38. Expected Value - Chapter 15 & Epilogue

This is final episode of this season and the closing chapter in the Expected Value Story. In this episode, we look at the future of innovation. Innovation without adaptation is just novelty. The future belongs to those who can continuously evolve their innovation system. 

Over this season, we've followed Freya and her team as they've moved from good intentions and busy innovation to clearer decisions, better evidence, and real value. But no system ever stands still. The world keeps changing, and the real question becomes how do we change with it without losing what we've built? 

As we prepare to say goodbye to Freya, she is asked a simple but powerful question, what's next? And her answer isn't about more ideas or bigger bets. It's about building an organization that can keep learning, adapting, and creating value, even when the future refuses to behave.

Transcript

SPEAKER_01: 0:00

What's a birthday? Uh uh uh uh uh What's a Barthy?

SPEAKER_03: 0:07

Uh uh uh uh Welcome back to the final episode of this season and the closing chapter in the Expected Value Story. In this final episode, we look at the future of innovation. Innovation without adaptation is just novelty. The future belongs to those who can continuously evolve their innovation system. Over this season, we've followed Freya and her team as they've moved from good intentions and busy innovation to clearer decisions, better evidence, and real value. But no system ever stands still. The world keeps changing, and the real question becomes how do we change with it without losing what we've built? As we prepare to say goodbye to Freya, she is asked a simple but powerful question, what's next? And her answer isn't about more ideas or bigger bets. It's about building an organization that can keep learning, adapting, and creating value, even when the future refuses to behave. Let's dive in. The future belongs to those who can continuously evolve their innovation system. Innovation has long been the most talked about, least measured, and most misunderstood word in the business lexicon. We've called it strategy, culture, capability, creativity, survival. We've rewarded volume over value, activity over impact, theatre over truth. And in doing so, we've built a global innovation infrastructure that is fundamentally fragile, but it doesn't have to be this way. Freya looked out at the faces around the boardroom table. It was her third anniversary presentation, and she'd been asked to address a simple but profound question. What's next? We've made remarkable progress, she began. Our XV system has transformed how we prioritize opportunities. Our portfolio approach has balanced our risk profile, our governance model has reduced friction while maintaining alignment, and our learning loops have accelerated how quickly we iterate and adapt. She paused, letting the summary of their journey sink in. But the world isn't standing still, and neither can we. Freya clicked to the next slide titled The Future of Value Creation. The innovation landscape is shifting beneath our feet. I'd like to discuss five forces that will reshape how we create value over the next decade and how we need to evolve our system to stay ahead.

SPEAKER_02: 3:24

From periodic to perpetual innovation.

SPEAKER_03: 3:30

The first major shift Freya identified was in the rhythm of innovation itself. We've traditionally thought about innovation as periodic, discrete projects with clear beginnings and ends. But increasingly value comes from perpetual innovation, from systems that continuously adapt and evolve rather than projects that complete and stabilize. This shift was evident in how their most successful innovations were developing. What began as specific solutions were evolving into continuously improving systems that created expanding value over time. Their supply chain optimization initiative, for example, hadn't stopped at implementing its initial recommendations. Instead, it had evolved into a perpetual innovation engine, constantly identifying new opportunities for efficiency and resilience through real-time data and machine learning. The most valuable innovations aren't those that solve a problem once, but those that create systems that continuously solve evolving problems, Freyer explained. This perspective changed how they thought about investment horizons and success metrics. Rather than focusing exclusively on immediate returns, they were increasingly valuing the creation of perpetual innovation capabilities, systems that could continuously adapt and create value over extended periods. In the future, we won't distinguish between innovation and operations as separate activities. The most effective operations will have innovation built into their DNA continuously evolving rather than periodically transforming.

SPEAKER_02: 5:16

From expected to realized value.

SPEAKER_03: 5:21

This perpetual innovation approach directly addresses one of the most significant challenges in innovation management, Freya continued, the gap between expected and realized value. Expected value is a prediction. Realized value is the reality, she explained, quoting their finance director. What matters isn't how good our predictions are, it's how effectively we translate those predictions into actual business outcomes. Leading organizations were developing more sophisticated approaches to tracking this translation process, creating what some called value realization pathways that connected early confidence assessments to eventual business impact. Value traceability models, systems that track how initial value estimates evolve through development, launch, and scaling phases, creating transparency into where value leaks or amplifies. Benefit realization frameworks, structured approaches to identifying, measuring, and capturing projected benefits with clear accountability for value delivery. Value inflection mapping, identifying critical transition points where value creation accelerates or decelerates, creating focused management attention at these moments. A retail banking group demonstrated the power of this approach. They developed a comprehensive value realization system that tracked innovations from initial concept through full implementation. Each initiative carried a value passport that evolved as the innovation progressed, documenting how confidence, evidence, and value projections changed over time. Most importantly, Freya emphasized, the system continues tracking for 24 months after implementation, creating accountability for actual value delivery rather than just successful launch. This approach increased their realized value capture from 38% to over 70% of projected value. We used to celebrate when an innovation launched, Freya quoted their finance director. Now we celebrate when an innovation delivers its projected value. That's a fundamental shift in mindset and behavior.

