29: Expected Value - Chapters 2,3 & 4

In this second episode we move from illusion to understanding. Chapters two through four unpack the real performance gap in innovation and introduce the X V system, a data-informed way to calculate the expected value of ideas. We explore how confidence, value, and time sensitivity come together to create a measurable forecast of innovation performance.

Transcript

Speaker 1: 0:00

What's a perfect? Uh uh uh uh uh uh uh uh uh what's a park?

Simon: 0:07

Uh-uh uh uh in this second episode we move from illusion to understanding. Chapters two through four unpack the real performance gap in innovation and introduce the X V system, a data-informed way to calculate the expected value of ideas. We explore how confidence, value, and time sensitivity come together to create a measurable forecast of innovation performance.

Speaker 2: 0:33

We've been counting all the pilots, posting metrics on the wall, but when the CFO is asking where's the value in it all.

Simon: 0:44

Chapter 2 From Theatre to Performance Innovation has a visibility problem. Innovation. Everyone loves the idea of it. Everyone, mostly, agrees it's important. But when it comes to tracking what matters, performance, outcomes, value, it becomes murky, soft, sometimes even performative. This isn't because innovation leaders don't care about results, it's because the system around them is set up to track activity, not impact, to track velocity, not value. Freya delivered the kind of update many innovation leaders are familiar with. The numbers look good, more pilots, more engagement, more participation, but when David, the CFO asked, What's the value of all this? The room fell silent. Freya didn't fail because her work was bad. She failed because the system didn't give her a way to express its value in language the business trusted. And this is where most innovation programs get stuck. They speak in ideas, stories, energy and effort. But executive decision makers are trained to listen for return, contribution, confidence, and risk. Innovation insights the innovation communication gap. Consider how a head of sales communicates. We added fifteen new logos in Q3 representing four point two million dollars in annual recurring revenue with a sixty eight percent confidence in retention based on similar client cohorts. Now consider how a typical head of innovation communicates. We ran four pilots in Q3 with positive user feedback and growing engagement metrics across business units. The difference isn't just in the metrics, it's in the entire language of performance. The measurement crisis in modern innovation. The disconnect Freya experienced is playing out in organizations worldwide, from startups to global enterprises across every sector. Countless surveys of business leaders reveal that while most consider innovation critical to their organization's future, typically less than 25% express any confidence in their ability to measure its impact. Research also shows that innovation budgets are assessed primarily through activity metrics rather than outcome metrics. This isn't a new problem. In fact, it's been masquerading as the nature of innovation for decades. Innovation is inherently uncertain and thus hard to measure, becomes both an explanation and an excuse. But the truth is more concerning. Innovation measurements hasn't evolved while nearly every other business function has transformed its relationship with data, evidence, and performance. This measurement gap is even more glaring in the era of agentic AI and cognitive offloading. While other business functions increasingly use AI to process complexity and identify patterns, innovation often remains stuck in rudimentary measurement approaches. Instead of leveraging these tools to understand the complex relationships between activities and outcomes, many innovation teams continue to track surface metrics that reveal little about actual value creation. How did we get here? A brief history of innovation measurement. The innovation measurement problem didn't emerge overnight. It evolved alongside changing perspectives on innovation itself. Nineteen fifties to nineteen sixties, the R and D era. Innovation was largely synonymous with research and development. Measurement focused on technical outputs, patents filed, papers published, and product features delivered. Innovation was the domain of technical experts, isolated from business metrics. nineteen seventies to nineteen eighties, the process revolution. As stage gate and other structured development processes gained popularity, innovation measurement shifted toward process efficiency, time to market, throughput metrics, and milestone achievement. The assumption was that a good process would yield good outcomes. nineteen nineties to two thousands The Portfolio Approach Organizations began balancing innovation portfolios across horizons, core, adjacent, transformational, and measuring the balance of investments. However, these frameworks often remained more theoretical than operational. twenty tens The Lean Startup Influence Eric Rees and Steve Blank introduced concepts like validated learning and minimum viable products. This led to more experiment-based metrics, but often without systematic approaches to aggregate learning into organizational value. As AI compressed innovation cycles and raised the stakes, the need for stronger measurement systems became acute. Activity-based metrics proved inadequate in an era where value creation and destruction happen at unprecedented speed. Throughout this evolution, one pattern persisted. We kept adding new innovation methods without fundamentally upgrading how we measure innovation's contribution to business performance. What's needed now isn't another innovation process, but a system that can make any innovation process more value visible and decision intelligent. Innovation metrics are broken. The problem isn't a lack of metrics, it's that we're using the wrong ones. Here's what we typically see in corporate innovation metrics. One vanity metrics for example number of ideas submitted. This measures motion, not value. two activity metrics, for example innovation events held, hackathons run, pilots launched. These ignore relevance and miss true value metrics. three cultural metrics, for example, engagement scores, participation levels in innovation activities, these can be indicatively useful, but they are far from sufficient. four anecdotal metrics. One big win becomes the case study of performance. Isolated proof is not systemic validation. These aren't inaccurate, but they're incomplete, and when they're used as the only way to justify investment, they set innovation up to fail. Consider the most common innovation metric, idea count. Organizations proudly report that their innovation platforms or campaigns generated thousands of ideas. But quantity has almost no correlation with innovation impact. In fact, research suggests that organizations with the highest idea volume often struggle most with execution. They've optimized for ideation without building the muscle for selection or implementation. While companies count internally generated ideas, the most valuable innovations often emerge from users solving their own problems in their own local context and frame. These user innovations which represent some of the richest sources of breakthrough potential remain invisible to conventional metrics. The activity illusion. Activity metrics create a comfort zone for innovation teams, workshops facilitated, design sprints run, prototypes built, pilots launched. Each creates the appearance of progress without necessarily delivering value. The activity illusion is particularly seductive because it aligns with how innovation is often visualized, as a funnel or pipeline with ideas flowing from left to right. But this visualization itself reinforces the problem. It suggests that innovation is primarily about movement, about pushing