32. Expected Value - Chapter 7

“Innovation doesn't happen in a vacuum. It happens in cycles”

In this chapter, we're diving into one of the most powerful and often misunderstood forces shaping innovation performance, the S-curve. Innovation doesn't happen in straight lines, it happens in cycles. Technologies evolve, markets shift, and every idea follows a natural rhythm, from emergence to acceleration, to maturity and eventually decline. Understanding this rhythm changes everything. It explains why confidence fluctuates, why value grows and then stabilizes, and why timing can be the difference between leading a market and missing it entirely. In Surfing the S-Curve, we'll explore how Freya and her team connect the life cycle of innovation to the XV model, transforming it from a static formula into a dynamic living system. This is a chapter about movement, timing and foresight, because the real art of innovation isn't just creating something new, it's knowing when to let go, when to optimize, and when to jump to the next curve.

Transcript

Speaker 1: 0:00

What's up?

unknown: 0:01

Uh-huh.

Speaker 1: 0:03

Uh-uh. What's it worth? Uh-uh.

Simon: 0:10

Thank you for joining me for the next part of Expected Value. In this chapter, we're diving into one of the most powerful and often misunderstood forces shaping innovation performance, the S-curve. Innovation doesn't happen in straight lines, it happens in cycles. Technologies evolve, markets shift, and every idea follows a natural rhythm, from emergence to acceleration, to maturity and eventually decline. Understanding this rhythm changes everything. It explains why confidence fluctuates, why value grows and then stabilizes, and why timing can be the difference between leading a market and missing it entirely. In Surfing the S-Curve, we'll explore how Freya and her team connect the life cycle of innovation to the XV model, transforming it from a static formula into a dynamic living system. This is a chapter about movement, timing and foresight, because the real art of innovation isn't just creating something new, it's knowing when to let go, when to optimize, and when to jump to the next curve. Let's begin. Chapter seven Surfing the S curve. Innovation doesn't happen in a vacuum, it happens in cycles. Technologies evolve, markets shift, businesses rise, plateau and reinvent, and every product, service or solution, no matter how transformative, follows a path. Emergence, acceleration, maturity, and eventual decline. This is the truth that many innovation frameworks ignore, and it's the insight at the heart of XV. The S curve is more than just a model for portfolio management. It's the underlying reality that informs every component of the expected value framework. While not explicitly featured in the XV formula itself, the S curve provides the essential context that makes XV dynamic rather than static, predictive rather than reactive. It's the force that shapes how confidence builds, how predicted value evolves, how time sensitivity shifts, and how strategic fit changes over an innovation's life cycle. I've always seen the S curve as the invisible hand behind innovation decisions, Freya explained to her team one morning. It's not just about where things sit in a portfolio, it's about understanding that all innovations follow natural life cycles, and those life cycles affect every aspect of how we evaluate them. Axel nodded. And that's why XV works differently than traditional ROI calculations. It adapts to where things are in their cycle. This insight transformed how Freya's team approached innovation assessment. The S curve wasn't just a tool for portfolio composition, it was the lens through which they viewed every component of XV. Confidence through the curve. The first revelation came when Freya mapped confidence patterns against the S curve. The relationship was striking but not straightforward. Look at this pattern, she said to Axel, pointing to a chart showing confidence trajectories for various projects. Confidence doesn't just steadily increase over time, it follows distinct patterns depending on where an idea sits on the curve. In the emergence phase, confidence typically started low, 0.1 to 0.3, and grew slowly, often with significant fluctuations as early experiments yielded mixed results. Confidence building here was fundamentally about reducing uncertainty through focused learning, validating core assumptions about problems, solutions, and market readiness. The acceleration phase showed a different pattern. Confidence typically grew more rapidly and consistently as market validation strengthened and implementation hurdles were overcome. This was where confidence often crossed the critical 0.5 threshold, marking the transition from more uncertain than certain to more certain than uncertain. In maturity, confidence typically plateaued at high levels, 0.7 to 0.9, with diminishing returns on further validation efforts. The challenge here wasn't building confidence but maintaining it as market conditions evolved. This means we need different confidence building strategies depending on life cycle position, Freya noted. Early stage requires rapid, low cost experimentation focused on core unknowns. Mid-stage needs systematic validation with real users and operational testing. Late stage needs vigilance against complacency. Understanding the S curve connection helped the team avoid two common confidence traps, expecting unrealistically high confidence too early and failing to recognize when confidence should be justifiably high based on life cycle position. Value through the curve. The relationship between predicted value estimation and the S curve proved equally revealing. Predicted value isn't static either, Freya observed. It evolves as innovations move through their life cycle. In emergence, value estimates typically had wide ranges and were based largely on assumptions. The focus was less on precision and more on directional potential. Was this a one million dollar opportunity or a ten million dollar opportunity? As ideas entered acceleration, value estimates became narrower and more evidence based. Initial market traction provided actual data on adoption rates, price points, and operational costs. Value calculation shifted from theoretical to observed. In maturity, value estimation became highly precise, but often began to decline as competitive differentiation eroded and new alternatives emerged. This phase required honest assessment of value sustainability rather than optimistic projection. Understanding this relationship helps us avoid comparing apples to oranges, Axel noted, a mature idea with proven but modest value shouldn't automatically outrank an emerging idea with uncertain but potentially transformative value. It's about the right value expectations for the right stage. This insight was particularly important for avoiding the short-termism that often plagues innovation portfolios. By recognizing that predicted value evolves along the S curve, teams could make more balanced investments across time horizons rather than always favoring the apparent certainty of late stage initiatives. Time sensitivity and the curve. Perhaps the most direct connection between XV and the S curve came through time sensitivity. The curve itself was fundamentally a time-based model, showing how value creation evolves over periods of innovation maturity. Time sensitivity isn't just about market windows, Freyer explained during a portfolio review. It's about understanding where an innovation sits in its natural life cycle, and how that position affects the urgency of action. Ideas approaching the inflection point between emergence and acceleration often had