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The Problem With Innovation in Large Organizations
An Austrian manufacturer of precision agricultural equipment invests 8% of annual revenue in R&D. The engineers are among the best in Europe. The patents are real. The product quality is demonstrably superior by any technical measure. And yet the company has been losing market share for four consecutive years to a competitor from Eastern Europe whose products are technically inferior by almost every specification.
This is not a story about manufacturing quality. It is a story about what the competitor understood that the Austrian company did not: which specific customer needs were going unaddressed — and which ones were being addressed so well that further investment there created no additional customer value.
The competitor did not win on engineering. It won on market understanding.
This pattern repeats across industries and company sizes. It is not confined to manufacturing. It appears in medical devices, in industrial software, in construction equipment, in instrumentation. Companies with superior technical capabilities consistently lose to competitors with inferior capabilities because the inferior competitors are addressing needs that actually matter while the superior companies are investing in needs that are already adequately served.
Decades of research on product failure rates have produced a consistent finding: 70-90% of new products fail to meet their commercial targets. The cause is almost never technical failure. Products fail because they solve the wrong problems — because the organizations that built them did not know, with any precision, which customer needs were genuinely underserved.
Systematic innovation is the alternative to this. Not innovation theater — the design workshops, hackathons, and innovation labs that generate enthusiasm without changing product outcomes. Systematic innovation is the organizational capability to identify, measure, and act on quantified customer need, repeatedly and reliably, across product lines and market segments.
This guide covers the what, why, and how of systematic innovation for enterprise product organizations — drawing on over 20 years of practice and the methodology that has produced the most documented evidence for dramatically improved product success rates.
Why Traditional Innovation Approaches Systematically Fail
The Lottery Problem
Most enterprise product development is organized like a lottery. The organization generates a large number of product ideas, filters them through a stage-gate process, invests in the ones that survive, and hopes that enough of them succeed to justify the portfolio.
The logic seems defensible: diversify your bets, let the market decide, use the stage gates to kill bad ideas early. But there is a fatal flaw in this reasoning. A lottery works because the prize is known. In product development, the prize — the specific combination of customer needs that a new product must address to succeed commercially — is unknown. Stage gates filter based on technical feasibility and market size estimates, neither of which tells you whether the product addresses a need that is genuinely underserved.
Generating more ideas does not improve your hit rate if you do not know which target to aim at. You are increasing the volume of shots without improving the precision. The result is the 70-90% failure rate that research has documented consistently for fifty years.
The Opinion Problem
In the absence of data on what customer needs are genuinely underserved, product decisions are made by opinion. The CEO has a vision. The VP of Engineering sees a technical opportunity. The VP of Sales aggregates feature requests from the accounts she spends most time with. The product manager tries to synthesize all of these perspectives into a coherent roadmap.
The product that emerges from this process is not designed around customer need. It is designed around the organizational power balance at the time of the decision. Well-resourced teams with persuasive leaders get their features built. Underserved customer segments whose advocates are not in the room go unaddressed.
This is not a criticism of the people making these decisions. They are doing the best they can with the information available to them. The problem is structural: the information available to them — sales anecdotes, qualitative customer feedback, competitive monitoring, technical assessments — does not tell them which needs are underserved and by how much.
The Method Problem
In response to the opinion problem, many organizations have invested in customer research. But the methods they use — interviews, focus groups, surveys, ethnographic studies, design thinking workshops — have fundamental limitations that prevent them from answering the strategic question that matters.
Interviews and focus groups produce qualitative themes. Themes are useful for discovery and for organizational empathy, but they cannot be directly used for strategic prioritization. “Customers want easier configuration” is a theme, not a priority. It tells you a direction, not a magnitude.
Surveys, as conventionally designed, ask customers to rate their satisfaction with specific products or to rank feature preferences. These instruments measure preference within the current solution frame — they tell you whether customers prefer your product to a competitor’s, not whether either product is adequately addressing the most important underlying needs.
Design thinking workshops produce creative concepts. They are excellent at generating candidate solutions, but they provide no systematic mechanism for determining which problems are worth solving. A design thinking process that generates 100 product concepts is not a prioritization tool — it is an ideation tool. Prioritization requires a different kind of input.
The result is that most enterprise organizations have a research function that generates a steady stream of qualitative insights, and a product function that uses those insights as one input among many in a decision process that is ultimately driven by opinion and politics.
Systematic innovation requires breaking this pattern. It requires replacing the qualitative insight stream with a quantitative measurement system that produces decision-grade inputs to product strategy.
