Decision-Making Under Risk in Innovation

As corporations grow larger, decision-making often becomes increasingly complex. Even minor decisions can require lengthy processes, delaying implementation. In many cases, organizations may adopt a plain vanilla approach to all decisions, regardless of their nature. Over time, this stifles experimentation, as the effort to secure a decision can exceed the effort required for the experiment itself. When such practices persist, experimentation reduces, innovation diminishes, and the culture of creativity erodes. Eventually, the company risks losing its innovative edge—a scenario with serious consequences. One way to counteract this is by adopting Amazon’s “One-Way Door/Two-Way Door” decision-making framework. Decisions that are irreversible and carry significant consequences (One-Way Door) should be approached with caution and thorough deliberation. However, decisions that are reversible or low-risk (Two-Way Door) should be made quickly, enabling teams to take more risks and act decisively. This approach fosters a greater appetite for risk-taking while maintaining prudence where it matters most. It encourages experimentation and ultimately builds a thriving culture of innovation. What is your organization’s appetite for innovation?

Decisions Don’t Wait for Perfect Information I was with one of my favorite banks last week working with them on their strategic plan. They are a high performing team that has more strategy chops than many of their peers. Yet, they reiterated a familiar frustration: they weren't moving as fast as they wanted (or needed) to achieve their lofty goals. The answer often lies in the question. More accurately, questions that can't or don't need to be answered are slowing things down. One of the biggest traps in decision-making is waiting for every question to be answered. Especially in situations exploring the unknown where there is the biggest potential upside, uncertainty is something to be managed, not avoided. Organizations that can navigate uncertain waters with confidence will be the winners in the increasing competitive world of financial services. Back when I worked in early-stage venture, I developed a simple framework that still guides my thinking today: ❓ Do I have enough information to make a decision? 🚀 If yes → make the decision. 🛑 If not, what information do I need? ❓Is that information knowable? 🎯 If it’s not knowable now, can I run a test or experiment to validate a hypothesis? 🚀 If yes → run the test. ❓ If it’s not knowable and not testable, what’s the cost of being wrong? 🧨 If the risk is acceptable → make the decision. 🛑 If the risk is too high → redefine the scope or revisit assumptions. This isn’t about being reckless. It’s about recognizing that the pursuit of perfect certainty is often just fear in disguise. The best decision-makers are great at scoping risk, setting clear hypotheses, and moving forward anyway. The faster we can make decisions with the best available data, the faster we can adapt the plan to fit known facts rather than addressing ungrounded fears. Don’t let “we need more data” become the enemy of progress.

"The data isn't conclusive yet. Let's wait." I watched a $50M opportunity slip through our fingers because of that sentence. The painful truth? Technical and business leaders often get trapped between two extremes: analysis paralysis or reckless speed. That $50M opportunity? It went to a competitor who had an imperfect solution but was willing to learn and adapt in the market. Sometimes the riskiest decision is waiting for perfect data. After guiding engineering, operations and manufacturing teams through transformation for over 20 years, I've discovered something counterintuitive: the most innovative organizations don't choose between rigor and speed. They embrace the paradox and design systems that deliver both. Beyond the classic frameworks like SWOT or RACI Matrix, what matters most is how we approach decisions, not just which tools we use. Here are four principles that consistently drive results, regardless of your chosen framework: 1️⃣ Classify by consequence, not complexity Map decisions on two axes: reversibility and impact. When a decision can be easily reversed and has modest impact, move quickly. Save the deep analysis for truly consequential choices. 2️⃣ Create "decision boundaries" instead of "decision points" For each initiative, establish clear parameters where teams have autonomy to experiment without additional approvals. This accelerates learning while maintaining control. 3️⃣ Separate learning decisions from scaling decisions Use small, rapid tests to generate evidence before making larger commitments. This approach lets you fail fast and adapt quickly. 4️⃣ Build feedback loops into every decision Make data collection automatic and continuous. The goal isn't perfect information - it's learning faster than your competition. This framework works because it: ☑️ Creates safe spaces for experimentation ☑️ Builds a culture of continuous learning ☑️ Enables incremental improvement over either/or thinking ☑️ Makes decision ownership clear and visible What decision-making approaches have helped your team balance speed with rigor? Share your experience below. --------------- #DecisionMaking #Leadership #Innovation #StrategicThinking

