Techniques for Breaking Down Complex Tasks

🏗 How To Tackle Large, Complex Projects. With practical techniques to meet the desired outcome, without being disrupted or derailed along the way ↓ 🤔 99% of large projects don’t finish on budget and on time. 🤔 Projects rarely fail because of poor skills or execution. ✅ They fail because of optimism and insufficient planning. ✅ Also because of poor risk assessment, discovery, politics. 🎯 Best strategy: Think Slow (detailed planning) + Act Fast. ✅ Allocate 20–45% of total project effort for planning. ✅ Riskier and larger projects always require more planning. ✅ Think Right → Left: start from end goal, work backwards. ✅ For each goal, consider immediate previous steps/events. ✅ Set up milestones, prioritize key components for each. ✅ Consider stakeholders, users, risks, constraints, metrics. 🚫 Don’t underestimate unknown domain, blockers, deps. ✅ Compare vs. similar projects (reference class forecasting). ✅ Set up an “execution mode” to defer/minimize disruptions. 🚫 Nothing hurts productivity more than unplanned work. Over the last few years, I've been using the technique called “Event Storming” suggested by Matteo Cavucci to capture user’s experience moments through the lens of business needs. With it, we focus on the desired business outcome, and then use research insights to project events that users will be going through towards that outcome. On that journey, we identify key milestones and break user’s events into 2 main buckets: user’s success moments (which we want to dial up) and user’s pain points or frustrations (which we want to dial down). We then break out into groups of 3–4 people to separately prioritize these events and estimate their impact and effort on Effort vs. Value curves (https://lnkd.in/evrKJUEy). The next step is identifying key stakeholders to engage with, risks to consider (e.g. legacy systems, 3rd-party dependency etc.), resources and tooling. We reserve special timing to identify key blockers and constraints that endanger successful outcome or slow us down. If possible, we also set up UX metrics to track how successful we actually are in improving the current state of UX. When speaking to business, usually I speak about better discovery and scoping as the best way to mitigate risk. We can of course throw ideas into the market and run endless experiments. But not for critical projects that get a lot of visibility — e.g. replacing legacy systems or launching a new product. They require thorough planning to prevent big disasters and urgent rollbacks. If you’d like to learn more, I can only highly recommend "How Big Things Get Done" (https://lnkd.in/erhcBuxE), a wonderful book by Prof. Bent Flyvbjerg and Dan Gardner who have conducted a vast amount of research on when big projects fail and succeed. A wonderful book worth reading! Happy planning, everyone! 🎉🥳

How I Learned Complex Things Quickly at Amazon (And Still Do Today) My first few months at Amazon felt like drinking from a firehose. New systems. New acronyms. New teams. And no one slowing down to explain any of it. I had two options: Fall behind quietly… Or figure out how to learn fast. Here’s the system I built…and still use today: 1/ I started with the system…not the task ↳ Instead of asking “what do I need to do?” ↳ I asked: “How does this entire system actually work?” ↳ Who owns what ↳ What depends on what ↳ Where things break Understanding the system made everything else easier. 2/ I asked simple questions…early and often ↳ “Can you walk me through this like I’m new?” ↳ “What usually goes wrong here?” ↳ Five direct questions saved me weeks of confusion Smart people ask basic questions faster. 3/ I studied past work like it was a playbook ↳ Old launch docs ↳ Postmortems ↳ Escalation threads ↳ That’s where the real lessons lived Not in theory…in what already happened. 4/ I wrote everything down…then simplified it ↳ Notes, diagrams, quick summaries ↳ Then I’d force myself to explain it in one paragraph If I couldn’t simplify it, I didn’t understand it yet. 5/ I taught it back immediately ↳ To a teammate ↳ To my manager ↳ Or even just out loud to myself ↳ Teaching exposed gaps instantly Learning isn’t complete until you can explain it clearly. 6/ I focused on patterns, not details ↳ Most “complex” systems repeat the same problems ↳ Ownership gaps ↳ Misalignment ↳ Unclear decision paths Once I saw the patterns, I could move faster every time. Here’s what Amazon taught me: You don’t need to know everything. You just need to learn faster than the problem changes. 📬 I write weekly about clarity, learning, and operating in complex environments in The Weekly Sync: 👉 https://lnkd.in/e6qAwEFc What’s one technique that helps you get up to speed faster?

