Continuous Improvement In Project Management

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.

Most companies are using AI for efficiency. Some are accelerating value creation. A great case study is how Colgate-Palmolive is driving innovation. Here are specific ways they are embedding GenAI across innovation processes to substantlly improve research and product development. These come from an excellent article in MIT Sloan Management Review by Tom Davenport and Randy Bean (link in comments). 💡 AI-Driven Product Concept Generation Accelerates Ideation By linking one AI system that surfaces consumer needs with another that crafts product concepts, Colgate-Palmolive can swiftly generate creative ideas like novel toothpaste flavors. This AI-augmented workflow produces a broader product funnel and allows rapid iteration, enabling more employees to participate in the innovation process under guided human oversight. 🔍 Retrieval-Augmented Generation Enhances Data Reliability The firm’s use of retrieval-augmented generation (RAG) integrates company-specific research, syndicated data, and real-time trends from sources like Google search data. This approach minimizes the risk of hallucinations and ensures that responses are deeply grounded in verified, internal content—delivering more accurate market analysis and trend detection. 🤖 Digital Consumer Twins Validate and Refine Concepts Moving beyond traditional focus groups, the company has developed “digital consumer twins”—virtual representations of real consumer behavior. These digital twins rapidly test hundreds of AI-generated product ideas. Early evaluations show a high level of agreement between virtual feedback and actual consumer responses. This innovation speeds up early-stage concept validation and reduces reliance on slower, more limited human panels. 🔐 Democratizing AI Through a Secure Internal AI Hub Colgate-Palmolive’s AI Hub provides employees with controlled access to advanced AI tools (including models from OpenAI and Google) behind corporate firewalls. Mandatory training on responsible AI use, including guardrails and prompt engineering best practices, ensures that employees harness these tools safely and effectively. Built-in surveys and KPI tracking further enable the company to measure improvements in creativity, productivity, and overall work quality. 🌐 Bridging Traditional Analytics with Next-Gen AI for Measurable Impact By integrating traditional machine learning with cutting-edge generative AI, Colgate-Palmolive is not only boosting operational efficiencies but also driving strategic growth. This seamless blend supports tasks ranging from market research and innovation to marketing content creation—demonstrating a holistic, value-driven approach to adopting AI that is a model for other organizations.

Most leadership advice wasn’t built for the AI era. And if you’ve been leading through complexity, you’ve likely noticed it. It’s still rooted in certainty, hierarchy, and control. But the world we’re leading in now moves faster than planning cycles, and rewards those who can adapt in real time. In this new landscape, the most effective teams aren’t the ones with the best ideas. They’re the ones with the best systems for learning. It’s not about having the perfect plan. It’s about knowing how to move, listen, and adapt, at speed. Because leadership today isn’t just about setting direction. It’s about designing environments where progress is the norm. → Environments where iteration is expected, not feared  → Where feedback drives action, not defensiveness  → Where learning loops replace static plans This shift isn’t theoretical. It’s practical. And it shows up in how leaders: ✔️ Organize for learning, not just efficiency  ✔️ Start before they’re “ready”  ✔️ Treat feedback as a strategy  ✔️ Build systems that evolve  ✔️ Share ownership    What actually scales is leadership that listens, adapts, and moves with purpose. Because progress isn’t a product of pressure, it’s the result of systems that learn. If your leadership model is still rooted in plans and approvals, it might be time to shift. Not toward complexity, but toward a structure that supports change. 👉Curious how to build that kind of leadership system? Let’s talk about what it could look like in your organization.  