things through stages rather than about making intelligent selections, building confidence and creating value. This approach fails to acknowledge that innovation isn't a linear process, but follows an S-curve pattern, with different stages requiring different metrics and management approaches. Ideas in emergence need different measures than those in acceleration or maturity. Yet most organizations apply the same metrics regardless of where innovations sit in their life cycle. The cultural bypass. Cultural metrics like engagement scores and participation rates are not inherently problematic. Innovation does require a supportive culture. The issue arises when cultural metrics become substitutes for performance metrics when people are excited about innovation is presented as evidence that innovation is working. This creates what we might call the cultural bypass, the belief that if we get the culture right, the results will follow automatically. But this isn't how innovation succeeds. Culture is necessary but insufficient. Toyota's famed culture of continuous improvement works because it's paired with rigorous measurement systems for quality, efficiency, and value. Without those systems, cultural enthusiasm alone would produce minimal impact. What's needed isn't just culture but performance tuning, systematic approaches to improving efficiency, reliability, and fit. This requires metrics that capture not just activity or engagement, but the effectiveness of the innovation system itself in generating and capturing value. Multiple perspectives the innovation measurement standoff The innovation measurement gap looks different depending on where you sit in the organization. The CFO's view I'm not against innovation, I'm against black boxes. When every other function can show me the trajectory of value creation, why should innovation get a pass? Give me something I can believe in, not just something I have to take on faith. The innovation leaders frustration. They want certainty in a function that's designed to explore uncertainty. How am I supposed to predict the ROI of something that's never existed before? And if I only pursue ideas with guaranteed returns, am I really innovating at all? The CEO's dilemma I need disruptive innovation to secure our future, but I can't justify unlimited exploration with no accountability. If there's no way to measure innovation effectiveness, how do I know if we have the right team, the right approach, or the right focus? The frontline innovators reality. I've got three great ideas, but I don't know which one to prioritize. My team is stretched thin, my budget is limited, and every stakeholder has a different opinion. Without some way to assess relative value, I'm just going with the idea that has the most political backing. The expected value system bridges these perspectives, creating a common language that honors both the uncertainty of innovation and the need for performance visibility. The impact gap. At the core of this failure is a simple truth. Innovation outputs are not the same as innovation outcomes. Launching a pilot doesn't mean it was valuable. Getting engagement doesn't mean you solved a meaningful problem. Running fast doesn't mean you're running in the right direction. This is what Freya ran into. It is what hundreds of innovation leaders encounter every quarter when they're asked to justify their work to finance, strategy or the executive team. They bring activity metrics to an outcome conversation. The three disconnects. The value disconnect. Innovation activities aren't clearly linked to business value drivers revenue, cost, risk, growth, sustainability. The confidence disconnect. Innovation updates don't communicate confidence levels or how they're evolving based on evidence. The strategic disconnect. Innovation priorities aren't visibly aligned with enterprise strategic priorities. These disconnects don't just create frustrating meetings. They fundamentally erode trust in innovation as a business function. And when that trust erodes, the consequences are predictable budget cuts, talent attrition, and a retreat to safe incremental improvements that don't threaten the status quo. Metrics that focus solely on activity fail to distinguish between the over ambitious moonshot destined to fail and the under ambitious project that will never create substantial value. The innovation accountability spectrum Most organizations fall somewhere on what we might call the innovation accountability spectrum level one, the activity trap. Innovation is measured purely by outputs and activities. Success is defined as doing innovation stuff. Metrics focus on workshops held, ideas gathered, and pilots launched. No clear connection to business outcomes exists. Level two the anecdote stage Innovation teams rely on success stories and case studies to demonstrate value. Individual wins are highlighted, but there's no systematic approach to measuring the function as a whole. Success is defined through narratives rather than numbers. Level three The Process Focus Organizations at this level have structured innovation processes with stage gates and milestones. Metrics track progression through these stages but often measure compliance with the process rather than the value being created. Success is defined as following the innovation method correctly. Level four The Strategic Alignment Innovation metrics are connected to strategic priorities but still focus mainly on activities within strategic themes. Success is defined as doing innovation in the right areas without necessarily proving value contribution. Level five The Performance System Innovation is measured as a performance function with clear outcome metrics, confidence tracking and value contribution. Success is defined through demonstrated impact, strategic relevance, and efficient resource allocation. Most organizations hover between levels two and three, creating the innovation measurement gap that undermines their efforts. The expected value system is designed to help organizations reach level five. Reflection questions one, where does your organization currently sit on the innovation accountability spectrum? Two, which of the three disconnects is most problematic in your context? Three, what would change if your innovation function could speak the language of value and confidence? From activity to evidence. The path forward requires a fundamental shift in how we think about innovation measurement from activity to evidence. Old thinking is number of pilots launched. New thinking is the predicated value expected from each pilot. Old thinking is number of ideas submitted. New thinking is a weighted confidence in the innovation pipeline. Old thinking is we need a culture of innovation. New thinking is measuring cross functional value contribution to strategic outcomes. Old thinking is fail fast rhetoric. New thinking is structured learning, kill credits and a failure balance sheet. Old thinking is volume and motion. New thinking is traction, relevance, progress and value. We need to start tracking the expected value of our work, not just what we did, but what we believe this is worth and how that value is changing over time. This approach aligns with modern cognitive practices. With agentic AI tools increasingly handling the processing burden of complex information, human judgment and decision making become even more crucial. An effective measurement system offloads the computational elements, tracking, correlating pattern recognition to appropriate tools, allowing human stakeholders to focus on the judgment aspects of innovation, the nuanced decisions about where to invest and how to evolve. Improving innovation measurement isn't just about adopting new metrics, it requires fundamental shifts in how innovation leaders think about measurement itself. From justification to insight. Many innovation leaders view measurement primarily as a way to justify their existence, a defensive