Adopting artificial intelligence has become necessary to stay competitive; its use will continue expanding.
Key takeaways
CEOs and board members need to successfully create an AI vision for their organization.
With the right strategy, CEOs and boards can leverage AI to elevate productivity and efficiency.
Artificial intelligence has become an integral part of discussions in leadership meetings and boardrooms. Its adoption has rapidly become necessary to stay competitive and it will soon become an element of every key corporate function and workflow to increase insight, productivity and efficiency. With AI’s deployment as a foundational aspect of business success, board members and CEOs need to develop a plan which aligns AI with business strategy with an eye toward effective risk management and governance.
While the middle market is experiencing a surge in the integration of AI and generative AI technology, 60% of companies feel they lack the vision and a plan for AI, according to the Microsoft 2024 Work Trend Index. Further, in the recent RSM Middle Market AI Survey: U.S. and Canada, 67% of organizations using generative AI report they need outside help to get the most out of the tool. With the growth and potential of AI solutions, CEOs and boards have a significant opportunity to shape the most beneficial AI strategies and use cases and help identify where investments should be focused.
RSM US LLP Director Robbie Beyer and Manager Joseph Fontanazza explored the role of CEOs and the board of directors in AI development and integration during RSM’s webinar AI strategies and insights for CEOs and board members.
Below, we summarize that presentation with a look at some critical details for CEOs and the board to consider when developing an AI strategy, as well as issues, opportunities and potential use cases for many AI tools and applications.
The role of the CEO and board in AI adoption
As with any relatively new innovation, it’s apparent that many organizations are finding it challenging to determine how to develop and integrate AI strategies. However, employees are using AI independently at a high rate, with a Microsoft survey finding that 78% of respondents bring their own AI to work. With users driving increased adoption, company leadership must focus on an effective implementation and governance strategy to enhance and increase the efficiency of processes while considering potential risks.
To secure a lasting competitive advantage, it’s imperative to focus on the long-term implementation of data and AI. With so many companies still finding their footing with AI, there’s a major opportunity to truly create differentiation and a foundation for growth. AI is here to stay, and CEOs and board members need to think about how to utilize it, what use cases will deliver the most value and how to develop a strategy for ultimate AI adoption success.
“With AI moving so quickly, you want to make investments in situations that will lead to you having long-term benefits from it,” said Fontanazza. “You can't have quick fixes that are just going to solely take care of something in the short term and hamstring you in the longer term.”
Key issues
The biggest issue for most CEOs and boards is how to develop their organizational vision for AI. Initially, leadership must determine where they want to be on the technology adoption curve. Do you want to be an innovator, an early adopter or someone who is slower to adopt AI technology? Eventually, AI will be ingrained in every process and workflow across the organization, but the speed of integration is an important determination; CEOs and boards should decide how quickly it happens as well as the level of investment and risk.
Leadership teams must be proactive to consider use cases where AI can provide the insights and predictive capabilities that are necessary to run the organization more effectively. Where can efficiencies be gained and what can be further automated with AI or generative AI?
Regardless of what direction CEOs and boards choose, a robust and secure data foundation is important for any AI implementation. The quality of data directly influences AI outcomes; ensuring high-quality, unbiased data is critical for building reliable, accurate and fair AI systems that drive efficiencies and equitable decision making.
“Ultimately, if people start to get bad information or inaccurate responses from an AI model, adoption can drop immediately,” said Beyer. “If it’s not providing value, it’s difficult to motivate people to continue working and driving adoption of it. It’s critical to facilitate accurate responses and accurate predictions right out of the gate.”
With many organizations finding that they need outside assistance to get the most from AI initiatives, CEOs and board directors need to quickly identify the right skills and talent to bring their AI vision to life. An outside perspective is often critical for progress with AI, and those resources will be in high demand as the technology only increases in importance.
"Ultimately, if people start to get bad information or inaccurate responses from an AI model, adoption can drop immediately,” said Beyer. “If it’s not providing value, it’s difficult to motivate people to continue working and driving adoption of it. It’s critical to facilitate accurate responses and accurate predictions right out of the gate."
Robbie Beyer, Director, RSM US LLP
Challenges and opportunities
AI can be a truly transformative solution, but to achieve its anticipated return on investment (ROI), CEOs and boards should consider focusing on some key lessons learned from RSM’s use of and experience with AI. These include:
- Data privacy and security: Data collected and used by AI systems needs to remain secure, comply with privacy laws and not leak outside the organization.
- User experience: Solutions that provide an exceptional user experience significantly strengthen adoption and outcomes.
- Expectations: Helping team members understand the technology and how they can use it helps drive innovation across the enterprise.
- Scalability and flexibility: Solutions that meet the growing needs of organizations need to be flexible and adapt to changing circumstances.
- Interoperability and integration: Alignment with existing infrastructure and technology is critical to maximize utility of AI solutions.
- Predictions leading to action: Integrate solutions with business processes and/or set change management expectations up front.
In addition, risk should be a primary consideration for CEOs and boards when implementing and navigating an AI strategy. In the context of AI, risk is the probability of a technical, reputational, legal, ethical or financial loss. However, effective governance of AI will decrease the probability of risks becoming realities.