heightened time sensitivity. This was the moment when market readiness aligned with solution capability, creating a critical opportunity to scale rapidly before competitors. Missing this window could mean losing first mover advantage. Similarly, ideas at the late maturity stage often had increased time sensitivity, but for different reasons. This was when renewal or replacement became urgent to avoid value erosion as the curve flattened or declined. The team began explicitly mapping time sensitivity modifiers to S-curve positions. Early emergence pre-inflection. Moderate time sensitivity 0.7 to 1.0. The focus is typically on building foundations and learning. Late emergence near inflection. High time sensitivity 0.8 to 1.2. The window is opening for acceleration. Early acceleration. Very high time sensitivity 1.3 to 1.5 Critical Scaling Period Maximum Mentum. Late acceleration. High time sensitivity 1.2 to 1.3. This is a time to solidify market position before maturity. Early maturity, moderate time sensitivity 1.0 to 1.2. The focus should be on optimizing for maximum value capture. Late maturity, increasing time sensitivity zero point nine to one point two. If prioritizing anything here it reflects the growing urgency for renewal or replacement. This is why the time component of XV is so powerful, Axel noted. It captures the life cycle urgency that raw value calculations miss. We need a way to visualize this, Freya said, looking at the portfolio map they'd been developing. It's one thing to talk about different life cycle stages theoretically, but I want the team to actually see how time sensitivity transforms our view depending on where something sits on the S curve. Axel nodded, already opening his laptop. I've been working on something that might help. The next morning Axel presented a comparative table to the portfolio committee. This shows four initiatives across different stages of the S curve with their complete XV calculations, he explained. The team leaned forward to study the analysis. What's particularly revealing, Freya explained, moving to the front of the room, is how time sensitivity transforms our view of these opportunities. The next gen UI has the highest raw value potential at four point zero million dollars, but receives a strategic delay factor of zero point seven, because moving too early would mean building on unstable foundations. Exactly what we discussed earlier for early emergence innovations where deliberate delay is advantageous. She pointed to the table. Meanwhile, the supply chain optimization, despite having less than half the raw value, becomes our top priority with an XV of nearly $2.1 million due to its critical urgency factor of 1.5 and strong strategic fit of 1.1, aligning perfectly with what we expect in the acceleration phase. And note how the legacy system enhancement, despite having our highest confidence score of 0.9, receives a strategic delay factor of 0.8 because we know it has a limited useful life before replacement, Axel added. Its strategic fit is also relatively low at 0.7, primarily due to weak market attractiveness, as this space is becoming less relevant. This demonstrates how mature innovations approaching end of life require careful consideration of both timing and strategic fit. The team studied the table thoughtfully. The time sensitivity factor is doing double duty, holding us back from premature investments at the emergence stage with factors below 1.0, while amplifying high urgency initiatives in acceleration with factors up to 1.5. And the strategic fit is providing an additional layer of refinement, showing which initiatives align best with our strategic profile. Exactly, Freya nodded. That's the power of thinking in S-curves while applying both time sensitivity and strategic fit. It shows us that different rules apply at different stages, and our resource allocation needs to reflect that reality. The table became a standard part of their portfolio reviews, helping teams understand not just where initiatives ranked by X V, but why those rankings made strategic sense across the life cycle stages. What this really demonstrates, a team member observed during a later review, is that surfing the S curve isn't about putting all your resources on one point. It's about having the right balance of initiatives across the entire curve, with appropriate timing considerations and strategic fit for each stage. Freya couldn't have put it better herself. They were no longer making isolated decisions about individual ideas. They were managing a living portfolio that spanned multiple horizons, with clear visibility into how timing and strategic fit affected value at each stage of development. Strategic fit across the curve. The S-Curve didn't just inform the confidence, value, and time components of XV, it also provided crucial context for the strategic fit framework. The fourth dimension in the refined XV formula, our core competencies don't exist in a vacuum, Freya explained to the team. They evolve over time just like the innovations we're evaluating. This insight led to a more dynamic assessment of strategic fit, particularly the company advantage dimension. An organization might have strong advantage in mature technologies but weaker positioning in emerging ones. Or it might have distinctive capabilities in early stage innovation, but struggle with scaling to maturity. To reflect this reality, Freya and Axel updated their strategic fit framework to ensure each dimension was rigorously defined and consistently applied across different life cycle stages. First, problem value range 0.1 to 0.3. Where? 0.1 low is where the problem is barely recognized by potential users, has minimal impact on operations outcomes, and solving it would create only marginal improvements. Users show little willingness to allocate resources or change behaviors to address it. Evidence might include low engagement with prototypes, difficulty finding users who acknowledge the problem, or minimal measurable impact in pilot tests. 0.2 is where the problem is recognized and acknowledged by users, has measurable negative impacts, and shows moderate expression of interest in solutions, users demonstrate willingness to allocate some resources to address it. Evidence might include consistent user feedback identifying the issue, documented inefficiencies or pain points, and moderate engagement with potential solutions. 0.3 high, where the problem is critical, with significant and measurable consequences. Users actively seek solutions and demonstrate high willingness to pay, financially or with time attention, evidence might include unprompted user complaints, quantifiable major impacts on key metrics, existing workarounds that users have created, or regulatory compliance requirements that make solving it mandatory. Second, company advantage 0.1 to 0.3. Where 0.1 low is where the organization has no distinctive capabilities relevant to this opportunity compared to competitors or new entrants. Implementation would require building entirely new capabilities with no leverage from existing strengths. The organization would be at a disadvantage against established players or specialists in this space. Evidence might include no relevant technology ownership, no specialized expertise, no valuable data assets, and no established relationships with relevant customers. While not uniquely positioned, the organization can credibly compete based on existing strengths. Evidence might include adjacent technical capabilities that can be extended, relevant customer relationships but in different contexts, or partial data intellectual property advantages. And 0.3 high is where the organization possesses significant