Indian IT firms spend approximately Rs 1.97 crore to train their employees each year, yet few become meaningfully smarter. Because training and learning capability aren’t the same thing. • Training is an event. • Learning capability is a system. • One transfers information. • The other builds adaptability. • When learning is scheduled, growth happens occasionally. • When learning is embedded, growth becomes continuous. That’s the real differentiator. Organizations that build learning capabilities adapt up to 60% faster, because learning becomes part of how people work, not something they attend. Here’s what that looks like in practice: 1. Rewarding application of insight, not just course completion. 2. Designing teams to share learnings real-time, not post-project. 3. Building systems where every experience teaches, automatically. The smartest organizations treat learning the way they treat technology as an essential operating system, not an occasional upgrade. Because when every person develops the capability to learn, unlearn, and reapply, the organization evolves naturally. Transformation no longer has to be forced. Is your organization scheduling learning or building it into its system? #LeadershipDevelopment #OrganizationalAgility #CapabilityBuilding

A learning organization is one where learning is intentionally BUILT INTO the daily management system. Many companies claim to be learning organizations, but in reality, they often confuse training with learning. They focus on courses and certificates but neglect the daily habits that drive growth... like reflection, feedback, knowledge-sharing, and collaborative problem-solving. Sound familiar? If so... Here are some ways to move toward becoming a true learning organization: 💡 Make learning visible. Start weekly team meetings with one question: What did we learn this week? 📢 Encourage challenges. Let people respectfully question the way things are done. Leaders need to show that it’s not only okay to ask “why?”- it’s welcomed. This is a great approach to build into your daily Gemba Walk! ⚠️ Use problems as lessons. Don’t jump to blame when something goes wrong. Instead, ask, What can we learn from this? What will we do differently next time? Make this a habit, not a once-off response in your 1:1's and everyday interactions. 📋 Make reflection routine. At the end of a project or during quality meetings, take 10 minutes as a team to ask: What went well? What didn’t? What did we learn? What should we change? 🗣️ Share learning across teams. Too often, learning stays stuck in silos. Create simple ways to pass it on like learning libraries, book clubs or monthly learning huddles across departments. ✨ Lead by example. Leaders who regularly admit they’re still learning create a culture where learning is normal. Asking questions instead of always having the answers is a key behaviour to set the tone. ......... And these are just the daily management options- there's lots more that you can do at a systems and systemic level ( tomorrow's post is on this). I'd love to hear your thoughts on shaping a learning organization through daily management systems...what else would you add?

Across industries, clients are sharing with me that something quiet, yet significant, is unfolding in boardrooms: strategic planning is being fundamentally rethought, not just refreshed. Two signals are driving the shift: 1️⃣ Corporate Restructuring Is Accelerating Kraft Heinz’s decision to split into two companies is just one recent example. We're seeing more leadership teams acknowledge that legacy structures built for scale may now be barriers to growth: nimble entities are far more adaptable in uncertain times. In my own practice, I’m currently working with a large-scale healthcare executive client reorganizing around service-line profitability (not geography), and a fintech firm exploring spinouts to unlock value in client-driven capabilities. Clarity is the new currency and leading strategy discussions. Exclusionary growth-oriented strategies are passe. 2️⃣ Capital Markets Are Opening Back Up Another observation is that IPO momentum is returning. Axios recently reported up to 60 IPOs are expected before year-end. Klarna, Gemini, and others are moving forward, and even mid-market firms are reevaluating M&A plans. One client postponed a deal this summer, not because of funding obstacles, but to sharpen their investor story in light of the competition. The most impactful shift? Strategic planning itself is being rebuilt. Traditional planning models are losing trust and relevance. In today’s politicized and noisy environment, many of my clients are curating their own data ecosystems. Some have added “noise filters” to adjust for narrative manipulation. Others are shortening cycles from annual to rolling 6–9 months. Here are 3 practices I’m seeing among forward-looking orgs: ✅ Scenario Loops over Static Models Dynamic updates based on volatile indicators (commodities, regulation, consumer trust) guide real-time adjustments. ✅ Strategy + Structure Are Now Linked One tech firm redesigned its org chart during its strategy retreat, not 6 months later. ✅ Investor Storytelling Is Part of Planning Especially for firms near funding or IPO, strategic planning now includes a messaging track. My O&G CFO client called it their “Investor GPS.” As you prepare for your next planning cycle, ask: ·       Is our structure aligned for where we’re going, not just where we’ve been? ·       If the capital window opens, are we ready? ·       Are we telling a story the market believes? In 2026, strategy is more abut being directionally clear, structurally agile, and ready to move. #ExecutiveLeadership #StrategicPlanning #CapitalMarkets #IPO #CorporateRestructuring #2026Strategy #BoardLeadership