SPEAKER_02: 7:47

From individual to ecosystem innovation.

SPEAKER_03: 7:58

We've traditionally thought about innovation as something that happens within our organization, led by our people, leveraging our capabilities, Freya noted. But increasingly, the highest value innovation emerges from ecosystems, not individual organizations. This reality was already reshaping their approach. Rather than trying to own all the capabilities needed for innovation, they were focusing on orchestrating networks of partners, suppliers, customers, and even competitors to co-create solutions. Their sustainable packaging initiative illustrated this approach. Rather than developing materials internally, they had created a consortium of material scientists, manufacturing partners, recycling experts, and regulatory specialists. Together, they had developed a solution far beyond what any single organization could have created alone. The innovation leader of the future isn't the organization with the most internal capabilities, but the one that can most effectively orchestrate ecosystem capabilities to create value, Freya explained. This ecosystem perspective required new approaches to collaboration, intellectual property, and value sharing. It wasn't enough to create value. They needed to ensure that value was appropriately distributed to sustain ecosystem participation. We need to shift from asking how do we capture maximum value to how do we create and share value in ways that strengthen our ecosystem.

SPEAKER_02: 9:38

From human-led to human AI collaboration.

SPEAKER_03: 9:44

The fourth shift Freya identified was perhaps the most profound, the evolution of human AI collaboration in innovation. Artificial intelligence isn't just another tool in our innovation toolkit, she explained. It's a fundamental shift in how innovation happens, from purely human-led to human AI collaboration. This shift was already visible in their work. AI systems weren't just automating routine tasks, they were becoming active partners in the creative process, identifying patterns humans might miss, generating novel combinations of ideas, and accelerating learning cycles. Their product design team had experienced this transformation first hand. What once took weeks of iterative prototyping could now happen in days through AI-enabled generative design. The AI didn't replace human creativity, it amplified it, allowing designers to explore far more possibilities and learn from each iteration. The future of innovation isn't humans versus AI or even humans using AI as a tool, Freya noted. It's humans and AI as collaborative partners, each bringing unique strengths to the creative process. Freya shared a framework that captured this new relationship. The most sophisticated innovation systems are now evolving beyond the false dichotomy of human versus machine to create integrated decision environments that combine the distinctive strengths of both, she explained. This integration had four key dimensions. One AI expands the possibility space. AI systems can explore vastly more potential solutions than humans alone, considering combinations and approaches that might never occur to even the most creative teams. This expanded exploration prevents premature convergence on familiar paths. two, humans apply contextual wisdom. People bring critical judgment about organizational context, implicit knowledge, and values that AI cannot fully replicate. Human wisdom ensures solutions are not just technically viable, but organizationally appropriate. three. AI accelerates evidence gathering. Intelligent systems can continuously scan for signals related to key assumptions, providing real time updates to confidence assessments across multiple dimensions simultaneously. four. Humans guide ethical boundaries. People set the normative constraints and priorities that ensure innovation serves human needs and values. This essential governance role cannot be delegated to algorithms. This partnership creates a powerful cognitive division of labour, Freya continued, where each party handles what it does best. AI optimally handles pattern recognition across vast data sets, simulation of multiple scenarios in parallel, continuous monitoring of weak signals, identification of non-obvious connections between initiatives, and bias detection in decision patterns. Humans optimally handle, setting strategic intent and priorities, making value judgments and ethical trade-offs, applying organizational wisdom and politics, managing stakeholder relationships, and interpreting contextual nuance. The new innovation leader's role, Freya concluded this section, shifts from direct decision maker to system architect, designing the interfaces between human and machine intelligence to maximize combined performance.

SPEAKER_02: 13:36

From efficiency trailing to efficiency leading.

