Project Management

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

McKinsey & Company 𝗮𝗻𝗮𝗹𝘆𝘇𝗲𝗱 𝟭𝟱𝟬+ 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝗳𝗼𝘂𝗻𝗱 𝗼𝗻𝗲 𝗰𝗼𝗺𝗺𝗼𝗻 𝘁𝗵𝗿𝗲𝗮𝗱: ⬇️ One-off solutions don’t scale. The most successful projects take a different path: They use open, modular architectures that enable speed, reuse, and control. → Designed for reuse → Able to plug in best-in-class capabilities → Free from vendor lock-in This is the reference architecture McKinsey now recommends — optimized to scale what works while staying compliant. It consists of five core components: ⬇️ 𝟭. 𝗦𝗲𝗹𝗳-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗽𝗼𝗿𝘁𝗮𝗹: → A secure, compliant “pane of glass” where teams can launch, monitor, and manage GenAI apps. → Preapproved patterns, validated capabilities, shared libraries. → Observability and cost controls built-in. 𝟮. 𝗢𝗽𝗲𝗻 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 → Services are modular, reusable, and provider-agnostic. → Core functions like RAG, chunking, or prompt routing are shared across apps. → Infra and policy as code, built to evolve fast. 𝟯. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 → Every prompt and response is logged, audited, and cost-attributed. → Hallucination detection, PII filters, bias audits — enforced by default. → LLMs accessed only through a centralized AI gateway. 4. 𝗙𝘂𝗹𝗹-𝘀𝘁𝗮𝗰𝗸 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → Centralized logging, analytics, and monitoring across all solutions → Built-in lifecycle governance, FinOps, and Responsible AI enforcement → Secure onboarding of use cases and private data controls → Enables policy adherence across infrastructure, models, and apps 5. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 → Modular setup for user interface, business logic, and orchestration → Integrated agents, prompt engineering, and model APIs → Guardrails, feedback systems, and observability built into the solution → Delivered through the AI Gateway for consistent compliance and scale The message is clear: If your GenAI program is stuck, don’t look at the LLM. Look at your platform. 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. This is why I’m excited about the future of Product Management, the discipline of developing and managing software products. I’m especially excited about the future of AI Product Management, the discipline of developing and managing AI software products. Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow. This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow. Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires: - Technical proficiency in AI. PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models. - Iterative development. Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process. - Data proficiency. AI products often learn from data, and they can be designed to generate richer forms of data than traditional software. - ... [Reached length limit; full text: https://lnkd.in/geQBWz6s ]

𝗧𝗼𝗱𝗮𝘆, 𝗣𝗠𝗜 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝘀𝘁𝘂𝗱𝘆 𝘄𝗲’𝘃𝗲 𝗲𝘃𝗲𝗿 𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗲𝗱 - 𝗼𝗻 𝗮 𝘁𝗼𝗽𝗶𝗰 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘁𝗼 𝗼𝘂𝗿 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻: 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘂𝗰𝗰𝗲𝘀𝘀. 📚 Read the report: https://lnkd.in/ekRmSj_h With this report, we are introducing a simple and scalable way to measure project success. A successful project is one that 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝘃𝗮𝗹𝘂𝗲 𝘄𝗼𝗿𝘁𝗵 𝘁𝗵𝗲 𝗲𝗳𝗳𝗼𝗿𝘁 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗻𝘀𝗲, as perceived by key stakeholders. This clearly represents a shift for our profession, where beyond execution excellence we also feel accountable for doing anything in our power to improve the impact of our work and the value it generates at large. The implications for project professionals can be summarized in a framework for delivering 𝗠𝗢𝗥𝗘 success: 📚𝗠anage Perceptions For a project to be considered successful, the key stakeholders - customers, executives, or others - must perceive that the project’s outcomes provide sufficient value relative to the perceived investment of resources. 📚𝗢wn Project Success beyond Project Management Success Project professionals need to take any opportunity to move beyond literal mandates and feel accountable for improving outcomes while minimizing waste. 📚𝗥elentlessly Reassess Project Parameters Project professionals need to recognize the reality of inevitable and ongoing change, and continuously, in collaboration with stakeholders, reassess the perception of value and adjust plans. 📚𝗘xpand Perspective All projects have impacts beyond just the scope of the project itself. Even if we do not control all parameters, we must consider the broader picture and how the project fits within the larger business, goals, or objectives of the enterprise, and ultimately, our world. I believe executives will be excited about this work. It highlights the value project professionals can bring to their organizations and clarifies the vital role they play in driving transformation, delivering business results, and positively impacting the world. The shift in mindset will encourage project professionals to consider the perceptions of all stakeholders- not just the c-suite, but also customers and communities. To deliver more successful projects, business leaders must create environments that empower project professionals. They need to involve them in defining - and continuously reassessing and challenging - project value. Leverage their expertise. Invest in their work. And hold them accountable for contributing to maximize the perception of project value at all phases of the project - beyond excellence in execution. 📚 Please read the report, reflect on its findings, and share it broadly. And comment! Project Management Institute #ProjectSuccess #PMI #Leadership #ProjectManagementToday

