Stuck in an endless loop of client changes? Lost track of what revision this constitutes? Yeah. Been there. Done that. The secret? It's not about saying no. It's about saying yes to the right things upfront. Every project that goes sideways starts the same way: Vague agreements. Fuzzy boundaries. Good intentions. Six weeks later you're bleeding money and everyone's frustrated. Here's my framework after 30 years of running two 8-figure businesses: The SOW is your salvation. Not some boilerplate template. A real document that covers: • Exact deliverables (not "design work" but "3 homepage concepts, 2 rounds of revisions") • Hours of operation ("We respond M-F, 9-5 PST. Weekend requests get Monday responses") • Revision rounds spelled out ("Round 1 includes up to 5 changes. Round 2 includes 3.") • Feedback cycles defined ("48-hour turnaround for client feedback or the project may be delayed or additional fees may be incurred") But here's what most people miss— Don't work on client notes immediately. Client sends 37 pieces of feedback at 11pm Friday? Producer sends conflicting notes from the CEO? Marketing wants one thing, sales wants another? Stop. Collect everything first. Resolve the conflicts. Get on the phone and discuss it with your client to get alignment. Separate the "have to haves" from the "nice to haves". Then present unified changes. "Based on all feedback received, here are the 8 changes we'll implement. This constitutes revision round 2 of 3." Watch how fast the random requests stop. No extra work that goes unappreciated. No more feelings of being taken advantage of. Communicate before the crisis, prevents the crisis from happening. "Just so you know, we're entering round 2. You have one more included. After that, it's $X per additional round." No surprises. No awkward money conversations. No resentment. Scope creep isn't a them problem. It's a you problem. And that's good news, because that means you are in control. They're not trying to take advantage. They just don't know where the boundaries are because you never drew them. Draw the lines early. Communicate them clearly. Everyone wins. What's your most painful scope creep story? What boundary would've prevented it? Small Business Builders #projectmanagement #clientmanagement #businessgrowth

🎣 “They didn’t even cc me.” This was how Yumi, a senior marketing director, found out her billion-dollar product had been repositioned, without her input. The project she had been leading for 18 months was suddenly reporting into someone else. She didn’t mess up. She wasn’t underperforming. She just wasn’t "there". Not at the executive offsite. Not at the Friday “golf and growth” circle. Not at the CEO’s birthday dinner her male peer casually got invited to. She was busy being excellent. They were busy being bonded. 🍷 When she asked her boss about the change, he was surprised: “You’re usually aligned with the bigger picture, so we assumed it’d be fine.” In Workplace politic-ish: Yumi was predictable. Available. Yet not powerful enough to be consulted. 🔍 What actually happened here? Women are told to build relationships. Men build alliances. Women maintain connections. Men maintain relevance in power circles. It’s not about how many people like you. It’s about how many people speak your name when you’re not in the room. And in most companies, the real decisions - about budget, headcount, succession, are made off-the-clock and off-the-record. 📌 So, how do you stop getting edited out of influence? Try these: 1. 𝗧𝗿𝗮𝗰𝗸 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗼𝘄𝗲𝗿 𝗺𝗮𝗽.    Not the org chart. The whisper network / shadow organistion.    Who gets invited to early product reviews?    Who influences without title?    Start mapping that!     2. 𝗔𝘂𝗱𝗶𝘁 𝘆𝗼𝘂𝗿 𝗻𝗮𝗺𝗲-𝗱𝗿𝗼𝗽 𝗰𝗼𝘂𝗻𝘁.    If your name hasn’t been mentioned by 3 different people in senior leadership this month, you are invisible to power, even if you’re a top performer.     3. 𝗥𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴.    Skip the webinars and female empowerment panels.    Start showing up where strategy happens: QBRs, investor briefings, offsite planning, cross-functional war rooms.     4. 𝗖𝗿𝗲𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗯𝗮𝗰𝗸𝗰𝗵𝗮𝗻𝗻𝗲𝗹.    Schedule recurring 1:1s with lateral stakeholders, not to “catch up,” but to co-build. Influence travels faster across than up.     5. 𝗕𝗲 𝘄𝗵𝗲𝗿𝗲 𝗮𝗯𝘀𝗲𝗻𝗰𝗲 𝗵𝘂𝗿𝘁𝘀.    If you vanished for 2 weeks and no one noticed, you’re not central enough to promote.     🧨 If any of this feels raw, it’s because it is. Brilliant women are being rewritten out of their own stories, not for lack of performance, but for lack of positioning. That’s why Uma, Grace and I created 👊 𝗙𝗿𝗼𝗺 𝗢𝘂𝘁𝘀𝗶𝗱𝗲𝗿 𝘁𝗼 𝗜𝗻𝘀𝗶𝗱𝗲𝗿: 𝗠𝗮𝘀𝘁𝗲𝗿 𝗪𝗼𝗿𝗸𝗽𝗹𝗮𝗰𝗲 𝗣𝗼𝗹𝗶𝘁𝗶𝗰𝘀👊 A course for women who are done watching strategic mediocrity rise while they wait for recognition. It’s not about becoming someone else. It’s about learning the rules that were never designed for us, and playing like you intend to win. 🔗 Get it if you’re ready, link in comment. Or wait until they “assume you’d be aligned,” too.