Hi - I'm Steve. I am a professional fail-er. Many times data teams are asked questions that pertain to things that the business has never done before. This might be creating opportunity sizing for a new feature, forecasting adoption or performance of our customers, or building recommendations, either for the business or the customers to suggest relevant improvements or features to adopt. The challenge with many of these problems is that there's not always a black-or-white answer, and in addition, we tend not to have complete datasets that enable us to paint the full picture. As a result, we end up having to build assumptions into our models, basing this off of past experience, similar features, user behavior and other correlational analysis. Data teams that are not comfortable with the concept of failing fast can fall into the pitfall of 'paralysis by analysis', whereby we fail to make a recommendation due to the uncertainty that implicitly exists in the data. The easiest thing to do to delay a project or deliverable is to ask for more data, which inevitably will beget more questions and sometimes cause us to lose sight of the goal we were trying to accomplish in the first place. A much more effective approach, I have found, is to clearly draw out what assumptions we must make to size the feature, or conduct the analysis. Establish clearly the risk of us being wrong on any of those assumptions, and clearly evaluate one-way (irreversible) and two-way (reversible) decisions. The goal is to have enough 'low stakes' experiments, where we can easily roll back the change to gain enough confidence in the assumptions you must make for the 'high stakes' decisions where reversing the change is either incredibly costly, or sometimes infeasible. Through this approach, we're able to dedicate a lion share of the analysis time firming up the hypothesis we must make for the 'high risk' decisions, and apply the highest level of rigor in terms of experimentation and burden of evidence. 'Low risk' areas enable us to broaden our scope of knowledge of the product, building confidence in our assumptions, and creating data for us to explore 'why wasn't my assumption accurate?' Creating controlled environments to fail fast will not only enable you to learn faster, but it will enable teams to build confidence in their abilities to test their assumptions and debug when the stakes are high. If you create an environment where *every* decision requires an insurmountable burden of evidence, you run the risk of stifling innovation and having a data team that's not equipped to debug situations when our assumptions were inevitably wrong. My suggestion to data teams is to embrace (controlled) failure. No one asks the question 'why did this roll-out go so well?', but certainly the question always arises 'what went wrong' when our predictions do not materialize. Ensure you're prepared for those situations by learning *how* to fail.

Risk hits different in the nonprofit world. “Innovative” is what we call it after we know it works. In the moment, it’s called “risky and unpopular.” And it’s one thing to take big risks with investment capital. It’s another thing entirely to take big risks with other people’s lives. That’s what nonprofit leaders contemplate ahead of a bold gambit. In my experience, this is why leaders hesitate. Not because of lack of imagination, or lack of funding, necessarily (lack of funding can be a great catalyst of innovation), but because we understand the weight of consequences. People can go hungry. They can lose their healthcare. Their homes. In some cases, their lives. But the fact is, avoiding innovation is also a risk. Needs evolve, systems shift, funding landscapes change—and if we don’t adapt, the people we serve pay that price too. So how do we innovate responsibly? We hedge the big bets. ✔️ We build portfolios of ideas or programs, not single-point failures. ✔️ We diversify our efforts so that if one initiative stumbles, it becomes a pivot rather than a crisis. ✔️ We test early and small. We learn fast, and scale only what proves real value. ✔️ We treat experimentation as a disciplined practice, not a gamble. Controlled fire. ✔️ We communicate everything we’re doing and why. No surprises within the tent. And you should expect your decision to be unpopular if it represents change. Change is hard; especially those who’ve known loss, or are managing trauma. Lean into it. Listen with the expectation that you might learn. If you do, you will. Innovation done well doesn’t endanger trust; it strengthens it. It grows your coalition.

