Tag: Artificial Intelligence

AI agents can’t work across teams because they lack the domain knowledge that exists only in developers’ minds (e.g., architectural patterns, business rules, design constraints). When agents make changes to another team’s code, they usually fail. Not because the agent lacks capability, but because it doesn’t know that team’s context. You could supervise the agent […]

In my previous blog, I shared how to evolve leadership for agentic AI using familiar mental models. As a CTO, I’ve been thinking about the corresponding architectural shifts required: We need to move from building predictable systems to developing autonomous capabilities that augment teams. Based on hands-on explorations and working with fellow technology leaders navigating […]

A single supply chain disruption today can erase millions in revenue and years of carefully built customer trust. While most organizations struggle with the balance between lean operations and reliability, some companies have discovered a different path. These market leaders have replaced traditional buffer strategies with a more responsive, efficient way to manage supply chains. […]

As organizations deploy generative AI technologies, they face challenges including lack of expertise, fragmented governance, unclear accountability, and immature tooling—issues that can be addressed through an integrated framework of governance mechanisms, repeatable processes, and embedded safeguards.

The exponential growth of enterprise data presents unprecedented opportunities for innovation, yet many organizations struggle to capitalize on it due to inadequate data governance. A robust governance framework is crucial for future-proofing and maintaining competitiveness. Effective data governance rests on four pillars: Data visibility: Clarify available data assets to inform decision-making. Access control: Balance accessibility […]

With AI’s rapid evolution, boards face multi-faceted risks requiring diverse oversight, technical expertise, agile risk-mitigation, clear values guiding deployment, and robust cybersecurity – proactively managing uncertainties while capturing AI’s transformative potential.

Data powers groundbreaking generative AI; curate diverse, high-quality datasets to cultivate innovation, while mitigating risks and biases.

Generative AI demands a pragmatic strategy to identify value, embrace agility, scale intelligently, experiment rapidly, and prioritize responsible deployment.

Some of us like to use the holiday season and early part of the new year to step back from day-to-day activities and reflect. It’s a good time to plan a bit for the coming year and catch up on what others are doing and thinking. For me it’s also a chance to work my […]

The emergence of generative AI has brought about transformative possibilities and the potential to benefit how we work, live, and interact with the world. However, it is crucial to recognize the responsibility that comes with such powerful technology. When I speak to executives today, there is a lot of enthusiasm and excitement to get started […]