distinctive capabilities directly relevant to this opportunity, creating sustainable competitive advantage. These capabilities would be difficult for competitors to replicate quickly. Evidence might include proprietary technology slash IP, unique data assets or algorithms, established customer relationships in the exact target market, specialized expertise embodied in the current team, or distinctive distribution channels. Third, market attractiveness zero point one to zero point three Where zero point one, low, the market exhibits challenging conditions such as small total addressable market, declining growth, intense competition with established leaders, high entry barriers, unfavorable regulations, commoditized offerings with low margins, or high customer acquisition costs. Evidence might include market analysis showing saturation, declining industry revenues, regulatory roadblocks or established competitors with dominant positions and resources to defend them. Zero point two medium, where the market shows moderate attractiveness with mixed conditions, reasonable size, moderate growth, manageable competition, moderate margins, and accessible entry points. Evidence might include industry reports showing steady growth, analysis of competitive landscape revealing viable entry points and reasonable customer acquisition economics. And zero point three high, where the market demonstrates exceptional attractiveness, large or rapidly growing size, favorable competitive dynamics with clear entry points, strong margin potential, limited regulatory barriers, and favorable network effects or economies of scale. Evidence might include documented rapid growth trends, fragmented competition without dominant players, high margins among existing providers, or analysis showing unmet customer needs. And finally, trend alignment 0.1 to 0.3. Where 0.1 low means the innovation runs counter to significant emerging trends in technology, regulations, demographics, or user behaviors. As trends continue to develop, they will create increasing headwinds. Evidence might include reliance on technologies or approaches being replaced by newer alternatives, targeting demographic segments that are shrinking, or positioning that conflicts with strengthening regulatory directions. Some trends support it while others may create challenges? The net effect is relatively balanced. Evidence might include analysis showing alignment with some industry trends, but potential challenges from others, or positioning that is neither strongly favoured nor threatened by major directional shifts, and 0.3 high, where the innovation aligns perfectly with multiple converging trends, creating strong tailwinds for adoption and growth. As these trends strengthen, the innovation's value proposition becomes more compelling. Evidence might include clear alignment with documented technology, evolution paths, demographic shifts favoring the solution, regulatory changes that create advantages for the approach, or documented changes in user behavior that increase demand. Calculating the strategic fit. The strategic fit is calculated by summing the scores across all four dimensions problem value plus company advantage plus market attractiveness plus trend alignment. This creates a scale from 0.4 minimum possible score to 1.2 maximum possible score. This strategic fit then becomes the fourth multiplier in the XV formula. For example, an initiative scoring 0.1 in all four dimensions would have a strategic fit of 0.4, significantly reducing its expected value. A balanced initiative scoring 0.2 in all dimensions would have a strategic fit of 0.8, neither amplifying nor diminishing its expected value. An initiative scoring 0.3 in all dimensions would have a strategic fit of 1.2, amplifying its expected value by 20%. This refined approach created a much more structured and evidence-based assessment of strategic fit that could be consistently applied across different life cycle stages and innovation types. Key concept the S-curve, innovation's natural life cycle. The S-curve represents the fundamental life cycle pattern that underpins all innovation. Unlike linear models that suggest steady progression, the S-curve reveals the natural rhythm of how innovations evolve and mature through distinct phases. The four life cycle stages one. Emergence phase, the early exploratory stage where innovations take shape, characterized by high uncertainty, limited evidence, and foundational learning. Growth is slow as core assumptions are being validated with confidence typically ranging from zero point one to zero point three. Investment should focus on rapid low cost experimentation and hypothesis testing. two acceleration phase, the steep upward trajectory where validated innovations gain traction, characterized by increasing confidence zero point four to zero point seven, market validation and scaling challenges. This critical inflection point requires shifting from exploration to execution with resources concentrated on capturing the rapid growth opportunity. Three maturity phase, the plateau where growth slows and optimization becomes the focus, characterized by high confidence 0.7 to 0.9, established market position and diminishing returns on investment. The key challenge shifts to extending the plateau through incremental improvements while preparing for the next curve. 4. Decline phase, the downward trajectory as relevance fades and newer solutions emerge. Recognizing this phase early is crucial for timely reallocation of resources toward emerging opportunities and deliberate management of sunset initiatives. Why the S-curve matters for XV. The S curve fundamentally shapes how we apply the expected value system. Confidence assessment, different evidence standards apply across the curve. Early stage requires proof of concept, while acceleration requires scaling evidence. Value estimation, predictability increases as initiatives progress. Emergence demands broad value ranges, while maturity enables precise forecasts. Time sensitivity, urgency peaks at critical transition points, particularly the emergence to acceleration inflection points and the early signals of decline. Strategic fit. Advantage evolves across the curve. Organizations may have different strengths at different life cycle stages, requiring dynamic fit assessment. Resource allocation. Each phase requires different investment approaches, exploration for emergence, amplification for acceleration, and optimization for maturity. The greatest innovation risk isn't failure within a curve but failure to jump between curves. Organizations must simultaneously optimize current curves while exploring and preparing for the next ones. This requires balancing the portfolio across all phases, with deliberate allocation to both exploit and explore next curve initiatives. Understanding where innovations sit on their natural life cycle transforms how we evaluate, resource, and manage them, not as static projects, but as dynamic entities with predictable, yet nonlinear, evolutionary patterns. The decision lens. With all of the components of XV, confidence, predicted value, time sensitivity, and strategic fit now viewed through the S-Curve lens, Freya's team had a much more sophisticated understanding of how to evaluate innovation opportunities. The S-Curve isn't just a way to categorize ideas, Freya explained during an executive review. It's the underlying reality that makes XV work. It's why we measure confidence dynamically rather than statically. It's why we adjust value expectations based on maturity. It's why time sensitivity matters beyond simple market windows, and it's why strategic fit must be evaluated in relation to