Agile-PM

ITIL and Microsoft’s Operations Framework in Cloud and AI Era: Why Operational Discipline Still Matters

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You may be thinking, why write about ITIL and Microsoft’s Operations Framework in 2026? I wrote this article because our technology industry finds itself at another inflection point. IT organizations are aggressively pursuing cloud modernization, automation, and artificial intelligence (AI) initiatives while facing economic pressures, evolving workforce models, and heightened expectations from customers, boards, and […]

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Announcing My New Book: CTRL-ALT-QA Testing

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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

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How to Effectively Manage Backlogs: Turning a Wish List into a Delivery Engine

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During a recent conversation with a few seasoned technology professionals, including a Development Manager and a Technical Program Manager, the subject of backlog management surfaced alongside several other operational discussions. As we exchanged field experiences, it became clear that organizations often view backlogs through very different lenses, and those perceptions can lead to unintended consequences.

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Smart Systems, Human Mistakes: Where Automation Meets Accountability

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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

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AI Won’t Replace IT Managers – But It Will Expose Weak Ones

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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,

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IT – Tech Debt or Leadership Debt? Where Projects Really Go Off the Rails

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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

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Devs vs. Deadlines in AI Era: When Good Code Meets Corporate Pressure

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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

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Info Tech Incident Post-Mortem Theater

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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

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Episode 6 Hidden AI Cost Ripple: Engineering Complexity

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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

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Hidden AI Cost Ripples – Observability & Model Evaluation – Episode 5

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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

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