Cultivating Organizational Learning and Adaptability

Success requires continuously hypothesizing, building, testing, analyzing, and refining AI-powered prototypes and business models at speed.

Fail fast, adapt, and iterate.

The availability of massive datasets and progress in machine learning vastly accelerate the pac...

• Build teams encompassing a diversity of AI-related skills including computer science, mathematics, ethics, design thinking, business strategy, change management and more.
• AI progress requires technical prowess combined with humanistic perspectives to produce well-rounded solutions.

AI's implications for business extend far beyond simply automating repetitive tasks. Applied thoughtfully, AI also enables vastly expanded leverage of data, personalization and customization at scale, superior prediction capabilities, and deep integration across platforms and ecosystems.

In environments where value is produced via networks, leverage platform business models that efficiently and intelligently match producers and consumers of value, while also capturing a share of that value for the platform via fees, data accumulation, or other means.

• Responsibly monitor jobs at high risk of automation displacement.
• Provide transition support, reskilling/upskilling, severance, and alternative role paths for workers affected.
• Manage societal and enterprise impacts simultaneously.

Instill and operationalize strong humanistic guardrails around ethics, fairness, transparency, accountability and control to ensure the development and application of AI technologies ultimately serve and benefit humanity, rather than threaten it.

Technological progress and humanis...