AVID Methodology (Paper)
Create New Offerings That Customers Want In Less Time With Less Risk
Business today is complex! Yet this did not happen overnight. Rather, complexity has been steadily increasing over the years driven by globalization, the Internet, faster development of technologies, more competitors entering the market, more diversity in what customers want, multiple stakeholder demands, and other factors. What is not obvious, however, is the effect that complexity has had on innovation risk.
The relationship between complexity and innovation risk is an indirect one. Complexity affects the business environment in two significant ways. First, complexity drives business uncertainty — the extent to which business conditions and outcomes range from predictable to unpredictable. Second, complexity drives industry clockspeed, a concept introduced by Charles Fine in 1998, which he described as the evolution rate of an industry.
Specifically, we define this evolution across two dimensions — how quickly products/services mature, and how often business models change. Industry clockspeed determines the extent that business conditions are stable or dynamic. In sum, complexity affects innovation risk via the impact it has on business uncertainty and industry clockspeed (see figure below).
Back in the day, the business environment was relatively stable and predictable. Industry boundaries were well compartmentalized, and most industry clockspeeds were slow and steady. Business uncertainty was relatively low because everyone followed the same business rules, practices, and strategies. Companies knew who their competitors were and where they stood in the pack. Back then, companies determined what products/services customers would get and when they would get them — a supply side economy.
This was a wonderful world where companies had the luxury of efficiently planning and executing profitable growth. A dramatic increase in complexity has changed all of this. The business environment is now dynamic and unpredictable (see figure below). Companies live in a world today where competitive response to new products/services is faster; disruptive business models appear with little notice; and customer priorities can change on a dime.
The upside of increased complexity is that there are more ways to create and deliver value. Ironically, this also makes it is more difficult to identify value creation opportunities and how best to exploit these opportunities. Thus, increased complexity, coupled with the fact that industry clockspeeds are much faster, means that there are more growth opportunities. The bad news is that complexity obscures these growth opportunities, making them difficult to identify and riskier to implement. We call this the innovation paradox.
Back in the day, the primary growth strategy for most companies was to incrementally extend the value of existing products/services for as long as it remained profitable to do so. They had plenty of time to exploit their core products/services because the business environment was relatively stable and predictable. In today’s business environment, however, incremental value enhancement does not have the horsepower to propel growth like it used to.
The window for exploiting existing products/service is much shorter because product/service maturity happens faster — commoditization sets in much sooner. The bottom line is that the life expectancy of core products and services is getting shorter in most industries, and the only way to compensate for this is to build more growth engines. Companies that are not able to consistently build new growth engines will see their earnings steadily decline.
The most profitable and enduring growth engines attract customers with new value propositions and open up new markets. To build these kinds of super growth engines, companies must engage in product/service innovation. The catch-22 is that innovation risk is high today. Because of this, only one in five new product/service innovations succeed.
We describe a successful innovation as one that produces an amount of profitable revenue sufficient to carry its pro rata share of a company’s growth target relative to the time and resources invested into the innovation. Thus, an innovation may produce marginal profits and still be considered a failure by our definition.
Failure to Resolve Innovation Risks
We cannot say that risk is the ultimate cause of innovation failure. That would be too bold a statement. Although innovation risk kills many new product/service projects, the real reason for these failures is that companies are not effectively resolving innovation risks prior to execution. The key to successful innovation is to first recognize risk factors, and then proactively eliminate and/or manage them so that success is in your favor, not the other way around. What are these risk factors? In short, a risk factor is anything that gets in the way of:
- Creating a product/service that will be valued by customers
- Converting this value into sufficient customer demand to generate revenue
- Developing a business model capable of converting the revenue into enough net profit to drive growth. We use the word “enough” because the success of an innovation is relative to the growth target that the innovation project is aiming for.
Given these objectives, you can image the incredible number of innovation risk factors working against success — selecting a poor innovation opportunity, not understanding what customers value, developing an innovation that can easily be imitated by competitors, unexpected costs that permanently decrease the profitability of the innovation, poor operational execution of an innovation project, conflicts and snags with partners in the ecosystem — the list goes on. All risk factors fall into one of two categories:
- Risk factors that are ignored or unrecognized
- Decisions, designs, strategies and/or activities that are informed by flawed assumptions.
Value Innovation Versus Value Enhancement
Let’s dig even deeper. The question now is: why do companies have a hard time recognizing and then effectively resolving these risk factors during the innovation process? To answer this question, we must first make the distinction between two very different types of product/service development projects.
Companies tend to view new product/service projects as if they were the all same. In fact, they are not all the same. At this point, we clarify the difference between value enhancement and value innovation. Improving and/or extending an existing value proposition, a product/service design, and/or business model is value exhancement. A value enhancement targets existing customers of a value platform in the same market. Value enhancements are relatively low risk projects because the path forward is well illuminated.
