Decision Quality Assessment

Decision Quality vs. Outcome Quality. One of the most subtle challenges in board oversight is distinguishing decision quality from outcome quality. When results are strong, decisions are often assumed to have been sound. When results disappoint, those same decisions are frequently re-evaluated as flawed. This is a natural human tendency—but it can quietly undermine learning and governance. Boards are responsible for overseeing decisions made under uncertainty. By definition, even high-quality decisions can lead to unfavorable outcomes when conditions change. Conversely, weak decisions can sometimes produce positive results due to factors outside management’s control. When governance focuses primarily on outcomes, boards risk sending an unintended signal: success will be rewarded regardless of reasoning, and thoughtful risk-taking will be questioned when conditions shift. This dynamic can discourage transparency. Management teams may become reluctant to surface assumptions or uncertainties if outcomes alone determine how decisions are judged. Strong boards work deliberately to separate these concepts. Decision quality depends on the information available at the time, the assumptions made explicit, the alternatives considered, and the logic connecting choices to expected results. Outcome quality reflects how reality ultimately unfolded. When boards evaluate both—explicitly and separately—oversight improves. Accountability becomes clearer without becoming punitive. And learning becomes cumulative rather than episodic. In my experience, the most effective boards are those that consistently ask: “Given what we knew then, was this a sound decision—and what should we learn for next time?” If discussions in your boardroom tend to blur decision quality and outcome quality, I’d be interested in how you’re working to keep those concepts distinct. --------------- #financeexecutives #cfo #futureoffinance Scott Sanders Andrew Reed, CPA, CMA Michael Corridon, CPA CGMA #corporategovernance #boardgovernance

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.

Nearly 60% of CEOs evaluate their strategic decision capability based on outcomes rather than the quality of their decision-making process (PwC). It’s easy to see why. Outcomes are tangible, measurable, and at the end of the day, they’re the bottom line. Yet, decades of research show that using smart decision processes thoroughly beats congratulating yourself on outcomes. This is because outcomes are influenced by factors outside your decision scope—like market shifts, new regulations, or good old-fashioned luck. You could have a positive result because the market suddenly changed in your favor, or because a competitor stumbled. Or, a great decision could lead to an unfavorable outcome simply because of unexpected variables—like an economic downturn or an unforeseen risk. By the way, some of the most brilliant, value-creating moves I’ve seen came after a bad misstep or unexpected event prompted exec teams with stellar decision practices to re-evaluate and take advantage of the new conditions. (Insert your favorite example from early COVID here!)  When you evaluate your strategic decisions through the lens of the quality of your decision-making process it can reveal key insights: ✨ Clarity of information: Did you gather the right data? Were there gaps in your information? ✨ Diverse perspectives: Did you get a variety of viewpoints? Did you challenge assumptions? ✨ Navigating uncertainty: What risks were identified? Did you fully explore what you were unclear about? ✨ Alignment with values and mission: Did your decisions consistently reinforce the org’s larger vision? Were the decisions aligned with your org’s core values? ✨ Flexibility and agility: Did you stay flexible to new information or changing circumstances? ✨ Room for improvement: What worked well? What changes might be made next time? Focusing on the quality of your decision-making process reveals whether your decisions are based on thorough analysis, aligned with your strategic goals, and designed to be repeatable for long-term success. What could change for your team if you started measuring success by increasing the quality of your decisions instead of waiting for the results?

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

A practical diagnostic that I use with my executive clients to map seven places where decision quality leaks - plus RAPID REPAIRS :) you can apply still this week. The 7 Silent Drains on Decision Quality: Vague problem framing — unclear problem, no single owner, fuzzy success. Effect: debate, delay, weak choices. Hidden vetoes in matrix organizations — informal veto power appears late; people feel surprised, not engaged. Effect: rework, reversals, slow rollout. Title power over expert power — decisions follow hierarchy; specialists stay quiet. Effect: blind spots, lower quality. Option collapse — only one path on the table; “approve or reject” instead of real alternatives. Effect: poorer decision, no contrast. No opponents — nobody tests the favorite idea; risks downplayed. Effect: groupthink, nasty surprises later. Risk language mismatch — legal/product/medical talk past each other; different scales/terms. Effect: confusion, stalled decisions. Post-decision amnesia — no review, no metrics, no learning loop. Effect: repeated mistakes. Quick Fix Kit to try this week :) 1. Name the decision type (reversible vs. one-way) and the single owner. 2. Map formal + informal vetoes early; clarify their role before you socialize options. 3. Require two viable alternatives (no straw men). 4. Assign a Red Team (or one designated skeptic) to attack the favorite. 5. Standardize risk language with a shared impact/likelihood scale. 6. Draft a one-page “as if decided” note (press-release/FAQ style) to expose gaps. 7. Calendar a 30-day decision review to capture lessons and adjust. What would you add to the list above? #DecisionMaking #Leadership #TeamPerformance #ExecutiveCoaching #MatrixManagement