Decision Quality Assessment

If you don’t measure decision quality, you’re grading luck. Example: A team hit target once. +7% on revenue. Six months later, the same bet lost 12%. The win was luck. The thinking wasn’t checked. Fix these misses with simple habits. This cuts audit, headline, and key-person risk - and helps you pivot faster. 1. Make a decision log. Owner, goal, 3 assumptions, base rate (what usually happens), options rejected, and a stop rule if the decision goes bad. 2. Run a 10-minute pre-mortem. “It’s a year later and we failed. What likely went wrong?” Add the top 3 risks to the plan. 3. Set review dates now. Day 60 and Day 180. Did reality match our assumptions? What did we learn? 4. Score decision quality (simple 5-point): • Assumptions written? (list 3) • Base rate used? (e.g., past conversion 18–22%) • Real alternatives considered? (≥2) • Reversible? Kill-criteria set? (e.g., CAC > $450 for 4 weeks) • Right speed for the risk? (fast/slow by design) 5. Tie rewards to the process, not slide polish. Show the log. Show the reviews. Promote the thinking, not the theater. Stop: • Doing “Are we on track?” with no “Should we stop?” • Celebrating wins without how we won. • Blaming people when the process was blind to decision quality. Start doing: • One page per bet. • One pre-mortem per bet. • Two reviews per bet. • Share lessons in public. Smart leaders don’t just ask, “Did it work?” They ask, “Was it a good bet when we placed it?” Raise decision quality. Outcomes will follow. 📩 Boards & CEOs: Build a Decision OS your teams trust. Let’s talk. 📬 Subscribe to BRIDGE: https://lnkd.in/gCdavukQ ♻️ Repost if your org still celebrates luck as skill ➕ Follow Adi Agrawal | Bridge the Gap

5 Mental Models Elite Leaders Use for High-Impact Decisions. When I led complex transformation projects, I noticed something: The highest-performing executives weren't necessarily smarter. They just had superior decision frameworks. Leaders make 35-50 critical decisions weekly that shape organizational outcomes. Yet 67% of executives report making the wrong strategic decision at least half the time. Here are the 5 mental models that transformed my decision quality: 𝟭/ 𝗧𝗵𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗶𝗺𝗶𝗻𝗴 𝗥𝘂𝗹𝗲 Big decisions have big consequences. Time your decision process accordingly: → Operational decisions (impact < 1 month): 24 hours max → Tactical decisions (impact < 1 year): 1 week deliberation → Strategic decisions (impact > 1 year): Minimum 2-week analysis 𝟮/ 𝗧𝗵𝗲 𝟳𝟬% 𝗥𝘂𝗹𝗲 (𝗝𝗲𝗳𝗳 𝗕𝗲𝘇𝗼𝘀) → Below 40%: High-risk gambling → 40-70%: Calculated risk with potential for first-mover advantage → Above 70%: Diminishing returns on information gathering 𝟯/ 𝗜𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 → Pre-mortem: "Imagine this initiative failed completely. What happened?" → Red-teaming: Assign your strongest thinkers to challenge your assumptions → Consequence mapping: Chart second and third-order effects 𝟰/ 𝗥𝗲𝗴𝗿𝗲𝘁 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 → Calculate the ROI of your best alternative option → Factor in hidden costs (team bandwidth, attention fragmentation) → Consider the compounding value of focus vs. dilution 𝟱/ 𝗦𝗲𝗰𝗼𝗻𝗱-𝗢𝗿𝗱𝗲𝗿 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 → Document assumptions and expected outcomes → Set explicit review dates (30/90/180 days) → Analyze prediction accuracy and process quality The difference between good and great leadership isn't working harder. → It's making better decisions consistently. Which of these models would have the biggest impact on your leadership effectiveness?

Breaking: Local man perfectly predicts he will get wet, then jumps into pool without towel, swimsuit, or phone in waterproof case. “But I was so accurate!” he shouts, while his iPhone dies. This is your supply chain on forecast accuracy metrics. 😏 Congratulations! You predicted demand would be 1,000 units. Actual demand was 1,003 units. You’re 99.7% accurate! You also ran out of stock on day 2, airfreighted emergency inventory at $50k, and your competitor took your customers. But hey, great forecast! 🎊 Now let’s ask the real questions… did your profits go up? Did inventory costs go down? Or did you just… forecast better? Nobody wants to admit that forecast accuracy ends up as a vanity metric. You can have a 95% accurate forecast and still make catastrophically bad decisions. You can also have a 70% accurate forecast and print money. Why? Because the real world doesn’t care about your MAPE score. The real world cares about: • Did you stock out during peak season? • Are you sitting on $2M of dead inventory? • Did you airfreight products at 10x cost because your “accurate forecast” didn’t account for lead time variability? Forecast accuracy measures how well you predicted the past. Decision quality measures how well you’re preparing for the future. These are not the same thing. A good decision framework accounts for uncertainty, asymmetric costs, and the economic impact of being wrong. It asks: “What should I do given what I don’t know?” not “Look how well I predicted this number!” Stop optimizing for forecast accuracy. Start optimizing for decisions that make money. Your CFO will thank you. What’s the worst “we improved accuracy!” celebration you’ve witnessed that changed absolutely nothing? #SupplyChainOptimization #DontBeADumDum

