Accelerate growth with strategic AI deployment

Middle market organizations are moving quickly to integrate artificial intelligence in meaningful and measurable ways to deliver long-term value, enhance business performance and achieve scalable results. In today’s ever-changing business landscape, companies must leverage AI to target high-value use cases within their operations, such as forecasting, compliance and customer engagement. To establish the right strategic scope, leaders must confirm that their AI solutions align with their desired business outcomes. This requires integrating AI tools into existing systems and workflows to optimize adoption, enhance efficiency and create sustainable success.

How quickly has AI advanced in the middle market? The 2025 RSM Middle Market AI Survey: U.S. and Canada found that 91% of middle market executives are either formally or informally using AI in business practices. But 53% of organizations that have adopted and implemented generative AI believe they were only somewhat prepared to do so, and 70% using generative AI report they need outside help to get the most out of that tool.

Furthermore, 92% of executives experienced challenges with implementation and 62% of respondents said generative AI has been harder to implement than expected. Given the rapid growth of AI and the implementation challenges that can follow, companies must establish a well-defined, results-oriented AI strategy and roadmap that truly fits their needs.

RSM US Principal Jonas Melton, Managing Director Sean Indick and Director Craig Niemoeller recently shared strategic insights on implementing AI solutions that align with business goals, infrastructure and organizational culture during RSM’s webinar Deploying AI that fits: Strategic choices for scalable, sustainable impact.

Below, we take a look at key considerations for building an effective AI adoption strategy, from use case development and solution identification to identifying opportunities that drive growth, improve outcomes and deliver long-term value.

AI use case development and categories

Before identifying AI use cases, organizations must develop a strong foundational framework. This process includes clearly defining business goals, establishing governance and selecting AI-supporting technologies. Solution identification is another critical component, which helps determine whether to build, buy or customize AI tools, or use a mix of all three, depending on the need. However, all of this depends on organizational readiness, including robust change management processes.

You don’t want C-suite leaders to decide where they think opportunities exist. From a use case perspective, it’s the people closest to day-to-day work who are most likely to recognize real opportunities. These groups should then work cohesively to identify opportunities and determine solutions.

Sean Indick, Managing Director, RSM US LLP

AI use cases typically fall into three categories:

Organizations often have use cases in all three categories and should aim to pursue opportunities across each as they mature.

Furthermore, successfully identifying use cases to maximize AI impact requires addressing the following measures:

Be granular when identifying use cases, especially for agentic AI. Break processes into specific tasks, such as a particular journal entry or account reconciliation, rather than attempting to automate broad, high-level workflows. While some higher-level bots can address larger problems, many high-value automations exist in the details. Evaluate feasibility by testing for clear ROI, checking data quality and reviewing the required architecture and tools before investing.

Jonas Melton, Principal, RSM US LLP

A business, experience and technology framework enables each use case to be assessed for viability, desirability, feasibility and success metrics. Use cases are scored against a rubric to determine business impact and execution fit, then mapped to categories such as ready-to-accelerate, needs more research, incubate later or shelve entirely. This approach helps teams prioritize high-value, realistic opportunities and avoid investing in use cases better addressed with non-AI solutions.

In addition, deploying a successful AI initiative requires assigning value that aligns with business objectives. Demonstrating a tangible impact to stakeholders is central to effective implementation and long-term scalability. Initiatives can demonstrate their value by how they support three key goals:

Identifying AI solutions

Organizations can choose from different types of AI solutions depending on their goals, data readiness and development needs. Three primary models exist, each offering varying levels of data considerations, flexibility, complexity, control and use cases. These include:

Many organizations assume AI tools just work, but out-of-the-box solutions only handle generic tasks well. When a business needs AI to operate according to its specific workflows and nuances, significant development effort is required. This includes discovery, defining success criteria, designing the solution and customizing it for the organization’s processes. While embedded AI may cover roughly 80% of a use case, the remaining 20%, often where the real business value lies, requires thoughtful design and developer involvement.

Craig Niemoeller, Director, RSM US LLP

When choosing between a buy versus build approach, organizations must also evaluate business value, feasibility, ROI and associated risk. Custom solutions require thorough testing, strong governance, security measures and compliance oversight, while embedded AI usually comes with vendor-managed controls in place.

“AI empowers the workforce by embedding agents alongside existing human resources, creating a hybrid model where repetitive, low-value tasks are automated, allowing employees to focus on higher-value, quality-reviewed work,” says Indick. “Successful deployment requires attention to the employee experience, including training, upskilling and providing the right tools and technologies.”

Frequently asked questions

The takeaway

Successfully deploying AI solutions within your business processes is not only about adopting technology; it’s also about thoughtfully assessing processes and their pain points. In addition, aligning tools, business units and people is necessary to deliver measurable business impact. By focusing on strategic initiatives, whether enterprise-wide or focused on specific use cases, your organization can move from experimentation to scalable outcomes.

AI tools and technologies may seem overwhelming at first. To achieve successful results and long-term value, your organization should consider working with technology providers or trusted advisors. This approach helps build trust, fill knowledge gaps and establish a competitive advantage.

Ready to get started? RSM’s experienced AI advisory team understands the enterprise AI journey and the foundational elements necessary to generate increased value and reduce risk. Contact our team to learn more about how AI can transform your key business operations.