SPEAKER_03: 13:43

The final shift Freya highlighted reflected a fundamental change in how organizations think about innovation economics. Traditionally, we've thought about efficiency as something that comes after innovation. We innovate first, then optimize later, she explained. But the future belongs to organizations that lead with efficiency, making it a primary design principle rather than an afterthought. The efficiency imperative 10 to 20x as the new normal. As they look to the future of innovation performance, perhaps no factor would be more critical than radical efficiency improvement. The organizations that thrive won't be those with the biggest innovation budgets, but those that generate the most value per dollar invested. Freya had recognized this early in her transformation journey, but it was during a strategic foresight workshop with the executive team that the full implications became clear. We've been thinking about innovation success in terms of absolute returns, she explained, projecting a series of graphs showing innovation performance across industries. But the real competitive advantage isn't coming from spending more, it's coming from extracting dramatically more value from each dollar spent. The data showed something striking. Organizations that had achieved the highest innovation ROI weren't necessarily investing more in total, but they were generating ten to twenty times more value per unit of investment than their peers. This isn't just about cost cutting, Freya emphasized, it's about a fundamental shift in innovation economics that will separate leaders from followers over the next decade. The convergence of several forces made this efficiency revolution inevitable. One. Open innovation maturity. When Freya first introduced open innovation approaches, many viewed them as experimental supplements to traditional RD, but as these approaches matured, something remarkable happened. They consistently delivered 10 to 20 times efficiency advantages over conventional methods. A challenge to redesign a critical component that would have cost$180,000 and taken nine months through traditional development was solved through an innocentive challenge for just$12,000 in under six weeks, a 15 times efficiency improvement. 2. AI powered efficiency. The second force accelerating the efficiency revolution was artificial intelligence, not just as a technology to be deployed, but as a fundamental amplifier of innovation capacity. AI doesn't just accelerate innovation, it dramatically reduces the cost per experiment, per iteration, and per insight, Freya noted. Early adopters report 50 to 80% reductions in innovation development costs. The team had seen this firsthand. Their AI-enabled insight engine could analyze thousands of customer interactions overnight, extracting patterns that would have taken a traditional research team months to identify. Their simulation tools could test hundreds of product variations virtually, compressing physical prototyping cycles by 90%. 3. Ecosystem economics. The third force reshaping innovation efficiency was the evolution of ecosystem approaches. Traditional innovation models emphasized ownership of capabilities and control of intellectual property. The emerging model emphasized orchestration of capabilities and strategic management of knowledge flows. When organizations shift from owning all capabilities to orchestrating them, the economics transform, Freyer explained. Why pay for full-time expertise when you can access world-class capabilities on demand? This ecosystem approach had allowed the team to tap into specialized capabilities that would have been prohibitively expensive to maintain internally from quantum computing expertise to advanced material science to cutting edge behavioral economics. four. The new competition landscape As the efficiency imperative took hold, the entire competitive landscape for innovation was being reshaped. Organizations that continue to measure innovation success purely by spending levels or activity metrics would find themselves at a structural disadvantage. In this future, Freya told the executive team, organizations compete not on innovation spending, but on innovation efficiency. The question shifts from how much can we invest to how much value can we create per dollar invested? five. The efficiency velocity advantage Perhaps most powerful was the compounding effect of efficiency and velocity together. When organizations could learn and create at radically lower costs, they could explore more paths, run more experiments, and take more calculated risks with the same resources. Efficiency doesn't just reduce costs, Freyer explained, it fundamentally changes how many shots on goal you can take, how quickly you can pivot, and how boldly you can explore. The evolution of innovation measurements. As innovation practice became more sophisticated, the metrics used to measure it needed to evolve. The simple counts of the past, ideas generated, patents filed, products launched were giving way to more nuanced systems that captured the dynamic, nonlinear nature of innovation value creation. Freya highlighted several emerging measurement approaches. One, system performance metrics. Rather than evaluating individual innovations in isolation, leading organizations were developing measures of how the entire innovation system works. Functions, including learning efficiency ratio, how much validated learning is generated per unit of investment, confidence calibration, how accurately teams confidence assessments match actual outcomes over time. Portfolio renewal rate, how effectively the organization replaces declining S curves with emerging ones. two network effect measurements As innovation increasingly happened across organizational boundaries, new metrics were emerging to capture ecosystem performance. Ecosystem value contribution, how much value is created by the organization's innovation network beyond direct internal returns. Collaboration multiplier, how much additional value is generated through external partnerships compared to internal only approaches. Knowledge flow indicators, how effectively insights and capabilities move across organizational boundaries. three retrospective quality metrics Building on the table of justice concept from earlier chapters, organizations were developing more sophisticated approaches to measuring decision quality decision consistency score how aligned actual decisions are with the stated principles and criteria of the innovation system Learning utilization rate how effectively lessons from past initiatives are applied to new decisions Kill Quality Index How cleanly and efficiently underperforming initiatives are terminated and their resources reallocated Digital Twins for Innovation Systems One of the most promising frontiers in innovation performance, Freya continued, is the emergence of digital twins for innovation systems, virtual replicas of an organization's innovation portfolio, processes, and performance patterns. Just as manufacturers use digital twins to simulate and optimize physical systems, forward-thinking organizations were beginning to create living models of their innovation ecosystems. These models captured the relationships between initiatives, resources, capabilities, and strategic objectives, allowing leaders to test scenarios, identify constraints, and optimize portfolio composition. The expected value system provided the essential foundation for these digital twins. By quantifying confidence, value potential, strategic fits, and learning velocity, it created the structured data needed to build meaningful simulations and predictive models. Early implementations of innovation digital twins showed significant promise. Portfolio optimization, identifying optimal resource allocation across initiatives based on confidence trajectories, strategic alignment and capability requirements. Learning acceleration, simulating different experiment sequences to determine which would build confidence most efficiently across the portfolio. Risk visualization mapping interdependencies between initiatives to identify cascade effects if certain assumptions prove false. Strategic alignment, testing how changes in strategic priorities would affect portfolio value and identifying adaptation strategies.