A Board Checklist for Data & AI Transformation Before approving or renewing any major data or AI investment, boards should be able to answer yes to the following: 1. Strategic Relevance: can we clearly articulate which enterprise-critical decisions this investment is intended to improve, and why those decisions matter to value creation or risk management? 2. Decision Ownership: is there a single, accountable executive for each of those decisions, with authority aligned to consequence not diffused across committees? 3. Information Fitness: have we agreed what information is necessary and sufficient for those decisions, rather than funding general capability or excess reporting? 4. Value at Stake: can we quantify the economic or risk impact of making those decisions better, or of continuing to make them as we do today? 5. Cost of Inaction: have we explicitly considered the downside of delay, indecision, or status quo and not just the cost of investment? 6. Decision Velocity: will this investment materially change the speed at which critical decisions are made, escalated, or revisited and is that speed appropriate? 7. Accountability for Outcomes: if outcomes disappoint, will we be able to distinguish whether the failure was due to poor information, poor judgement, or poor execution? 8. Learning Loop: is there a defined mechanism for reviewing decisions against original assumptions, updating models and thresholds, and improving decision quality over time? 9. Governance Ownership: is this investment overseen through strategy and risk lenses, or has it been delegated entirely as a technology matter? 10. Board Visibility: will the board receive ongoing, decision-focused insight into whether decision quality is improving and not just delivery milestones and spend? Data and AI do not create value by themselves. They only matter insofar as they improve the quality, speed, and accountability of decisions. So the real board-level question is this: Are we governing technology or are we governing the decisions that determine enterprise outcomes? Bill Schmarzo Mark Stouse John Thompson Malcolm Hawker Matthew Small Dan Blake Eddie Short Edosa Odaro Robin M. Dan Everett Arvind Murali M.B.A., M.S

There’s a subtle difference between decisiveness and decision quality. In a recent LP Perspectives piece on family office investment leadership, one theme stood out: as complexity increases, the shift from speed to judgment. That observation aligns with research from Harvard Business Review on “decision hygiene” and the idea that strong outcomes are less about intelligence and more about disciplined process. Under pressure, leaders don’t lack insight….. sometimes they lack pause. And pressure has a way of disguising itself as urgency. This struck me because we often talk about decision-making styles as if competence and intelligence are the differentiators. They’re usually a given. After extensive study, a third trait emerged as the strongest predictor of decision quality: cognitive style …. actively open-minded thinking. Described as: ·      People who love to change their mind ·      People who look for information that might prove them wrong ·      People who can acknowledge & explain they’ve changed their position … because the facts changed In governance, investing and AI ethics, this may be one of the most undervalued capabilities at the table. Not speed. Not certainty. Judgment. It makes me think of the ancient symposia; ideas exchanged, challenged, refined. The goal was not to win the argument. It was to elevate the thinking. As complexity compounds, actively open-minded thinkers aren’t a nice to have. They’re essential. 📸: OpenAI

It’s not every day millions witness a masterclass on judging decision quality. The 2025 Qatar GP gave us just that, if we look beyond the results. On lap 7, a crash brought out the safety car. Nine teams pitted for fresh tires. McLaren, leading 1-2 with Piastri and Norris, stayed out to protect track position on a tough overtaking track. Commentators hailed the strategy’s flexibility and potential late-race advantage. By race end, Verstappen won, Piastri was second, and Norris fourth. Praise turned criticism and even McLaren’s boss admitted they “got it wrong.” But did they? Judging only by outcome: yes. But that’s outcome bias. McLaren’s decision was based on: - Track position importance in Qatar. - Likelihood of another safety car. - Late-race tire offset advantage. What they couldn’t control: - No further safety cars. - Rival’s tires held up. - A green-flag finish negating their edge. This happens often in business: good decisions meet bad luck, and are labeled mistakes. Here’s how to improve decision-making: 1. Focus on decision process, not just outcome. 2. Separate skill from luck. 3. Use the right data at the right time. 4. Explicitly manage uncertainty. 5. Reward strong processes, not just results. Most of us don’t have million-dollar split-second calls but daily decisions shape our success. Ask: Was the process logical? Did we control what we could? Would we choose the same again? If yes, you’re winning. Outcomes are often luck and context—decision quality is yours.

