Leaders Insights — AI. The weekend podcast for everyone learning to use AI well. Foundations of LLMs, prompting, responsible AI, and getting the most out of ChatGPT, Claude and Gemini. New episodes on weekends at www.mba-training.com.
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Morgan Stanley's deployment of an AI assistant for its financial advisors exposed a problem most teams overlook: feeding the model more information does not produce better answers. The real discipline is selecting which context matters, and why that distinction changes how you build prompts entirely.
Klarna rebuilt one of its highest-volume, most repetitive operations around an AI agent rather than bolting AI onto an existing process. The decisions they made, and the ones they got wrong initially, offer a practical template for any team facing a similar problem.
As AI agents take on multi-step tasks autonomously, the question of when and how humans should intervene has become one of the more consequential design decisions in enterprise AI. This article unpacks the mechanics of oversight in agentic systems and explains how to think about it practically, not theoretically.
Bloomberg built a domain-specific large language model from scratch rather than retrieving over generic ones, and the results clarified a decision that still confuses most enterprise AI teams. The logic behind that choice, and where it breaks down for other organizations, is more instructive than the model itself.
Most organizations tracking AI ROI are measuring the wrong thing at the wrong time, then drawing conclusions that either kill good projects or protect bad ones. This article breaks down what ROI actually means in an AI context, how to calculate it honestly, and where the method breaks down.
In 2016, an algorithm called COMPAS was put under the microscope by ProPublica journalists, and what they found split the AI community down the middle. The argument that followed is one of the clearest illustrations of why "fairness" in AI is not a technical setting you dial in, but a choice with real consequences.
Context windows determine how much information an AI model can hold and reason over in a single session. Understanding their mechanics changes how you design prompts, structure documents, and decide when to trust a model's output.
Most professionals using ChatGPT, Claude, or Gemini are still copying and pasting between tabs, which turns AI into a toy rather than a working tool. This playbook walks you through connecting AI to your actual business systems, step by step, so the output lands where the work happens.
Klarna's AI deployment in customer support became one of the most cited cases of LLMs producing measurable operational results. The prompt discipline behind it offers concrete lessons that transfer well beyond fintech.
Reasoning models like OpenAI's o3 and Google's Gemini 2.0 Flash Thinking have captured attention by visibly "thinking through" problems before answering. The consensus says to use them everywhere you need accuracy, but that prescription is wrong in ways that will cost you money and slow your teams down.
A major retailer celebrated millions in projected AI savings, then watched the number quietly shrink to almost nothing once someone counted the full cost. That moment, repeated across industries throughout the early 2020s, explains why measuring AI returns remains the most underrated skill in enterprise technology.
The EU AI Act is now producing real obligations for companies deploying AI in Europe, and ignorance of the legal text is not a defence your board will accept. This playbook gives you a concrete sequence of steps to assess your exposure, assign ownership, and take action before regulators come looking.
Most teams adopt AI applications based on demos and vendor promises, then discover the gaps only after deploying them in production. This playbook gives you a structured sequence to test what actually matters before you commit budget, data, or workflows to any AI tool.
Generic AI gives generic answers. This playbook shows you how to connect large language models to your organisation's own data so that every response is accurate, specific, and actually useful.
Most AI tools you're using today will look different or obsolete within two years. The professionals who stay effective aren't the ones who memorize features, they're the ones who've learned to think in terms of problems, constraints, and outputs.
A corporate lawyer's frustration with AI tools that forgot everything between sessions quietly pushed a wave of professionals toward a different way of working. The shift from treating AI as a one-shot tool to giving it persistent context is one of the most underappreciated productivity changes of the past two years.
Most organizations pick their AI model deployment strategy based on a simple story: open source is flexible and cheap, closed APIs are powerful and fast. That story leaves out the parts that actually determine whether a deployment succeeds or fails.
Most professionals waste hours each week on tasks that follow the same pattern every time. This playbook shows you how to identify those tasks, convert them into structured AI workflows, and make the output reliable enough to actually use.
Not every task benefits from a reasoning model, and using one indiscriminately wastes time, money, and attention. This playbook gives you a concrete decision process for matching the right model type to the right problem.
AI bias is not a glitch or an edge case. It is a structural feature of how models are built, and understanding its origins is the first step to catching it before it damages a decision, a product, or a reputation.
Most professionals assume that longer, more detailed prompts produce better AI outputs. The real skill is something narrower: identifying which specific context actually changes the answer, and leaving everything else out.
Most LLMs confidently answer questions using knowledge that stopped updating months or years ago. RAG fixes that, and this playbook shows you exactly how to build it without getting lost in the technical weeds.
Klarna ran one of the most cited enterprise deployments of AI agents in financial services, and the results were genuinely mixed. Here is what actually happened, what the numbers mean, and what any organization should take from it before committing to agent-based automation.
Coding assistants like GitHub Copilot, Cursor, and Claude have made it genuinely possible for non-engineers to build working software. But the dominant narrative around "vibe coding" is flattening a more complicated reality that professionals need to understand before betting on it.
JPMorgan Chase deployed AI agents across legal review and trading operations, then discovered that automation without structured human checkpoints created compliance exposure it hadn't anticipated. The decisions they made to redesign those workflows offer a concrete template for any organization running agents at scale.