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

It’s easy as a PM to only focus on the upside. But you'll notice: more experienced PMs actually spend more time on the downside. The reason is simple: the more time you’ve spent in Product Management, the more times you’ve been burned. The team releases “the” feature that was supposed to change everything for the product - and everything remains the same. When you reach this stage, product management becomes less about figuring out what new feature could deliver great value, and more about de-risking the choices you have made to deliver the needed impact. -- To do this systematically, I recommend considering Marty Cagan's classical 4 Risks. 𝟭. 𝗩𝗮𝗹𝘂𝗲 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗦𝗼𝘂𝗹 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 Remember Juicero? They built a $400 Wi-Fi-enabled juicer, only to discover that their value proposition wasn’t compelling. Customers could just as easily squeeze the juice packs with their hands. A hard lesson in value risk. Value Risk asks whether customers care enough to open their wallets or devote their time. It’s the soul of your product. If you can’t be match how much they value their money or time, you’re toast. 𝟮. 𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗨𝘀𝗲𝗿’𝘀 𝗟𝗲𝗻𝘀 Usability Risk isn't about if customers find value; it's about whether they can even get to that value. Can they navigate your product without wanting to throw their device out the window? Google Glass failed not because of value but usability. People didn’t want to wear something perceived as geeky, or that invaded privacy. Google Glass was a usability nightmare that never got its day in the sun. 𝟯. 𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗼𝘀𝘀𝗶𝗯𝗹𝗲 Feasibility Risk takes a different angle. It's not about the market or the user; it's about you. Can you and your team actually build what you’ve dreamed up? Theranos promised the moon but couldn't deliver. It claimed its technology could run extensive tests with a single drop of blood. The reality? It was scientifically impossible with their tech. They ignored feasibility risk and paid the price. 𝟰. 𝗩𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗲𝘀𝘀 𝗚𝗮𝗺𝗲 (Business) Viability Risk is the "grandmaster" of risks. It asks: Does this product make sense within the broader context of your business? Take Kodak for example. They actually invented the digital camera but failed to adapt their business model to this disruptive technology. They held back due to fear it would cannibalize their film business. -- This systematic approach is the best way I have found to help de-risk big launches. How do you like to de-risk?

Conflict is inevitable. Emotional intelligence is the antidote. This “conversation guide” is a blueprint for emotional intelligence in action. ✅ Every step here reflects self-awareness, empathy, impulse control, and respect for others’ perspectives — the core pillars of EQ. ✅ Difficult conversations often go wrong not because of what we say, but how and when we say it. ✅ Mastering these skills turns conflict into collaboration. ✅ You create safety, preserve dignity, and move toward solutions — not stand-offs. Bottom line: 🧠 The emotionally intelligent leader doesn’t avoid hard conversations because they know how to have them well. That’s where trust is built, relationships deepen, and real progress happens. Give it another read, and tell me what you think... HOW TO MASTER DIFFICULT CONVERSATIONS 1️⃣ Timing Matters ❌ Don’t ambush someone when they’re stressed or busy. ✅ “Can we find a time that works for both of us?” 2️⃣ Starting With Empathy, Not Ego ❌ Don’t jump in with blame or judgment. ✅ Begin by acknowledging their perspective and emotions. 3️⃣ Staying Steady, Not Reactive ❌ Don’t snap back or shut down. ✅ “Okay, I hear you. Can you help me understand what happened?” 4️⃣ Tackling It Early ❌ Don’t let negative feelings fester. ✅ Bring up issues when they’re still small. 5️⃣ Creating The Right Setting ❌ Don’t have tough talks in public or around peers. ✅ “Mind if we step aside and talk in private for a minute?” 6️⃣ Focusing On The Issue ❌ Don’t bring up past grudges or performance issues. ✅ Stay on topic and address one concern at a time. 7️⃣ Finding Common Ground ❌ Don’t frame the conversation as “winning” vs. “losing.” ✅ “We both want [X] by [date and time], right?” 8️⃣ Accepting Responsibility ❌ Don’t deflect or minimize your role in the situation. ✅ “I could’ve handled that better — my bad.” 9️⃣ Avoiding Absolutes ❌ Don’t use words like “always,” “never,” or “impossible.” ✅ Recognize nuance and exceptions to patterns. 🔟 Offering Solutions ❌ Don’t just present problems without plans for moving forward. ✅ “Here’s what I think could help... what do you think?” --- ♻️ Repost if this resonates. ➕ Follow Travis Bradberry for more and sign up for my weekly LinkedIn newsletter. Do you want more like this? 👇 📖 My new book, "The New Emotional Intelligence" is now 10% off on Amazon and it's already a bestseller.

