Top 3 Governance Considerations when Deploying GenAI

Summary:

• A multidimensional governance framework contributes to responsible GenAI development and use while accounting for emerging risks and challenges.
  
  
• GenAI governance frameworks must prioritize transparency, responsibility, and trust to ensure consistent outcomes that align with stakeholder and consumer expectations and evolving regulations.
  
  
• With global AI regulations continuously evolving and GenAI mistrust increasing, organizations must address three crucial governance practices to foster trust, drive innovation, and ensure compliance. 

Organizations that want to responsibly harness GenAI's transformative potential must define and implement a meaningful GenAI governance framework.

Strong GenAI governance practices safeguard organizations against diverse risks and create a foundation that accelerates adoption, compliance, and innovation. Considering that approximately 64% of organizations are projected to fail with their GenAI applications by 2026, how an organization approaches its governance strategy will be a definitive factor influencing success with this emerging technology. 

Pressure is rising to jumpstart GenAI adoption to secure a competitive market advantage. However, reputational, financial, and compliance risks may escalate the longer organizations hesitate to act on implementing GenAI governance policies while waiting on global AI regulations to take shape. 

Intellectual property, training data, privacy and compliance, mistrust, third-party risk, and algorithmic bias are among the concerns associated with GenAI, which often result in stagnant initiatives or even complete abandonment.

A robust GenAI governance framework can promote confidence in stakeholders, customers, and regulators while positioning organizations to anticipate future AI compliance requirements.

Below are 3 critical GenAI governance considerations for development and use.

#1: Establish Data Ownership Policies

Client or customer data misuse and potential intellectual property infringement are among the top concerns associated with GenAI governance, at 34% and 35%, respectively. High-profile copyright infringement lawsuits, such as the New York Times lawsuit against OpenAI and Microsoft, have illustrated the emerging data complexities associated with GenAI - particularly given evolving AI regulations.  

Effective data ownership policies help prevent misuse, protect intellectual property, support transparency and accountability, and foster stakeholder trust. It also contributes to responsible and ethical GenAI initiatives and governance standards that help organizations better respond to forthcoming global AI regulations. 

Organizations must establish explicit frameworks regarding data ownership, particularly data sourced from third parties or users. Data ownership policies should define who can access, modify, and benefit from that data and ensure data contributors or providers retain appropriate control or compensation where applicable. Updating existing data acquisition strategies may be necessary in many scenarios when the organization lacks sufficient internal data for training GenAI systems.

Additionally, evaluate GenAI data protection and privacy policies, and ensure data sets meet compliance standards, like CCPA, HIPAA, or GDPR. It might also be necessary to re-establish contract terms with data providers before using purchased data sets for GenAI use cases.

#2: Enforce GenAI Transparency 

GenAI mistrust is a continuous hurdle as AI becomes nearly ubiquitous. Organizations that overhype their capabilities or fail to divulge when GenAI is being leveraged can result in consumer and user resistance or pushback.

Organizations must thoughtfully navigate the GenAI mistrust challenge to foster trust and address rampant cynicism regarding output accuracy. For example, over 75% of surveyed consumers want companies to reveal when GenAI is used, indicating the value of embracing transparency as an essential strategic practice for customer-facing GenAI products.

Maintaining operational and decision-making transparency is another best practice that nurtures trust among stakeholders, employees, and users. Organizations should thoroughly document and disclose data sources, algorithms, and decision-making processes used by AI models. 

Furthermore, organizations should implement transparent reporting practices that clarify to stakeholders how AI decisions are made and how data is utilized. A centralized repository containing a model inventory that is classified per relevant regulations can further support routine audits and assessments to verify compliance.

Any biases or limitations within the AI system should also be openly communicated to enhance accountability, facilitate improved stakeholder understanding, and ensure the responsible deployment of GenAI technologies. 

#3: Implement a GenAI Center of Excellence (CoE)

Many GenAI market leaders are establishing a GenAI Center of Excellence (CoE) to drive innovation, mitigate risks and negative job impacts, and ensure best AI development and deployment practices. 

A GenAI CoE serves as a centralized hub that consolidates expertise, resources, and governance frameworks in a tactical approach that incorporates both technical and human considerations. It sets standards for data quality, model development, and ethical AI usage to ensure consistency and compliance across the organization. It can also foster collaboration among cross-functional teams, promote continuous learning, prioritize viable use cases, and align AI initiatives with business objectives to accelerate AI adoption.

Organizations should establish a GenAI CoE and rely on it as a long-term roadmap for sustained, human-centric AI innovation that produces desired business outcomes under the umbrella of quality, fairness, security, and privacy.

While by no means an exhaustive guide, some key steps to implementing a GenAI CoE include defining objectives and scope; assembling a multidisciplinary team with appropriate expertise; developing governance policies and standards; promoting cross-functional collaboration between departments; investing in training and development opportunities; and monitoring performance and impact. 

Effectively Communicate Your GenAI Messaging

As the AI regulatory landscape becomes increasingly complex and GenAI adoption continues to surge, effective communication is critical to supporting AI marketing agendas.

Orange Bridge leverages our copywriting and marketing expertise to help tech companies amplify their messaging, nurture credibility, and promote client and user trust. Enhance transparency regarding your GenAI governance practices and create content that resonates with diverse stakeholders. 

Our skilled technology writers combine technical knowledge with SEO-driven copywriting to distill complex concepts into engaging and informative content.

Ready to boost your GenAI messaging? Contact Orange Bridge today to learn how our content writing services can help optimize your technology firm’s marketing strategy.