There is relatively little ambiguity as to what is needed, who it will be sold to, how it will work, how it will be sold, and how it fits in with the current business model. The success of an existing value platform validates these assumptions. Value enhancement is about exploiting existing products/services by incrementally moving the value proposition up a sustaining value trajectory. Because industry clockspeeds are getting faster, the window for exploiting core products/services is much shorter today. This is due to the effects of complexity.
A value innovation, on the other hand, is a product/service that creates a completely new value proposition. Specifically, the new product/service involves helping customers get an important “job” done in a new way. A new value platform is created that can be exploited over time by moving the value proposition up a sustaining value trajectory. You know you are dealing with a value innovation if the value proposition:
- Requires a fundamental change in the current business model or requires a completely separate business model.
- Involves a new market.
- Sells to new customers.
- Involves changes to the company’s business ecosystem.
Unlike value enhancement, value innovation is high risk because the path forward is ambiguous — there is no existing value platform to inform decisions about what customers value, the business model, and product/service design. It is seldom the case that a good value innovation opportunity falls at the front door of a company. To find these opportunities, companies must engage with customers, suppliers, partners, and others in different ways to discover new ways to create, deliver, and capture value.
Value innovation opportunities and risk factors elude conventional business analysis techniques. For instance, companies often use the SWOT framework to access opportunities and risks. They use traditional “voice of the customer” methods to capture customer requirements; they develop a business case that argues for a growth opportunity; they quantify the profit-generating potential of a new product/service; and they assess the risk factors. If the business case passes the ROI test, management approves the project, and off it goes to planning and execution.
The problem is that the entire business case is premised on the notion that innovation opportunities and risk factors are already “out there” and therefore can be captured and analyzed. The business case is assumed to be complete and accurate when, in fact, it is replete with omissions and flawed assumptions. Even though the business case may look reasonable on paper, the failure of the project is already guaranteed, because the opportunity is at best marginal and at worst fictional. The innovation risk factors cannot be resolved because they are not known. This is the strange case where the company doesn’t know what it doesn’t know.
Now we can better answer the question posed above — why do companies have a hard time recognizing and then effectively resolving risk factors during the innovation process? The answer is that they are using conventional product/service development tools and methodologies designed for low risk value enhancement projects to drive high-risk value innovation projects. They fail to recognize that value innovation projects require a radically different approach to identifying growth opportunities and resolving innovation risks.
From a project management perspective, a key difference between a value enhancement project and a value innovation project is in the discovery phase (also referred to as the pre-development phase). Before a value innovation project can move out of the discovery phase and into the development phase, three critical outputs are necessary for innovation success:
A compelling value proposition for a customer segment.
A product/service design that can fulfill the customer value proposition better than competing solutions.
A business model that can create and deliver the product/service to customers while generating the required net profit for the company.
All assumptions associated with these critical outputs must be valid, or the project is doomed to fail.
The Discovery Phase and Critical Outputs
The discovery phase of an innovation project is often called the “fuzzy front end” because of the ambiguous nature of innovation under conditions of high uncertainty; i.e., the critical outputs are not clear. Innovation risk increases to the extent that the assumptions underlying critical outputs are flawed. The ambiguity surrounding value innovation makes it easy to generate fictional or weak critical outputs. Further, activities in the discovery phase for a value innovation project are viewed as seemingly chaotic, unpredictable, and unstructured.
By contrast, the discovery phase of a value enhancement project is relatively straightforward (more pre-development than discovery). Because the critical outputs in a value enhancement project are in line of sight, activities are structured, predictable, and formal. The purpose of a value enhancement project is to move an existing value proposition up a sustaining value trajectory. As such, the path forward is relatively clear because the value platform that is being extended already exists. That is, the existing value platform provides a baseline from which to identify/define critical outputs and to validate the accuracy of these outputs before moving to the development phase.
Because the critical outputs are known up front, the risk factors for a value enhancement project are conspicuous and can therefore be readily identified and described in the business case. Uncertainty for a value enhancement project is relatively low. Most of the risk associated with a value enhancement project has to do with execution variances, which are managed during the development phase. A value innovation project, on the other hand, does not have an existing value platform which can be used as a baseline to identify/define critical outputs. Uncertainty obscures the critical outputs, which drives up innovation risk. Unlike execution risk, which can be managed in the development phase, innovation risk must be resolved in the discovery phase before execution. Because of the extreme difference in the level of uncertainty, the dynamics of the discovery phase for a value innovation project are much different from the dynamics of the discovery phase for a value enhancement project.
The high level of uncertainty in the discovery phase partially explains why the success rate for value enhancement projects is generally much higher than that of value innovation projects. Conventional methods/tools effectively handle the discovery phase for value enhancement projects because the critical outputs are in line of sight with an existing value platform. Due to higher uncertainty, however, these same methods/tools are less effective at handling the discovery phase for value innovation projects.
