Importance of Experimentation in Business Strategy

Most companies claim they embrace failure. But walk into their Monday meetings, and watch people scramble to hide their missteps. I've seen it countless times. The same leaders who preach 'fail fast' are the first to demand explanations for every setback. Here's the uncomfortable truth:  Innovation dies in environments where people feel safer playing it safe. But there's a difference between reckless failure and strategic experimentation. Let me show you exactly how to build a culture that genuinely embraces productive failure: 𝐂𝐡𝐚𝐧𝐠𝐞 𝐲𝐨𝐮𝐫 𝐩𝐨𝐬𝐭-𝐦𝐨𝐫𝐭𝐞𝐦 𝐦𝐞𝐞𝐭𝐢𝐧𝐠𝐬 Stop asking "Who's fault was this?" and start asking: "𝘞𝘩𝘢𝘵 𝘩𝘺𝘱𝘰𝘵𝘩𝘦𝘴𝘪𝘴 𝘸𝘦𝘳𝘦 𝘸𝘦 𝘵𝘦𝘴𝘵𝘪𝘯𝘨?" "𝘞𝘩𝘢𝘵 𝘴𝘱𝘦𝘤𝘪𝘧𝘪𝘤 𝘥𝘢𝘵𝘢 𝘥𝘪𝘥 𝘵𝘩𝘪𝘴 𝘧𝘢𝘪𝘭𝘶𝘳𝘦 𝘨𝘪𝘷𝘦 𝘶𝘴?" "𝘏𝘰𝘸 𝘤𝘢𝘯 𝘸𝘦 𝘶𝘴𝘦 𝘵𝘩𝘪𝘴 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 𝘧𝘰𝘳 𝘰𝘶𝘳 𝘯𝘦𝘹𝘵 𝘪𝘵𝘦𝘳𝘢𝘵𝘪𝘰𝘯?" 𝐂𝐫𝐞𝐚𝐭𝐞 '𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭 𝐬𝐡𝐨𝐰𝐜𝐚𝐬𝐞𝐬' Monthly meetings where teams present their failed experiments and the insights gained. The key? Leaders must go first. Share your own failures openly, specifically, and without sugar-coating. 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐭𝐡𝐞 "24-𝐡𝐨𝐮𝐫 𝐫𝐮𝐥𝐞" After any setback, give teams 24 hours to vent/process. Then require them to present three specific learnings and two potential next steps. This transforms failure from a dead end into a data point. Most "innovative" teams are just risk-averse businesses in disguise. They've mastered innovation theater, not actual innovation. Don't let your people think they need permission to innovate. Instead, start building systems and a culture that make innovation inevitable.

**We do not learn from EXPERIENCE. We learn from EXPERIMENTS.** Experience alone is often mistaken as the source of learning, but simply going through an event doesn't guarantee growth or insight. What truly drives learning is treating our decisions as experiments, where we are consciously testing assumptions, observing outcomes, and adjusting based on the lessons learned. So what makes a good experiment vs. a bad one? 1. **Clear Purpose:** A good experiment starts with a clear objective. What are you trying to understand or improve? Before taking action, define your goal. A bad experiment is one that’s vague and unstructured—aiming to ‘see what happens’ without a specific purpose in mind. 2. **Testing Assumptions:** A good experiment identifies specific assumptions to test. These are hypotheses about how certain actions or decisions will lead to particular outcomes. For example, “If I improve our product’s delivery time, customers will stay loyal.” A bad experiment skips over this step, not clarifying what assumptions are being tested, which makes learning difficult to measure. 3. **Methodology:** A well-designed experiment has a clear method. This includes knowing how you'll collect data, what actions you’ll take, and how you'll measure success. For instance, you might conduct surveys, track customer retention rates, or analyze financial data over time. A bad experiment has no structure or strategy in place to evaluate results. 4. **Adaptability:** Even when experiments don’t produce the desired result, they are still valuable—if we can learn from them. An experiment is only “ba” when it stops when the results aren’t what we wanted, without reflecting on the outcome and adjusting the approach accordingly. ## A Warning: Don’t Skip the Minimally Viable Experiment (MVE) Entrepreneurs often rush into creating a Minimally Viable Product (MVP), as popularized by the Lean Startup methodology. While an MVP is crucial for testing your product with real users, I believe there is an important step before this—often skipped—that could save entrepreneurs a lot of time and money. I call it the **Minimally Viable Experiment (MVE)**. An MVE is a small-scale test where you validate key assumptions or ideas before building even a basic version of your product. It helps you determine whether your concept holds water without investing significant time and resources upfront. By conducting an MVE, you ensure you're not prematurely jumping into product development, which might lead to wasted efforts if the idea doesn’t perform as expected. By treating every decision as an experiment—starting with an MVE—you create a structured learning process. Each test becomes an opportunity to gain insights and adapt, whether or not the result aligns with your expectations. The next time you face a decision or a product development idea, ask yourself: What assumption am I testing? How can I structure this as a MVE before moving to the MVP stage? #mvp #mve

