Tag: AI

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 […]

AI agents are as transformative as the advent of the internet. They will change how we organize work, manage operations, and drive value A question I often hear from AWS customer executives is how they should think about leading in this new era. I use the same mental models I use to lead my most […]

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.

Navigating technological upheaval requires foresight – this insightful guide explores emerging trends like AI, cloud, IoT, and data explosion poised to upend industries, urging executives to experiment and strategize proactively to stay ahead of disruptive forces.

Harvard Business Review Analytic Services has conducted sponsored research to gain insight into how organizations are currently using data, with a particular focus on its application to generative AI. The resulting report, titled “Scaling Generative AI for Value: Data Leader Agenda for 2025,” examines trends across various industries and companies worldwide. Prior sponsored research with […]

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 […]

Automate tedious tasks, gain faster insights through conversational analytics, and innovate using synthetic data – accelerating time-to-value from data assets.

Maximize AI’s value without breaking the bank – this guide explores cost-optimization strategies across the AI lifecycle, from model selection and data management to FinOps practices.

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.

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