Avoiding tough talks is a direct path to losing team trust. Here's how top leaders handle conflict: 1/ The Real Problem → Leaders stall, hoping conflict resolves itself → Feedback gets softened until it’s meaningless → The issue festers, and performance suffers 2/ Why It Matters → Projects halt because no one says what needs to be said → The wrong people stay in the room, the right ones leave → Culture declines and misalignment becomes the norm 3/ The CLEAR Framework → Cut the Fluff: Skip the warm-up and get to the point → Label the Behavior: Focus on actions, not identity → Explain the Impact: Make it real, why does it matter? → Ask for Alignment: Invite a response, not a lecture → Recommit or Redirect: Don’t end vague, end with clarity 4/ What Happens Next → Tension goes down, not up → People feel respected, not ambushed → Projects move forward, with trust, not silence 5/ Why You Need This → Leading isn’t about avoiding discomfort → It’s about creating clarity when others won’t → This framework gives you the words to do it right What's your biggest takeaway?

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data. ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose key tasks and see how successful users are. It won’t be right at first, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/eUBScaHp 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush: https://lnkd.in/dUgWtwnu 👍 UKO: https://lnkd.in/eNFv2p_a 👍 Wireframing Kit: https://lnkd.in/esqRdDyi 👍 [continues in comments ↓]

Have I mentioned we are data geeks?🤓🤓 Performance uncertainty remains one of the biggest barriers to wider uptake of #energy #efficiency technologies.💡 #Wind-assisted propulsion,💨 air-lubrication systems🫧 and other proven #retrofits can cut fuel use by double-digit percentages.📉 But real-world savings swing with weather, routing and operations. Without clarity on a retrofit’s actual contribution, neither shipowners nor charterers can forecast returns with confidence.🤷🏻♀️ And because we’ve always believed that #data📊 can give us the clearest truth, we set out to address this challenge.👊🏻 Our friends at Eastern Pacific Shipping Pte. Ltd. gave us access to the Pacific Sentinel, on which we installed a high-frequency data acquisition system as three suction #sails⛵️ were retrofitted onboard the MR tanker in March 2025. Calibrated sensors captured #power consumption, vessel speed, engine load, heading and wind conditions every 15 seconds. Over four months as the vessel traded spot around the Americas,🌎 we saw #weather and #performance at a fidelity far beyond the single daily datapoint in a noon report. Building on #ITTC and DNV methodologies, Global Centre for Maritime Decarbonisation (GCMD) and EPS implemented an “on-off’’ testing protocol,🎛️ comparing power consumption with the sails activated and deactivated under otherwise similar environmental and operational conditions to isolate the sails’ true contribution. Under the predominantly near-headwind conditions sampled, the vessel saw an average instantaneous power savings⚡️ of 7.2%, with a 95% confidence interval between 6.2% and 8.2%. Instantaneous savings ranged from +28% to –14%. These rare outliers highlight just how sensitive power savings are to wind speed and direction, and underscore the importance of tracking dynamic operational data.⚠️ Access report here:  https://lnkd.in/g_dRFtJp If we want to scale energy-efficiency retrofits, we must tackle performance uncertainty head-on. Shipowners won’t invest, and charterers won’t commit, if they can’t trust that the #savings will show up in their fuel bills.💵 We therefore developed a power savings polar heat map to predict energy and fuel savings with wind conditions. With 3rd-party verification, this will enable performance-linked financing of the retrofits.💰 This case study is but a first step in building that validation layer. And it ladders🪜 up to what we launched last week: #FEET — the world’s first blended-finance fund designed to support energy-efficiency retrofits through a pay-as-you-save repayment structure. Progress is incremental, and this marks a big step in the right direction.👊🏻 Together, we are stronger; together, we can💪🏻 Shane Balani, Zheng Yang Cheng 钟正扬, Bhushan Taskar, Goh Wan Ni, Pavlos Karagiannidis, Mirtcho Spassov, CFA, Mike Wilson, Rashim Berry, Cyril Ducau

