There’s a pattern I keep seeing in organizations that are struggling with AI governance, and it starts the same way every time: the CISO or the General Counsel gets assigned ownership of “the AI policy,” produces a document, and then watches as the rest of the organization ignores it — not out of bad intent, but because nobody connected the policy to how work actually gets done.
A little over a year ago, I wrote that the build vs. buy debate was the wrong frame — that the real question was how to orchestrate the right blend of built, bought, and integrated capabilities to deliver value faster. I still believe that. Composability and platform thinking haven’t gone anywhere.
A few years ago, I wrote about scaling an engineering team from 6 to over 100 — the lessons learned, the hard pivots, and the patterns that held up under real pressure. That article was grounded in a specific kind of scaling: more people, more process, more structure.
Autonomous engineering teams are reshaping the future of work. But what exactly defines an autonomous team in today’s AI-driven environment, and how do they differ from Agile and DevOps models?
An autonomous team is a self-managing group of engineers empowered to make decisions independently, aligned with overall organizational goals. Unlike traditional Agile teams that often rely on external direction, autonomous teams own their roadmaps, prioritization, and execution, fostering faster decision-making and innovation.
The transition from developer to executive is one of the most challenging yet rewarding career shifts in technology. It’s not just about writing great code anymore—it’s about leading people, influencing business strategy, and aligning technology with company goals. Many developers aspire to leadership roles, but few understand what it truly takes to succeed at the executive level.
Scaling an engineering team is a journey filled with challenges and opportunities. Reflecting on my experiences, particularly during my tenure at eXp Realty, where we expanded our technology organization from a modest group of 6 to a robust team of over 100 professionals, I’ve gathered several key insights. This article delves into the strategies and lessons learned from that transformative period.
In our blog post about Scaling Tech Teams we touched on the important of assigning mentors. Today’s entry dives into the importance of mentors and provide additional detail on how to setup a successful mentorship program.
When scaling an engineering team, assigning mentors is one of the most effective ways to onboard new hires, transfer institutional knowledge, and create a sense of belonging. Without structured mentorship, new employees often struggle to acclimate, leading to inefficiencies and potential attrition. A well-executed mentoring program ensures that both technical and cultural knowledge are passed down effectively.
The role of enterprise technology leaders is shifting faster than ever. In the past, IT executives focused primarily on system stability, infrastructure, and cost control. Today, they must be product-driven strategists, aligning technology with business growth while keeping up with an ever-expanding digital landscape.
As we move into 2025, five key trends will define successful enterprise technology leadership. If you’re leading a technology organization—or aspiring to—these are the areas where you need to focus.
Copyright © Ledwith Technology Consulting, LLC. All rights reserved. 2009 - 2026.
Template by Bootstrapious. Ported to Hugo by DevCows.