life cycle position. This understanding transformed how they applied XV in practice. Rather than treating each component as an isolated assessment, they understood the interconnections between them through the life cycle model. A low confidence score on an early stage idea wasn't a failure, it was an appropriate reflection of emergence phase uncertainty. A high time sensitivity on a rapidly accelerating idea wasn't an arbitrary rating. It was capturing the critical momentum period of the S curve. A strategic fit assessment that showed strong problem value but weaker company advantage might indicate the need for partnership rather than in-house development. XV works because it adapts to innovation reality, Axel summarized, and innovation reality follows the S curve. The Icarus Paradox Navigating Ambition on the S-Curve. One evening, as Freya and Axel were discussing the challenges of S curve navigation, Freya brought up an ancient metaphor. Have you ever thought about the full story of Icarus? she asked. You mean flying too close to the sun? Axel replied. That's only half the story, Freya explained. Daedalus actually warned his son of two dangers flying too high, where the sun would melt his wings, but also flying too low, where the sea spray would soak them and drag him down. It wasn't just about excessive ambition, it was about finding the right middle path. This insight resonated deeply with their S curve work. Innovation required balancing two opposing risks the danger of over ambition, attempting to jump curves too early, or with insufficient capability, and what absolutely Axel termed ambition deficit disorder. The equally dangerous tendency to stay too long on flattening curves, optimizing the familiar rather than pursuing necessary renewal. Most organizations focus only on the risk of flying too high, Freya observed. They fear spectacular failure from overreaching. But the more common failure, the one that's killed countless successful companies, is flying too low, lacking the ambition to jump curves when necessary. This balanced perspective transformed how they approach the innovation portfolio across the S curve. In emergence, they guarded against both premature scaling, flying too high and insufficient experimentation flying too low. In acceleration they balanced aggressive growth, flying high enough, with sustainable execution, not overreaching. In maturity they focused on both optimization, extracting current value, and renewal, preparing for the next curve. The Icarus paradox is everywhere in innovation, Freya noted, companies that succeeded by flying at the right height on one curve often either become reckless or more commonly, too cautious on the next one. This framework gave them a powerful metaphor for communicating portfolio balance to leadership. It wasn't about minimizing risk, it was about navigating between equally dangerous extremes, maintaining sufficient ambition without crossing into recklessness. Finding the right altitude isn't easy, Freya acknowledged, but it's essential for surfing the S-curve successfully. User-led innovation across the curve. As Freya's team deepened their understanding of the S-curve, they encountered a phenomenon that challenged traditional thinking about where innovations originate. Drawing on research on user-centered innovation, they recognized that different types of users played critical roles at different points on the S-curve. Innovation doesn't just flow from companies to users, Freya explained during a strategy session. Often, it flows in the opposite direction, with users themselves creating the earliest versions of what later becomes mainstream. This insight led to a more sophisticated approach to sourcing and evaluating innovations across the curve. Lead users in emergence. At the early stages of the S-curve, they discovered that lead users, those facing needs months or years before the mainstream market, often developed their own solutions to problems the market hadn't yet recognized. These users weren't just early adopters, they were actually innovating out of necessity. The research shows that important innovations often come from users, not manufacturers, Axel noted, we need to systematically identify these lead users and learn from their solutions. The team developed approaches for identifying and engaging lead users, incorporating their innovations into the emerging portfolio, and using their insights to validate assumptions earlier in the process. As innovations moved into the acceleration phase, they found that user communities played a crucial role in scaling adoption and evolving solutions. These communities created complementary innovations, developed new use cases, and served as powerful diffusion networks. The acceleration phase isn't just about our execution, Freyer observed. It's about creating the conditions for user communities to thrive around our innovations. This led to deliberate strategies for supporting user communities, including open interfaces, development tools, knowledge sharing platforms, and co-creation opportunities. In the maturity phase, the focus shifted to mainstream users and their feedback on refinement and optimization. These users provided crucial insights for performance tuning, the systematic improvement of efficiency, reliability, and fit that extended the productive life of mature innovations. By incorporating user-centered innovation perspectives into their S-curve management, the team created a more balanced approach that recognized the bi-directional flow of innovation between company and users. This often revealed opportunities for curve jumping that would have been invisible from a purely internal perspective. When we listen to lead users, Freya noted, we often discover the next S-curve before our competitors even recognize that one exists. Performance tuning, the art of curve extension. As innovations matured along the S-curve, Freya's team developed a disciplined approach to what they called performance tuning, the systematic process of improving efficiency, reliability, and fit to extend the productive life of proven innovations. Performance tuning isn't about transformative change, Freya explained to her team. It's about making mature innovations work better, last longer, and deliver more value before their inevitable replacement. This approach recognized that while all innovations eventually plateau, the length and height of that plateau could be significantly influenced by deliberate optimization efforts. Efficiency tuning. This focused on reducing resource requirements and streamlining operations to improve margins as growth slowed. The team developed methodologies for identifying efficiency opportunities through operational analytics, automation potential assessment, and systematic waste reduction. Efficiency isn't exciting, but it's essential, Axel said. It's what funds our ability to explore the next curve while maintaining credibility on the current one. Reliability tuning. As mature offerings reached wider adoption, reliability became increasingly critical. The team developed approaches for systematic quality improvement, error reduction, and resilience enhancement that maintained user trust through the maturity phase. Reliability issues kill mature products faster than competition, Freyer observed. Users forgive glitches in emerging innovations but expect mature offerings to work flawlessly. Fit tuning. Perhaps most importantly, fit tuning focused on continuously refining how well innovations aligned with evolving user needs, adjacent systems, and strategic priorities. This prevented the gradual drift away from relevance that often accelerates decline. Fit isn't static, said