🎯 Why Most Business Problems Remain Unsolved (And How to Fix That) Last week, I had the privilege of facilitating a Problem Solving & Business Acumen workshop for our teams at L'Oréal Indonesia. 💡 The Problem We All Face (But Rarely Talk About) Here's an uncomfortable truth: we're wired to jump to solutions. In business, this looks like: ✔️ Launching promotions without understanding why sales declined ✔️ Hiring more people without diagnosing process inefficiencies ✔️ Copying competitor tactics without validating if they fit our context The cost? Wasted resources, frustrated teams, and recurring problems that never truly go away. According to the World Economic Forum's Future of Jobs Report 2023, analytical and critical thinking are the #1 and #2 most important skills for workers. Yet, most of us were never formally taught how to think critically or solve problems systematically. 🛠️ The Problem-Solving Process: A Step-by-Step Guide Step 1: Define the Problem (Don't Jump to Judgment!) 📝 Craft a Problem Statement with 6 components: "How can [responsible party] improve/reduce [reality] to meet [expectation] within [timeline] without [anti-goals], in order to fulfill [reason]?" Example: "How can the product team launch a new product on time in Q4 2024 without sacrificing key processes, in order to meet the sales target?" Step 2: Find Alternatives (Issue Tree + MECE) Once the problem is clear, break it down using an Issue Tree. For instance, if mascara sales dropped -14% YoY: 📦 Placement → Gondola compliance, visibility, signage 🎁 Promotion → BOGO mechanics, POS materials 💰 Price → Elasticity, perceived value 🎨 Product Claims → Content freshness, reviews 🔥 Competition → Share of voice, endcap presence ✅ Ensure hypotheses are MECE (Mutually Exclusive, Collectively Exhaustive)—no overlaps, no gaps. Step 3: Test Your Hypotheses Don't fall in love with your first idea. Run quick tests: 📊 For a skincare serum declining in pharmacies, we tested: ✔️ Hypothesis A: Reduced pharmacist advocacy is the issue → Micro-detailing pilot in 10 stores ✔️ Hypothesis B: Cold chain OOS drives lost sales → Warehouse SOP audit + temperature logs ✔️ Hypothesis C: Execution gaps suppress promo ROI → Endcap compliance audit Each hypothesis had clear KPIs and timelines—no guessing, just data. Step 4: Make the Decision (Impact vs. Effort Matrix) Not all solutions are equal. Prioritize: 🟩 Quick wins—do this! 🟦 Strategic bets 🟨 Fill-ins 🟥 Avoid Focus on low effort, high impact moves first. Build momentum, then tackle the big bets. 🚨 What Happens When We Skip These Steps? A mascara brand saw sales drop -14% YoY. The reaction? "Let's run a BOGO promo!" The result? Sales stayed flat. Why? Because the real issues were: ❌ Poor gondola compliance (only 68% correct facings) ❌ Weak influencer share of voice ❌ Competitor secured prime endcap space The lesson: Solutions applied to the wrong problem = wasted budget and missed targets.

