AI for CXOs: Achieving enterprise AI adoption

Artificial intelligence is constantly evolving and simultaneously transforming the middle market business landscape. Business leaders are more focused than ever on turning AI ambition into execution by integrating and scaling enterprise AI strategies across critical operations, from finance and customer experience to risk management. As AI advancements continue, use cases mature and investments expand, C-suite leaders must play a pivotal role in implementing an effective AI strategy to drive growth and deliver measurable, long-term impact.

How quickly is enterprise AI adoption advancing 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, up from 78% in the 2024 survey. But 53% of respondents who have adopted and implemented generative AI feel their organizations were only somewhat prepared to do so, and 70% using generative AI report they need outside assistance to get the most out of that tool.

With the immense transformative potential of AI solutions, C-suite leaders are strongly positioned to create effective AI adoption strategies that emphasize clear implementation, strong compliance practices, and a sensible balance between short-term wins and long-term scalability.

RSM US Director Robbie Beyer and RSM Canada Managing Director David Brassor recently provided insights into real-world strategies, governance considerations and actionable takeaways to lead AI transformation with confidence and clarity across the enterprise during RSM’s webinar The executive AI playbook: Lessons learned and takeaways from RSM’s CXO series.

Below, we explore key considerations for CXOs when shaping an AI strategy—focusing on scalability, competitive advantage, emerging opportunities and challenges, and practical use cases across AI tools and applications.

The role of CXOs in enterprise AI adoption

As AI strategies increasingly become catalysts to redefine how firms operate, CXOs can leverage emerging AI tools to address specific business needs, revolutionize outcomes and drive informed, data-driven decisions. More opportunities to enhance AI strategies are on the horizon for many middle market companies, as 88% of RSM AI survey respondents with a generative AI budget report they expect a budget increase in the coming year. Reflecting this momentum, RSM announced plans to further capitalize on AI innovation.

“Aligning with the growing demand for AI tools within middle market firms, RSM in June 2025 announced $1 billion investment in technology to accelerate AI strategy and drive next-level solutions for our clients,” says Beyer. “This AI investment over the next three years is supporting our ability to deliver AI solutions for clients that are meaningfully enhancing their businesses while also enhancing internal efficiency and innovation.”

C-suite executives can prepare companies for successful AI implementation by focusing on several trends and themes that can inspire successful AI deployment with measurable long-term results. These include:

• AI implementation plan: Companies must identify real business use cases, aligning return on investment (ROI) with business priorities and establishing data readiness as the foundation. In addition, employees must be educated on critical aspects of AI tools and technical processes.
• AI governance and risk management: AI strategies must address data privacy, security, fairness and global regulatory compliance, creating responsible and transparent AI deployment.
• AI industry and technology enablement: With industry accelerators from advisors such as RSM, as well as pre-built connectors and hyperscaler platforms including Azure, Amazon Web Services and Google Cloud Platform, organizations can deploy AI quickly and scale efficiently.
• AI talent development and user adoption: Critical talent considerations include educating employees, driving change management and fostering a culture of innovation where teams surface use cases and further embrace AI.
• AI functional integration: Integrating AI into existing workflows can lead to quick wins while preparing for transformative, long-term opportunities that reshape business models.

Change management and communication strategies, involving employees and addressing concerns about impacts to jobs, are some of the most important factors that directly influence the AI-readiness of any organization.

Key enterprise AI adoption issues

Organizations are rapidly scaling AI adoption but often face key issues, particularly in upskilling their respective teams. For example, RSM’s AI survey found that 39% of respondents experience a shortage of in-house expertise. In addition, the absence of a clear AI strategy and ROI parameters can hinder the desired results. AI adoption fails without a defined plan, measurable ROI and clear, designated timelines.

Without a clear AI strategy, companies will have difficulty getting the expected value and impact from AI investments. An effective AI strategy includes defining operational and technical processes, as well as aligning AI initiatives with business goals. Companies should evaluate what processes can benefit the most from AI, balancing quick wins with long-term growth potential.

Furthermore, AI governance is a necessary step in addressing implementation issues and extends into multiple operational areas of the business, including:

As the technology evolves, adopting a proven governance approach, such as RSM’s AI Governance Framework, is essential for both effective risk management and successful AI implementation. A comprehensive governance framework provides guardrails for regulatory compliance and unbiased outputs while maintaining alignment with business strategies.

“Responsible AI governance is rapidly evolving,” says Brassor. “Early adopters like NIST [the National Institute of Standards and Technology] introduced regulatory compliance years ago, covering both cybersecurity and AI risk management. More recently, governments, such as those in Australia and Canada, have issued directives on AI adoption. In addition, major technology providers, including Microsoft and Google, have developed their own responsible AI compliance standards to avoid such issues.”

Enterprise AI adoption challenges and opportunities

Because AI’s power depends largely on the quality of a company’s data, strong data governance is a cornerstone for building a successful, results-driven AI strategy.

Data quality issues can significantly hamper generative AI outcomes, with 41% of AI decision makers in RSM’s AI survey identifying data quality as a key barrier to successful deployment. It’s simply a “garbage in, garbage out” principle. Poor data not only undermines outcomes but can also heighten risks around privacy and security. To avoid unreliable results—and potentially harmful business decisions—organizations must rigorously adhere to regulatory compliance standards.

In addition, integrating AI into existing operations and workflows is another crucial challenge, but successful AI deployment can come in many forms.

“There are ways to implement AI that are minimally disruptive to the business,” says Beyer. “Often these tools are either embedded in technology platforms that you're using today or quick implementations that come with prebuilt connectors or accelerators. You can embed those right into existing workflows and make them more efficient. But when you think about some of the long-term opportunities, there are new innovative ways of doing business that have become available. Starting with a plan at the beginning helps you navigate all those strategic elements.”

To deploy a successful AI plan, it is important to have clear values aligned with business objectives. Demonstrating tangible value to stakeholders is central to successful AI implementation and long-term, scalable solutions. For example, RSM has developed an AI value proposition that focuses on three key value drivers:

AI solutions and use cases

AI is inclusive of data science, machine learning and automation, and can perform tasks like humans, such as learning, reasoning and decision making.

Currently, organizations are using AI in three main areas:

In addition, certain use cases and solutions specific to domain functions are as follows:

• Predictive analytics: AI models can predict outcomes in industries such as health care, where tools can combine patient and insurance data to forecast adverse events, enabling proactive case management and faster follow-ups.
• Demand planning: Intelligent forecasting with the use of customized models can enable precise inventory management and operations planning.
• Speed to insights: Dashboards with natural language interfaces improve data literacy, supporting faster and more informed decision making.
• Customer analysis: AI predicts customer behavior and buying patterns, helping sales and marketing teams drive growth and retention.
• HR and information technology: AI applications can answer and maintain service desks, handle employee policy queries and perform task management.
• Workflow automation: AI streamlines document processing, extracts data from unstructured sources and integrates with existing systems.
• Audit management: AI tools can identify anomalies and risks in financial data, improving efficiency and reducing organizational risk.