And for many if not most employees, AI delivered value by writing emails faster, serving as a brainstorming partner and synthesizing data in lengthy reports. Since improved productivity was touted as a success in early use cases, survey respondents expressed satisfaction with AI. 

Pilot success is not enterprise transformation

Based on the success of an initial use case, many organizations figured, “Oh, wow, we can do this everywhere.” They invested in larger-scale AI deployments but struggled to succeed without the foundations in place to scale quickly, such as reliable data tooling and upskilled talent.

Many organizations underestimate the variables that creep into post-pilot use cases. In pilots, everyone is focused on the excitement of new technology, and businesses typically choose projects with readily available data rather than undocumented knowledge that lives in team members’ heads or data scattered across the enterprise in disconnected systems. 

To advance from pilots to enterprise transformation, an organization must implement AI where humans are not in the loop, which increases complexity. Because this step requires the enterprise to provide data, context and tooling, satisfaction typically decreases. Moving to that next level means overcoming challenges related to data quality, permissions, process complexity, organizational change and undocumented knowledge.

Organizations that succeed at scale have operationalized AI, moving it almost into the background and making it part of how they do business?

The gap between individual satisfaction and success at scale

Many organizations that say they are using AI have actually implemented deterministic automation, such as routing invoices through a predefined workflow for human approval. A fully AI-enabled process would involve AI interpreting the invoices and applying judgments within guardrails, such as flagging unusual spending patterns or resolving common invoice exceptions. While the difference may seem subtle, to use AI at scale, an organization must move beyond task automation to transform how work gets done.

Few organizations are far enough along on their AI journey to feel confident in their ability to provide an AI tool with the guardrails, context and data for it to do the job consistently without human intervention. The good news is that the window to operationalize and still gain a competitive advantage is likely longer than originally predicted.

Organizations that succeed at scale have operationalized AI, moving it almost into the background and making it part of how they do business. Rather than serving as a test, AI simply gets the job done and employees no longer talk about the discrete parts of the end-to-end process.

AI operationalization happens only when organizations reimagine thousands of individual processes. Organizations must seamlessly roll out AI at scale while also understanding each component. They must also implement strong AI governance to keep costs predictable and prioritize data privacy. Most importantly, organizations must move from viewing AI as just another technology to adopting a digital labor mindset. 

Moving to the next level means overcoming challenges related to data quality, permissions, process complexity, organizational change and undocumented knowledge that often lives only in employees’ heads?

Closing the gap through operationalization

AI success is achieved not by generating isolated AI wins, but by operationalizing AI to change how work gets done across the enterprise. That means prioritizing issues, determining how AI can solve them and executing those ideas.

After establishing priorities, many organizations focus on AI use cases for execution. Instead, they need to embrace the Great Unthink—redesigning existing processes rather than layering AI on top of them.

Enterprises should focus on the desired outcome and explore how to leverage AI to perform it. The key is to view AI as digital labor—not as a technology tool—and manage it accordingly, with processes to govern, measure and optimize its performance. 

Satisfaction was never the only goal with AI

While many organizations are realizing value from AI, its full potential remains largely untapped. Organizations now have the chance to rethink how work gets done and create new growth opportunities in the process.

We are only beginning to understand AI’s potential. The technology continues to evolve at a rapid pace and in ways never thought possible. But organizations are learning that scaling AI across an enterprise is far more complex than deploying it for individual tasks. Over the next decade, the greatest value will accrue to organizations that stop asking whether AI works and start asking how it can fundamentally change the way work gets done.