Let me walk you through the math that should make every CFO question their resource allocation. Using the latest 2025 industry benchmarks from SaaS Capital, here's the stark reality for a typical $200M ARR company: Revenue Responsibility: • Sales team: Manages $40M in new ARR (20% of total revenue) • CS team: Manages $160M in existing/expansion ARR (80% of total revenue) Budget Allocation Reality: • Sales: 13% of ARR ($26M) - up from 10.5% in previous years • Customer Success: 8% of ARR ($16M) - down from 8.5% in previous years Enablement Investment (based on industry benchmarks): • Sales enablement: ~$780K annually (3% of sales budget) • CS enablement: ~$160K annually (64% of CS teams spend <$200K on all programs, tools, and training combined) Investment per revenue dollar managed: • Sales: $780K ÷ $40M = $19.50 per $1M managed • CS: $160K ÷ $160M = $1.00 per $1M managed They're spending 19.5X more per revenue dollar on the team managing 20% versus the team managing 80%. In what other business context would this allocation be considered rational? Imagine if manufacturing allocated 19.5X more maintenance budget to machines producing 20% of output versus those producing 80%. Or if airlines invested 19.5X more in routes generating 20% of revenue versus those generating 80%. The CFO would be fired. Yet this exact irrational allocation persists in SaaS because of tradition, not logic. The Efficiency Data only makes this more baffling: • CS Efficiency: 1 CSM manages $2-5M in ARR • Sales Efficiency: 1 rep manages $600K-$1M in quota • CS is 2-5X more capital efficient, yet receives proportionally less investment The Revenue Economics defy conventional business wisdom: • According to BCG, "Over 25X more value is generated over a customer's lifetime than in the year when the customer is acquired" • TSIA data shows companies with dedicated CS teams achieve 17% base revenue growth vs. just 5% with a sales-only approach • Forrester Research found dedicated CS teams deliver 107% ROI within 3 years Remember the 120-day challenge from my earlier post? For this company, achieving a 1% churn reduction and 3% expansion increase would be worth millions, yet they're investing $1 per $1M in revenue for the team responsible for making that happen. The reality: McKinsey explicitly states that "slower-growing SaaS companies underinvest in customer success." This investment imbalance explains why many companies struggle to achieve the critical 3-5% improvements that transform business fundamentals. Next week, I'll explain why training is the most obvious investment decision in CS and why it's the most overlooked. What's the enablement investment ratio in your organization? Does it match your revenue responsibility ratio? Calculations based on industry benchmarks from SaaS Capital's 2025 Private SaaS Company Spending Benchmarks #CustomerSuccess #Enablement #Investment #ARR #ROI Previous Post: https://lnkd.in/g_bpYGzr Next Post: https://lnkd.in/g76FYFMf

𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝘀 𝗕𝗿𝗼𝗸𝗲𝗻 𝗔𝗻𝗱 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗙𝗶𝘅 🔧 We’ve all sat through those endless meetings. The ones where "solutions" create more problems than they solve. After 20 years in consulting, I’ll let you in on a secret 👇 Most teams don’t solve problems. They debate symptoms. 𝗝𝗼𝗵𝗻 𝗗𝗲𝘄𝗲𝘆’𝘀 𝟳-𝘀𝘁𝗲𝗽 𝗺𝗲𝘁𝗵𝗼𝗱 isn’t just philosophy, it’s battlefield medicine for business. Here’s why it works when others fail: 1️⃣ 𝗗𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Most teams skip this to sound "action-oriented"... Big mistake. 📌 Pro tip: Try the "5 Whys" before moving forward. 2️⃣ 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗽𝗮𝗿𝗮𝗹𝘆𝘀𝗶𝘀 We confuse data with insight. Real analysis answers one question, "What’s really stopping us?" 3️⃣ 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗰𝗿𝗶𝘁𝗲𝗿𝗶𝗮 This separates professionals from amateurs. No guardrails? Prepare for shiny object syndrome. 4️⃣ 𝗕𝗿𝗮𝗶𝗻𝘀𝘁𝗼𝗿𝗺𝗶𝗻𝗴 The step everyone loves... and gets wrong. Rule: No idea is stupid, but every idea must be stress-tested. 5️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘁𝗶𝗺𝗲 Where ego kills more solutions than facts do. The antidote?... "What would we advise our client to do?" 6️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 The graveyard of good intentions. Secret weapon? Pilot everything. 7️⃣ 𝗙𝗼𝗹𝗹𝗼𝘄-𝘂𝗽 The step 83% of teams skip (Harvard study). Because admitting "We were wrong" feels like failure. Here’s the uncomfortable truth: 🔴 Your last "solution" probably created 3 new problems 🟢 The best teams solve fewer problems, but solve them completely We train teams to spot the difference between a problem worth solving and a symptom masquerading as the real issue The results speak for themselves: ✅ 40% faster decision cycles ✅ 70% fewer "solution rollbacks" ✅ 3x more stakeholder alignment So I’ll ask what no one does... What’s one "solution" your team implemented that actually made things worse? 💡