Category: Thought Leadership

Supply chain managers understand inventory reconciliation. Compliance officers know audit requirements. Marketing teams grasp campaign workflows. What if they could build their own enterprise applications directly from that expertise? This isn’t theoretical. Citizen development tools like Kiro replace traditional coding with natural language specification. Business users describe what they need in plain English, and Kiro […]

  “The speed of typing out code has never ever been the bottleneck for software development (not since keyboards became widespread from the 60s or 70s)” —Gergely Orosz Software development is a complex value delivery system involving many interdependent roles, including developers, product managers, and platform engineers. Dependencies create potential bottlenecks, such as pull request […]

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 […]

Fortune 500 CEOs face a choice in 2025: Migrate to the cloud now and capture its powerful advantages or watch competitors build their leads while you continue with legacy infrastructure. That might sound dramatic, but several converging forces make now the right moment to move to the cloud if you haven’t already done so. In […]

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 an Enterprise Strategist, I’ve noticed that discussions about multicloud are often marked by confusion and contradictory advice. Some advisers warn against adopting a multicloud strategy, while others suggest that you will miss an industry-wide transformation if you don’t. There are legitimate reasons for and against multicloud strategies; success depends on balancing potential business value […]

AWS achieved a 15.9% year-over-year cost reduction by measuring the total economic cost of delivering software units rather than relying on traditional development productivity metrics.

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.

Your stakeholders expect you, as a technology leader in life sciences, to master generative AI, implement next-generation sequencing platforms, and deploy digital twins for clinical trials—all while accelerating innovation, increasing delivery speed, and managing costs. And of course you have to do this with your existing team. Just don’t forget to maintain flawless system uptimes […]

Driving successful innovation quickly is a major challenge for many organizations, even in the generative AI era. Only 21% of organizations achieve their innovation goals.1 The typical concept-to-launch journey still takes about 22 months, and 82% of engineers say they’re “constantly looking” for ways to speed it up.2 Slow go-to-market timelines mean missed opportunities and […]