posture that creates the wrong incentives. The shift is toward measurement as a source of insight. What's working? What isn't? Where should we double down or pull back? From point in time to trajectory, traditional innovation updates provide static snapshots. Here's where we are today. The shift is toward trajectory thinking. Here's how our confidence and evidence are evolving over time and what that tells us about future value. From activity, pride to learning pride. Innovation teams often take pride in how much they've done. The shift is toward taking pride in how much they've learned, including what they've learned by stopping work that isn't generating value. From scoring to storytelling. Effective innovation measurement isn't just about having the right numbers. It's about telling the right story with those numbers. A story that connects innovation work to business outcomes in ways that resonate with different stakeholders. These mindset shifts don't happen automatically with new metrics. They require deliberate culture building, leadership alignment, and consistent reinforcement through rituals, language, and incentives. The issue wasn't lack of activity, nor was it a shortage of talent or ambition. The issue was visibility. We need a system. This book is not about adding another metric to your dashboard, it's about building a performance system for innovation. That means tools that give you visibility over time. Filters that test strategic fit before execution. Portfolio level KPEs that track real contribution. Cultures that reward intelligent failure, not only shiny wins. These tools work because they do three things that traditional innovation measurement doesn't. They track movement over time. Innovation is a journey, not a moment. Blend quantitative with qualitative, not everything that counts can be counted. Create shared language across functions, finance, ops, product, innovation, all aligned. Freya didn't fail because she didn't do the work. She failed because she couldn't show what the work was worth. That boardroom silence wasn't a reaction to her numbers. It was a reaction to the void her numbers left. And it's a moment many innovation leaders know far too well, the sense of standing on solid ground only to realize it's soft underneath. The metrics that once felt impressive suddenly feel irrelevant. Because the people you're speaking to, finance, strategy, the CEO aren't looking for activity. They're looking for impact, and that's where the illusion breaks. Innovation often operates in a haze of motion. There are ideas, pilots, design sprints, partnerships, hackathons. On the surface, it all looks dynamic and feels cool. But underneath it's often built on weak foundations, metrics that describe effort but say nothing about progress. Volume without value, movement without momentum. This isn't just frustrating. It's fatal. Because when the pressure comes, when budgets tighten, when the next crisis hits, only what's measurable and trusted survives. And innovation, for all its energy, still struggles to show up in a way that executives believe in the credibility crisis. We can trace this credibility gap to three problematic narratives that have become embedded in how organizations think about innovation one innovation is special. The belief that innovation can't or shouldn't be measured like other business functions creates a separate set of standards and expectations. two innovation is about creativity. The overemphasis on the creative aspects of innovation, ideation, design thinking, at the expense of its performance aspects, selection, execution, scaling. three innovation success is unpredictable. The fatalistic view that innovation outcomes are inherently random, making measurement futile. These narratives aren't just incorrect, they're actively harmful. They position innovation as perpetually exceptional, perpetually exempt from the disciplines that drive sustainable business performance. The reality is that innovation success isn't random. It follows patterns that can be observed, measured, and improved through deliberate performance tuning. Just as a Formula One team continuously adjusts their car for optimal performance, organizations can tune their innovation systems for optimal value creation, finding the right balance of efficiency, reliability, and fit. As we close this chapter and prepare to dive into Freya's journey toward a solution, one thing should be clear. The innovation measurement problem isn't just an annoying obstacle, it's an existential threat to innovation's legitimacy within organizations. In Act 2, we'll discover the building blocks of the expected value system. How XV provides a dynamic way to assess both value potential and our confidence in it. How the strategic fit framework ensures strategic fit alongside value potential. How the S-curve helps position innovation bets at the right moment in their life cycle. Together, these tools don't just measure innovation differently, they change how innovation decisions are made, resources are allocated, and value is understood across the organization. This isn't about making innovation more bureaucratic, it's about making it more believable. Not just to executives, but to everyone involved in creating the future of the business. Because when innovation becomes measurable, it becomes manageable. And when it becomes manageable, it becomes unstoppable. That's why this isn't a call for new buzzwords or bolder decks. It's a call for a system, a performance system that gives innovation the same tools every other business function relies on. Visibility, confidence, telemetry, and contribution. One that shifts the conversation from outputs to outcomes, from excitement to evidence, from busy work to belief. In the next chapter we return to Freya. The meeting didn't go the way she planned, but it planted something, a restlessness, a refusal to keep measuring motion when what the business needs is meaning. And so she begins to look elsewhere for a different way to understand her work, her team's potential, and the bets her organization is making every day without knowing it. She doesn't yet have a name for it. But she's about to ask a question that will change everything. What if we could track what an idea is worth? But before she could track what innovation was worth, she needed something even more fundamental. That evening, Freya sat at her kitchen table, laptop open, staring at a search bar. The question felt almost embarrassingly basic for someone who'd been leading innovation for eighteen months. What is innovation? She'd built processes, hired teams, launched initiatives, but David's challenge had exposed an uncomfortable truth. If she couldn't clearly define what innovation was, if her organization couldn't agree on what actually counted as innovation versus mere activity, how could she ever measure its value? The cursor blinked, waiting. She typed the question and hit enter. TLDR Most innovation programs suffer from measuring the wrong things, vanity metrics, activity counts, and cultural engagement rather than actual value creation. This creates a critical gap between innovation outputs and business outcomes. Organizations need to shift from tracking volume to demonstrating confidence weighted predicted value, from celebrating engagement to measuring strategic contribution, and from focusing on process compliance to learning quality and performance tuning. Chapter 3 Not everything new is innovation. Invention is possibility. Innovation is performance. Freya sat at her kitchen table with her screen aglow. David's question from the boardroom still echoed. What's the value of all this? She'd realized they needed a system to measure innovation's worth, but first she needed something more fundamental. How could they measure the value of innovation if they couldn't even define what innovation was? So, somewhat embarrassingly, she had just typed a deceptively simple question