Project Management

Everyone celebrates disruption. Almost nobody celebrates permanence. We tend to assume that innovation means constant reinvention, which is why companies often redesign products, processes, and even organizations simply because they can. And yet the highest compliment for many inventions is that they disappear from the conversation altogether. When something solves a problem so elegantly that nobody feels the need to improve it for decades, that isn't a failure of innovation. It's often the end goal. I think organizations should aim for the same outcome. We spend enormous energy chasing the next breakthrough, while quietly overlooking the systems that already work remarkably well. Sometimes the smartest decision isn't to redesign. It's to protect what has already reached its optimum. The best innovation isn't the one that changes every year. It's the one that makes change unnecessary. Before launching your next transformation, ask a simple question: are you fixing a real problem, or redesigning something that already works? What is one product, process, or idea that you believe got it right the first time? #AIReadiness #HumanAgentOrchestrator #FutureOfManagement #DesignThinking Source: Stacey Horrick

One lesson that has served me well over the years: Don’t carry bad blood. In any long career you will be misunderstood. You’ll disagree. You’ll feel wronged. Sometimes even betrayed. Decisions won’t go your way. People will make calls you don’t agree with. The temptation is to keep score. But resentment is expensive. It clouds judgment. It narrows perspective. It limits future opportunity and is neither rational nor helpful. I’ve learned that making things right is both good relational practice and good business practice. Industries are small. Reputations compound. People you disagree with today may become partners tomorrow. And often, when you sit down and have the hard conversation, you discover the situation was more complex than you initially believed. There is real freedom in choosing not to carry offense. In picking up the phone and saying, “Let’s reset.” Over time, it builds a network of people who know you value relationships. Early in my career I valued results over relationships. Now, I know relationships trump results. Relationships are far more enduring and valuable than the near term results I used to favor. That’s good for business. More importantly, it’s good for the soul.

Context graphs are quickly becoming one of the most talked about ideas in enterprise AI, with investors Jaya Gupta and Ashu Garg calling them a “trillion-dollar opportunity.” The reason? If we want agents to do real work, they don’t just need better models. They need a reliable model of how work gets done inside an organization. A context graph connects your people, content, and systems to the time‑ordered traces of actions between them, so agents can see real processes, not just static data. At Glean, we’ve found that building a useful context graph comes down to three core elements: • Observe real work, not just final states. Capture fine‑grained activity across the tools where work happens (edits, comments, messages, status changes, meetings) instead of relying only on the current record in a single system. • Put structure on top of that activity. Use knowledge graphs (projects, customers, products, teams) and personal graphs (what each person is working on) to turn noisy events into coherent tasks and end‑to‑end processes. • Continuously learn from humans and agents. Treat every successful resolution (whether done by a person or an agent) as another trace in the graph, so the system’s “playbooks” improve over time. Before we shipped this, we tested it on ourselves. With employees’ opt‑in, we analyzed real work sequences for flows such as AE mid‑market deal cycles, SE proofs‑of‑concept, on‑call incident response, and PM feature launches to understand what effective paths actually look like in practice. Our team has published a deep dive on how we build these context graphs at Glean, from deep connectors and knowledge graphs to agentic feedback loops. https://lnkd.in/gBXRmvM5

Big news: Microsoft just dropped a new AI formula in Excel — =Copilot() Yes… Excel basically has ChatGPT built in now. I created a full guide showing how CFOs, Finance and FP&A teams can put this to work immediately: https://lnkd.in/erM6KiNv And a video guide too: https://lnkd.in/ePYdTaCZ Inside the walkthrough, I show how the new =COPILOT formula can help you: ✅ Automate variance analysis against budget ✅ Generate KPI summaries in seconds ✅ Speed up Accounts Payable workflows ✅ Auto-draft monthly budget memos ✅ Even create Python code for deeper analysis The Excel file also includes 30 finance & FP&A use cases using the =COPILOT formula. You can download it here: https://lnkd.in/eXwB7Vhm A few examples: =COPILOT("Write a one-line variance explanation for", A2:H2) → instant commentary for any P&L row. =COPILOT("Label this row as Risk, Opportunity, or On Track based on actual vs budget", A2:H2) → risk tagging, fully automated. =COPILOT("Tell me whether this is driven by revenue growth or cost pressure", C2) → quick driver classification. =COPILOT("Summarize the top 3 variance drivers for this selection", A2:H20) → monthly snapshot in one click. =COPILOT("Create a CFO-level narrative for this month’s FP&A results", A2:H50) → board-ready storytelling. =COPILOT("Flag any unusual variances that might require leadership attention", A2:H20) → automated red-flag detection. Hope this helps more finance and FP&A teams to use this great new Excel formula!