The Framework: Outcome-Driven Innovation
Systematic innovation, as practiced at MYLES and as codified by Tony Ulwick and die ODI-Praxis over 30 years of research and practice, is built on a specific framework: Outcome-Driven Innovation (ODI).
ODI is grounded in a foundational insight from Jobs-to-be-Done theory: customers do not want products; they want to accomplish goals. Products are the means. When product teams define their market around their product — and ask “how can we make this product better?” — they limit their innovation space to what is achievable within the current product paradigm. When product teams define their market around the customer’s goal — and ask “where does the customer’s current approach to accomplishing this goal fall short?” — they open an innovation space that includes every possible approach to addressing the customer’s need.
This shift in framing is not cosmetic. It changes what you measure, how you segment your market, what innovations you pursue, and how you evaluate whether those innovations are working.
The Core Claim
Ulwick’s practice — involving over 400 client engagements across a wide range of industries — reports that products developed using the ODI methodology succeed at an 86% rate, compared to an industry average of 17-30%. This figure comes from the practice’s own client tracking and is not independently audited, but the directional claim is supported by the mechanism: products designed to address quantifiably underserved customer needs succeed because they address real demand, not assumed demand.
MYLES is an experienced practitioner of Outcome-Driven Innovation. Our practice is built on the ODI methodology, refined through 20+ years of engagements with companies including Liebherr, B.Braun, Hilti, Palfinger, Eaton, Teleflex, Pöttinger, MAM, and others across the industrial, MedTech, and B2B spectrum.
For a comprehensive look at the evidence behind ODI’s success rate, see the detailed treatment in Why 86% of ODI-Guided Products Succeed.
The ODI Process: Six Steps from Market Definition to Growth Strategy
Step 1: Define the Market Around the Job
The first — and most consequential — step in systematic innovation is defining the market correctly. Not in product terms (“we are in the industrial pump market”), not in customer terms (“we serve mid-size manufacturers”), but in job terms (“we serve organizations whose operators need to transfer fluid reliably within process systems under variable pressure conditions”).
This sounds like a minor semantic shift. The strategic consequences are profound.
When you define your market around the job, your competitive set expands to include every alternative way customers accomplish the goal — not just competing products in your category. When you define your market around the job, your addressable market typically grows significantly, because non-customers who use alternative approaches to the same goal are now visible. And when you define your market around the job, the outcomes you need to measure are the outcomes that matter to all job executors — not just the ones who currently use your product.
The job statement has a specific syntax: verb + object + contextual clarifier. It must be solution-agnostic — no product references — and stable over time. “Perform minimally invasive cardiac procedures safely and efficiently” is a job. “Operate our cardiac catheterization system” is a product activity.
Getting this step right typically takes three to five rounds of iteration and involves qualitative interviews with job executors to validate that the statement captures the full scope of what they are trying to accomplish. For a full treatment of why the ODI market definition is so consequential, see the ODI market definition guide.
Step 2: Discover Customer Needs as Desired Outcomes
With the market defined, the next step is to discover the full set of customer needs related to the job. In ODI, customer needs are expressed as desired outcome statements — specific, measurable articulations of what success looks like at each step of the job execution process.
Outcome statements follow a strict syntax: direction of improvement + metric + object of control + contextual clarifier. For example: “Minimize the time it takes to confirm that the instrument is correctly calibrated for the specific application before beginning the procedure.” This is a measurable statement that any solution — hardware, software, process, or hybrid — could address. It is not a feature request; it is a customer need.
The discovery process uses the Universal Job Map — a framework developed by Ulwick that decomposes any job into eight functional stages: Define, Locate, Prepare, Confirm, Execute, Monitor, Modify, Conclude. By systematically exploring what customers are trying to accomplish at each stage, the discovery process produces a comprehensive set of 50–150 desired outcomes covering the full job process.
This comprehensiveness is critical. Most product development processes capture needs primarily from the Execute stage — the core product interaction — and miss the needs at the Define, Confirm, and Conclude stages. These “edge stages” are disproportionately underserved because no product team focuses on them, and they represent some of the highest-opportunity innovations available.
Qualitative interviews with 15-30 diverse job executors drive this phase. The interviews follow a specific protocol designed to elicit process description rather than feature preferences. For a detailed guide to the job mapping process, see The Job Map: Mapping What Customers Are Trying to Accomplish.
Step 3: Quantify Satisfaction with Current Solutions
With 50–150 desired outcomes identified, the next step is to quantify how well current solutions — your product, competitors’ products, and alternative approaches — are addressing each outcome.