into Google What is innovation? The answers came fast and left her more confused than ever. From a leading consultancy, innovation is the process of translating an idea or invention into a good or service that creates value or for which customers will pay. Pay Freya frowned and thought to herself So on that basis our internal process improvements that save millions, but customers never see aren't innovation. That automated testing system Dennis built that transformed our development cycle but never touched a customer. That doesn't count. From a tech giant, innovation is about staying ahead of the curve and anticipating customer needs before they know they have them. It's all about predicting the future, she wondered. But what about penicillin? Indoor plumbing, solutions to problems everyone already knew they had. From two dictionaries, the use of a new idea or method, and a new idea method or device novelty. She muttered to herself New to whom? New to the world, new to our industry, new to our company. This definition includes everything from CRISPR gene editing to color coding your calendar. From an academic journal, innovation is the successful exploitation of new ideas. She almost laughed. Define successful, define exploitation, define new. Every word is a rabbit hole. From a government innovation lab, innovation means doing things differently and doing different things to deliver better outcomes. This could describe literally any change, is switching from Times New Roman to aerial innovation now. From a startup accelerator, innovation equals invention multiplied by commercialization. Elegant, she admitted, clean, mathematical. But then she thought, what about Wikipedia, Linux, the polio vaccine that Sulk refused to patent? What about that internal dashboard that revolutionized our decision making but will never see a market? From a design agency. Innovation is creativity that ships. Punchy. Memorable. But what about Japan's kaizen? Tiny, uncreative improvements that compound into transformation. What about that wildly creative app we piloted that shipped, flopped, and created zero value? From a business school, innovation is the embodiment, combination or synthesis of knowledge in original, relevant and valued new products, processes or services. Freya rubbed her temples. Original according to whom, relevant to what context, valued by which stakeholders. This definition is so broad it's almost meaningless. The more she read, the more the confusion compounded, each definition crystal clear in isolation, maddeningly incompatible together. Innovation was simultaneously a product, a process, a mindset, or a system always new except when it's not customer facing or purely internal. About creating value but for whom? Inherently creative or just methodically better? No wonder David questioned their value, no wonder she couldn't answer his challenge. They couldn't even agree on what they were measuring. Three hours into her research dive, just as frustration peaked, Freya encountered a definition that stopped her scrolling. Innovation is something new or improved that creates or redistributes value. She read it three times. The simplicity was almost shocking, not just new, not just creative, not just technological. The definition demanded performance, actual value creation or redistribution. This wasn't philosophical wordplay, it was crisp, simple, succinct. New or improved, acknowledging that innovation didn't require radical novelty. Creates or redistributes, recognizing that value could be generated fresh or shifted from one place to another. Value, the non negotiable outcome that separated true innovation from mere activity. She closed her laptop and grabbed a marker. On her whiteboard, she wrote the definition large enough to see from across the room. Innovation is something new or improved that creates or redistributes value. Innovation is change that matters. But even as clarity dawned, new questions emerged. If innovation required value creation, how could they assess whether their current initiatives would actually deliver value? How could they predict which activities would successfully transform into innovation? The next morning's innovation showcase felt different. Freya watched the demonstrations with new eyes, her discovered clarity making the performance even more obvious. Welcome to where the magic happens, announced the innovation lab manager, gesturing at walls covered in colourful sticky notes. We're ideating on breakthrough concepts, black blue sky thinking, no bad ideas. Freya studied the wall. Uber for pet food, Netflix for textbooks, Airbnb for parking spaces. Every idea was just successful company for random industry. These are all just copies of existing models, she thought. And more importantly, where's the evidence any of them will create value? We're taking proven innovation and applying it to new verticals, the lab manager explained enthusiastically when someone asked about originality. Freya bit her tongue. They'd turned innovation into a performance complete with beanbag chairs and walls of sticky notes. Innovation theatre at its finest. Later in the monthly review she watched as solid improvements were dismissed one by one. The team reduced server costs by forty percent through optimization, someone reported. That's efficiency, not innovation, came the response. Customer service created a new workflow that cut resolution time in half. Process improvement, not innovation. We developed an internal tool that's saving five hours per week for finance teams. Incremental improvement? Where's the breakthrough innovation? Freya felt her frustration rise, but now it was paired with understanding. They'd fetishized novelty while dismissing actual value creation. The customer service tool was already delivering two hundred sixty thousand dollars in annual savings through productivity gains. Meanwhile, Project Phoenix, their celebrated AI prototype, had consumed eight hundred thousand dollars without delivering a penny of value or even evidence of future value. Which one was actually innovation? That evening Freya sat in her office surrounded by her notes. The paradox was maddening. Everyone agreed innovation was critical. No one agreed what it was. It was like building a house without agreeing on what house meant. Some thought mansion, others thought cottage, a few insisted on spaceship. She pulled up a document and started typing. We've turned innovation into a performance metric instead of a practice. We've confused the map for the territory, the metric for the goal, the appearance for the reality. The word innovation has become so diluted, so overused, so poorly defined that it's lost all meaning. It's everything and nothing. It's whatever we need it to be to justify our decisions, impress our stakeholders, or fill our presentations. This isn't just semantic nitpicking. When we can't define innovation, we can't measure it. When we can't measure it, we can't manage it. We end up in innovation theatre performing elaborate rituals that create the illusion of progress while real opportunities pass us by. She paused, then added. But if innovation equals value creation, then we need a way to assess which of our activities are likely to become true innovations. We need to predict value, not just hope for it. The next morning Freya arrived early. She needed space to think. She mapped out what they'd been measuring, covering her whiteboard with their current metrics. Number of ideas generated. Patents filed prototypes built technologies deployed research papers published Design Thinking Workshops held Hackathons completed. Standing back she saw it clearly, we're measuring ingredients, not meals. The data science team's demo from yesterday was a perfect example. They'd been so excited about Project Phoenix, their new generative AI prototype, it could write full product descriptions from simple sketches, learn user style, improve with feedback. They