I interviewed 7 decision professionals and the pattern is undeniable. The quantitative stuff I obsess over? Models, frameworks, statistical analysis? It's not what matters most. Here's what I learned from these experts at the Society of Decision Professionals (SDP): 1. Process beats perfection Andrew Thrift: "When you've met the criteria for decision quality, you can defend it and the chips will fall where they fall." 2. Inclusion trumps expertise Lee Failing: "People without technical expertise still have the right to have a say in decisions that affect them." 3. Natural feels better than structured Audrey D.: "Think of it as a mindset, not a procedure. People can get clarity without knowing they're going through a structured process." 4. Bias awareness is transformative Walter Kosi: "Once you become sensitive to biases, you can't unsee them. They jump to your eye everywhere." 5. Engagement drives effectiveness Samantha Rush, CSP and M.npn: "If people don't want to participate, you won't get their piece of the puzzle." 6. Values diversity can coexist Reidar B Bratvold: "People should be allowed to have different value systems. I can disagree with your values but never say they're wrong." 7. Human skills matter most Tony Kenck: "Five of the six elements in our decision quality framework are on the soft skill side." This week’s episode of Ask a Decision Engineer explores how these "power skills" help people work together better and navigate the messy realities of human decision-making. Listen to Episode 4: "How is Decision Science Applied? Part 2: Power Skills" wherever you get your podcasts. Which of these insights resonates the most? #powerskills #decisions #decisionmaking

Decision Intervention Framework for Lifting Operations (Behavior-Based, Risk-Focused, Non-Technical) Most lifting failures do not happen during the lift. They happen 𝗪𝗛𝗘𝗡 𝗗𝗘𝗖𝗜𝗦𝗜𝗢𝗡𝗦 𝗔𝗥𝗘 𝗠𝗔𝗗𝗘 — 𝗢𝗥 𝗔𝗩𝗢𝗜𝗗𝗘𝗗. Through behavioral analysis of lifting operations, I developed a Decision Intervention Framework focused on how people think, judge, and accept risk. The framework targets six critical decision points: • 𝗥𝗶𝘀𝗸 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 — routine work often hides real risk • 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 — unclear authority leads to silent risk acceptance • 𝗔𝘂𝘁𝗵𝗼𝗿𝗶𝘁𝘆 𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁 — people see risk but stay silent • 𝗣𝗿𝗲𝘀𝘀𝘂𝗿𝗲 𝘃𝘀 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 — schedule and cost override safety thinking • 𝗘𝗮𝗿𝗹𝘆 𝗪𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 — discomfort and near misses are ignored • 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝘃𝗶𝗲𝘄 — outcomes are reviewed, decisions are not This framework does not replace standards or procedures. It strengthens 𝗵𝘂𝗺𝗮𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝘂𝗻𝗱𝗲𝗿 𝗿𝗶𝘀𝗸. Safer lifting starts when organizations stop asking: “Did we follow the procedure?” And start asking: “Was this a good decision at the time?” That is where 𝗿𝗲𝗮𝗹 𝘀𝗮𝗳𝗲𝘁𝘆 begins. Stay safe, everyone! FDP CONSULTING LLC (Engineering & Business Consultant) ---- I work with leaders, managers, and governance teams in high-risk and complex operations to improve decision quality, risk judgment, and leadership behavior through executive conversations, decision labs, and behavioral reviews. If your role involves approving, accepting, or inheriting risk, let’s talk about decisions — before incidents do

A good decision can produce a bad outcome. A bad decision can produce a good outcome. I have found that this is one of the most important distinctions in governance and is rarely discussed explicitly. If boards evaluate management purely on outcomes, they unintentionally reward luck and penalize sound thinking. Decision quality asks a different question: "Was the decision process rigorous, explicit, and economically grounded?" This includes: - Clear assumptions - Visible trade-offs - Integrated economic logic In my experience, boards that focus on decision quality improve outcomes over time—because they improve the system that produces them. I have a question for the directors out there: "How does your board distinguish between decision quality and outcome quality?" If this is an area you’re refining, I’d welcome a discussion on how leading boards are approaching it. --------------------------- #CorporateGovernance #BoardLeadership #DecisionQuality #ExecutiveDecisionMaking #ProfitabilityAnalytics Achille Ettorre, MBA Nike Ajao - CFO - MBA - Forbes - MSN - Global CFO Award Virginia Roby Christian Cuzick Lukas Sundahl, CMA, CSCA, MBA

7 decision frameworks that eliminate mental fatigue. A chess grandmaster ignores 31 pieces to focus on the one move that matters. Your brain makes 35,000 decisions daily. Research confirms it. No wonder you're exhausted by dinner. Here's what I learned working at Fortune 500 companies as an executive: 1. The Impact Matrix - Plot choices on Effort vs Impact axes - Only focus on high-impact decisions - Automate or delegate everything else 2. The 10/10/10 Rule - Ask: How will this matter in 10 minutes? - Then 10 months? - Then 10 years? - Perspective kills unnecessary stress 3. The Regret Minimization Framework - Ask: "At 80, which choice will I regret not making?" - Your future self has clearer judgment - Used by Bezos to start Amazon 4. The 5-Why Cascade - Ask "why" five consecutive times - Surface the real motivation behind choices - Most stop at surface-level reasoning 5. The Morning Decision Block - Reserve your first 90 minutes for key decisions - Research shows cognitive peaks happen early - Save routine choices for afternoon hours 6. The Premortem Technique - Imagine the decision failed completely - Work backward to identify failure points - Reveals blind spots before they become problems 7. The 70% Rule - Act at 70% confidence - Waiting for 100% certainty creates paralysis - Winners move before they feel ready Decision quality isn't about having better answers. It's about having better frameworks for finding answers. Which technique will you implement today? ♻️ Repost if this shifted your perspective 🔔 Follow Kabir Sehgal for more decision frameworks