“Stop calling everything an agent” Folks are slapping 'agent' on anything that touches AI. It’s confusing. I jumped on Peter Yang’s podcast to unpack why that’s misguided, and why the middle ground (agentic workflows) is where real ROI lives. Workflows = determinism and reliability Agents = judgment and flexibility Stitch them together and you get the safest, most powerful form of AI orchestration. Give your agent just enough tools and context to do one job exceptionally well, and orchestrate the rest with workflows. Two examples of this I’m using personally: 𝗘𝘅𝗲𝗰𝗖𝗼𝗻𝗻𝗲𝗰𝘁 (Workflow / Determinism)  👉 A teammate submits an Interfaces form to request exec engagement, the AI drafts an on-brand message, posts to Slack, and tracks it in Tables. This one runs hundreds of times a month. Zero chaos, total clarity 𝗜𝗻𝗯𝗼𝘅 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (Agent / Inference) 👉 When a new email arrives, the agent reads it, checks HubSpot, uses reasoning to categorize it, and sorts it in Slack. It flags customers, intelligently routes the right things to my EA Cortney, and clears all of the noise. It reasons like a human assistant, saving us hours each week with full transparency logs Cortney’s Inbox Management agent is actually available as a template, you can steal it here: https://lnkd.in/gR8UjTCN Full episode coming soon. Thanks for having me, Peter.

Rick Rubin went on stage in Helsinki the day after my talk. Someone asked how he resolves creative differences with artists. His answer was simple: change the conversation from "I disagree" to "let's build it." Then he shared a story: An artist played him a song. The transition didn't work. Rubin told him so. The artist said, "We'll just cut that part in half." Rubin thought to himself: What a dumb idea. But he didn't say that. He said, "Let's try it." The artist played it. It worked. Rubin is a legend. He's produced everyone from Johnny Cash to Jay-Z. Instead, he bit his tongue and let the artist prove him wrong. The principle: when you make an idea tangible, it stops being the person's idea. It becomes something you can both look at objectively and improve together. Once you build it, the truth is obvious. Here's what this looks like in practice: Your designer wants to change the entire homepage layout. You think it's too risky. Instead of three meetings debating it, you say: "Let's build a prototype and test it with 50 users this week." Your sales team wants to restructure the pricing page. Instead of blocking it because you're worried about conversions, you say: "Let's run it as an A/B test on 20% of traffic for two weeks." Your engineer wants to rebuild a core feature from scratch. You think it's overengineered. But instead of killing it in the planning phase, you say: "Spike it out in three days and show me if the performance gain is real." You're not saying yes to everything. You're saying, "Let's find out." Rubin also said something that stuck with me: "If there's disagreement, I always side with the artist's vision. Because to them, it's their career. To me, it's just one piece of my portfolio." Most leaders think backing down makes them look weak. Rubin knows that siding with the person who has the most at stake makes better work happen. Your job isn't to be right. It's to create the conditions where the best idea wins. Stop debating. Start building. P.S. This insight is from this week's newsletter where I break down why Yamaha dominates while Steinway got sold to private equity: https://lnkd.in/efSqP_9K P.P.S. Access additional research links, the podcast, and the full archive in the first comment 👇 Thank you to Nordic Business Forum!

I’ve had 4 legal battles since starting my business. Could I have avoided them? Probably. But I didn’t have the funds for a proper lawyer. I didn’t have the founder network to ask the right questions. I was figuring it out as I went - like most of us do. So, let me help you not learn the hard way. Here are 5 clauses I now include in every contract to protect my work, my business, and my sanity: 1. Non-cancellable, non-refundable agreements If you’ve qualified your clients properly, this shouldn’t be a problem. But if someone signs, onboards, and then disappears? We still get paid. And so should you. 2. Immediate or short payment terms We don’t do 30- to 90-day terms. You wouldn’t work for 3 months without pay - so why should your business? Cash flow isn’t just admin. It’s survival. 3. Enforceable payment protection Your contract should include: Interest on late invoices A “stop work” clause if payment isn’t made A clause that guarantees you still get paid even if the client delays the project Your time is not free. Put it in writing. 4. Intellectual Property stays yours Anything we bring to the table = ours. Anything we create for you = yours. Clear. Simple. No grey area. We once had a client record a training session… and try to resell it behind a paywall. Now our contract includes a £10,000 fine per breach. And in that case, per breach = per view. 5. Don’t work with d*ckheads. Not a legal clause - more like legal wisdom... 😂 🚩 If they’re pushing for discounts before asking about outcomes 🚩 If they want to start work before signing or paying 🚩 If they delay, ghost, or act shady in the first 10 days… Walk away. Trust me. Yes, contracts are important. But court is expensive, stressful, and slow. The best legal advice I can give you; - Protect your business. - Trust your gut. - And don’t work with d*ckheads. Learning from someone else’s mistakes is a hell of a lot cheaper than learning from your own. You’re welcome 💜 😉 P.S - Want to finally get the confidence to start building your personal brand online? This is your sign. I’m hosting a FREE Zoom masterclass SEPT 10th. Join here: https://lnkd.in/gMwytmS3 and I'll show you exactly how to build your personal brand (and the life you want!).