had working code, live demonstrations, even some early user tests. But when she'd asked the simple question, what's the value outcome? The room had fallen silent, mumbles about efficiency and automation, but no concrete projections of save time, reduced costs, or increased sales. They had invention, technology, even creativity, but no clear path to value creation. Not yet. Freya mapped out the fundamental inputs that organizations often confuse with innovation itself. She drew ten circles on her whiteboard labelling each one. The visualization illustrated the ten essential components that organizations often mistake for innovation itself. At the center is true innovation, something new or improved that creates or redistributes value, surrounded by the inputs that contribute to but don't independently constitute innovation. One invention, the novel idea or device. Two research. The systematic investigation that generates knowledge. ten ecosystem the external networks and partnerships. While each of these elements is essential to the innovation process, none qualifies as innovation on its own until it contributes to demonstrable value creation. This distinction forms the foundation of the expected value system, measuring not just innovation activity, but innovation outcomes. Freya stepped back from the board. Ten circles, ten essential ingredients, but none of them innovation on their own, not until they combined to create or redistribute value. The implications cascaded through her mind. Every initiative in their portfolio needed reexamination through this lens. Take Project Phoenix, their generative AI platform Technology State of the Arguage models Creativity Novel applications for content generation. Research extensive studies on implementation approaches Capital eight hundred thousand dollars invested over eighteen months. But when she asked the brutal question what value has it created or redistributed? The answer was uncomfortable. Zero. They had a sophisticated capability looking for a problem to solve. Contrast that with the customer success automation tool technology, basic scripting, nothing revolutionary. Creativity minimal, just connecting existing systems. Capital, fifteen thousand dollars and three weeks of Dennis's time. Value created two hundred sixty thousand dollars annually in time savings. That was innovation not because it was new to the world, but because it performed. We've been blinded by brilliance, she said aloud to the empty office, seduced by sophistication, while real innovation, unglamorous, practical, value creating innovation, has been happening under our noses. That afternoon Freya called an emergency team meeting. The skepticism was palpable as she unveiled her new framework The Innovation Litmus Test one. Can you identify specific value to be created or redistributed? two can you measure that value in concrete terms? three, do you have evidence this will actually happen? four. Is there a clear path from current state to value delivery? If the answer to any question was no, they had innovation inputs, not innovation. The pushback was immediate. But we need to experiment, argued a team member. Not everything has clear value from day one. Absolutely, Freya agreed. But then let's be honest about what we're doing. Early experiments are about building confidence in value potential. They're innovation investments, not innovations. Let's measure them differently. This will kill creativity, someone muttered from the back. Will it? Freya challenged. Or will it channel creativity toward problems worth solving? There's infinite creativity in finding ways to create real value. A team member raised her hand. So the automation tool that everyone dismissed as boring Is one of our only true innovations this quarter, Freya confirmed. Because it's actually creating measurable value today, not someday, not maybe. Now the room shifted. She could see minds recalibrating. But how do we assess the others? Another team member asked. The ones with potential but no current value. Freya paused. This was the key question. We need a way to evaluate expected value, to quantify our confidence that these inputs will successfully convert to actual innovation. Not just gut feeling, but structured assessment. That evening Freya reflected on the day's revelations. The innovation paradox wasn't that innovation was hard to achieve, the paradox was that by mythologizing it, by turning it into something magical and mysterious, they'd made it impossible to manage. She pulled out her notebook and wrote We now know innovation equals value creation or value redistribution, but how do we predict which activities will successfully deliver value? We need a scoring system that captures how confident we are based on evidence. How much value we expect when that value might materialize? Whether the requirements fit our capabilities, something that turns hope into hypothesis, gut feeling into structured assessment. She thought about other fields that dealt with prediction under uncertainty. Finance had models for expected returns, insurance had actuarial tables, even weather forecasting had probability scores. Surely innovation could have something similar. As she turned off the lights one thing was clear, they'd finally defined what innovation actually was. Now they needed to figure out how to predict it. The foundation was set. The definition was clear. Next would come the discovery of how to measure what didn't yet exist, the expected value of innovation. Too long didn't read. Innovation isn't invention, research, technology, creativity, or any other input alone. It's the outcome when these inputs create or redistribute value. Freya lands on a clear definition, something new or improved that creates or redistributes value, which cuts through the confusion of countless competing definitions. Most organizations measure innovation inputs, ideas generated, patents filed, workshops held, rather than innovation itself leading to innovation theatre. By focusing on value creation as the defining characteristic, we can begin to distinguish true innovation from mere activity. This clarity sets the stage for developing a system to predict which activities will successfully convert to value creating innovations. Freya sat at a corner table in the cafe waiting for Axel to arrive. Saw this and thought of you. She'd immediately texted back. Now, as she checked her watch, she wondered if this sports analytics approach could really translate to her innovation challenges. It seemed promising, the idea of measuring potential rather than just outcomes, but would it hold up to scrutiny? The bell above the cafe door jingled, and Axel strode in, scanning the room before spotting her. Unlike Freya's corporate polish, Axel had the casual confidence of someone whose currency was ideas rather than impressions. His laptop bag was worn at the edges, and he had that slightly distracted look of someone who might have been solving equations in his head on the walkover. Sorry I'm late, he said. Giving her a brotherly hug, and then dropping into the chair across from her, got caught in a rabbit hole looking at more of those football analytics models. I ordered you a flat white, she said, pushing the cup toward him. Thanks. So why did that article hit such a nerve with you? Axel asked, wrapping his hands around the warm cup. Your message seemed pretty urgent. Freya took a deep breath. I'm in a bit of a professional crisis, to be honest. I haven't really talked to you about my work lately. The innovation director gig at Nexus, right? I thought that was going well. It was until last week. She recounted the boardroom presentation, the slides full of metrics and activities, David's pointed question about value and her inability to provide a satisfying answer. What I hear is you've been measuring activity, not impact, Axel summarized. Exactly. Idea submissions, hackathons launched, pilots running, but when