The Role of Effective Leadership in AI Development
The surge in AI tools and technologies has introduced new risks to organizations, particularly linked to human factors. These risks affect not only financial outcomes but also wellbeing, safety, productivity, and engagement. Effective leaders today recognize that growth isn't just about numbers—they also focus on broader measures of value creation that include these human-centered factors.
Focus on Tangible and Intangible Assets
Intangible assets are becoming a larger piece of corporate value, especially with rapid technological changes like AI and quantum computing. Traditional economic and risk metrics don’t fully capture these new dimensions. Leaders need to adopt broader measures that quantify both the benefits and risks of these emerging technologies.
Recognize the Need for New Revenue and Valuation Models
Technological breakthroughs in the past—like mechanized production and electric power—transformed economic theories and value measurement. Digitization is driving similar change today, but economic models haven't yet caught up. Effective leaders use consistent, reliable revenue and valuation models for digital assets such as algorithms, large language models, data, and intellectual property.
Measure Value Creation and Risk Management from AI
To maximize AI’s benefits while managing its risks, leaders complement financial metrics with measures reflecting societal value. Examples include:
- Physical and emotional health value: Assessing how AI impacts employee health, productivity, and cyber risk.
- Product and service value: Quantifying the value AI-enabled offerings provide to customers and users.
Traditional metrics like total shareholder return remain important, but combining them with these new measures provides a fuller picture of net societal value.
Measure Net Societal Value and Risk
Leadership must include the effects of AI on all people involved with or impacted by the organization—employees, contractors, customers, community members, and beyond. Organizations often track wellbeing across multiple dimensions, but metric quality varies based on data availability, benchmarking, and standardization. Improving these measures helps better manage AI’s human impact.
Practice Effective AI Governance
Traditional governance structures often fall short for AI’s unique challenges. AI tools require flexible governance that can adapt to fast changes and different scenarios. Effective leaders adopt dynamic governance models with regular updates and feedback loops to ensure responsible, ethical AI development and deployment. This approach allows leadership teams to adjust governance as AI technologies and organizational goals evolve.
Use Case: Social Media Algorithms
Societal value: Social media algorithms personalize content, boost user engagement, help people connect, and amplify underrepresented voices.
Societal costs: However, they can create echo chambers, spread misinformation, and contribute to mental health problems through addictive designs aimed at maximizing usage and profit.
Net societal value equation: The balance between these benefits and costs determines the true societal value of social media. Some experts believe its value has peaked, while others see room for growth, highlighting the need for updated economic measurement models.
For HR professionals, understanding these dynamics is crucial. Effective leadership in AI development means aligning growth with both financial and societal metrics. It also means adopting governance practices that keep AI development ethical and responsible. Leaders who integrate these approaches create sustainable value for their organizations and stakeholders while managing AI-related risks.
To deepen your knowledge of AI’s impact on organizations and human factors, explore Complete AI Training’s latest AI courses.
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