The middle market AI playbook

Artificial intelligence is rapidly transforming key operations within companies in all industries—driving innovation and opportunities to amplify growth and build a competitive advantage. While AI investments have quickly become essential for sustainability as new tools and use cases emerge, the path to effective AI utilization is not linear—there is no one-size-fits-all approach for AI success. To fully capitalize on AI’s potential for increased efficiency, insight and productivity, companies must develop a strategy to address challenges related to readiness, governance and skill gaps, and align technology to business goals.

*Source: 2025 RSM Middle Market AI Survey: U.S. and Canada

The rapid advancement of AI in the middle market is demonstrated in the 2025 RSM Middle Market AI Survey: U.S. and Canada as 91% of surveyed middle market executives reported either formally or informally using AI in business practices, a significant increase from 78% in 2024’s report. However, increased implementation has not come without challenges, as only 20% of organizations feel that they have integrated AI meaningfully and 70% using generative AI report they need outside help to get the most out of that tool.

With the growing importance of AI to overall business success, thoughtful planning is necessary; this entails conducting initial solution selection and implementation, and establishing governance and ongoing management. Without this preparation and attention to detail, AI projects often do not deliver on their anticipated value or can fail completely. In fact, the recent Massachusetts Institute of Technology State of AI in Business 2025 study determined that 95% of generative AI projects produce no return on investment. These surprising results were mainly driven by flaws in the project approach, such as a lack of focus, defined expectations or metrics for success.

AI should not be treated as a feature, but as a foundation. It is now imperative to view AI as a fundamental focus for the business, woven into every layer of an enterprise’s core architecture and operations. AI should not be seen as an add-on, nor a separate team operating independently, but instead as a common thread that embeds AI capabilities across functions to drive integrated decision making, automation and continuous improvement at scale.

RSM’s AI value proposition captures the three key value drivers executives care about most: operational efficiency, revenue growth and risk management. By effectively leveraging AI-driven solutions, your enterprise can run smarter, grow faster and stay protected.

Ultimately, effective strategy and planning are the most important elements of a successful AI strategy. These critical steps will help your organization establish overall goals, understand where the technology could have the most impact, identify any potential skill and experience gaps, and implement a framework that protects your environment against evolving regulatory and data security risks. By laying this groundwork, you can better align AI solutions with organizational objectives; set realistic, manageable expectations; and improve your return on investment.

Defining your AI strategy and governance approach

Initially, you must establish your overall vision and strategic priorities for AI initiatives. What do you want AI to accomplish within your organization? However, in addition to considering AI’s features and potential benefits, you must also institute an effective governance program prior to implementing technology. AI solutions require guardrails and controls to confirm output is unbiased and processes align with business goals and any regulatory guidelines.

For example, RSM’s proven AI Governance Framework guides our risk approach and our work with clients. It constantly evolves and incorporates critical elements from many best-practice frameworks, creating a more comprehensive approach encompassing more potential risks than a single framework. While extensive, the RSM framework is also adaptable, with the level of AI governance built to align with the level of risk your systems are exposed to. 

Identifying, evaluating and measuring use cases

How do you want AI to improve and enhance your critical processes? To tap into AI’s full potential, you must outline what use cases will optimize operations and help you create a competitive advantage. With AI’s vast potential to create positive change, you should build a pipeline of AI initiatives, prioritized by their potential ROI. 

Of course, no two businesses are the same, and use cases can vary drastically between industries and organizational structures. However, a sampling of potential AI use cases includes:

After defining your AI strategy, use cases and governance approach, your AI journey advances to developing an implementation plan and putting solutions into action. In some cases, you may already have platforms with AI capabilities that can help you reach your goals, while other use cases require critical decisions around whether to buy or build AI functionality.

Solution architecture and tech selection

First, you should undergo an AI architecture development process, considering how inputs and outputs are structured and where human intervention is necessary. Evaluate how you can leverage the AI capabilities within your existing technology and determine whether to buy or build AI solutions to fill potential gaps.

On one hand, you can buy additional AI tools to integrate into your current tech stack, but building a more customized solution may meet more specific goals or metrics. Both are proven strategies for success, but the right choice for your organization depends on cost and time-to-market, as well as use-case intentions, in-house talent capabilities and data foundations.

AI solution development

Once your technology solutions are in place, tailored models should be developed to test and confirm desired functionality and outputs. After AI models are verified, they should be integrated into workflows with automation enabled. This process can be fast-tracked with the help of pre-built templates designed for specific use cases and industries, such as RSM’s industry accelerators that are purpose-built to meet the unique operational and regulatory demands within several sectors. 

AI integration and change management

With automation enabled, connecting AI systems to your enterprise platforms is the final step before seeing the results of your AI investments. The work does not stop there though, as ongoing training is necessary to strengthen user adoption and communicate any process changes as AI platforms and goals continue to advance and evolve.