True understanding isn’t about how well we communicate, but how deeply we comprehend others. It’s about listening with empathy, recognizing the emotions behind the words, and connecting on a deeper level. Here are five tips to improve your comprehension: 👉 Listen Actively: Give your full attention to the speaker, eliminating distractions, and focus on both their words and the emotions they’re conveying. 👉 Ask Clarifying Questions: If something isn’t clear, ask questions that help you better understand the meaning and intent behind the message. 👉 Practice Empathy: Put yourself in the other person’s shoes to gain a deeper insight into their perspective, feelings, and motivations. 👉 Pay Attention to Non-Verbal Cues: Body language, tone of voice, and facial expressions offer valuable context that words alone may miss. 👉 Reflect and Paraphrase: After a conversation, take a moment to reflect and paraphrase what was said to ensure you’ve fully grasped the message. Prioritize comprehension, and watch your relationships and communication thrive.

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

WEF's Global Risks Report 2026 is out 👉 (https://lnkd.in/eaMrdW67).. I put the findings in a 20-year perspective. I mapped 20 years of risk rankings. Two patterns stand out. Both troubling. The headline findings in this report: 🔵 geoeconomic confrontation is now the #1 risk in the short term, 🔵 economic risks are spiking, 🔵 50% of experts expect a turbulent or stormy outlook over the next two years. But the deeper signal only appears when you track the rankings over time (what I did, see 👇 ). ⚫ 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 𝟏 – 𝐋𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐫𝐢𝐬𝐤𝐬 𝐦𝐢𝐠𝐫𝐚𝐭𝐞 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐬𝐡𝐨𝐫𝐭 𝐭𝐞𝐫𝐦 Not overnight. Not mechanically. But persistently. In 2007–2010, short-term risks were concrete and immediate: asset bubbles, oil shocks, chronic diseases. Fast forward to today. The long-term top risks for 2026 are: 🌪️ extreme weather 🌍 biodiversity loss 🧠 misinformation 🤖 adverse AI outcomes What changed is not that economic risks disappeared. It’s that structural risks began to act as crisis amplifiers. Extreme weather didn’t replace financial shocks, it reshaped them. Climate risks first entered the short-term top 5 around 2014. By 2020, climate action failure topped the list. “Tomorrow’s risks” became today’s stress multipliers, and increasingly, direct crisis drivers. The future didn’t wait. ⚫𝐏𝐚𝐭𝐭𝐞𝐫𝐧 𝟐: 𝐍𝐚𝐭𝐮𝐫𝐞 𝐢𝐬 𝐛𝐞𝐢𝐧𝐠 𝐟𝐨𝐫𝐠𝐨𝐭𝐭𝐞𝐧, 𝐚𝐠𝐚𝐢𝐧 This year, environmental risks dropped sharply in the short-term rankings. More worrying: their severity scores also declined in absolute terms. Yet over the 10-year horizon, environmental risks dominate the top 10. Twenty years of WEF risk data tell the same story: we consistently recognise long-term environmental threats, then consistently deprioritise them when short-term pressures mount. It's not that we don't know. It's that our attention economy is structurally biased toward the urgent over the important. The most interconnected risk for the second year running? Inequality (👇). It fuels everything else: polarisation, migration, political instability, resistance to climate policy. Perhaps that's where to start: 𝐢𝐟 𝐰𝐞 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐚𝐝𝐝𝐫𝐞𝐬𝐬 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬, 𝐰𝐞 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐫𝐞𝐝𝐮𝐜𝐞 𝐭𝐡𝐞 𝐬𝐡𝐨𝐫𝐭-𝐭𝐞𝐫𝐦 𝐝𝐞𝐬𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐤𝐞𝐞𝐩𝐬 𝐮𝐬 𝐭𝐫𝐚𝐩𝐩𝐞𝐝 𝐢𝐧 𝐜𝐫𝐢𝐬𝐢𝐬 𝐦𝐨𝐝𝐞. #GlobalRisks #WEF #ClimateChange #Sustainability #SystemChange