Axel. Even when an innovation isn't fundamentally changing, its context is, continuous fit tuning maintains its relevance. Performance tuning provided a disciplined framework for managing mature innovations without succumbing to either premature abandonment or excessive attachment. It created the sustainable plateau from which effective curve jumping could be launched, generating the resources, credibility, and organizational learning needed for successful transitions. The art isn't squeezing every last drop from a flattening curve, Freya told her team. It's optimizing it just enough to fund the jump to the next one. AI energetics accelerating curve navigation. The emergence of advanced AI capabilities introduced a powerful new dimension to S-curve management. Freya and Axel recognized that AI fundamentally changed how organizations could navigate S-curves across all phases. AI is both creating new curves and transforming how we move along existing ones, Freya explained to the executive team. It's accelerating the natural life cycle in ways we need to understand. The team identified several critical ways AI was reshaping S-curve navigation. In the emergence phase, AI tools enabled the team to rapidly explore vastly more possibilities than human teams could evaluate alone. This dramatically increased the efficiency of early stage experimentation and learning. AI isn't replacing human creativity, Freya said. It's amplifying it by handling routine analytical tasks and revealing patterns humans might miss. The team developed specific methodologies for AI augmented emergence that combined human insight with machine scale, using generative AI to explore diverse solution spaces beyond human anchoring biases, deploying machine learning to analyze experimental results and identify non-obvious patterns, applying simulation tools to test assumptions before physical or market experimentation, creating feedback loops where AI suggested new experiments based on previous results. As innovations moved into the acceleration phase, AI offered unprecedented ability to personalize, optimize, and scale solutions. This often compressed the acceleration timeline, creating steeper S curves with faster time to value. AI doesn't just help us climb the curve, said Axel. It actually steepens the curve itself, creating more value faster than traditional scaling approaches. The team developed frameworks for identifying where AI could most effectively amplify acceleration, focusing on opportunities for automated personalization that adapted innovations to diverse user needs, dynamic optimization that continuously refined performance based on usage data, predictive resource allocation that anticipated scaling requirements, intelligent automation of repetitive tasks that would otherwise limit scaling. Enhanced curve jumping. Perhaps most importantly, AI offered new capabilities for navigating the critical jump between curves. By systematically monitoring weak signals, identifying emerging patterns, and simulating potential futures, AI tools helped the team spot jumping opportunities earlier and prepare for them more effectively. The most dangerous moment in innovation is when you need to jump curves but don't see the next one clearly, Freya told the team. AI is becoming our early warning system, helping us see beyond the plateau of our current success. This approach led to the development of what they called agentic innovation management, frameworks where human teams and AI systems worked as partners to navigate S-curves, with each contributing their unique strengths to the process. AI accelerates S-curves in both directions, Axel cautioned. It helps us climb them faster, but it also causes mature curves to flatten and decline more quickly as new alternatives emerge. This makes effective curve jumping more important than ever. Refining the formula, XV, through the S-curve lens. As these insights accumulated, Freya's team recognized the need to refine how they applied the XV formula across different stages of the S curve. The core formula, confidence times, predicted value times time, sensitivity times fit remained consistent, but their understanding of each component evolved significantly. Each element of XV has a different character depending on where an innovation sits on its curve, Freya explained during a framework review session. We need to be explicit about those differences. This led to more nuanced application of each component, confidence across the curve. In emergence, confidence, typically 0.1 to 0.3, was primarily about validating fundamental assumptions. Does the problem exist? Is the solution technically feasible? Is there market interest? Confidence building focused on rapid, low cost experiments designed to test these core hypotheses. In acceleration, confidence, typically 0.4 to 0.7, shifted to evidence of scalability. Can the solution grow? Will unit economics work at scale? Can the organization deliver consistently? Confidence building became more systematic with structured validation of scaling assumptions. In maturity, confidence, typically 0.7 to 0.9, centered on optimization potential and transition readiness. Can performance still improve? Are renewal options identified? Confidence assessment incorporated both current performance and future options. Predicted value through life cycle stages. The predicted value component of XV also evolved significantly across the curve. This wasn't just about changing numbers, it was about fundamentally different approaches to prediction. In emergence, predicted value estimates were broad ranges based largely on market size, problem severity, and theoretical solutions. The focus was on directional accuracy rather than precision. In acceleration, predicted value became more concrete, incorporating actual adoption data, usage patterns, and early financial results. Predictions narrowed in range and increased in reliability. In maturity, predicted value estimates were highly precise but needed to account for sustainability and decline. The focus shifted to time bounded projections that acknowledged eventual flattening. Time sensitivity as S curve position. The time sensitivity component directly correlated with S curve positioning, with specific multipliers for different stages. Early emergence pre inflection 0.7 to 1.1 moderate. Late emergence near inflection one point two to one point four high. Early acceleration one point three to one point five very high. Late acceleration one point two to one point three high. Early maturity one point zero to one point two moderate. Late maturity one point one to one point three increasing. These multipliers reflected the natural urgency patterns along the curve with particular emphasis on the critical transition points between phases. Strategic fit as dynamic compatibility. Finally, the strategic fit component evolved from a static assessment to a dynamic evaluation of compatibility across the innovation life cycle. Emergence fit focused on foundational advantage. Did the organization have distinctive capabilities relevant to the emerging opportunity? The four dimensions problem value, company advantage, market attractiveness, and trend alignment were assessed with emphasis on future potential rather than current position. Acceleration fit emphasized scaling advantage. Could the organization grow the innovation more effectively than alternatives? The fit assessment now required evidence of actual advantage rather than theoretical positioning. Maturity fit considered both optimization advantage and transition potential. Could the organization extend the curve while preparing for the next one? The assessment incorporated evidence of operational