SPEAKER_02: 23:53

Evolving the system, not just the solutions.

SPEAKER_03: 23:59

As Freya wrapped up her presentation, she emphasized that these shifts wouldn't happen automatically. They require deliberate evolution of their innovation system. The systems we've built have served us well, she acknowledged, but they need to evolve to capture the opportunities ahead. She outlined several concrete adaptations they were making. One XVI two point zero enhancing their expected value framework to incorporate platform potential, ecosystem value and efficiency metrics. two portfolio expansion moving beyond project based portfolios to include platform investments, ecosystem partnerships, and capability development. three adaptive governance evolving from stage gate processes to continuous adaptation cycles that could respond to rapidly changing conditions. four enhanced learning systems integrating AI powered learning acceleration and cross ecosystem knowledge sharing. five cultural evolution developing new mindsets and skills for platform thinking, ecosystem collaboration, human AI partnership, and efficiency led innovation. The greatest innovation risk isn't betting on the wrong solutions, Freya concluded. It's failing to evolve our innovation system to match the changing landscape of value creation.

SPEAKER_02: 25:35

The innovation metabolism.

SPEAKER_03: 25:41

As the executive team discussed these shifts, the CEO offered a powerful metaphor. What you're describing isn't just a set of tactics or strategies, he observed. You're talking about developing what we might call an innovation metabolism, the ability to continuously convert ideas into value, learning into adaptation, challenges into opportunities. Freya nodded. That's exactly right. The organizations that thrive in the future won't be those with the best innovation process or the biggest innovation budget. They'll be those with the healthiest innovation metabolism, the ability to continuously sense, respond, learn, and create value in a rapidly changing world. This metabolism perspective reframed innovation not as a function or a process, but as a fundamental organizational capability, the ability to continuously adapt and create value in response to changing conditions. Our job isn't just to deliver specific innovations, Freya concluded, it's to build an organization that can continuously innovate, not as a special activity, but as its natural way of operating.

SPEAKER_02: 26:58

Implementation pathways starting where you are.

SPEAKER_03: 27:05

As the presentation concluded, the executive team engaged in a wide ranging discussion about the implications of these shifts. The CEO eventually brought the conversation back to practical next steps. This is compelling, he acknowledged. But how do we move forward? Where do we start? Freya had anticipated this question. She displayed a simple framework titled The Continuous Evolution Path. One, measure current metabolism, assess the organization's current capacity for continuous adaptation and value creation across each dimension. two identify metabolic constraints. Determine which aspects of the innovation system are limiting overall performance and adaptability. three Design strategic interventions. Develop targeted interventions to address the most significant constraints and accelerate evolution. four create rapid learning cycles. Implement interventions through short focus cycles with clear learning objectives. five. Scale what works, rapidly expand successful approaches while continuously identifying the next constraints. The beauty of this approach, Freya explained, is that we don't need to transform everything at once. We can evolve progressively, starting where we'll create the most impact.

SPEAKER_02: 28:39

Different paths for different organizations.

