As artificial intelligence (AI) becomes more embedded in business strategies, its governance has emerged as a critical issue for companies seeking to leverage AI’s capabilities while managing regulatory and operational risks. The adoption of AI tools requires thoughtful, structured governance to ensure compliance, protect sensitive data, and maintain investor confidence. Effective AI governance is not just a regulatory necessity; it is also a strategic move that can significantly enhance a company’s credibility and appeal to investors, positioning it for long-term success and sustainable growth. Here, we discuss key best practices for AI governance that can help safeguard both operational integrity and investor trust.
Establish Clear & Comprehensive Processes for AI Usage
The foundation of effective AI governance starts with clear processes on how AI will be integrated into organizational workflows. For companies seeking to maintain investor confidence, ensuring that AI is used responsibly and transparently is essential. Companies must determine:
- Which AI tools will be implemented? Different AI models offer varying degrees of customization, functionality, and risk. Companies should carefully evaluate which AI models best suit their operational needs and strategic goals, ensuring that the tools chosen align with investor expectations for stability, security, and compliance.
- How will AI tools be integrated into existing and new workflows? Clearly defining how AI will be incorporated into business processes is crucial for minimizing risks and avoiding surprises that could negatively impact performance and investor sentiment.
- What data is fed into these systems? Companies must be vigilant about the type of data used in AI tools. Sensitive or material, non-public information (MNPI) should never be input into public or third-party AI tools, as mishandling such data can not only lead to regulatory issues but also erode investor confidence.
- What safeguards are in place to protect proprietary or sensitive information? Robust security measures and transparency in how AI systems manage and protect data are key to preserving investor trust and ensuring that the company remains compliant with privacy laws and regulations.
Establishing clear guidelines and proactively addressing these questions helps companies reduce the risks of operational failures or data breaches that can negatively impact stock prices and investor sentiment. Moreover, companies should adopt a mindset that treats AI outputs as drafts or preliminary insights, requiring rigorous human oversight to ensure decisions based on AI are accurate and aligned with business objectives.
Involve Legal & Compliance Teams Early
To ensure that AI is fully compliant with all relevant regulations and corporate policies, it’s essential for legal and compliance teams to be involved at every stage of the AI implementation process. This proactive approach is critical not only for regulatory adherence but also for maintaining investor confidence by demonstrating that the company is managing risks responsibly.
- Involve the General Counsel (GC) and ensure their understanding of AI frameworks and who within the organization has access to material, non-public information (MNPI). Additionally, there should be clear definitions in place for what is in and out of scope for AI tools. This step minimizes the potential for inadvertent disclosures of sensitive or MNPI that could lead to significant financial and regulatory repercussions.
- Align AI use with Regulation Fair Disclosure (RegFD): AI’s impact on how and when material information is disclosed must be considered. Any AI-driven decision-making process that affects investor communications or material disclosures should be carefully scrutinized to prevent any appearance of selective or improper disclosure.
- Consistency Across Departments: Ensure that AI governance processes are consistent across all departments and teams, particularly when it comes to handling sensitive financial data. Investor confidence is strengthened when companies demonstrate that their AI governance is uniform, transparent, and subject to oversight at every level.
Third-Party Vendor Oversight & Data Protection
For organizations that rely on third-party AI tools, ensuring robust data protection is paramount. Companies need to ask these critical questions of their providers:
- How is data safeguarded? It is essential that AI vendors have strong data protection measures in place. This can help reassure investors that the company is taking adequate steps to protect its intellectual property, client data, and any other sensitive business information.
- How are safeguarding processes audited? Regular audits of third-party vendors’ data protection practices are necessary to ensure that these safeguards remain effective and in line with evolving regulatory standards.
- What happens if the AI tool’s functionality or terms of service change? Changes to third-party AI tools, such as new terms of service or alterations in data handling practices, should be disclosed and carefully reviewed by the company before implementation. Transparent vendor management will assure investors that the company remains in control of its risk exposure.
Additionally, it’s important to assess whether third-party providers carry adequate insurance policies to protect against data breaches or misuse of information, further instilling confidence in both the regulatory and financial aspects of the company’s operations.
Other Helpful Tips
- Adopt a Centralized AI Governance Platform: One of the most effective ways to manage AI governance is through a single platform with clear ownership and oversight. This approach not only ensures consistency in processes but also enhances transparency, which is critical for any investor relations strategy. By having a central governance framework, companies can mitigate the risks associated with fragmented AI applications.
- Develop a Prompt Library & Training for Employees: Generative AI tools are only as effective as the prompts provided. To maintain high standards and ensure AI tools generate the desired outputs, companies should create a prompt library and provide ongoing training to employees. Additionally, keeping a record of these prompts can offer valuable insights into how to refine the system and improve its outputs over time. This is an easy way to enhance the efficiency of AI tools while ensuring that AI-driven insights are both relevant and reliable for stakeholders.
- Identify Key AI Use Cases with Clear Protections: Companies should start by identifying specific use cases where AI can add the most value. Engaging stakeholders early in the process, including the legal, compliance, and investor relations teams, ensures that AI use is aligned with the company’s strategic goals and investor interests. By integrating these protections from the outset, the company can avoid scenarios where AI use inadvertently harms data security or investor perceptions.
By establishing clear processes, working closely with legal and compliance teams, and ensuring strong vendor oversight, companies can build an AI governance framework that not only meets regulatory requirements but also fosters investor confidence. Demonstrating a commitment to responsible AI usage is a powerful way to maintain long-term investor trust and protect the company’s reputation in the market.
Gilmartin Group partners with healthcare and life sciences companies to create durable shareholder value and craft compelling strategic messaging that resonates with the investor community. To find out more about how we partner with our clients, please contact our team today.
Authored by: Isabella Luong, Analyst, Gilmartin Group