excellence alongside flexibility for evolution. By explicitly adapting each component of XV to the S curve position, the team created a more sophisticated evaluation system that acknowledged the different challenges and opportunities at each life cycle stage. The formula itself is constant, Axel observed, but our application of it evolves with the innovation's position on the curve. Jumping S curves. Perhaps the most powerful insight from S curve thinking wasn't just about managing innovation along a single curve, but understanding when and how to jump to the next one. The most dangerous moment in innovation isn't failure, Freya explained to her executive team, it's success that traps you on a flattening curve. Organizations that had reached the top of one S curve but failed to jump successfully to the next emerging curve in their industry left themselves open to disruption. By the time they tried to make the transition, new players had established dominant positions on the curve. The trap is insidious, Axel said during a strategy session. Success on the current curve creates powerful incentives to optimize what's working rather than invest in what's next. Yet by the time the current curve visibly flattens, it's often too late to catch the next one. This insight transformed how the team approached mature innovations. Rather than simply optimizing them until decline, they began actively searching for the next curve while the current one was still growing, looking for emerging technologies, business models, or market needs that would eventually replace their current offerings. Most importantly, they recognized that jumping curves required deliberate timing and preparation. Jump too early before the next curve is viable and you risk abandoning a successful business for an unproven alternative. Jump too late after the next curve has already accelerated and you risk being left behind by more agile competitors. Jump without proper capabilities and you fail to gain traction on the new curve, despite good timing. The optimal window for jumping curves is narrower than most realize, Freyer observed. You need to begin investing in the next curve while the current one is still generating strong returns. That feels counterintuitive, but it's essential. Implications for XV valuing the jump. The concept of jumping S curves had profound implications for how the team applied XV to their portfolio. If the greatest risk is being trapped on a flattening curve, then we need to explicitly value the ability to jump, Freya told her team. That means our XV assessments need to account for curve position and jumping potential. This led to several important refinements in how they applied XV. Time sensitivity adjustment for curve position. Ideas that enable jumping to the next curve received higher time sensitivity multipliers even when the current curve was still performing well. This created a counterbalance to the natural tendency to focus resources on optimizing the current successful curve. For example, an early investment in a technology that might eventually replace a current profitable product would receive a time sensitivity multiplier of 1.2 to 1.3, even though the immediate urgency wasn't obvious. This reflected the strategic importance of establishing a position on the next curve before it accelerated. Value estimation across curves. The team developed more sophisticated approaches to value estimation that considered not just the direct value of an innovation, but its option value for jumping curves. Some innovations matter less for what they deliver today and more for the position they establish on tomorrow's curve, Axel explained. This insight led them to include curve transition value in their calculations, the strategic value of building capabilities, market position, or knowledge that would enable successful jumping later. Confidence building for curve jumping. Perhaps most importantly, they recognized that confidence building looked different for innovations designed to jump curves. These required parallel tracking of confidence on the current and emerging curves, earlier investment in learning about nascent technologies or market shifts, deliberate experimentation across multiple potential next curves, different validation approaches for disruptive versus sustaining innovations. Building confidence for curve jumping isn't just about validating a specific idea, Freya noted, it's about systematically reducing uncertainty about which curve to jump to and when. Strategic fit for transition The strategic fit assessment was particularly valuable for evaluating curve jumping potential. The team refined their approach to evaluate not just current fit but transition fit. Problem value assessment included not just current problems but emerging ones. Company Advantage considered transferable capabilities that could bridge between curves. Market attractiveness evaluated both current markets and emerging adjacent spaces. Trend alignment became crucial for identifying which emerging curves aligned with the major shifts. A high strategic fit for curve jumping doesn't just mean we fit the current landscape, Freya explained. It means we have the right foundation to build a bridge to the next curve. The cannibalization conundrum. One of the thorniest challenges in jumping S curves was the inevitable cannibalization of successful existing products or services. This created both psychological and financial resistance to investing in the next curve. Our most successful business unit leaders are measured on current performance, Freyer acknowledged in a discussion with David, the CFO. We're essentially asking them to invest in things that might undermine their own success. This tension made objective evaluation through XV even more critical. By separating the assessment of an innovation's value from the political considerations of who might lose, in a transition, the team could make more objective decisions about when and where to invest in jumping curves. They developed specific approaches to address the cannibalization challenge. Protected innovation spaces. They created structurally separate teams with explicit mandates to develop innovations that might cannibalize current offerings. These teams reported to corporate leadership rather than the business units they might disrupt, allowing them to pursue next curve opportunities without inherent conflicts, dual curve incentives. They revised incentive structures for senior leaders to include metrics related to both current curve performance and next curve positioning. This aligned individual rewards with the organization's need to successfully navigate transitions between curves. Cannibalization neutral evaluation. Most importantly, they modified their XV and strategic fit assessments to evaluate new opportunities on their own merits, independent of their cannibalization impact. This prevented the natural bias against ideas that might threaten current success. Cannibalization is inevitable in a changing world, Freyer observed. The only question is whether we'll cannibalize ourselves or be cannibalized by competitors. Our evaluation system needs to recognize that reality managing multiple curves. Simultaneously. As the team's understanding deepened, they recognized that effective innovation management wasn't about navigating a single S curve, but about managing multiple overlapping curves simultaneously. At any given time we need to be optimizing mature curves, accelerating mid-stage curves, and seeding potential future curves. Freya explained during a strategy session. The art is in the balance. This multicurve perspective led to a more sophisticated portfolio approach that explicitly