The New York Times profiled a start-up with 28 employees serving nearly 50 million users. That company is us. The traditional startup playbook: raise massive funding, hire hundreds of employees, and worry about profitability "later." But there's another way. Everyone at Gamma could fit in a small restaurant. We're not just surviving—we've been profitable for 15+ consecutive months, with revenue growing month over month, and lifetime negative net burn (we have more money in the bank than we've raised). This isn't an accident. We've deliberately designed our organization to maximize impact per person. Instead of creating specialist silos, we hire versatile generalists who can solve problems across domains. Rather than building management hierarchies, we find player-coaches who both lead and execute. Our team leverages AI tools throughout our workflow - Claude for data analysis, Cursor for coding efficiency, NotebookLM for customer research synthesis. These aren't just productivity hacks; they're force multipliers. Examples: — When our growth PM needed better analytics, he didn't file a ticket with a data team—he built a self-serve system that anyone can use without SQL knowledge. — When our marketing lead needed to understand our customers better, she fed thousands of interactions into an LLM and created actionable personas that now guide our entire strategy. — When our design team needs to test a hypothesis, we create a rapid prototype and show it to our power users. What we're seeing isn't just about "doing more with less." It's about fundamentally changing what's possible per person. The most valuable employees aren't specialists who excel in narrow domains - they're resourceful problem-solvers who continuously expand their capabilities. This approach creates remarkable resilience. Since everyone understands multiple functions, we don't have single points of failure when someone leaves or moves to another project. If you're building today, the question isn't how quickly you can scale headcount … it's how much impact you can create with the smallest possible team. The future belongs to tiny teams of extraordinary people.

"Vibe Coding !== Low Quality Work: a guide to responsible AI-assisted dev" ✍️ My latest free article: https://lnkd.in/gjMdjMWV The allure of "vibe coding" – using AI to "move faster and break even more things" – is strong. AI-assisted development is undeniably transformative, lowering barriers and boosting productivity. But speed without quality is a dangerous trap. Relying uncritically on AI-generated code can lead to brittle "house of cards" systems, amplify tech debt exponentially, and introduce subtle security flaws. Volume ≠ Quality. A helpful mental model I discuss (excellently illustrated by Forrest Brazeal) is treating AI like a "very eager junior developer." It needs guidance, review, and refinement from experienced hands. You wouldn't let a junior ship unreviewed code, right? So how do we harness AI's power responsibly? I've outlined a field guide with practical rules: ✅ Always review: Treat AI output like a PR from a new hire. ✅ Refactor & test: Inject engineering wisdom – clean up, handle edge cases, test thoroughly. ✅ Maintain standards: Ensure AI code meets your team's style, architecture, and quality bar. ✅ Human-led design: Use AI for implementation grunt work, not fundamental architecture decisions. The goal isn't to reject vibe coding, but to integrate it with discipline. Let's use AI to augment our craft, pairing machine speed with human judgment. #softwareengineering #programming #ai

Working with multiple LLM providers, prompt engineering, and complex data flows requires thoughtful organization. A proper structure helps teams: - Maintain clean separation between configuration and code - Implement consistent error handling and rate limiting - Enable rapid experimentation while preserving reproducibility - Facilitate collaboration across ML engineers and developers The modular approach shown here separates model clients, prompt engineering, utils, and handlers while maintaining a coherent flow. This organization has saved many people countless hours in debugging and onboarding. Key Components That Drive Success Beyond folders, the real innovation lies in how components interact: - Centralized configuration through YAML - Dedicated prompt engineering module with templating and few-shot capabilities - Properly sandboxed model clients with standardized interfaces - Comprehensive caching, logging, and rate limiting Whether you're building RAG applications, fine-tuning foundation models, or creating agent-based systems, this structure provides a solid foundation to build upon. What project structure approaches have you found effective for your generative AI projects? I'd love to hear your experiences.