the CFO asked what it was all worth, the actual value of all this innovation work, I had nothing concrete. Just stories and anecdotes. Freya sighed. I've been leading innovation for eighteen months, and I can't answer the most basic question about what it's delivering. And you think this expected goals concept might help? I'm not sure, but something about it resonated. The article talked about looking beyond immediate results to measure the quality of performance that should eventually lead to goals. That's exactly what I need. A way to show the expected value of our innovation work even when we don't have final outcomes yet. Axel nodded, understanding dawning in his eyes. So that article accidentally hit on exactly what you're struggling with. Serendipity, Freya said with a small smile. Sometimes you send the right thing at the right time without even knowing why. Let me explain how it works in football, Axel said, leaning forward. Goals are rare events. A team might dominate a match, create fifteen perfect chances, and still lose on a flute goal. If you evaluated players just on goals and assists, you'd miss the real value creation. Like my innovation problem, Freya said. We might have great ideas that don't immediately translate to measurable outcomes. Exactly. In football, researchers and data scientists develop something called expected goals XG. Instead of just counting actual goals, they measure the quality of chances created. Each shot gets a probability score based on position, defensive pressure, angle to goal, all these factors that influence whether a team should score, not just counting when they do. Freya nodded, already connecting dots. They're measuring the quality of the process, not just the outcomes. Right. And here's where it gets interesting for your problem, Axel continued, pulling out his phone to show her a graph. The clubs that use this don't panic when results don't match the underlying performance. If a team is creating high quality chances but not scoring, they know the goals will eventually come. They make decisions based on the expected value, not just the current results. Freya stared at the phone, the possibilities already taking shape in her mind. That's exactly what I need, a way to show the expected value of our innovation work even when we don't have final outcomes yet. The cafe lights flickered briefly, the universal signal that closing time was approaching. Freya glanced at her watch, surprised to see how late it had become. I think we can adapt this to innovation, Axel said. But we'd need to work out the components that create innovation value, the way shop position and angle create goal probability. Freya was already gathering her things. The cafe's closing, but I'm not ready to stop. Come to my office, nobody will be there this late, and we'll have the whiteboards to ourselves. I want to break the back of this tonight. Axel grinned. I don't have any other plans. Let's do it. Fair warning, Freya said as they stepped out into the evening air. We might be there late. I work best at night anyway, Axel replied, and this is the most interesting problem I've tackled in months. By the time they reached the Nexus Global Office the building was nearly empty. A few lights still burned on various floors, but the innovation lab that Freya had set up was dark and quiet. Perfect, she said, flipping on the lights and revealing a large space with multiple whiteboards, flexible furniture, and a small kitchen area in the corner. Coffee machine works and there's a takeaway menu drawer if we need sustenance later. Axel immediately gravitated to the largest whiteboard. Let's start with what we know from the XG model and translate it to innovation, he said. Confidence and value. They began by identifying the two most fundamental components. In football, Axel explained, drawing on the whiteboard, expected goals combine the position of the shots with the probability of scoring from that position. In innovation, we need something similar. Freya nodded. We need the predicted value, what it's worth if it succeeds, weighted by our confidence in that outcome. Exactly, Axel said, writing on the board. Expected value equals confidence times predicted value. They spent the next two hours defining what confidence meant in innovation terms. Not a gut feeling, but an evidence based assessment across multiple dimensions, technical feasibility, user demand, market size, and operational viability. Confidence isn't a guess, Axel insisted. It's a measure of what you've validated. Freya grabbed a marker and started listing factors. Technical feasibility validated User needs verified Market size confirmed Business model tested Organizational capability assessed regulatory barriers examined. Confidence is composite, she said. It's not one score. It's a roll up of multiple dimensions of evidence. They settled on a zero point one to one point zero scale. A score of zero point one meant we believe this could work but have tested almost nothing. A score of one point zero meant we've thoroughly validated all key assumptions. By nine PM they had ordered Thai food and were testing their initial two factor model against several projects from Freya's active portfolio. As they reviewed the results, Freya spotted a pattern. The confidence weighted value works, she explained, pointing to two projects with similar scores, but it treats all opportunities as equally timely. Project A might be worth one million dollars with fifty percent confidence. Project B might be identical in those terms, but if A needs to launch in three months, while B has a two year window, they're not equivalent. Axel nodded, immediately seeing the issue. We need a time factor, a multiplier for urgency. They debated the scale, eventually settling on zero point seven to one point five, zero point seven for valuable but not time sensitive, Freya explained. One point zero for standard timing, and up to one point five for now or never opportunities. With that, their formula evolved. Expected value equals confidence times predicted value times time. Sensitivity. The security guard making his rounds poked his head in around ten thirty PM, surprised to find them still working. Freya waved and explained they'd be late. He nodded and continued on, leaving them to their increasingly crowded whiteboards. Shortly before midnight, Freya stood back from the board frowning at their latest calculations. Something's still missing, she said. I've been mentally scoring our projects and some high scoring ones feel wrong. They might be valuable in abstract, but they don't align with our capabilities or strategic direction. Axel watched as Freya mapped projects on a second whiteboard, clustering them by strategic alignment. What if we had a fourth component, Axel suggested, a measure of strategic fit? Yes, Freya agreed. Some ideas score well on confidence, value and timing but feel wrong for us. They're outside our capabilities or far from our strategy. They spent the next hour defining what strategic fit meant, alignment with corporate priorities, leverage of existing capabilities, and contribution to competitive advantage. By one AM fueled by their third round of coffee, they had added the fourth dimension to their formula. XV equals confidence times predicted value. Times time sensitivity. Time strategic fit. XV, Freyer asked, noticing the change in notation. Expected value, Axel replied. Like XG in football, but for ideas. The efficiency breakthrough. They were both tired now but running on the adrenaline of these breakthroughs. As they applied their four factor formula to more examples, Freya had another realization. Wait, she said suddenly, stopping mid pace, me we're missing something crucial. Axel turned from the board. What do you mean? Think about it. Two projects might have the same XV, but if one costs ten times more to develop, they're not really equivalent, are they? She moved to the whiteboard and added