SPEAKER_03: 28:45

Freya acknowledged that moving from concept to practice required different approaches based on organizational context. She outlined specific implementation pathways for four common starting points. For large enterprises with established innovation functions, their challenge is often coordination across complex structures and overcoming entrenched measurement systems. Key steps include one portfolio assessments, apply the XV model retroactively to existing innovation portfolio. two executive alignment workshop, establish shared language around confidence based assessments and expected value. three governance pilots implement the three tier governance model for a distinct subset of the portfolio. four value bridge construction create explicit connections between innovation metrics and existing strategic KPIs. five AI enhancement plan develop a roadmap for integrating AI tools that augment human judgment. For mid market companies seeking innovation structure, their advantage is flexibility combined with sufficient resources. Their implementation path should focus on one leadership calibration exercise conduct workshops where leaders score sample initiatives to reveal implicit assumptions. two lightweight X V Implementation Introduce simplified X V scoring for all new initiatives. three Learning rituals establish regular portfolio reviews focused explicitly on confidence movement. four Strategic fit mapping create a clear strategic fit radar template aligned to critical priorities. Open innovation ecosystem develop relationships with external solver communities and talent networks for startups and small organizations, their constraints are time and resources, but their advantage is agility. They should start with one streamlined confidence framework. Adopt a simplified confidence assessment with three dimensions. two weekly learning reviews implement short, focused sessions tracking what's been validated or invalidated three FITSelf assessment. Develop an honest strategic fit profile to guide opportunity selection. four dynamic resource pool allocate fifteen to twenty percent of available resources to a flexible fund redistributed based on confident signals. five AI productivity enhancement leverage accessible AI tools for market signal analysis and assumption testing for public sector and non profit organizations, their context often emphasizes impact over financial returns with distinct governance considerations. one value definition workshop explicitly map what value means, including social, environmental and capability dimensions. two stakeholder inclusive governance adapt the governance model to include beneficiary perspectives three cross sector learning exchange establish relationships with organizations facing similar challenges four confidence transparency system create public facing documentation of confidence assessments. Five policy to delivery bridge develop explicit connections between policy objectives and innovation initiatives from innovation theatre to total innovation When we began this journey Freya reflected innovation had become theatre rich in activity but poor in measurable impact. We've since built a comprehensive system to transform innovation from performance art to performance discipline, from potential to proof the CEO nodded thoughtfully This feels right not a revolution but an evolution building on what we've accomplished while adapting to what's next she turned to the executive team I'd like each of you to work with Freya to assess your area's innovation metabolism and identify the highest leverage interventions. Let's reconvene in a month to align on our collective path forward The path forward three commitments As the meeting concluded Freya offered not a summary but an invitation to make three commitments that could transform how the organization approached innovation performance. Commit to evidence over opinion Pledge to base innovation decisions on structured assessment of confidence and evidence rather than intuition or influence. Create processes that explicitly separate what you know from what you believe and track how that relationship evolves over time. Commit to learning over activity promise to measure innovation not by volume of activity but by quality and speed of learning. Establish systems that reward insight generation, assumption testing and evidence building as much as or more than idea generation and implementation. Commit to system over projects Dedicate yourself to building innovation as an organizational capability rather than a collection of initiatives. Invest in the frameworks, rituals and cultural elements that enable sustained performance rather than occasional breakthroughs. These commitments aren't easy, Freya acknowledged they require courage to challenge established patterns, discipline to maintain structured approaches even under pressure, and patience to build capabilities that compound over time rather than deliver immediate results. But the organizations that make these commitments gain something invaluable the ability to create value consistently in conditions of uncertainty. They develop what might be called uncertainty advantage the capacity to navigate complexity more effectively than competitors to learn faster from both success and failure and to allocate resources with greater precision to opportunities with genuine potential The Innovation Performance revolution As Freya reflected on how far they'd come from the initial challenges of prioritization to building a comprehensive innovation system and now to evolving that system for the future she realized the journey wasn't ending. In many ways it was just beginning but they now had something invaluable a capacity for continuous adaptation that could evolve with changing conditions and emerging opportunities. Innovation doesn't have to be unpredictable performance doesn't have to be a guessing game value doesn't have to be retrospective. We can see it now, measure it now build it now and if we do we might just finally stop talking about innovation as the thing that might save us and start treating it like the system that already can in a world of accelerating change and increasing complexity that might be the most valuable innovation of all TLDR The future of innovation is being shaped by five major shifts from projects to platforms from periodic to perpetual innovation from individual to ecosystem innovation from human led to human AI collaboration and from efficiency trailing to efficiency leading organizations that thrive will develop an innovation metabolism the ability to continuously sense, respond, learn and create value in a rapidly changing world this requires evolving innovation systems to incorporate platform thinking, ecosystem collaboration, human AI partnership and radical efficiency improvements with organizations achieving 10 to 20 times gains in innovation efficiency becoming the new competitive standard. The ultimate challenge isn't just creating more but creating better solutions that contribute to sustainable prosperity and human flourishing. By committing to evidence over opinion learning over activity and system over projects organizations can develop the uncertainty advantage needed to consistently create value in an increasingly complex world Epilogue The Journey from here For more than a decade I've worked alongside innovation leaders inside global companies, startups, governments and everything in between and time after time I've watched brilliant work fall apart not because the ideas weren't good but because very few, if any, know how to talk about value in a way that speaks to the business in the way the business works. This disconnect between data and decision making isn't unique to corporate innovation. In sports analytics teams have discovered that sophisticated metrics are worthless without effective communication to the people who need to use them. In professional sports having the best data in the world generated and analyzed by the brightest minds in the sport is of little or no advantage if the signals it can send are not communicated well to the managers, the coaches, the scouts and the players who can benefit from it. Corporate innovation faces this same challenge, arguably a greater scale and higher stakes it's not enough to have better measurements. We need better interfaces between those measurements and the human systems they're designed to serve from idea to impact the value journey at its core this book has been about one simple truth ideas without impact aren't innovation, they're imagination The expected value system emerged from a recognition that the gap between creative potential and realized value isn't primarily about the quality of ideas It's about how we select, develop, and scale those ideas through disciplined learning and evidence-based confidence building. These organizations share a commitment to measuring what truly matters, confidence-weighted value across their innovation portfolios. As we conclude this journey, let's examine the critical elements of the path from idea to impact. Challenge framing. The critical first step. While we've explored numerous components of the expected value system throughout this book, perhaps the most foundational element, one that shapes everything that follows, is how we frame challenges. Properly framed challenges are the secret to successful innovation. They determine not just what solutions we consider, but how we evaluate success, who we involve and what value pathways we explore. This challenge-driven approach represents a fundamental shift in how innovation begins, from solutions first to outcomes first. Rather than starting with ideas looking for problems, we begin with clearly defined outcomes seeking the most effective paths. From internal expertise to diverse perspectives. By framing challenges rather than solutions, we open the aperture to insights and approaches from beyond our domain boundaries. From fuzzy goals to measurable outcomes, challenge framing forces explicit articulation of what success looks like, creating clearer evaluation criteria from the start. From technical focus to value focus. Challenges center on the value to be created rather than the technology to be deployed, preventing solution bias. The relationship between challenge framing and expected value is synergistic. Good challenge framing creates better inputs for XV assessment, while the XV framework provides structured discipline for evaluating solutions to well framed challenges.

