Operationalizing Machine Learning for Business Impact

Companies have spent over 500 billion dollars on AI initiatives that failed. Because nobody could see what was happening across their AI footprint. MIT and Harvard both landed on the same conclusion. 90% traced to governance failures.   I spent years watching this pattern from inside some of the largest companies in the US. The gap between deploying AI and actually governing it gets wider every quarter.   If you are leading an AI initiative right now, here are three questions your board should be able to answer today:    1. How many AI systems are running across the organization, and who owns each one?  2. What is the financial exposure if any single system produces a wrong output at scale?  3. Is there a measurement framework in place that connects AI performance to business risk in dollar terms?   If your team cannot answer all three, you have a governance problem putting your organization at risk.   Message me for more info on how to get the answers to those questions.

The 1% Problem: Why Your GenAI Strategy Is Missing Critical Risk Detection Stop treating rare events like common problems. Your GenAI strategy is missing the hidden risks. Most enterprises leverage standard ML pipelines on datasets where 99% of observations are noise—incomplete queries, failed transactions, edge cases nobody planned for. Your model optimizes for scale and ignores what actually disrupts your business. This is the LNRE problem: Large Number of Rare Events. It's fundamental to statistical science, but most GenAI implementations completely miss it. When you deploy language models or recommendation engines across high-dimensional data where rare events drive value—fraud detection, anomaly detection, supply chain disruption—classical distributions break down. Your model becomes useless exactly when it matters most. The fix isn't more data. It's a paradigm shift in how you architect AI systems. Forward-thinking teams are solving this by: Implementing separable statistics frameworks for outlier detection Leveraging specialized statistical models alongside foundation models Optimizing model governance and data quality for edge case detection Building hybrid AI approaches that synergize traditional and deep learning Your enterprise might achieve 99% accuracy. But in risk management, compliance, or fraud prevention? That 1% is mission-critical. The question isn't whether your GenAI works. It's whether it works when it matters most. How is your organization tackling rare event challenges in your AI transformation roadmap? #GenAI #MachineLearning #DataScience #AnomalyDetection #FraudDetection #AI #Statistics #EnterpriseTech #DataQuality #RiskManagement

AI can explain your data in seconds. But it still can’t tell a story people care about. I’ve been looking at outputs from tools like ChatGPT, Claude, Grok, and Gemini recently, and while they’re amazing at taking care of the repetitive grunt work, and (when implemented correctly) are technically accurate, they all tend to land in the same place which is clear, but generic. You get a summary of what’s happening, along with trends and patterns, but what’s missing is a clear point of view and a story that helps someone decide what actually matters. And that’s the gap… Because good data storytelling isn’t just about explaining what’s there, it’s about choosing what matters, challenging what doesn’t add up, and cutting the rest so the message is clear and relevant. That’s what turns information into direction. AI is amazing at describing what’s there, but without that context, it doesn’t highlight tension, prioritise what matters, and ultimately guide a decision. That part [context] still needs a human touch. And that’s exactly why data storytelling matters more now than ever. Keen to hear how others are finding it, is AI helping drive decisions, or mainly just explaining what’s already there?

The hardest part of forecasting isn't the model. You can have the best tools, the cleanest data, the fanciest AI overlay - and it still falls apart if people aren't comfortable saying "this one's not looking great." No dashboard fixes that. No process either. The best forecasting cultures I've seen aren't the ones with the most sophisticated models. They're the ones where people feel safe being honest about their pipeline before the quarter ends. That's the real competitive advantage. How does your team handle it?

Everyone’s suddenly very worried about AI taking their job. Interesting timing. Because I remember a slightly different era — not that long ago — where: Requirements were written after the build “Final version v7” was still not final Dashboards were created, admired, and never opened again And entire processes existed purely because “that’s how we’ve always done it” I was there. Not watching. Mapping. — So when I hear “AI will replace us,” I can’t help but think: Replace what, exactly? The meetings that produce no decisions? The flows nobody maintains? The complexity we built to signal effort instead of solving problems? If anything, AI is just very good at exposing what shouldn’t have been there in the first place. — The uncomfortable truth is this: Most of the value was never in doing more. It was in seeing clearly. What the system is actually supposed to do. What should exist — and what shouldn’t. Where to simplify. Where not to touch. That part doesn’t get automated easily. — Anyway. Curious to see who adapts by adding more layers… …and who quietly removes them. 🙂

The “𝟭𝗠 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘄𝗶𝗻𝗱𝗼𝘄” sounds impressive, but, it can be misleading in practice. In long AI sessions, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗿𝗼𝘁 is real. After a certain point, models start to: • Forget earlier decisions • Contradict prior reasoning • Drift silently, with no warning So while benchmarks may show strong retrieval performance, even small failure rates become risky when AI is working on real systems (like production code). 𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁: Bigger context ≠ better context. What actually works:  • Keep sessions shorter and focused  • Rotate context before it degrades  • Use structured handovers (clear decisions, file paths, identifiers) Treat large context limits as a 𝗰𝗲𝗶𝗹𝗶𝗻𝗴, not a 𝘵𝘢𝘳𝘨𝘦𝘵. The real advantage isn’t how much context you can fit. It’s how well you manage it. Are you actively managing context, or just assuming the model will handle it? #ClaudeCode #AIEngineering #AgenticAI #SoftwareEngineering #DeveloperTools

One thing I never understood properly during the AI rush: Why were companies talking so much about AI tools before fixing their data problems first? Maybe I’m wrong, but this thought stayed in my mind for a long time. If a company’s data is scattered everywhere, teams work differently, documentation is incomplete, and systems don’t properly talk to each other… then how will AI suddenly give great results? At times it felt like companies were trying to build the top floor before fixing the foundation underneath. And honestly, I’m still trying to understand this space better. Because the more I observe enterprise technology discussions, the more I feel that technology alone is never the real problem. A lot depends on how well the company itself is organized before adding another layer on top of it.

Financial AI doesn’t fail because models are weak. It fails because the data is wrong. Most datasets: look realistic sound intelligent but are not factually grounded We built a Factually Grounded Financial Q&A Dataset where: ✔ every calculation is computed ✔ every answer is validated ✔ every distractor is intentional If you're building financial AI, the question isn’t scale. It’s truth. https://lnkd.in/gxy9yu2m

Prompting is not a skill anymore Hot take: "prompt engineering" is already outdated as a skill. The real skill in 2026 is CONTEXT engineering. Giving an AI a clever prompt with no context = getting clever nonsense. Giving it: → Your company's data schema → Past examples of good output → Relevant business rules → What "good" looks like → What to never do ...that's where the magic happens. The companies winning with AI aren't writing better prompts. They're building better context pipelines. #AI #ContextEngineering #LLM #PromptEngineering

Most AI tools are bought before anyone in the business has answered three questions: 1. What data will it run on? 2. Which process will it sit inside? 3. Who's accountable when it goes wrong? If you can't answer all three in one sentence each, you're not ready to buy. This isn't about being slow. It's about not wasting money on a pilot that fails because the foundations weren't there. The AI Readiness Audit gives you all thirty questions. Free, 7 minutes, honest result. https://lnkd.in/ef96_6jH