below the formula. Cost per XV point equals total investment divided by XV. This changes everything, Axel said, his eyes widening as he processed the implications. Traditional R and D might spend one hundred thousand dollars to achieve an XV of five hundred thousand dollars. That's twenty cents per X V point. But what if we could achieve the same XV for ten thousand dollars? Through open innovation, freelance experts or challenge platforms, Freya continued, excitement building despite the late hour. That would be two cents per XV point, a ten times efficiency advantage. They spent another hour refining the complete framework, creating what would become their standard visualization. This isn't just about measuring expected value, Freya said, studying their work. It's about understanding the economics of innovation. We can finally answer not just what's it worth, but what's it worth relative to what it costs? It was nearly three AM when they finally packed up. The whiteboards were filled with equations, matrices, and examples. Freya took photos of everything with her phone. There was no way she was erasing this work. As she snapped the photos, Freya thought about Matilda's challenge when she'd hired her. Make innovation a business capability, not just a creative exercise. For the first time Freya felt like she had the framework to do exactly that. Let's test it, Freya said, stifling a yawn but still energized by what they'd created. I'll pull together real data tomorrow. Four concrete examples from our portfolio. Axel nodded, gathering his things. Send me the details. I want to work through the calculations myself. As they left the building, the night guard giving them a bemused look, Freya felt a sense of achievement she hadn't experienced in months. In one marathon session they'd created not just a metric but the foundation of an entire system for understanding innovation value. We still need to refine it, she said, as they parted ways on the street. The strategic fit component especially needs more structure. One step at a time, Axel replied with a tired smile, but I think we've broken the back of it. Freya nodded. We have. And David's not going to know what hit him. The next day, despite the late night, Freya arrived at the office early. She pulled together data on four key initiatives from her portfolio and prepared the information to send to Axel. Let's test it, Freya said to herself, pulling out her laptop. Real ideas, real numbers. She opened her portfolio dashboard and selected four initiatives, excluding both the customer service project and Project Phoenix, the AI prototype. Project Alpha, an AI powered customer service assistant. Predicted value if successful. Two million dollars in cost savings and revenue. Current confidence low zero point three. Still in concept phase with key assumptions untested. Time sensitivity. Normal one point zero. No urgent competitive or market pressures. Strategic fit zero point eight. Reasonable alignment, but some capability gaps. Development approach. Internal RD Estimated cost. Four hundred fifty thousand dollars. Project beta. An internal process automation. Predicted value if successful. Five hundred thousand dollars in efficiency gains. Current confidence. High zero point eight. Pilot has shown strong results. Time sensitivity elevated one point three. Aligned with upcoming system migration. Strategic fit. One point one. Strong alignment across all dimensions. Development approach. Internal team with some external support. Estimated cost. Seventy-five thousand dollars. Project Gamma. A blockchain-based verification system. Predicted value if successful. Four million dollars in new revenue streams. Current confidence. Very low. Zero point two. Highly speculative major technical hurdles. Time sensitivity. Normal one point zero. Emerging market with no immediate pressure. Strategic fit. Zero point six. Weak strategic alignment. Limited advantage. Development approach. External partnership. Estimated cost. Two hundred fifty thousand dollars. Project Delta. A customer facing mobile app enhancement. Predicted value if successful. Three million dollars in new revenue. Current confidence. Moderate zero point five. Concept validated with users. Technical approach unclear. Time sensitivity. Low zero point seven. Market adoption likely eighteen to twenty-four months away. Strategic fit zero point nine. Good but not perfect alignment. Development approach. Open innovation challenge. Estimated cost. $35,000. After he'd signed an NDA, Freya sent the data to Axel. When they met again that afternoon, Axel had already worked through both the XV formula and efficiency calculations for each. Project Alpha. XV calculation is zero point three confidence times two million dollars predicted value times one point zero times sensitivity times zero point eight strategic fit gives an expected value of four hundred eighty thousand dollars. Cost per X Vat point Point. four hundred fifty thousand dollars cost divided by four hundred eighty thousand dollars expected value derives an XV efficiency of zero point nine four. Project beta colon dot X for calculation is zero point eight confidence times five hundred thousand dollars predicted value times one point three times sensitivity times one point one strategic fit derives an XV efficiency of five hundred seventy two thousand dollars. Cost per X V point seventy five thousand dollars estimated cost divided by five hundred seventy two thousand dollars expected value derives an X V efficiency of zero point one three dollars Project Gamma X FIV calculation is zero point two confidence times four million dollars predicted value times one point zero times sensitivity times zero point six strategic fit derives an expected value of four hundred eighty thousand dollars cost per X V point two hundred fifty thousand dollars estimated cost divided by four hundred eighty thousand dollars expected value derives an X V deficiency of fifty two cents. Project Delta Sviv calculation is zero point five confidence times three million dollars predicted value times zero point seven times sensitivity times zero point nine strategic fit derives an expected value of nine hundred forty five thousand dollars cost per X V point point thirty five thousand dollars estimated cost divided by nine hundred forty five thousand dollars expected value derives an XV efficiency of four cents. Freya stared at the numbers astonished This completely changes our view. Look at Delta, it has the highest XV despite the low time sensitivity, and it's by far the most efficient at just 0.04 dollars per XV point. Because you're using open innovation, Axel noted, the efficiency multiplier is dramatic. Meanwhile, Project Alpha has decent XV but terrible efficiency at 0.94 per point, you're spending almost as much as the expected value you're creating. And beta, Freya observed, has strong efficiency at 0.13 per point because of the high confidence and strong fits, even though the absolute value is smaller. The efficiency lens reveals something else, Axel said, creating a quick chart. If you had five hundred thousand dollars to invest, traditional thinking might put it all into alpha but look what happens with efficiency based allocation. Traditional approach favors the highest XV all five hundred thousand dollars invested in project alpha total XV generated four hundred eighty thousand dollars Efficiency We are losing value, spending one dollar to make ninety six cents using the efficiency based approach thirty five thousand dollars for Project Delta with an XV of nine hundred forty five thousand dollars seventy five thousand dollars to Project Beta with an XV of five hundred seventy two thousand dollars two hundred fifty thousand dollars for Project Gamma with an XV of four hundred eighty thousand dollars one hundred forty thousand dollars allocated for partial investment in project alpha or held in reserve total XV generated one million