Author: Keith Baldwin

News just broke that a hacking group claims to have stolen over a billion customer records from a major CRM company’s databases.A billion. Whether every detail of that claim holds up or not, one thing is clear: a lot of businesses are about to have some uncomfortable conversations about security, platform choices, and misplaced trust. […]

The arrival of Copilot within Microsoft Office and Teams represents a bold push by Microsoft to embed generative AI at the core of everyday workflows. But for many organizations, the vision of frictionless intelligence has become tangled in overload, confusion, and underutilization. In this article, we adopt a Problem → Solution lens: first diagnosing the […]

Introduction: Reliability Has Always Been the True Test of Engineering When Roman engineers built aqueducts, they didn’t think in terms of algorithms or model accuracy. They thought in centuries.Their success wasn’t measured by innovation but by reliability — water still flowed long after the builders were gone. Modern AI engineers face a similar test. We […]

Introduction: The Paradox of Fairness in Machines Every company racing to “make AI fair” is, in a sense, chasing a ghost. Fairness sounds like an unimpeachable virtue — who wouldn’t want fair systems, fair algorithms, and fair outcomes? Yet the moment we try to define fairness, we collide with its contradictions. Is fairness equality? Is […]

This week, I read Dr. Jeffrey Funk’s insightful LinkedIn post on Disney and Lionsgate’s experiments—and frustrations—with generative AI in Hollywood. Richard Self’s comment got me thinking. He highlighted a fascinating contrast: studios that once boasted about AI generating anime versions of John Wick or cloning Dwayne “The Rock” Johnson for Moana sequels are now scaling […]

Introduction Forecasting has always been at the heart of supply chain management. The difference today? The complexity of global supply networks makes “gut instinct” forecasting obsolete. Inaccurate predictions lead to overstocked warehouses, stockouts, and disappointed customers. But there’s good news: AI-driven forecasting is no longer the exclusive domain of data scientists coding in Python. Thanks […]

Introduction Let’s face it: many AI projects don’t fail because of bad algorithms. They fail because AI team communication collapses somewhere between the boardroom and the buildroom. Developers speak in acronyms, stack traces, and C# snippets that might as well be Klingon. Executives counter with ROI forecasts, compliance demands, and slide decks that feel like […]

Introduction Artificial Intelligence (AI) has shifted from boardroom buzzword to boardroom mandate. For executives leading Microsoft-centric enterprises, the question is no longer “Should we adopt AI?” but “How ready are we to scale AI across our business?” That readiness is not a binary yes/no. Instead, it’s a progression—a journey marked by stages of maturity. Just […]

Introduction: Failure as a Teacher In AI development, failure is not a risk—it is an inevitability. The question is not if an AI project will stumble, but when and how. What distinguishes successful organizations is not immunity from failure, but the ability to catch it early, learn from it, and redirect before losses spiral out […]

Introduction: Why ROI Matters More Than Ever AI is no longer confined to research labs or pilot experiments. Executives and business leaders now demand measurable returns. In an age of budget scrutiny and heightened expectations, the question is no longer “Can we do AI?” but rather “Should we, and what value will it deliver?” The […]

Introduction: The Fragility of Trust In software engineering, and especially in AI, the act of deploying code is no longer a purely technical gesture—it is an act of trust. We trust the pipeline to safeguard sensitive data, the infrastructure to comply with regulations, and the organization to honor the confidence placed in it by clients, […]

Introduction In my previous article, we explored the big idea: why large enterprises lose their innovative edge, and how they can revive it in the age of AI. We looked at Intel’s missed opportunities, NASA’s bureaucratic slowdown, and the lessons from disruptors like SpaceX and TSMC. The conclusion was clear: innovation requires autonomy, speed, and […]

Introduction Success can be a trap. The very processes and structures that allow an organization to dominate can eventually suffocate the creativity that made it great. Intel once set the pace for the entire semiconductor industry, only to stumble as AMD and TSMC overtook it. NASA put humans on the moon, but decades later private […]

Introduction: Why Backcasting? When organizations talk about bias mitigation in AI, the conversation often sounds like compliance training: tick the boxes, fill the forms, move on. Yet fairness in AI is not about checklists—it’s about long-term trust, systemic resilience, and societal impact. To break free from the checklist trap, we’ll use future backcasting: envisioning a […]