mapped each innovation to its corresponding curve and life cycle stage. Rather than simply categorizing ideas as explore, expand or exploit, they now understood these categories as positions on interconnected curves. The team developed visualizations that showed how these curves overlapped and where critical resource allocation decisions needed to be made. When to begin reducing investment in a flattening curve, when to increase investment in an accelerating curve, how much to allocate to the exploration of potential future curves, which capabilities needed development for successful jumping. These visualizations made the strategic implications of resource allocation clearer to leadership. Instead of seeing innovation as competing with the core business for resources, executives could now see how deliberate investment across multiple curves created sustainable growth and reduced the risk of disruption. We're not choosing between today and tomorrow, Axel observed. We're creating the bridge between them. This multicurve perspective also reinforced the importance of the XV formula's dynamic nature. By continually reassessing confidence, value, time sensitivity, and strategic fit as innovations evolved along their respective curves. The team could make smarter decisions about when to shift resources between curves. The key insight is that innovation isn't a linear process, Freya noted. It's a series of overlapping cycles that need to be managed as a system, not as individual projects. Strategic layering and S-curves. The S-Curve Insight transformed how Freya's team approached strategic layering of their innovation portfolio. Rather than simply mapping innovations to current strategic priorities, they began thinking in terms of curve transitions, how today's investments positioned them for tomorrow's opportunities. Strategic alignment isn't just about fitting today's strategy, Freya explained to her executive team. It's about creating options for evolving that strategy as curves shift. This perspective led to a more dynamic approach to strategic layering that considered not just current priorities, but future transitions. How innovations on the current curve enhanced competitive position. How innovations on emerging curves created options for strategic evolution. How capabilities developed on one curve might transfer to another. How the timing of investments across curves affected strategic flexibility, the integration with a strategic fit framework became particularly power. By assessing company advantage across multiple curves rather than just the current one, the team could identify where they had the potential to lead transitions rather than simply follow them. Some organizations have natural advantages in spotting and jumping to new curves, Axel observed. That's as important as advantage on any specific curve. This insight led them to add a fifth dimension to their strategic fit radar chart. Transition potential, the degree to which an innovation built capabilities, knowledge, or market position that would enable successful business. Jumping to future curves. This dimension helped identify investments that might not show strong fits with current priorities, but were strategically crucial for future transitions. It created space for deliberate investment in capabilities that might not pay off immediately but would be essential for navigating future curve jumps. Practical tools for curve navigation. To make these concepts operational, Freya's team developed several practical tools that integrated S-Curve thinking into their daily innovation management. The curve position assessment. This simple but powerful tool helped teams identify where an innovation sat on its life cycle curve. By assessing eight key indicators market adoption, competitive intensity, technical maturity, profit margins, growth rate, knowledge certainty, capability requirements, and disruption potential, they could place innovations at specific points on the curve. This positioning then informed appropriate application of XV with different confidence thresholds, value estimation approaches, time sensitivity considerations, and strategic fit evaluations based on curve position. The curve jumping readiness assessment. This tool helped evaluate organizational readiness to jump to emerging curves. It assessed current curve positioning, next curve identification clarity, capability gaps, cannibalization readiness, and leadership alignment. Low scores indicated the need for deliberate preparation before attempting significant jumps. The multicurve portfolio map. This visualization tool showed how the organization's innovations mapped across multiple overlapping S curves. It revealed resource allocation patterns, highlighted potential gaps in curve coverage, and identified critical transition points where investment decisions would determine future options, using color coding for different types of innovations and sizing bubbles. By resource allocation, the map made complex portfolio dynamics visible at a glance. The curve informed resource allocation model. This model provided guidelines for appropriate resource distribution across curve positions. Emergence phase investments focused on learning and positioning, lower cost, higher experimentation. Acceleration phase investments focused on scaling and market capture, higher cost, focused execution, maturity phase investments focused on optimization and transition preparation, moderate cost, balance between current returns and future options. Decline phase investments focused on managed sunsetting and value harvesting, minimised cost, disciplined focus on remaining value. By aligning resource allocation to curve position, the team avoided both underinvesting in promising accelerating innovations and over investing in flattening mature ones. From theory to practice The power of integrating S-Curve Thinking with XV became clear when Freya's team applied it to a critical strategic decision. Their company's core product, a specialized financial analytics platform, had dominated its niche for years. It generated substantial profits and had high customer satisfaction. By conventional measures, it was a success that warranted continued investment. But when they mapped it on the S curve and applied a curve informed XV assessment, a different picture emerged. Confidence was high, zero point eight. They understood the product and market thoroughly. Predicted value was stable but flattening. Growth had slowed as market penetration approached saturation. Time sensitivity was increasing one point three. New technologies were emerging that could potentially disrupt their approach. Strategic fit was strong for current curve but weakening for future positioning. Problem value zero point two, current problems being solved but new ones emerging Company Advantage zero point two strong in current technologies weaker in emerging ones Market attractiveness zero point one mature market with increasing competition Trend alignment zero point one technology approach becoming outdated Overall strategic fit zero point six. Meanwhile, they had been experimenting with a new AI based solution that showed promising early results but remained unproven. Confidence was low zero point three, many technical and market assumptions remained unvalidated. Potential predicted value was substantial. Initial estimates suggested it could eventually deliver three times the value of the current product. Time sensitivity was high one point four, competitors were exploring similar approaches. Strategic fit was improving. Problem value zero point three addressing emerging critical needs Compta zero point two building relevant capabilities Market