nine hundred ninety seven thousand dollars efficiency three point nine nine to four times value multiplication. We generate over four times more expected value with the same investment, Freya breathed. This is the strategic advantage we've been missing. And it gets better, Axel added as you build confidence through open innovation approaches, your cost per confidence point is also dramatically lower. It might cost one hundred thousand dollars to build confidence from zero point three to zero point eight internally but only ten thousand dollars through targeted external challenges From insight to system Over the next week Freya refined the model. This wasn't just polishing what they had created it involved substantial development work. one confidence scoring matrix She developed a detailed assessment grid for the confidence component with specific evidence criteria for each zero point one increment on the scale. For example, to move from zero point three to zero point four, confidence required documented user validation through structured interviews rather than anecdotal feedback. two time sensitivity guidelines she created decision trees to help teams consistently evaluate time sensitivity, considering market windows, competitive threats and internal deadlines three Value calculation templates For the predicted value component she built spreadsheet models for different innovation types cost saving, revenue generating, risk reducing, ensuring teams had standardized approaches to estimating predicted value four dynamic dashboard She developed a simple dashboard to track XV over time, showing how confidence and other components evolved with new evidence. five Assumption mapping She created templates for mapping assumptions to evidence, tracking which aspects of the confidence score were supported by data versus still hypothetical. Freya brought her brother into the team officially, offering him a freelance contract to continue their collaboration. Axel accepted, sharing her enthusiasm for the potential of what they were building. Together they tested the refined model on her team's active portfolio, calculating XV and efficiency metrics for 20 different initiatives. The results were revealing and sometimes uncomfortable. Ideas they'd championed showed weak XV due to low confidence or poor efficiency. Others they'd neglected suddenly looked promising when the cost per X V point was considered. As Axel had suggested she also considered how both XV and efficiency might be adapted across the S curve. For early stage ideas in the emergence phase they might accept higher cost per X V point for critical learning. For accelerating innovations efficiency became paramount targets under 50 cents for mature initiatives the focus shifted to optimization and maintaining sub $1 efficiency. This could be enhanced with AI, Axel suggested during a check-in agentic systems could help identify the most cost effective approaches to building confidence, predict where efficiency gains are possible and even automatize certain validation experiments. I can see that, Freya agreed it's called cognitive offloading letting AI handle the optimization while humans focus on judgments and strategic decisions. With each calculation the pattern became clearer the combination of XV and efficiency wasn't just about measurement it was about fundamentally rethinking innovation economics. Later that night she opened a new page in her notebook. She didn't draw arrows this time she just wrote innovation isn't unpredictable it's just under instrumented and it doesn't have to be expensive it can be radically efficient. And for the first time in months she felt like she was on the right trajectory with the tools to prove it. Before presenting to her team Freya met with Axel once more. She'd been working with the model for days and something was nagging at her formula is strong, she said, but I'm realizing the strategic fit component needs more development than our initial framework provides. Axel looked up from his laptop. What's missing? We've established that it's a crucial component of the formula, Freya explained, but we've only defined it at a high level alignment with strategy, fit with capabilities and contribution to advantage. What we haven't done is create a structured assessment framework for scoring it consistently Ah, Axel replied we need a systematic way to evaluate and score our strategic right to win. In other words, to answer the question is this a good idea for Nexus? Exactly, Freya said. Right now, different team members might score the same innovation completely differently based on their understanding of strategic fit. We need clearer dimensions, consistent questions and scoring guidelines, similar to what we developed for the confidence component. That makes sense, Axel nodded. The strategic fit needs to be as evidence based as the other components. But even with just these components we've already transformed the conversation from trust me to here's what we believe, why we believe it, and how efficiently we can develop it. She gathered her notes, energized by a clarity she hadn't felt in months. The anxiety of the boardroom felt distant now, replaced by a sense of purpose. David asked what all this was worth, she said. Now I can tell him not just the expected value but the cost per unit of value we're creating. That's a language any CFO understands. She knew this was just the beginning. The formula provided a lens to start seeing value differently but they still needed to develop the complete system one that would fully incorporate strategic fit, life cycle positioning and all the other dimensions that would make this truly operational. We need to transform this from an insight into a comprehensive operating model, she said. And that's the real work ahead, Axel agreed, but we've laid the foundation and what a foundation it is not just measuring value but understanding the economics of creating it efficiently That would be the real test building the complete system that could transform how innovation was measured, managed and valued with efficiency as a core strategic advantage rather than an afterthought innovation insights Why XV changes everything The expected value XV concept represents a fundamental shift in how innovation value is understood, even in this early developmental stage. Unlike traditional stage gate metrics that track activities, the emerging XV approach focuses on four critical elements What we believe an idea could be worth predicted value how confident we are in that belief confidence score how timing affects the opportunity time sensitivity how well it fits our organization strategic fit. This dynamic measurements evolves with learning and evidence, transforming innovation from a static judging process into a continuous learning system. Most importantly by adding the efficiency dimension cost per XV point the system reveals not just what innovations are worth but how economically they can be developed often showing a 10 to 20 times advantage for open innovation approaches over traditional RD. TLDR Freya collaborates with her data scientist brother Axel to develop Expected Value XV, a dynamic scoring model inspired by sports analytics. During one intensive all night session they build a comprehensive formula combining four factors confidence based on evidence, predicted value if successful time sensitivity and strategic fit. Crucially they add an efficiency dimension, cost per X V point, revealing that open innovation approaches can be 10 to 20 times more efficient than traditional RD $5 to $5 versus $10 to $50 per X V point. When applied to real projects the system transforms portfolio decisions by showing how the same budget can generate four times more expected value through smarter resource allocation. This systematic approach evolves innovation assessment from static forecasts to dynamic signals that reflect both value potential and development economics.