Cloud Computing

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

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

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

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

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

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

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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AI Cost Ripple: Networking & Data Movement – Episode 4  

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

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Tech Articles: My Perspective and Intent

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

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Hidden AI Cost Ripple: Vector & Retrieval Infrastructure – Third Episode

In our previous two episodes, we explored some of less obvious but critical ripple effects of scaling AI systems. In Episode #1, we unpacked Hidden AI Cost Ripple #1: Compute Infrastructure Cost Explosion; how increasing model complexity and usage can rapidly drive-up compute demands, often faster than teams anticipate. In Episode #2, we dove into

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Second AI Hidden Cost Ripple: Data Pipeline – Second Episode

While compute infrastructure forms a foundation of AI’s cost structure, it is only the first ripple. As organizations move from experimentation to production, a second, often larger wave emerges of data pipelines, where continuous movement and transformation of data introduce a new layer of complexity, scale, and hidden cost. If compute infrastructure is described as

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