This isn't a look at my n8n workflow post. It's an important lesson I've learned building AI workflows that ACTUALLY work. Most people (including me) do wayyyy too much with a single prompt. They throw everything at the model and wonder why the output is inconsistent and a bit crap. When I was building out CS2's call recording workflow, I saw a post from Justin Norris about breaking the workflow into steps. So I tried it and it dramatically improved the output. Our original approach was • One prompt that summarized the call, extracted actions, and formatted for Slack. • Result: Inconsistent. Sometimes it missed actions. Formatting broke. Our new approach: Three separate steps. (1) Note summary: Just focus on capturing what was discussed (2) Action extraction: Pull out actions, dates, and owners (3) Slack formatting: Take the unstructured data and format it based on predetermined rules Each step does one thing well. And we can test each step independently. The output is night and day different. I haven't had to edit this workflow in weeks. Break complex tasks into smaller, focused steps. Each step has clear inputs and outputs. Each step can be optimized independently. It takes more time to set up. But it's worth it.

In a recent segment on NBC News Daily, we discussed a common issue many students face: difficulty starting homework or study tasks. 🧠This challenge isn't about laziness or lack of motivation; it's often rooted in executive functioning difficulties, particularly task initiation. Research indicates that a significant percentage of students with executive functioning challenges struggle with initiating tasks. This is true for adults, too! Interestingly, these same challenges are prevalent among adults in the workplace. Professionals often experience task avoidance and procrastination, especially when faced with complex or overwhelming projects. Understanding that these behaviors stem from cognitive barriers, not personal shortcomings, is crucial for developing effective strategies. Here are three practical techniques to help overcome task initiation difficulties: ✈️Establish "Initiation Rituals" Instead of nagging or self-criticism, create consistent pre-task routines. For example, clear the workspace together or engage in a brief 2-minute brainstorming session. These rituals signal the brain to transition into work mode. 🪜Break Tasks into Ridiculously Small Steps Rather than focusing on the entire task, start with a tiny action, like opening a document or writing your name and the date. When I am avoiding writing, I make myself open a previous writing document, set a timer for 20 minutes, and re-read work. Small steps can build momentum and reduce feelings of being overwhelmed. 👥Implement "Body Doubling" Sit next to others while they work, even if you're engaged in a different task. This shared presence provides a calming effect and reduces the neurological barrier to starting. (There are online Zoom rooms that create this same effect, like Caveday). These strategies are not just for students; they are equally effective for professionals dealing with task initiation challenges. By recognizing and addressing these cognitive barriers, we can create environments that support productivity and reduce shame associated with procrastination. #work #studytips #education #brain #homework #middleschool #highschool #college #procrastination

Whenever I meet a person/team regularly missing deadlines, I teach them 2 things that eliminate 95% of the problems Let me explain with an example… Let’s say your team has to write a detailed report on a new opportunity. You have 3 months to do this. The usual way people do this: → List down all the tasks they have to do: research, draft, edit → Try & put time to these tasks: Research in 1st month, draft in 2nd month… But this never works. It’s unclear what has to get done (’Research’ can mean different things for different people) & none of these tasks take the amount of time you estimated. To fix this, make 2 changes: 1️⃣ LIST OUTPUTS (instead of listing tasks) → Write down all the outputs you will have to deliver → I define Output as ‘something that someone else can see & react to’ → ‘Research’ is not an output. But ‘A document capturing all our takeaways from our calls & reading’ is an ouput → In our eg, the outputs might be: (a) A document capturing our takeaways from our calls & reading (b) A 1st draft formatting this doc for external consumption (c) A 2nd draft based on leadership team’s feedback (d) Final document after all rounds of feedback (e) A 10 slide ppt summarising the final doc 2️⃣ BREAK DOWN TIME & FIT WORK INTO IT (instead of breaking down work & fitting it into time) → Time is fixed but our work is variable (eg: scope of our ‘research’ can change based on time available but our time is fixed: 3 months) → So its better to break down the fixed thing (time) and fit the variable thing (work) into it → In our eg, we break time available into blocks of say 3 weeks & then fit our outputs in them: —— Block 1 (weeks 1 - 3): Deliver Output (a) —— Block 2 (weeks 4 - 6): Deliver Output (b) & (c) —— Block 3 (weeks 7 - 9): Deliver Output (d) … Now when we start the project, our only focus is Block 1 and we will do everything we can to deliver on it. At the end of the block, we will review remaining outputs & blocks, make changes as needed & then just put our head down & focus on Block 2. This way at any given point in time we are only planning & focussing on 1 block. We have absolute clarity on what success means for this block (Deliver X output and not just do Y actions). There is a regular sense of achievement as we deliver on each block. After each block we re-calibrate based on learning & go on to next block. Every team I teach this to a team (& if they apply it well) they give me the same feedback: ”We have never delivered so much, so well, with so little stress. Thank you!” If you or your team are struggling with planning & meeting deadlines, try this approach. It might just be what you are looking for. #Planning #WorkManagement #Leader #Team —————————— My course 'New Manager Academy' is full of practical frameworks like these on topics of Delivering Results & Leading People. It's been taken by 700+ leaders from 100+ orgs & 7+ countries. Next cohort is starting Mid Aug. Link in comments

