Governance of Artificial Intelligence as a value driver

  • Article
  • 4 minute read
  • 20 Jun 2024

The emergence of Artificial Intelligence (AI) in organizations has been paired with the need for governance as a critical component, establishing more than compliance by driving value through harnessing the full potential of AI, while ensuring ethical practices. AI governance refers to the framework and processes that set strategy and objectives, guide the responsible development and deployment, and use of AI in organizations. Such practices have received much attention considering the emergence of the EU AI Act and the potential compliance gaps organizations will have.

However, AI governance is crucial for organizations beyond compliance. It drives value by enabling innovation and scaling of use cases in a structured way and assessing and establishing AI in a trustworthy way. Governance structures involve defining and communicating strategic goals and company values, adapting organizational responsibilities and communication, establishing processes throughout the AI lifecycle, and implementing conformity measures. A comprehensive approach to AI governance not only ensures compliance but also maximizes the potential of AI in a trustworthy way while driving immense value for the organization.

Infographic: Governance of Artificial Intelligence as a value driver

Innovating and scaling AI supported by governance structures

In today’s rapidly evolving technological landscape, staying competitive requires embracing innovation in the field of AI. The innovation and subsequent scaling of AI use cases is one of the greatest hurdles in its development. Governance structures offer stability and guidance to innovation in an organization. Regardless of the AI maturity of the organization, there is value in establishing organizational structures early on to define pathways for AI development and procurement processes. Governance measures support the innovation fitted to the demands of (potential) customers as well as guiding experimental AI.

Innovation often starts with identifying market needs, customer demands, and promising trends, and developing AI solutions to address those specific requirements. Governance frameworks support such an approach by providing mechanisms and protocols for gathering feedback from customers and stakeholders, and performing market research. By incorporating customer-centricity through governance measures, companies effectively pull innovation opportunities and align their AI initiatives with market demands.

Another approach to innovation involves proactively exploring and experimenting with new technologies and ideas, even before specific market demands are identified. AI governance frameworks support the push approach by providing a framework for experimentation, risk-taking, and exploration of new AI technologies. This includes establishing dedicated innovation labs or sandboxes where employees can freely explore and test new AI ideas. By allocating resources and providing guidelines, companies encourage the creativity of employees to push the boundaries of AI innovation through their governance measures.

Governance structures support defining goals for evaluating use cases, risks to assess and templates for establishing a proof of concept. Such measures are essential for supporting the necessary filtering of ideas and scaling AI. Aligning these governance measures along an innovation funnel for AI, helps to push use cases from ideation all the way to scaling.

Infographic: Governance of Artificial Intelligence as a value driver

Driving trustworthy AI with governance structures

AI governance plays a crucial role in understanding and managing the impact of AI tools throughout their lifecycle. This includes translating and communicating organizational values for trustworthy AI and defining suitable mechanisms/systems to monitor their performance. Trustworthy AI can be defined along various principles, such as the ones defined at the bottom of the page.

To adequately monitor AI, governance measures emphasize the involvement of all relevant stakeholders, including users of AI tools, to understand the impact on relevant principles. By incorporating participatory processes, organizations gain insights into the potential benefits and risks associated with AI use cases.

Furthermore, a comprehensive governance framework recognizes the importance of addressing impact at the early stages of AI development or procurement. This proactive approach allows organizations to anticipate and mitigate potential negative consequences before they materialize. It also promotes reflexivity, encouraging continuous examination of the impact of AI and its integration into the research and innovation process.

Importantly, it also provides a framework for responsiveness to societal concerns, operating beyond specific legal frameworks. By considering ethical, social, and environmental aspects, AI governance frameworks based on trustworthiness principles establish inclusive processes for early AI impact assessments. This fosters trust in AI systems for both employees working with such tools or potential customers segments of AI products.

By adopting a robust AI governance framework, organizations gain a holistic understanding of the impact of their AI. This enables them to make informed decisions, identify areas for improvement, and optimize their AI strategy. Ultimately, AI governance supports responsible and ethical use of AI technologies, driving value for organizations while building trust with both customers and employees.

Building trustworthy AI can take place along various principles

Infographic: Building Trustworthy AI

Organizations establish human oversight and agency through the introduction of various governance measures. This is achieved by providing users of the AI tool with information on how the model makes decisions and why decisions are made. Through the involvement of users in design processes and having employees participate directly in designing AI tools, trust can be established. By incorporating these measures, organizations establish accountability and empower users. This fosters a sense of empowerment, confidence, and inclusivity, leading to stronger trust relationships with both customers and employees.

Transparency and explainability are closely related to human oversight and agency. AI algorithms can be complex and opaque, making it difficult for customers and employees to understand how decisions are being made. By implementing transparency measures, such as providing explanations for AI-generated recommendations or disclosing the data sources used, organizations enhance understanding and trust in AI technologies. This also helps to mitigate concerns about bias or discrimination in AI systems, as customers can see how decisions are being made and have confidence in the fairness of the technology.

Governance measures establish robustness and safety through implementing testing and validation protocols for tools and continuously monitoring the AI model's functioning. This ensures the quality and stability of the functioning of the tools, giving the user a sense of safety. Rigorous testing and monitoring may also lead to the mitigation of model bias, which makes the decisions of AI tools more predictable and fairer for the person using them. Such measures foster a sense of understanding and trust in the AI tools that are used.

One way the governance of AI helps build trust with customers is by establishing clear guidelines and standards for data privacy and security. Customers are increasingly aware of the importance of protecting their personal information, and they expect companies to handle their data with care. By implementing strong data protection measures, such as encryption and secure storage, organizations demonstrate their commitment to safeguarding customer data. This not only helps to build trust but also protects the brand from potential data breaches and the resulting reputational damage.

Fairness and greater societal wellbeing are achieved through the implementation of guidelines and policies that promote fairness, such as bias detection and mitigation techniques, diverse training data, and impact assessments. Established protocols for crisis management can help organizations react quickly to prevent or minimize the societal impact of AI tools. By prioritizing these measures, organizations generate values of equity, inclusivity, and social responsibility. This fosters trust among customers and employees, as they perceive the organization as committed to non-discrimination and the broader welfare of society. This leads to stronger relationships, positive brand reputation, and a positive social impact, contributing to the long-term success and sustainability of the organization.

The Authors

Hendrik Reese

Hendrik Reese, Partner Responsible AI at PwC Germany

Dr. Sebastian Becker

Dr. Sebastian Becker, Manager Responsible AI at PwC Germany

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