I am pleased to announce the upcoming release of my fourth book in the CTRL-ALT series, CTRL-ALT-QA Testing, scheduled to launch on Amazon on July 17, 2026. This book represents our next logical step in a software delivery lifecycle. While software development often receives much of the attention, quality assurance (QA) testing are equally essential […]
Announcing My New Book: CTRL-ALT-QA Testing Read More »
Human Mistakes and Automation Bias Smart systems do not eliminate human error, they reshape it. Automation bias describes tendency to over-trust system outputs while ignoring conflicting signals. As system confidence increases, human skepticism often decreases. This creates new categories of failure: Moral Crumple Zones Human operators absorb blame when systems fail, even when their control
Smart Systems, Human Mistakes: Where Automation Meets Accountability Read More »
Over last couple of years, I have noticed an interesting pattern in conversations about Artificial Intelligence (AI). Whether I am speaking with IT executives, project managers, technical program managers, software engineers, or professionals outside information technology industry, discussion often gravitates toward same question: “Will AI replace our jobs?” Debate usually focuses on software developers, analysts,
AI Won’t Replace IT Managers – But It Will Expose Weak Ones Read More »
A recent conversation with IT peers, development managers, engineering leads, and QA leaders surfaced a pattern many have faced repeatedly across projects. If you are an IT executive, TPM, or PM, you have likely encountered a high-pressure moment when a project veers off track and one question dominates: is this a technical debt, or leadership
IT – Tech Debt or Leadership Debt? Where Projects Really Go Off the Rails Read More »
Navigating Human Side of Software Delivery Note: I distinctly remember something a speaker said during an AI forum discussion: “AI is a tool, not a replacement for human judgment.” A few years ago, many organizations IT leadership believed AI would solve one of software engineering’s oldest challenges; delivering more software with less effort. AI promised
Devs vs. Deadlines in AI Era: When Good Code Meets Corporate Pressure Read More »
Thanks for following along through our six-part AI Cost Ripples series. In this article, we’re switching gears to something different. In my TPM career, I’ve attended countless post-mortem meetings and exercises. Each one has been a learning experience, almost like a living environment with constantly changing variables. Yet, within that dynamic setting, certain rules remain
Info Tech Incident Post-Mortem Theater Read More »
As we conclude our journey through ‘hidden cost ripples of AI’, we arrive at our final and perhaps most underestimated ripple of all: Engineering Complexity. In our previous episodes we explored visible infrastructure layers such as compute, data pipelines, vector systems, networking, and observability. Yet as organizations move from experimentation into large-scale operationalization, another challenge
Episode 6 Hidden AI Cost Ripple: Engineering Complexity Read More »
I hope you enjoyed our previous four episodes. As we continue our journey through these hidden cost ripples of AI, we now move into Episode Five. Fifth Ripple marks a critical shift from deploying AI systems to actively managing lifecycle in production. Early ripples focus on development and deployment, but this stage addresses hidden challenges
Hidden AI Cost Ripples – Observability & Model Evaluation – Episode 5 Read More »
Our description of first three cost ripples: compute infrastructure, data pipelines, and vector/retrieval systems, quietly built the foundation for modern AI hidden cost ripples. Each layer solved a critical constraint, enabling models to scale, learn, and respond with increasing sophistication. However, once those pieces are in place, a new constraint emerges: “how fast intelligence can
AI Cost Ripple: Networking & Data Movement – Episode 4 Read More »
Thoughts, observations, and reflections across evolving information technology landscapes. The ideas shared on this website come from personal experience, research, and observation across rapidly evolving information technology landscapes. They are not written to promote a fixed stance, pro or anti anything, but to explore how systems behave in practice, where they succeed, and where they
Tech Articles: My Perspective and Intent Read More »