“AI governance is not in and of itself a new form of governance—it’s nothing new to the organization,” said Fontanazza. “It really should be part of your corporate governance, your IT governance and your data governance, with a few specific elements that are tailored to AI considerations.”
AI governance is not in and of itself a new form of governance—it’s nothing new to the organization,” said Fontanazza. “It really should be part of your corporate governance, your IT governance and your data governance, with a few specific elements that are tailored to AI considerations.
Joseph Fontanazza, Manager, RSM US LLP
From an AI perspective, the risks leadership need to account for generally fall into five categories:
1. Technical and semantic
Inaccuracy: AI systems may produce incorrect or misleading results
Bias: AI models can perpetuate biases in the training data, leading to unfair outcomes
Security: Risks include data breaches or malicious misuse of AI
Obsolescence: Fast AI evolutions can render systems obsolete, requiring expensive, regular upgrades
2. Legal and regulatory
Compliance: As regulatory frameworks for AI emerge, noncompliance can lead to penalties and reputational damage
Privacy and data protection: Misuse of AI could violate privacy laws or lead to unauthorized data sharing
Liability: Determining responsibility for decisions made by AI can be complex, potentially leading to legal disputes
3. Operational and financial
System and integration issues: Failure and integration problems can cause disruptions and cost overruns
Data management and scalability: Poor data handling and scaling issues can limit progress and add costs
Costs and regulatory fines: Potentially large and ongoing costs can be coupled with regulatory fines
4. Reputational
Public perception: AI misuse or failures can damage your company’s image
Trust: Potential loss of customer trust can occur due to irresponsible data handling or adverse AI decisions
5. Ethical
Fairness: The use of AI could potentially exacerbate social inequities if it is biased or misused
Transparency: Lack of transparency in AI decision making can lead to mistrust and ethical concerns
Job displacement: AI-led automation could lead to job losses and social disruption if not managed responsibly
AI use case successes
RSM has worked with many CEOs and boards to develop strategies to drive AI success within their organizations. Some specific AI use case successes include:
AI governance strategy and AI transformation: The RSM team has worked directly with clients to define key use cases, spending time with the C-suite and executives as well as finance, operations and marketing teams. Those use cases were prioritized and ROI was assigned to understand their transformational potential and how they aligned with the broader digital strategy. A roadmap was developed to determine where companies initially stood and what was necessary for implementation, providing a technical lens to think through solution architecture. The data that would support the solutions and dictate the strategy was also outlined.
Customer relationship management AI enhancement: We have clients that spend hundreds of thousands, if not millions, of dollars implementing customer relationship management (CRM) systems to capture robust data about their customer base. We have worked with clients to drive further enhancements to those investments by integrating AI directly into those systems. If somebody is interacting with the CRM system already, they no longer have to build a separate application for personalized outreach. Information can be analyzed quickly, with content provided for salespeople with key insights.
Intelligent forecasting and demand planning: Companies typically have structured sales data with detailed information about products, customers, regions and channels. In addition, companies have access to internal information from enterprise resource planning (ERP) and CRM systems as well as external signals in the market that are relevant to the business. We have worked with clients to integrate AI solutions that leverage that data to generate a forecast to target customers better, understand when sales may drop or increase in a particular dimension, and determine when to increase or boost inventory.
AI data analyst: A common question is how AI can be leveraged to get answers from data more rapidly. We have worked with clients to leverage Microsoft Fabric to perform data extraction, data processing and valuation creation while generating predictions. For example, with an authoritative central source of truth, a user can prompt the AI model to generate the top sales or top customers by region and generate a dashboard from scratch. The model provides a significant head start from a time savings perspective by allowing dashboards to be customized and deployed across the organization or allowing business users to get instant insight into their data.
Customer churn: By looking at past data, we have trained AI models to assign a risk score for clients with detail into when they may churn or become inactive. Analysts can then interpret that information and work with operations or marketing teams to perform any necessary customer outreach. Organizations can prioritize workflows to target the highest-risk revenue that they are in danger of losing.
Frequently asked questions
CEOs can combine AI technology with existing personnel to elevate productivity and efficiency. Companies need to initially create an AI vision with specific goals in mind, such as improving the customer experience, strengthening service delivery, automating manual tasks or processing data, and gathering insights more rapidly.
Generative AI can enable companies to predict future behaviour by analyzing and understanding past activity. While generative AI should not be seen as a replacement for human knowledge, it can inspire more rapid and agile decision making and uncover new opportunities that may have been otherwise overlooked.
To effectively capitalize on AI, boards need to work closely with management to align the technology with business objectives, considering how AI can be used in the industry, whether any changes to the business model must be made and what potential risks may emerge.
The takeaway
Choosing an optimal AI path for your business can be complex, but an effective strategy can create a significant competitive advantage, driving growth as well as successful outcomes for business shareholders and stakeholders. CEOs and board members need to successfully create an AI vision for their organizations, identifying advantageous use cases and mitigating potential risks while enlisting the right outside support and guidance along the way.
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.
RSM contributors
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Joseph FontanazzaManager
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