A survey instrument sends each outcome to a representative sample of 200-600 job executors, asking two questions per outcome: how important is this outcome to you (rated 1-10), and how satisfied are you with how well your current solutions allow you to accomplish it (rated 1-10)?
The opportunity score for each outcome is then calculated using Ulwick’s formula:
Opportunity Score = Importance + max(Importance − Satisfaction, 0)
Scores above 10 indicate significant opportunity — outcomes where importance exceeds satisfaction and where a product that addressed the gap would create real customer value. Scores above 12 represent substantial strategic opportunity. Scores above 15 are the “golden opportunities” — needs that are critically important and dramatically underserved by every current solution.
This quantitative measurement is what distinguishes ODI from other research approaches. The opportunity score is not a sentiment or a theme — it is a calculated metric that directly indicates where product investment will create the most customer value. For teams who want to understand how opportunity scores translate directly to roadmap priorities, see From Opportunity Scores to Product Roadmap Priorities.
Step 4: Discover Hidden Market Segments
Traditional market segmentation — by industry, company size, geography, job title — assumes that customers who look similar have similar needs. In practice, this assumption is systematically wrong. Two engineers at the same company, with the same job title and the same product, may have dramatically different patterns of underserved outcomes based on how they work, what they prioritize, and what trade-offs they have learned to make.
ODI’s segmentation approach uses cluster analysis on the importance data to identify groups of customers whose patterns of underserved needs are similar to each other and different from other groups. These need-based segments reveal the true structure of the market — not the demographic structure, but the structure of customer need.
In most ODI engagements, the cluster analysis reveals two to five distinct opportunity segments. Each segment represents a different strategic opportunity: different outcomes are most underserved, different product configurations would address their needs best, and different go-to-market approaches are appropriate.
This segmentation insight is often the most strategically valuable output of the entire ODI process — more valuable, even, than the opportunity scores themselves — because it reveals that the market is not homogeneous and that a product designed to address the most important outcomes for one segment may be irrelevant to another.
Step 5: Formulate the Growth Strategy
With opportunity scores identified and segments characterized, the growth strategy becomes a data-driven decision rather than a judgment call.
The strategic framework is built on two axes: how many high-opportunity outcomes does a candidate product address, and how large and attractive is the segment that values those outcomes? The combination of these two dimensions defines four strategy options:
Market differentiation: Target a segment with a high concentration of underserved outcomes. Build a product specifically designed for that segment’s most pressing unmet needs. Win decisively in that segment before expanding.
Market creation: Identify an outcome cluster that no current solution addresses at all. Build the product that addresses this cluster. Create a new market category.
Product improvement: Identify high-opportunity outcomes in the current product’s target segment. Make targeted improvements that address those outcomes while maintaining the product’s existing strengths.
Market expansion: Identify segments that have similar but distinct outcome profiles to the current target. Expand the product or develop product variants to address the underserved outcomes in adjacent segments.
The strategy is chosen based on the data, not on organizational preference. This is the key difference between systematic innovation and opinion-driven innovation: the data tells you which strategy has the highest expected value.
Step 6: Ideate, Develop, and Test Against Outcome Targets
Only at Step 6 does the ODI process move to ideation — the generation of specific product concepts, features, or solutions. This sequencing is deliberate and important. Ideas generated without outcome targets are generated in an information vacuum; ideas generated against specific outcome targets can be evaluated objectively for how well they address the identified need.
Each product concept is evaluated not just for technical feasibility and cost but for outcome coverage: how many of the high-opportunity target outcomes does it address, and how significantly does it move the needle on each? Concepts that address more outcomes more effectively are prioritized over concepts that address fewer outcomes or make marginal improvements.
This approach produces products that are designed to succeed from the beginning — not products that are designed based on what seems feasible or what engineering finds interesting, and then validated against customer response after launch.
Systematic Innovation in Practice: Building the Capability
The First Project: Proof of Concept
The most effective way to introduce systematic innovation into an enterprise organization is through a single, well-scoped pilot project. Choose one product line or market where you suspect significant growth opportunity is being missed — ideally one where sales are flat despite consistent feature investment, or where you are losing share to a competitor you believe is technically inferior.
Run the full ODI process on that market. Invest in the qualitative discovery phase — 15-20 customer interviews conducted by trained interviewers using a rigorous protocol. Invest in the quantitative validation phase — a properly designed survey with a representative sample of at least 200 respondents. Invest in the analysis — cluster analysis, opportunity scoring, strategic synthesis.
