In this piece, I explain why AI rarely improves GCC productivity on its own and why the real gains are decided by redesigning the delivery system that surrounds the team. Introduction When engineering productivity slips in a scaling GCC, the cause is almost never that the team got too big. It’s that the system around
Introduction Global capability centers have moved well beyond their original role. In many organizations, they take ownership of delivery, contribute to product development, and influence how work flows across teams. The way these centers are designed has not kept pace with that shift. The model still draws from an earlier reality where work was segmented,
This content was originally featured in a Forbes article in June 2026. Introduction If you want to know whether a company is getting real value from AI, stop reading their press releases. Look at what is running in production, because that is where most of this falls apart. A lot of AI work looks impressive in
Introduction You can tell a lot about a GCC’s operational maturity before you speak to the IT team. How procurement handles vendor security reviews, whether talent acquisition screens for security awareness, how leadership talks about risk in quarterly reviews— these are the real indicators. When cybersecurity lives only inside a dedicated security team and the
In this piece, I explain why knowledge transfer failures in GCC transitions are rarely about training or documentation and are often decided by the structural choices that shape long term capability. Introduction Knowledge transfer failures in Global Capability Center transitions rarely show up during handover. They surface later, after go‑live, as quiet underperformance, with delivery
Introduction Most organizations struggle with AI not because the technology falls short. They struggle because AI walks into operating environments that simply were not built to handle it beyond the pilot stage. Experimentation has become the norm. Companies fund pilots, encourage proofs of concept, and see promising early results. Many of these isolated efforts actually
This content was originally featured in a Forbes article in February 2026. Introduction Artificial intelligence (AI) is reshaping how work gets done across nearly every knowledge profession. Much of the conversation today focuses on productivity, automation and cost, or on speculation about entirely new roles. Less attention is paid to a deeper structural question for jobs
This content was originally featured in a Forbes article in January 2026. Introduction Most enterprises now have an AI strategy. Slide decks are polished, pilots are launched and tools are licensed. Yet little about how work gets done has changed. Technology moves quickly, but organizational thinking and operations often don’t. The execution gap doesn’t come from
Introduction The role of the CFO has expanded significantly in recent years. Finance leaders are now expected to drive sustained EBITDA expansion while supporting revenue growth without proportional increases in cost. Margin pressure is rising, capital has become more expensive, and traditional approaches built on manual process improvement and linear cost reduction no longer deliver
Introduction Most organizations don’t fail on strategy. Their roadmaps are ambitious, investments aligned, and intent clear. The failure point is execution, translating strategy into measurable outcomes at the pace markets demand. Traditional delivery models can’t keep up. In-house teams hit talent and bandwidth constraints. Outsourcing adds short-term capacity but dilutes ownership and context. The gap