The rapid development of AI tools and technologies in recent years has increased risks to organizations due to human factors. Concurrently, relative to both people’s impact on AI and AI’s impact on people — as well as on financial and intangible factors. Today, effective leaders complement financial growth metrics with new measures of value creation that reflect factors such as wellbeing, safety, productivity and engagement.
With thanks to a recent collaboration with Dr. Keyun Ruan (Founder and Chair of the Happiness Foundation, Risk Economics & Strategy, Alphabet, Google Cloud CISO), effective leaders take the following actions:
As intangible assets continue to increase as a percentage of corporate valuations — and as the increase in the pace and extent of technological development drives increased focus on intangible assets — the adequacy of traditional mainstream economic growth and risk metrics comes into question. Effective leaders adopt broader intangible measures that seek to quantify the benefits and risks associated with new AI technologies as well as others such as quantum computing.
Historically, technology breakthroughs — for example, mechanized production, electric power, information technology — and the industrial revolutions they enabled, drove major developments in economic value theories. Digitization has played such a role in the current industrial revolution without a parallel economic measurement breakthrough that accounts for the new ways value is created, distributed and consumed. Effective leaders employ dependable and consistent revenue and valuation models for digital assets such as large language models, algorithms, agents, data and intellectual property.
To use technology to its full potential amid rapid disruption while minimizing risks, effective leaders complement financial growth metrics with new measures of societal value that reflect wellbeing, safety, productivity and engagement. For example:
The sum of these measures becomes the net societal value, which can be tracked within and across industry sectors.
Effective leaders introduce measures of societal value and risk that include the impact of AI on people who work for the organization (either directly as employees or indirectly as contractors, temporary workers or vendors) as well as people who are impacted by AI more broadly. This can include customers, end-users, community members and those outside the community.
Organizations are already experienced in measuring components of human-focused factors across multiple dimensions of wellbeing. The quality of each metric varies considerably based on several factors related to measurability and reliability. This includes access to data, an organization’s history tracking it, the availability of benchmarks and the presence of uniform standards within and across industries and countries.
Effective leaders understand that traditional governance structures and processes often fail to address AI’s unique challenges and opportunities because they are unable to keep up with rapid advancements in AI technologies, including ever-changing large language models. AI tools (such as agents) require different governance protocols for different circumstances.
Effective leaders employ new models of dynamic governance that include relevant measures of performance and risk, providing a blueprint for the responsible and ethical development and deployment of AI. Dynamic governance models are flexible and responsive, with mechanisms for regular updates, feedback loops and continuous improvement. These attributes allow boards and senior management teams to tailor governance practices to their AI objectives and adapt to internal and external changes.
The following use case shows an example of how these components fit together:
Societal value: Social media algorithms play a key role in content personalization, enhancing user engagement by delivering relevant content, facilitating social connections and enabling the discovery of new interests and communities. They can also amplify voices and causes that might otherwise go unnoticed.
Societal costs: These algorithms are often criticized for creating echo chambers, spreading misinformation and exacerbating mental health issues, such as anxiety and depression through addictive design practices to maximize usage and profit. The impact on emotional and relational wellbeing as well as the potential manipulation of public opinion, represents significant societal costs.
Net societal value equation: Societal benefits (content personalization, social connections) minus societal costs and risks (echo chambers, misinformation, mental health issues) equal net value. The societal value of social media has changed over time. Some experts believe it may have already peaked, while others say it has yet to peak — indicating the potential value of updated and consistent new economic measurement models.
Effective leaders align AI development with metrics focused on both economics and societal value to guide growth as well as balance opportunities and risks. By practicing dynamic governance, they ensure that AI development is aligned with ethical and responsible practices. As AI continues to evolve, leaders who ensure that their governance and risk processes keep pace with AI are better positioned to create sustainable value for all stakeholders while managing the risks AI creates.
A version of this article originally appeared on Forbes on April 30, 2025.