SPEAKER_02: 42:10

Lead users The future in the present.

SPEAKER_03: 42:17

Throughout our exploration of the expected value system, we've emphasized the importance of evidence-based confidence building. One of the most powerful sources of such evidence comes from lead users, those who encounter future needs today and often create their own solutions to address them. The integration of lead user methodologies with the expected value system creates several powerful advantages. Accelerated confidence building patterns observed across lead users provide stronger early evidence of both problem importance and solution direction. Reduced market risk. Understanding solutions that lead users have already implemented reduces uncertainty about market acceptance. Expanded solution spaces lead users often develop approaches that traditional RD would never consider, expanding the innovation possibility space. Enhance value estimation. Lead user solutions provide tangible reference points for estimating potential value and adoption patterns. This emphasis on learning from those at the edges of need or capability creates another dimension of the expected value system, one that expands beyond internal opinion to incorporate diverse forms of user generated evidence.

SPEAKER_02: 43:40

From expected to realize value, closing the loop.

SPEAKER_03: 43:46

The ultimate test of any innovation system isn't how well it predicts potential, it's how consistently it delivers actual value. Throughout this book, we've emphasized that XV is not just a metric but a dynamic signal that evolves with learning. The most advanced practitioners of the expected value approach have developed sophisticated methods for tracking the journey from expected to realized value. Value realization pathways explicit mapping of how value hypotheses translate into actual business impact with clear accountability for each transition point. Learning velocity optimization systems for accelerating evidence gathering and confidence building, reducing the time between initial assessment and value capture. Scaling disciplines structured approaches to expanding successful pilots into full scale implementations, ensuring that proof of concept value translates into business level impact. Value evolution tracking, longitudinal measurement of how actual value compares to initial expectations, creating feedback loops that improve future assessments. This completion of the value loop is what distinguishes innovation rhetoric from innovation performance. It's not enough to have good metrics for potential value. Organizations require systems that consistently transform that potential into realized impact.

SPEAKER_02: 45:24

Navigating the S curve. A unified perspective.

SPEAKER_03: 45:32

As we have explored in chapter seven, the S curve provides the contextual framework for understanding how innovations evolve through their natural life cycle. This perspective is essential to implementing the expected value system effectively. Different positions on the S curve require different measurement approaches, learning strategies and resource allocation models. Emergence phase. Early stage initiatives require rapid, low cost experimentation focused on confidence building. Their XV scores will typically appear modest due to low confidence factors zero point one to zero point three, but their learning velocity becomes the critical measurement. Acceleration phase as confidence builds zero point four to zero point seven and initiatives gain traction, measurement shifts to scaling efficiency and market validation. Time sensitivity often peaks during this phase, creating urgency for resource allocation. Maturity phase. With high confidence established, measurement focuses on optimization, extension, and preparation for renewal. The balance shifts toward performance tuning alongside developing options for the next curve. The organizations that excel at innovation performance understand this life cycle pattern and adapt their measurement approach accordingly. They don't compare early stage initiatives with mature ones using the same metrics. They apply life cycle appropriate measurements at each stage. Most importantly, they systematically prepare for and execute S curve jumps, developing capabilities for the next curve while optimizing the current one. Their portfolio balance explicitly accounts for initiatives across multiple curves with deliberate resource allocation for exploration, acceleration, and optimization.

SPEAKER_02: 47:42

The Enablers How Innovation Performance Scales.