Explaining the Evaluation method LLM-as-a-Judge (LLMaaJ). Token-based metrics like BLEU or ROUGE are still useful for structured tasks like translation or summarization. But for open-ended answers, RAG copilots, or complex enterprise prompts, they often miss the bigger picture. That’s where LLMaaJ changes the game. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? You use a powerful LLM as an evaluator, not a generator. It’s given: - The original question - The generated answer - And the retrieved context or gold answer 𝗧𝗵𝗲𝗻 𝗶𝘁 𝗮𝘀𝘀𝗲𝘀𝘀𝗲𝘀: ✅ Faithfulness to the source ✅ Factual accuracy ✅ Semantic alignment—even if phrased differently 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: LLMaaJ captures what traditional metrics can’t. It understands paraphrasing. It flags hallucinations. It mirrors human judgment, which is critical when deploying GenAI systems in the enterprise. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗟𝗟𝗠𝗮𝗮𝗝-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Answer correctness - Answer faithfulness - Coherence, tone, and even reasoning quality 📌 If you’re building enterprise-grade copilots or RAG workflows, LLMaaJ is how you scale QA beyond manual reviews. To put LLMaaJ into practice, check out EvalAssist; a new tool from IBM Research. It offers a web-based UI to streamline LLM evaluations: - Refine your criteria iteratively using Unitxt - Generate structured evaluations - Export as Jupyter notebooks to scale effortlessly A powerful way to bring LLM-as-a-Judge into your QA stack. - Get Started guide: https://lnkd.in/g4QP3-Ue - Demo Site: https://lnkd.in/gUSrV65s - Github Repo: https://lnkd.in/gPVEQRtv - Whitepapers: https://lnkd.in/gnHi6SeW

Grid bottlenecks are a feature — not a bug — of the energy transition. For years, we viewed economics as the main hurdle to scaling clean energy. High costs for wind, solar, heat pumps, and storage dominated the conversation. But the world has changed. Thanks to extraordinary innovation and dramatic cost reductions in renewables and electrification technologies, the bottlenecks we face today are different. They’re no longer about whether clean energy is affordable — it is. Instead, the challenge is whether our energy systems can evolve quickly enough to integrate it. A recent Financial Times piece highlights this clearly: across Europe, the rapid build-out of renewable generation now outpaces the ability of grids to move electricity to where it’s needed. Curtailment, congestion, and long queues for grid connections already cost billions annually — and without decisive action, these costs will grow. This isn’t a sign of failure. It’s a sign of success. It means the transition is happening faster than the infrastructure built for the fossil era can handle. The rise of decentralised, variable renewables and electrified heating and transport requires a fundamentally different approach to planning — one that anticipates growth rather than reacts to it. The EU’s move toward more coordinated, top-down scenario building and cross-border grid planning recognises exactly this. Better alignment between countries and system operators, faster permitting, and prioritisation of critical projects are essential steps to unlock the full value of cheap clean energy. Because every euro lost to bottlenecks is not a cost of climate action — it’s a cost of not modernising our grids fast enough. The more successful we are in deploying renewables and electrification, the more urgently we must upgrade and expand our grids. Grid constraints are not a reason to slow down. They’re a reason to speed up the transformation of an energy system that was never designed for the technologies now powering our transition.