#1 skill companies hire McKinsey alumni for? Structured problem solving. Why? Because people who turn ambiguity into action and chaos into clarity are the most scarce, valuable assets. Here's the 3-step system McKinsey consultants use to tackle any business challenge: 1️⃣ Define the Problem - Clarify exactly what needs solving - Create a SMART problem statement - Identify key stakeholders and success criteria - Set clear constraints and deliverables 2️⃣ Decompose the Problem - Break down complex issues into manageable parts - Use issue trees to map relationships - Ask "how might we" questions to spark solutions - Find the root causes, not just symptoms 3️⃣ Prioritize Issues - Rank challenges based on key criteria (e.g., impact, feasibility) - Focus energy where it matters most - Make data-driven decisions about where to start - Avoid the trap of trying to solve everything at once This methodical approach is what separates strategic problem-solvers that senior leaders trust. Not magic. Not genius. Just process. And the best part? You can master this methodology too. Start with clear problem definition. Move to logical decomposition. Finish with structured prioritization. Most business problems don't need genius solutions. They need good-enough answers that create progress. Some action is almost always better than paralysis. What complex challenge can you apply this to today? ♻️ Find this valuable? Repost to help others. Follow me for posts on leadership, learning, and excellence. 📌 Want free PDFs of this and my top cheat sheets? You can find them here: https://lnkd.in/g2t-cU8P Hi 👋 I'm Vince, CEO of Sparkwise. I help orgs scale excellence at a fraction of the cost by automating live group learning, practice, and application. Check out our topic library: https://lnkd.in/gKbXp_Av

I’m kicking off an exciting project next week with an AI start-up, helping them map workflows to uncover execution gaps that they can solve with their AI solution. To do that, we will need to get really granular. Here’s the framework I’m using to break down the milestones of the process: Break up each process milestone into 3 parts: Pre-Dependencies → what must happen before you start Sub-Tasks → the steps that make it happen Post-Dependencies → what must happen after it’s done Next Steps → What, who and how Example: Equipment Procurement Pre: Signed proposal, drawings approved, deposit received Sub: Create POs, confirm lead times, update schedule Post: Notify PM team, update milestone, schedule warehouse check-in Next Steps: Change status of project in software to notify team Why this matters: The company I’m working with has a platform built to close the execution gap which is the space between what companies aim to do and what they can actually pull off. Their platform ingests data, detects operational signals & bottlenecks, and helps companies act faster. By pairing this framework (milestones broken into pre-, sub-, post- dependencies) with value stream mapping (capture current state, identify waste, then design a future state with their AI tool), we can make two big things happen: Spot exactly where execution drags and what dependencies or missing steps stall things. Design workflows that are simpler, cleaner, and more aligned so the AI signal is clear and actionable. What's your process?