How do you decide when to double down on an initiative or when to walk away? This kind of strategic decision making is one of the top skills innovators must master. Here's how I make the decision without letting my pride take over: 1. Data-Driven Decisions 📊 I insist that every project measure what I call Pivot Indicators. These are pre-defined metrics that answer two important questions: "When will we know if it's not working?" and "How will we know?" By establishing these indicators upfront, we remove ambiguity from the evaluation process. Whether it's a certain threshold of user engagement, a predefined ROI, or customer satisfaction scores, these indicators act as the guardians of our resources, ensuring that we stay agile, respond promptly to feedback, and invest in avenues that truly resonate with our overarching goals. 2. The Passion Parameter ❤️ Numbers are vital, but they're not everything. The intangible yet palpable energy of passion is often the difference between projects that fizzle out and those that thrive. If you had to make the decision today to start this idea from scratch, would you do it? Are you still as excited about the opportunity as you were when everything kicked off? If your team exudes excitement and genuine belief in the project, this collective energy can transform challenges into opportunities. 3. Opportunity Costs and the Big Picture ⚖️ Every resource committed to one initiative means potential neglect for another. It's essential to ask: What could these resources achieve elsewhere? I actually sit down and make a list of where I would invest the resources if I had more time and effort. Then, I'm not deciding whether I failed at something or didn't, I'm just deciding between option A or B for my time and resources. Our resources—whether time, talent, or capital—are finite, and successful innovators understand that we have to continuously reassess to make sure that we don't get stuck. How do you decide whether to re-invest or walk away? #innovation #management #strategy

Lately, I’ve been talking a lot about risk. Unsurprisingly, it’s only becoming more important. One theme that keeps coming up is the idea that “compliance slows us down.” In reality, it’s often more a timing problem than a compliance problem. When risk shows up at the end, it creates friction. When it’s embedded early, it creates clarity. The most innovative organisations work with risk early, openly, and across teams. Leading fintechs are already moving beyond compliance as a checkpoint, integrating risk and innovation into a single operating model where risk thinking shapes decisions, not just reviews them. When this happens, organisations can move faster, building better decision-making, stronger trust, and greater resilience along the way. This is where CFOs have real leverage, shaping decisions upstream and embedding risk into how the business operates. The advantage today isn’t found in minimising risk, but in understanding, anticipating, and using it to move with speed and confidence. The organisations that win will be those that turn risk into a catalyst for innovation. There’s a helpful piece from Deloitte in The Wall Street Journal  on this topic. I’ll link to it in the comments.

Your company's growth is a tightrope walk between innovation and complacency. Take too few risks? You'll be forgotten. Take the wrong risks? You'll compromise your brand. Plenty of the world’s most innovative companies we work with at Motto have figured it out, and we’ve seen some patterns. They expand boldly *without* compromising who they are. How’s this possible? By aligning innovation with their core values at the foundational level. Here's what that looks like in practice ↓ ⦿ Value-driven decision making Every new initiative should be measured against your company's fundamental beliefs. If it doesn't align, it's not worth pursuing. ⦿ Create a "failure budget." Allocate resources specifically for experimental projects Reward people for trying, not just succeeding. This tells your team it's okay — wonderful, even — to take calculated risks. ⦿ Implement an innovation framework. Set clear guidelines for new ideas. Leaders should ask themselves… → What will keep our company in the leader position? → What is the impact if we play it safe? → How will this innovation align (or not align) with our values? Make sure innovations contribute positively, inside and out. ⦿ Foster cross-pollination Form diverse "skunk works" teams. Give them a specific goal and deadline. Then, watch as fresh perspectives lead to groundbreaking ideas. ⦿ Embed values through education. Your team should breathe your company's values—When they do, even their boldest ideas will align with your core identity. Innovation isn’t about recklessness— It’s about daring to fly while staying true to your roots. When you master this balance true growth happens. Motto® helps tech companies align vision with bold growth. Let's talk about your next big move. → wearemotto.com