Most projects fail. But there’s a simple technique to give yours a fighting chance. It’s not a to-do list. It’s not a fancy tool. It’s not a 12-step system. It’s a single question that flips the way you think. Here’s how it works: It’s called a “premortem.” You’ve heard of a postmortem what went wrong after a project dies. A premortem asks: What if we ran that analysis now? Before anything dies. Before the first misstep. Before failure sets in. The premortem comes from psychologist Gary Klein. Here’s how to run one: → Gather your team. → Imagine it’s 2 years in the future. → The project has completely failed. → Ask: What went wrong? No sugarcoating. No happy talk. Start listing the causes of failure. Budget misfire? Wrong team? Lack of buy-in? Scope creep? Missed deadlines? You’ll be shocked how quickly people identify risks—once they feel safe predicting failure. Why this works: It defeats irrational optimism. • It turns hindsight into foresight. • It makes risk visible. • It aligns the team before chaos hits. Because the best time to fix a problem… is before it happens. Pre-mortems don’t require special skills. Just a shift in mindset: Don’t assume success. Assume failure—and reverse-engineer your way out. Ask: What will future-you wish you had done? Then… do that now. I run a premortem for every big project I take on. Writing a book? Premortem. Launching a podcast? Premortem. Planning an event? Premortem. It never guarantees success—but it always makes success more likely. Summary: The Premortem Playbook → Imagine future failure. → List the causes. → Turn those risks into action steps. → Adjust your plan today. It’s one of the most underrated tools in your productivity toolkit. Try it before your next project. You won’t regret it.

𝗢𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗠𝗢𝗦𝗧 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗲𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝗛𝗼𝘄 𝘁𝗼 𝗽𝗶𝗰𝗸 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲? The LLM landscape is booming and choosing the right LLM is now a business decision, not just a tech choice. One-size-fits-all? Forget it. Nearly all enterprises today rely on different models for different use cases and/or industry-specific fine-tuned models. There’s no universal “best” model — only the best fit for a given task. The latest LLM landscape (see below) shows how models stack up in capability (MMLU score), parameter size and accessibility — and the differences REALLY matter.  𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: ⬇️ 1️⃣ 𝗚𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝘁 𝘃𝘀. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁: - Need a broad, powerful AI? GPT-4, Claude Opus, Gemini 1.5 Pro — great for general reasoning and diverse applications.   - Need domain expertise? E.g. IBM Granite or Mistral models (Lightweight & Fast) can be an excellent choice — tailored for specific industries.  2️⃣ 𝗕𝗶𝗴 𝘃𝘀. 𝗦𝗹𝗶𝗺:  - Powerful, large models (GPT-4, Claude Opus, Gemini 1.5 Pro) = great reasoning, but expensive and slow. - Slim, efficient models (Mistral 7B, LLaMA 3, RWWK models) = faster, cheaper, easier to fine-tune. Perfect for on-device, edge AI, or latency-sensitive applications.  3️⃣ 𝗢𝗽𝗲𝗻 𝘃𝘀. 𝗖𝗹𝗼𝘀𝗲𝗱   - Need full control? Open-source models (LLaMA 3, Mistral, Llama) give you transparency and customization.   - Want cutting-edge performance? Closed models (GPT-4, Gemini, Claude) still lead in general intelligence.  𝗧𝗵𝗲 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆? There is no "best" model — only the best one for your use case, but it's key to understand the differences to make an informed decision: - Running AI in production? Go slim, go fast. - Need state-of-the-art reasoning? Go big, go deep. - Building industry-specific AI? Go specialized and save some money with SLMs.  I love seeing how the AI and LLM stack is evolving, offering multiple directions depending on your specific use case. Source of the picture: informationisbeautiful.net

Want to implement 𝗥𝗼𝗹𝗲-𝗕𝗮𝘀𝗲𝗱 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 (𝗥𝗕𝗔𝗖) in .NET? Users get roles. Roles have permissions. You enforce permissions on individual actions — simple in principle, but tricky in practice. I built a custom permission-based authorization handler without stuffing permissions into the access token. Instead, I used claims transformation in ASPNET Core to enrich user claims on the backend — cleaner, more flexible, and secure. Here’s how it works: https://lnkd.in/emuVhbsQ How are you handling authorization in your APIs today?