Stop having students do assignments (have AI do the work instead) This is one of my favorite right-now-in-the-AI-era activities Here’s how this one works: Instead of writing the essay or solving the case, students prepare everything an AI would need to succeed The challenge isn’t in doing the work as much as it is designing the system that makes the work possible This is not only better learning, it is also a critical work skill where codifying workflows and expertise unlock system efficiencies and make room for subject matter outputs Activity Breakdown 1️⃣ Understand the Task - Instructor drops in a sample prompt (e.g., “Write a persuasive essay on the ethics of AI in education”). - Students analyze what’s really being asked. 2️⃣ Build the Training Package - Essential facts, definitions, and concepts. - Key sources (and one or two to *ignore* — with reasons why). - Examples of good responses, common mistakes to avoid. - A metaphor, diagram, or hook that captures the core idea. - A step-by-step process the AI should follow. - A rubric or checklist for evaluating the result. 3️⃣ Optional Challenges - Test the package by prompting AI and critiquing the output. - Swap with a partner to see if their package gets a strong result. - Revise for clarity and efficiency. 4️⃣ Reflection - Which was harder: doing the task or training the bot? - What gaps in your own understanding did this reveal? This activity shifts the focus from doing the task to designing the system that produces expert work That’s just good metacognition right there - Breaking down complex assignments into clear, repeatable steps - Codifying what quality looks like (and what to avoid) - Building the skills to guide and evaluate AI outputs That’s not only better learning in the classroom — it’s also preparing them for a workplace where the most valuable people aren’t the fastest doers, but the clearest system designers ✨ How would you modify this activity to make it even better?

[Complicated vs Complex Problems] Sometimes we try to tackle problems head-on without clearly examining their different parts. Seen as a whole, they can seem overwhelming, unmanageable, or impossible to solve. But complicated problems almost always have solutions, even if it takes multiple steps to reach them. Complex problems, on the other hand, aren’t consistently replicable. There’s an unknown aspect about them that makes them harder to solve. Building a brand-new car model is a complicated problem. There are always numerous mechanical issues to resolve. But like all complicated problems, building a brand-new car becomes easier to manage when it’s broken down into smaller parts. In this case, creating specifications for new vehicles helps companies to automate the manufacturing process. What about complex problems? Like the stock market, they are fast-moving, fleeting, and unpredictable. Stocks can move for countless reasons, from an event in the news cycle to the length of the quarterly earnings report. But there are always opportunities to turn parts of a complex challenge into simpler solutions. Why is it important to identify the type of problem we are dealing with?  1) It helps us to manage the pace of change. When we see a space that’s rapidly evolving – AI is a great example – we can break down its component parts faster than ever. 2) It allows us to identify approaches we’ve used to address other problems to solve new ones. 3) It helps us to break down problems into smaller ones that may be easier to solve. When creating solutions, I start by building a framework around the challenges early in the process. I aim to create predictability around all the moving parts. I start out by seeking familiar solutions. When it comes to AI, for example, I think of bringing it to automation, just like building a car. We may not have full instructions yet, but we still know quite a lot — a car has windows, air conditioning, radio, etc. Complex problems are unpredictable. Yet, by grounding ourselves in simple, known facts, we can reduce their complexity, transforming them into multiple complicated problems instead. Here are some tips I’ve found helpful when trying to find a solution.  1)   Ask yourself if you've seen a similar type of problem before? How did you solve it? 2)   Ask yourself what factors are wholly in your (or your company’s) control, and which aspects are solvable? 3)   Make sure to eat an elephant only one bite at a time!  Break complex problems down into smaller ones: By solving the simple ones, you’ll eventually get to a bigger picture. In the case of AI example, even if we assume we’ll always get new models (ie complex), we can still create frameworks for the adoption lifecycle, regional needs, enablement, and more that can be broken down into repeatable solutions.  

How do you eat an elephant? One bite at a time. Us Comms folks run into lots of big, complex assignments: - Navigating a crisis - Repositioning a brand - Leading M&A comms - Introducing a new CEO (Obviously, the list is far longer--and growing.) Presented with massive projects, the younger me would freeze up. - How do I do it? - Where do I start? - What if I mess up? Reframing my mind was important: Even complex problems have a beginning, middle and an end. Here's how I'd tackle: Be clear on the outcome. → Many authors write the ending first and work backwards. Start writing. → A blank page is daunting; get those ideas on paper. Break it down. → Divide into manageable components. Prioritize actions. → Focus on high-impact areas first. Identify best practices. → Chances are someone, somewhere has already created a blueprint. (Google is your friend.) Enlist others. → You don't need to do all the work--delegate and collaborate. Adapt → Be flexible and refine your approach as you go. All of a sudden... ...I'd eaten that elephant.