GTM Systems teams CANNOT be order-takers in the AI era. Innovation won’t come from requirements - it will come from experiments. And these teams must evolve into true Product orgs. I see this as the most overlooked challenge with adopting AI in GTM Systems. A successful strategy means you need to move FAST. Experiment. Prototype. Iterate. This is the default standard in Product culture. The Problem: this approach runs counter to Biz Tech culture. Salesforce & Internal Tools experts will hear this and say I’m crazy. “You need strict governance & careful planning to scale systems infrastructure.” And previously, I would completely agree. But the AI era is a different beast for a few reasons. 1) Teams don’t know what’s possible or what they want from AI. • Success is judged by behavior change, not completion of a backlog item. • The value of AI will emerges through usage and iteration • New features will not result from traditional requirements gathering. 2) AI has completely shifted the delivery timetable. • Historically, the goal is to craft a long-term GTM Systems roadmap. • Then, you break key initiatives into months long implementation cycles. • But AI innovation is moving too fast to only ship 1x in 3 months. • Companies need to adopt a rapid experimentation mindset. 3) You CAN move fast by investing in composability. • An API-first approach allows you to ship outside core infrastructure. • Previously, all new feature build happened in tools like Salesforce. • You’re constrained by technical debt, dependencies, and more. • Now, you can deploy AI solutions in isolation. • An app that communicates to other systems via API is relatively low risk. Realistically, this approach will make most Biz Tech teams uncomfortable. Rapid experimentation historically led directly to scalability issues. But this is the default way of operating for core Product teams. A few ways they get it right without leaving a wake of technical debt: 1) Use MVPs with clear scope • Ship measurable slices of value to learn, not solve a whole problem up front. 2) Invest in composability • Every test is built with future modularity in mind - winning ideas can scale. 3) Leverage Users for Research • Stakeholders & Users are a source of insights, not requests. • It’s the old Henry Ford quote: “If I asked people what they wanted, they would have said faster horses.” 4) Document Assumptions • Experiments have clear hypotheses - learn from every test, even if it fails. GTM Systems teams have the opportunity to lead innovation like never before. AI is delivering the much-needed attention and investment in this function. And for the first time, they are less constrained by stakeholder requests. These teams can finally DRIVE strategy, not just support it. But success will depend on their ability to embrace this new approach. __ #AI #GTM #CRM

How do you plan when uncertainty only seems to grow? Through embracing disciplined experimentation. Here’s new writing from our Partner Charlotte Desprat on the five-step process we use to make a company great at it: 1. First, establish what you know as fact and what you don’t know – including the X-factors that could upend your plans. 2. From there, tease out the key hypotheses that you want to test. Keep in mind that some hypotheses might be more fundamental than others, and therefore might need to be tested earlier. 3. Then, consider how you might investigate each of these hypotheses using the scientific method. How can you break hypotheses into small, easily-testable components? Depending on the degree of unknowns, a rapid-fire approach might be enough to determine the key components of change. 4. Once you’ve designed your experiments, consider the time, cost, and risk associated with each. Together with the importance of each hypothesis, decide which experiments must come first vs. later. This will give you a priority list to adjust along the way. 5. Finally, set up a system by which you can quickly capture learnings and adjust. Obtain tangible measurements from these experiments. Your system should include a way to decide which experiments to follow up with, know if more are needed, and determine when you’ve learned enough from a given test. Critically, it should include a mechanism to end new ideas. Remember that about 80% of venture capital investments fail, and yet venture capitalists earn higher return on capital employed than public companies; their secret is that most of their failures come early, quickly, and cheaply. By treating experimentation as a discipline, not a one-off, you can capture the upside of uncertainty. That will be one of the most important capabilities to win in a turbulent future. Interested in our book chapter on experimentation? Click here for a direct download: https://lnkd.in/eAnUrC2t

Leading with humility, not just authority -- In a world of constant disruption, what’s the biggest risk a leader can take?  It is believing they have all the answers. I was reminded of this by Tim Harford’s classic TED talk on trial, error, and the "God Complex."  For those of us driving strategy in complex organizations, his message is more relevant than ever. It's not about having the perfect plan; it's about building a system that finds the best plan. My key takeaways for any leader today: - Challenge the "God Complex": True leadership isn't about being infallible. It's about fostering a culture of psychological safety where your best people are empowered to challenge assumptions and point out the blind spots you inevitably have. - Embrace Rapid Iteration: Harford’s Unilever example—developing a nozzle through 45 prototypes—is brilliant. The goal isn't a perfect first draft; it's a rapid learning cycle. Value progress over perfection. - Treat Failure as Data: Every "mistake" is simply a data point telling you what doesn't work. When we build systems that measure outcomes and learn from them without blame, we aren't failing—we're getting smarter, faster. - Build an Evolutionary Engine: Your strategy should be designed to evolve. Instead of placing one huge bet, place many small, intelligent ones. Let real-world results—not just boardroom theory—pick the winners. Leadership isn't about having the map; it's about building a better compass. How do you build experimentation into your team's DNA? #Leadership #Experimentation #AdaptiveStrategy #LearningCulture #Innovation #RRD #BusinessResilience #ContinuousImprovement