attractiveness zero point three growing market with favorable dynamics Trend Alignment zero point three perfectly aligned with technology evolution Overall Strategic Fit one point one when they calculated the complete XV for both initiatives. Current platform zero point eight times eight hundred thousand times one point three times zero point six gave an XV of four hundred ninety nine thousand two hundred dollars, whereas the AI solution zero point three times two million four hundred thousand times one point four times one point one giving an XV of one million one hundred eight thousand eight hundred dollars. The conventional approach would have been to heavily invest in the proven product while treating the AI solution as a speculative side bet, but the S-curve perspective revealed that the current product was approaching the top of its curve, while the AI solution represented a potential jump to the next curve. By applying curve-informed XV, they made a counterintuitive but strategic decision. They maintained the current product with focused optimization investments while significantly increasing resources for the AI solution, not because it was currently more valuable, but because it represented the necessary jump to the next curve. Without the S-curve lens, we would have overinvested in optimizing what was working and underinvested in what would work next, Freya reflected. The XV formula captured that reality when we applied it with curve positions in mind and incorporated the full strategic fit assessment. The result was a more balanced approach that both maximized returns from the current product and established a strong position on the emerging curve, avoiding the trap that had caught so many successful companies that failed to jump effectively. Building an S curve. Beyond specific tools and processes, Freya recognized that effective navigation of S-curves required a fundamental shift in mindset throughout the organization. S-curve thinking isn't just a portfolio management approach, she explained to her team. It's a way of seeing innovation as a natural cycle rather than a linear process. This mindset shift had several key elements. One, from static to dynamic evaluation. Traditional evaluation approaches treated innovations as fixed entities with relatively stable characteristics. S-curve thinking embraced the dynamic nature of innovation, recognizing that confidence, value, time sensitivity, and strategic fit all evolved as innovations moved through their life cycles. An idea isn't good or bad in some absolute sense, Axel observed. Its potential changes based on where it sits on its curve and what other curves are emerging. two. From success slash failure to life cycle management. Instead of seeing innovations as either successes or failures, S curve thinking reframed performance as appropriate management across life cycle stages, recognizing that even highly successful innovations eventually flatten and decline. The question isn't whether an innovation succeeded, Freya noted, it's whether we navigated its entire life cycle effectively, from emergence through to graceful replacement. Three. From competition to transition. Perhaps most importantly, S-curve thinking reframed the relationship between current and future innovations. Rather than seeing them as competing for resources, this mindset viewed them as sequential stages in an ongoing evolution. Current success should fund future transitions, Freyer explained, and future options should protect against current stagnation. This mindset shift was reinforced through language, rituals, and stories. The team celebrated not just successful launches, but successful transitions. Times when they had effectively jumped from one curve to the next, maintaining growth and relevance through changing conditions. Our greatest successes aren't individual innovations, Freya told her team. They're the bridges we build between them. Integrating S-curves into the full system. As Freya reflected on how thoroughly S-Curve thinking had transformed their approach, she realized it wasn't just a component of their innovation system, it was the unifying principle that made everything else work together. The S curve isn't just another model, she explained to David, the CFO, during a planning session. It's the lens through which everything else makes sense. This integration manifested across the entire expected value system. Each component of the XV formula, confidence, predicted value, time sensitivity, and strategic fit was now understood in relation to curve position, creating more nuanced and accurate assessments. Strategic fit framework. The dimensions of fit were evaluated not just for current circumstances, but for curve transitions, identifying where the organization had sustainable advantage across cycles. Portfolio management resource allocation explicitly considered curve positions, ensuring appropriate investment across emergence, acceleration, maturity and transitional initiatives. Governance Decision rights and processes adapted to different curve positions, with different thresholds and criteria applied to early versus late stage innovations, learning systems, knowledge capture and transfer became focused on buildings, curve jumping capabilities, preserving insights that would enable successful transitions. This full integration created a coherent system that could adapt to changing conditions while maintaining strategic direction. It wasn't rigid or formulaic, but provided a consistent logic for navigating the inherent uncertainty of innovation. We're not trying to predict the unpredictable, said Freya. We're aligning our decision processes with the natural patterns of how innovation actually works. That alignment was perhaps the greatest strength of the expected value system. It didn't fight against the S-curve reality of innovation, but embraced it, building tools and approaches that worked with rather than against these fundamental dynamics. As Freya looked ahead, she could see how this framework would continue to evolve. The specific tools and metrics might change, but the core principles, understanding life cycle positions, managing transitions between curves, balancing investment across multiple horizons, would remain essential to effective innovation management. The S curve isn't a trend or a technique, she reflected, it's innovation physics. And that's why it makes our entire system work. Too long, didn't read. The S curve represents the fundamental life cycle pattern that underpins all innovation, emergence, acceleration, maturity, and decline. While not explicitly named in the XV formula, it critically informs how each component should be applied. Confidence builds differently at each life cycle stage. Predicted value estimates evolve from broad ranges to precise calculations as evidence accumulates and time sensitivity peaks at critical transition points. The strategic fit assessment, ranging from 0.4 to 1.2 based on four clearly defined dimensions, ensures innovations not only have potential value but align with organizational capabilities across different life cycle stages. The formula must be balanced with both user-led innovation insights and the transformative potential of AI and agentics. Most crucially, organizations must avoid the Icarus paradox, neither flying too high over ambition, nor too low, ambition deficit disorder. By deliberately preparing to jump to emerging curves before current successes flatten, all while using performance tuning to extend valuable plateaus. By integrating S curve thinking into X V, teams make dynamic assessments that reflect innovation's natural evolution and avoid being stranded on declining curves.