SPEAKER_03: 47:50

As we've explored the expected value system throughout this book, we've primarily focused on the core frameworks XV, Strategic Fit Radar, the S curve, learning loops, and portfolio governance. But as you implement these approaches in your own context, several enabling factors will determine how successfully they scale. Ecosystem integration beyond boundaries. The most effective innovation systems today recognize that value creation happens at the interfaces between organizations, disciplines, and communities. This integration manifests in three critical dimensions Open innovation networks, challenge broadcast to solver communities, and expert networks rapidly expand solution diversity and accelerate evidence gathering, increasing both the quality of ideas and the confidence in their assessment. Open talent models, treating expertise as a fluid resource rather than a fixed asset, allows even small teams to orchestrate much larger innovation efforts through on-demand access to specialized skills. AI Energentics, the integration of artificial intelligence, represents perhaps the most transformative enabler, not just as a technology to innovate with, but as a fundamental amplifier of innovation capability. AI systems can identify non-obvious patterns across thousands of initiatives, calibrate confidence assessments based on historical accuracy, simulate portfolio scenarios, and even autonomously perform certain innovation tasks, dramatically expanding what's possible without replacing human judgment. These enablers transform how organizations think about innovation capability, moving from owning all necessary expertise to orchestrating it effectively, from making decisions based on limited perspectives to incorporating diverse evidence and from resource constraints to possibility expansion.

SPEAKER_02: 50:01

Strategic binding from alignment to integration.

SPEAKER_03: 50:09

Throughout this book, we've emphasized the importance of connecting innovation to strategic priorities. Advanced practitioners have moved beyond simple mapping to create structural connections that make innovation inseparable from core business performance. Value driver integration directly connecting innovation initiatives to the specific value drivers in the company's business model and strategic plan. Risk portfolio balancing positioning innovation initiatives as deliberate hedges against both current and emerging risks on the enterprise risk register capability development pathways making explicit how innovation initiatives build critical future capabilities identified in strategic planning. Option creation logic framing certain innovations explicitly as strategic options that create future flexibility rather than immediate returns.

SPEAKER_02: 51:11

The systems approach beyond frameworks.

SPEAKER_03: 51:18

What truly makes the expected value approach different from previous innovation frameworks is how its elements work together as a coherent system. This systems perspective creates unique integration across multiple dimensions. Vertical integration, connecting strategic intent to portfolio composition to initiative selection, to experiment design, creating coherence across all levels of innovation activity. Horizontal integration, linking innovation performance to core business metrics, risk frameworks, and capability development, making innovation an integral part of enterprise value creation. Temporal integration, unifying the forward looking potential of early stage initiatives with the backward looking evidence of launched innovations, creating a continuous learning cycle. Functional integration, breaking down silos between innovation, strategy, finance, and operations, fostering a shared language and decision framework across functions. Organizations that lead in innovation performance don't treat innovation as exceptional, they treat it as essential. They don't have an innovation strategy separate from their business strategy. They have a business strategy that innovation helps execute. This perspective shift is perhaps the most profound contribution of the expected value system. It doesn't just give us better tools for measuring innovation, it gives us a new way of thinking about what innovation is and how it creates value.

SPEAKER_02: 52:59

From potential to proof, your next steps.

SPEAKER_03: 53:06

As we close this exploration of the expected value system, the question becomes what now? How do you translate these concepts into action within your own context? The journey from innovation theater to innovation performance doesn't happen overnight. It's an evolution that unfolds through deliberate practice, consistent application, and continuous learning. Based on the experience of organizations that have successfully implemented the expected value approach, several starting points emerge. One begin with honest assessment. Use the innovation accountability spectrum from chapter two to evaluate where your organization currently stands. This creates a baseline for improvement and helps identify the most pressing gaps. two start small, learn fast. Rather than attempting comprehensive implementation immediately, select a subset of your innovation portfolio five to seven initiatives for initial application. This creates space for learning and adaptation before scaling. three Build the Cultural Foundation. Focus first on the mindset shifts and behaviors that enable honest assessment, evidence based decision making and learning from both success and failure. Technical tools will fail without this cultural foundation. four. Create coalition, not compliance. Engage key stakeholders from finance, strategy and operations as partners rather than targets. Their perspectives will strengthen the implementation and build broader ownership. five. Measure implementation impact. Assess how the expected value approach is changing decision quality, resource allocation, and learning velocity, not just innovation outcomes. This creates feedback for continuous improvement. six. Evolve with experience. Treat the frameworks and practices in this book as starting points, not final answers. Adapt and customize based on your specific context, challenges, and learning. The most important thing is to begin. Innovation performance isn't about perfection, it's about progress, about creating more value more consistently from your innovation investments. Remember, the organizations that excel at innovation performance didn't get there through dramatic transformation initiatives. They got there through persistent evolution, through consistently asking better questions and making better decisions over time. The final word from expected to extraordinary. We began this book with a simple premise. Innovation isn't broken, but the way we measure it is. The expected value system isn't just another framework, it's a fundamental reimagining of how we create, evaluate and deliver value through innovation. It transforms innovation from theatre to performance, from activity to impact, from potential to proof. But make no mistake, its power isn't in spreadsheets or radar charts. It's in how it revolutionizes our relationship with uncertainty. It's in the courage to say we don't know yet, and the discipline to systematically find out. It's in the clarity to see what matters, and the conviction to act on it. The hard truth is this in a world where AI accelerates decision cycles, where sustainability demands new solutions, where disruption doesn't wait for quarterly reviews, organizations clinging to innovation theatre are choosing irrelevance. The time for pretty pilot projects and vanity metrics is over. The time for gut feeling and hope driven portfolios is past. Your future belongs not to those with the loudest innovation claims or the flashiest labs, but to those who build intelligence into every decision, evidence into every confidence score, and learning into every outcome, even the failures. This isn't about perfection, it's about progression. It's about building systems that get smarter with each choice, stronger with each cycle, and more valuable with each iteration. When I began this journey over a decade ago, I was frustrated by brilliant ideas lost to poor metrics and broken systems. Today, I'm inspired by what happens when organizations embrace the expected value approach, not as a rigid methodology, but as a living practice that evolves with experience and adapts to context. So I invite you to consider. What if innovation wasn't left to chance but approached with the same rigour as other business functions? What if we valued evidence over activity and built systems for consistent breakthroughs rather than hoping for occasional wins? The choice between treating innovation as magical thinking or as disciplined practice isn't just philosophical, it's strategic. And in today's rapidly evolving landscape it might be the difference between thriving and merely surviving. The tools are in your hands. The choice is yours. But know this innovation can be, must be, more than occasional brilliance. It can be the systematic creation of extraordinary value. It can be your sustainable advantage in an unsustainable world. Let's not just hope for extraordinary. Let's expect it, let's build it, let's measure it, and then let's exceed it.