The outputs will surprise you. In every ODI engagement I have run, the opportunity data reveals at least one major finding that the product team did not expect — an outcome cluster that scores dramatically higher than the team assumed, a segment that turns out to be much larger and more underserved than the demographic segmentation suggested, or a competitive threat from outside the product category that was invisible in the existing competitive analysis.
Use this surprise as the business case for systematic innovation. The question to ask leadership is: “If our intuition missed this in a market we thought we understood well, how much are we missing in markets we have not studied?”
Building Internal Capability
A single ODI project, however valuable, is not systematic innovation. Systematic innovation requires organizational capability — people who understand the methodology, processes that integrate it into planning cycles, and metrics that hold the organization accountable to outcome-driven priorities.
Phase 1: Pilot (months 1-6). Run the first ODI project with external support, using it simultaneously as a capability-building exercise. Train two to three internal product managers or strategists as participants in the research design, fieldwork observation, and analysis phases. The goal is not just a research output — it is a team that understands the methodology well enough to apply it internally.
Phase 2: Expansion (months 7-18). Run two to three additional ODI projects using a combination of internal capability and external support. Integrate ODI outputs into the standard planning cycle — using opportunity scores as a standing input to roadmap prioritization discussions. Begin building an internal outcome database: a library of job maps, outcome statements, and opportunity scores for each product market.
Phase 3: Institutionalization (months 19-36). Integrate systematic innovation into the organizational fabric. This means annual outcome re-measurement for active markets, outcome-based OKRs for product teams, a standard briefing format for new product initiatives that includes the job statement and top-priority opportunity outcomes, and internal training programs that propagate the methodology across the product organization.
The organizations that reach Phase 3 — and sustain it — have built a genuine competitive advantage that is difficult to replicate. The methodological tools are available to everyone; the organizational capability to use them consistently is rare.
The Culture Problem
The most significant obstacle to systematic innovation is not methodological — it is cultural. Enterprise organizations are built for efficiency, not for inquiry. Their processes, incentives, and power structures are designed to execute against known plans, not to discover and respond to information that challenges those plans.
When ODI data reveals that the highest-priority investment on the current roadmap is addressing a low-opportunity outcome — that the feature that Engineering has been building for six months addresses a need that 68% of customers say is already adequately served — the data is threatening, not helpful, to the people who championed that investment.
Managing this dynamic is not primarily a research problem. It is a change management problem. The organizations that succeed with systematic innovation are ones where senior leadership has explicitly endorsed the principle that data about customer needs outranks organizational opinion in product decisions — and where they have demonstrated that commitment when the data conflicts with existing plans.
This requires courage from senior product leaders. It is much easier to commission customer research and then acknowledge it selectively than to build a process where the research outcomes genuinely drive decisions. But the organizations that build this culture are the ones that systematically outperform their peers on product innovation outcomes.
The companies I have worked with that have truly built systematic innovation capability have one thing in common: a leader — usually a VP of Product or a CEO — who was willing to let the data override their most cherished convictions at least once, publicly, in front of their team. That one moment of intellectual courage changes the culture more than any amount of process change. It tells the organization that the data is real, and that the commitment to customer-driven decisions is real.
What Systematic Innovation Enables
Better Products, Faster
The most direct benefit of systematic innovation is a higher product success rate. When product investments are made based on quantified customer need, the probability that the resulting product addresses real demand is dramatically higher than when investments are made based on opinion or qualitative themes.
But there is a second, less obvious benefit: the eliminated investments. In every ODI engagement, the opportunity score analysis reveals that some significant fraction of the current development roadmap is addressing low-opportunity outcomes — needs that are already adequately served. These investments can be redirected toward high-opportunity outcomes, increasing the overall expected value of the development portfolio without increasing the budget.
This means systematic innovation is not just about building better products. It is about eliminating the waste embedded in a product development process that regularly invests in features nobody needs while leaving genuinely underserved needs unaddressed.
Defensible Competitive Positioning
Products designed around quantified, underserved customer needs are positioned differently from products designed around engineering capability or competitive imitation. They address needs that competitors have not identified — because competitors are also operating on intuition and vocal customer feedback — and they can articulate that positioning in terms of specific customer outcomes.
“Our product minimizes the time required to confirm that the system is correctly configured before beginning operations” is a positioning claim tied to a specific, measurable customer need. It can be validated by customers in the language of their own work. It is not a marketing claim — it is a product fact. And product facts that address genuinely underserved needs are defensible in ways that feature lists and specification tables are not.
