Agile-PM

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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AI Cost Ripple: Networking & Data Movement – Episode 4  

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

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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

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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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3 Enterprise AI Myths Holding You Back This Year

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As we move into Q2 of 2026, enterprise AI has moved beyond mere experimentation into a phase of “operational reckoning.” While 90% of manufacturers and countless other enterprises are employing machine learning (ML), a significant gap remains between pilot programs and real-world, scalable return on investment (ROI). In my view, many business organizations are still

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