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Acknowledgements.

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This book would not exist without the work, wisdom, and generosity of many others. To the giants of innovation thinking whose research underpins much of this work. Clayton Christensen, whose theory of disruptive innovation and S curves gave us the scaffolding to see growth differently. Michael Tushman and Charles O'Reilly, whose work on ambidexterity and innovation systems directly inspired the fit quotient framework. And Peter Drucker, who reminded us that what gets measured gets managed, and that management is, in the end, a human. Art. I'm especially grateful to Eric von Hippel, whose pioneering research on lead users and user-centered innovation has profoundly influenced my thinking about where innovation truly begins. His insight that users often develop solutions long before companies recognize the problems has been instrumental in shaping the confidence assessment dimension of the expected value system. To Shannon Heald, who generously took the time to challenge and test my thinking on XV, pushing me to refine the model and ensure its practical applicability. Your thoughtful critique and insightful questions made this work immeasurably stronger in its final miles. To Nadia Jekgsembaieva, whose relentless championing of reinvention continues to sharpen our thinking about adaptability and relevance. To Aidan McCullen, whose podcast, The Innovation Show, is the key to helping unlock and revisit so much of the amazing research and thinking in the broad innovation space. To Andy Binns and the corporate explorer community for showing that corporate innovation doesn't have to be an oxymoron. To Dan Tomer and Esther Gons, whose innovation accounting gave this movement rigour and backbone. To John Windsor, whose work on open innovation and the transformation of talent ecosystems has helped expand how we think about capability and creativity. To the sports analytics pioneers whose work on expected goals and performance measurement beyond outcomes provided such rich inspiration for the expected value approach and the table of justice concept. To my own professional journey with Wazoku, where this thinking has been battle tested, broken, rebuilt, and brought to life over more than a decade of working with clients, solvers, and some of the most committed innovation leaders I've ever known. To my core team at Wazoku, David, Sarah, Ian, and Rosemarie, thank you for believing in the value of clarity, for challenging me when it counted, and for helping turn ideas into impact. To the extraordinary inascentive solver community, you are the embodiment of possibility. Every breakthrough you've helped create, every solution you've brought to life has deepened my belief in the power of distributed intelligence.

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About the author.

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Simon Hill is an entrepreneur, systems thinker, and CEO of Wazoku, a company at the forefront of innovation transformation. With two decades of experience helping global organizations turn ideas into outcomes, Simon has spent his career challenging the status quo of how innovation is led, measured, and valued. He's built frameworks, scaled platforms, and worked with some of the world's most complex businesses to unlock the true potential of their people and portfolios. A lifelong lover of sport, data, and strategy, Simon's thinking fuses performance analytics with practical execution, bringing clarity to the chaos of innovation. Expected value is his call to action for every innovator who's ever been told to prove it, and his playbook for how to do exactly that. And that's it, the end of this season of the podcast, and the close of the expected value story for now. Thank you for listening, for sharing episodes, for sending messages, and for taking these ideas into your own work. It genuinely means a lot, and it's been a privilege to build this season with you. Season four is coming in 2026, and we'll go further into the topic of innovation value with some incredible guests. Until then, I'm wishing you a happy festive season, a restful break if you can take it, and a brilliant start to the new year. Thanks again for your support, and I'll see you in 2026.