A Research Asset That Compounds
The job maps, outcome statements, and opportunity scores produced by systematic innovation are reusable assets. The job map for “move heavy materials to elevated installation positions on construction sites” — once built — is valid for years, because the job itself is stable. The opportunity scores can be refreshed annually at a fraction of the cost of the original research, tracking how market dynamics shift as competitors improve their products.
Over time, an organization that practices systematic innovation builds a proprietary research asset: a detailed, quantitative understanding of customer needs across its core markets that no competitor has. This asset compounds: each year of measurement adds to a longitudinal dataset that reveals trends in customer need and competitive dynamics invisible from cross-sectional data alone.
Common Failure Modes
Stopping at the Job Map
The job map is a valuable artifact, but it is the input to ODI, not the output. Organizations that run qualitative discovery — building job maps, identifying outcome themes — and stop before the quantitative phase have invested in insight but not in decisions. The most common reason for stopping is cost or timeline pressure; the cost of this decision is a research investment that does not produce the strategic outputs it was designed to generate.
Validating Existing Assumptions
The most insidious failure of systematic innovation is using its outputs selectively — surfacing the opportunity scores that confirm existing roadmap priorities and rationalizing away the scores that challenge them. This is harder to prevent than it sounds. The social pressure to maintain existing commitments is real, and the appetite for research that overturns those commitments is limited.
Building process structures that require explicit engagement with the full dataset — particularly the findings that are inconvenient — is the organizational design challenge that systematic innovation requires.
The One-Project Mistake
Running one ODI project and declaring victory on systematic innovation is common and counterproductive. A single project produces a research output; it does not change how the organization makes product decisions. Systematic innovation requires integration into the planning cycle, institutionalization in the metrics and incentive systems, and sufficient repetition that the methodology becomes normal — not exceptional.
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The Broader Context: Why DACH Organizations Are Particularly Well-Positioned
DACH-region enterprises have an engineering culture that is among the strongest in the world. The precision, quality orientation, and technical depth of Austrian, German, and Swiss manufacturers are genuine competitive assets — recognized globally and hard to replicate.
The risk that comes with this strength is also genuine: technology fixation. When organizations define themselves through their technical capabilities rather than through the problems they solve for customers, they invest in technical improvements that customers do not value while leaving valuable opportunities unaddressed.
Systematic innovation does not threaten the engineering culture. It directs it. The output of an ODI process is not a customer wish list — it is a set of specific, measurable outcome statements that translate directly into engineering requirements. Engineers who understand which outcomes are most underserved have precisely the information they need to direct their technical capability toward the highest-value problems.
This is what systematic innovation looks like in practice: engineering excellence, directed by rigorous market intelligence, applied to the specific problems that matter most to the customers who will determine the company’s market position for the next decade. Not innovation theater. Not guesswork. A repeatable process that produces better products, more efficiently, with fewer costly failures.
Frequently Asked Questions
The Path Forward
Enterprise organizations face a choice. They can continue to invest in innovation processes that are fundamentally driven by opinion, anecdote, and organizational politics — accepting the 70-90% product failure rate as an industry norm. Or they can build the organizational capability to know, with quantitative precision, which customer needs are genuinely underserved and to direct their considerable engineering and execution capability toward addressing those needs.
The path to systematic innovation is not a technology investment or an organizational restructuring. It is a research discipline and a decision-making practice — a commitment to replacing guesswork with measurement and organizational hierarchy with evidence.
The methodology for making this change exists. It has been validated over 30 years and 400+ engagements across industries. It is available to any organization willing to invest in learning it and applying it consistently.
The organizations that build this capability will not just innovate more successfully. They will accumulate a proprietary understanding of their markets that compounds over time — becoming more accurate, more actionable, and more defensible with every cycle of research and product development. That is the durable competitive advantage that systematic innovation builds.
Explore the Core Concepts
These articles provide deeper treatment of specific elements of the systematic innovation approach:
- What Are Jobs to Be Done? — The foundational framework for understanding customer goals
- What Is Outcome-Driven Innovation? — The complete ODI methodology from market definition to growth strategy
- The History of JTBD: From Christensen to Ulwick — The intellectual lineage of the methodology
- Why 86% of ODI-Guided Products Succeed — The evidence base for the methodology’s performance
- The ODI Market Definition — Why the first step changes everything
- From Opportunity Scores to Product Roadmap Priorities — How quantitative research drives roadmap decisions
- Quantitative Research in Innovation — Why gut feeling is not enough for product strategy
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