Consensus feels safe. It is also slow. Your job is not to keep everyone happy. Your job is to make the next right decision, own the risk, and move. Consensus tries to average preferences. Operators create direction. The difference is costly: consensus optimizes for feelings, direction optimizes for outcomes. Here is a simple operating view of decision-making that scales from a 3-person team to a 300-person org: 1️⃣ Define the decision and the owner ↳ One DRI. One clock. One sentence problem statement. ↳ Timebox debate. “We decide by Tuesday 3:00 PM.” 2️⃣ Separate door types ↳ Reversible (two-way): bias to speed and small tests. ↳ Irreversible (one-way): slow down just enough to protect downside. 3️⃣ Gather signal, not noise ↳ Ask for the strongest counterargument and the cheapest test, not opinions. ↳ Pull data that shrinks uncertainty, not decks that grow it. 4️⃣ Force alternatives ↳ At least two viable options with trade-offs stated plainly. ↳ Include a “do nothing” case to anchor costs. 5️⃣ Decide in writing ↳ One page, max: • Decision: X • Why now: drivers, constraints • Options considered: A/B (+ trade-offs) • Risks & mitigations: top 3 • Success metric & review date 6️⃣ Communicate for alignment (not agreement) ↳ “We chose X because Y. We will measure Z. We will recheck on [date].” ↳ Invite dissent before the call, commitment after it. 7️⃣ Close the loop ↳ Log the decision. Set the review. If wrong, fix fast, do not assign blame. Learning speed beats perfect aim. Decision hygiene beats decision theater. You do not need more meetings. You need clearer ownership, tighter clocks, and smaller experiments. When should you slow down? ↳ One-way door with existential risk. ↳ High cost of reversal, long tail liability, or brand trust at stake. ↳ When the cheap test is still expensive. Otherwise, ship the test. Leader’s checklist for “hard and clear”: ↳ Name the owner and the deadline out loud. ↳ Refuse vague language: “maybe,” “kinda,” “circle back.” ↳ Tie every decision to one measurable and one de-risking action. Use this micro-template in Slack/Email: ↳ Decision: Launch pricing test at $X for Segment Y ↳ Why now: Competitor moved, CAC rising ↳ Options: A/B/C (trade-offs noted) ↳ Risks: Churn ↑, margin ↓, confusion → Mitigations: FAQ, support script ↳ Metric: Net revenue per signup ↳ Review: 14 days, DRI: Pat Three moves you can make today: ✅ Pick one stalled decision and set a 24-hour clock. ✅ Write a one-page decision note and share it for alignment. ✅ Assign a DRI to every open decision and schedule the review. Hope this helped! How could it be improved? 👇 ♻️Repost & follow John Brewton for content that helps. ✅ Do. Fail. Learn. Grow. Win. ✅ Repeat. Forever. ⸻ 📬Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗in profile).

Monday’s post about Dr. Bose’s 50-year innovation journey sparked some discussion. More than one asked: “How do you know when to be patient vs. when to kill a project?” That’s exactly what I break down in today’s video: “Why ‘Fail Fast’ Innovation Advice Is Wrong.” After decades making billion-dollar innovation decisions, I’ve identified 5 critical elements that determine when an idea deserves patience: ✓ Mathematics Validation (Does the science work?) ✓ Ecosystem Readiness Assessment ✓ Market Evolution Analysis ✓ Sustainable Advantage Evaluation ✓ Organizational Patience Capacity These became core to my decision making framework after witnessing how quarterly reporting pressure killed promising breakthrough innovations at HP. ✅ Watch the full framework breakdown video: https://lnkd.in/dbMddfJD ❓What’s your biggest challenge in balancing patient innovation with business pressure? You can find the full post on Dr Bose in Studio Notes at: https://lnkd.in/dJsVKiY4 #InnovationDecisions #DecisionFrameworks #FailFast #InnovationStrategy #BusinessDecisions #TechLeadership #InnovationLeadership