Companies that use conventional methods/tools for the discovery phase when uncertainty is high, often end up with fictional or marginal critical outputs based on omissions and flawed assumptions. In such cases, value innovation projects are literally designed to fail, regardless of how well the project is executed because innovation risks have not been resolved. To increase the success rate of value innovation projects, companies need to adopt an approach for the discovery phase that involves a different mindset, different methods, and different tools than those conventionally used for value enhancement projects.
The discovery approach is premised on the idea that critical outputs are “discovered” through exploratory activities that engage customers, employees, managers, partners, and other stakeholders in the search for new ways to create, deliver, and capture value. These exploratory activities involve:
- Re-conceptualizing the market and the value that a company is capable of creating,
- Interacting with customers and non-customers in different contexts to gain insights into the important “jobs” that they need to get done,
- Designing profitable business models that offer customers compelling new value propositions,
- Designing new product/services based on how customers perceive value rather than how the company defines value, and
- Finding new ways to work with business suppliers and partners to create value for customers and the business ecosystem.
In short, the discovery process is about collaborative learning. Discovery is not about executing a plan or applying conventional analytical methods/tools coming from the mindset that critical outputs and risks can readily be identified because they are already “out there”. The outputs of the discovery phase are critical outputs that have been validated through empirical procedures effectively resolving the innovation risks. Only with correct critical outputs can a company move a value innovation project to the development phase with a reasonable expectation that it will succeed.
A Better Way to Manage the Discovery Phase
The Agile Value Innovation Discovery (AVID) methodology is a best practice for structuring the discovery phase for value innovation projects. The AVID methodology is fast and flexible under conditions of uncertainty because it does not require that the critical outputs be known up front. It does not require a set of assumptions that can be fixed throughout the innovation process, as is often the case for conventional development methodologies. In fact, just the opposite is true. Ideas and assumptions cycle through the discovery process and may change frequently as a result of ideation, collaboration, and empirical testing.
Assumptions that fail the reality test generate new learning loops that are immediately cycled back into the next iteration of ideation and testing. Failed assumptions are viewed as “off target”, which becomes the impetus for continued discovery learning. The AVID methodology ensures that the critical outputs of the discovery phase define a profitable growth opportunity, and that the underlying assumptions have been validated. When companies invest the time to do the discovery phase well, execution moves quickly and efficiently and hits the right target.
The traditional phase/stage gate methodology has come under fire for being inadequate for use with value innovation projects. Many criticize this methodology as too rigid and planning-oriented for innovation purposes. Specifically, the phase/stage gate methodology is criticized for stifling creativity with too much structure, inhibiting learning, killing nascent innovation ideas that do not meet financial criteria, generally slowing things down to a crawl during the innovation process, et cetera. We would suggest, however, that the problem is not the phase/stage methodology per se. Rather, the problem is that the phase/stage gate methodology does not work well in the discovery phase when uncertainty is high.
The phase/stage gate methodology emerged in the late 1980s as the best practice for structuring the development of new products/services in a business environment where uncertainty was low. That is, the phase/stage gate methodology was designed for incremental value enhancement projects where the critical outputs are in line of sight. The problem is that over the last few decades, uncertainty has increased dramatically. This uncertainty manifests itself in the discovery phase which exploits the weakness of the phase/stage gate methodology — its inability to generate critical outputs under conditions of high uncertainty.
That said, there is no need to discount the part of the phase/stage gate methodology that is useful for innovation projects, which is the execution phase. Once a lucrative growth opportunity has been identified, and the innovation risks have been resolved in the discovery phase, the phase/stage methodology is the best practice for managing the development phase of the project.
When it comes to value innovation projects (or radical value enhancement projects) where the critical outputs are obscured by uncertainty, the best way to increase the odds of success is to use a methodology better suited for the purpose. The Agile Value Innovation Discovery (AVID) methodology effectively structures the discovery phase for value innovation projects where uncertainty obscures the critical outputs.
Since the focus of AVID is solely on the discovery phase, AVID can be used on the front-end of a phase/stage gate methodology. AVID helps teams to identify the best growth opportunities and to resolve innovation risks prior to the development phase. Further, using the AVID methodology significantly decreases the time to market for a new product/service by compressing both the discovery and execution phases.
This is possible for two reasons. First, the agility of the AVID methodology enables teams to move through the discovery phase much faster and with much more precision than the conventional phase/stage gate methodology. Second, the testing and validation steps of the phase/stage gate methodology along with the subsequent decision gate (gate 5) can be eliminated because a solution design has already been empirically validated by the AVID methodology in the discovery phase. Removing these steps significantly compresses the execution phase (see figures below), thereby reducing the costs associated with this phase.
Execution goes much faster and more smoothly because execution teams can work in parallel with one another — each receives execution handoffs from the discovery phase that make the critical outputs actionable for them. That is, various management/administrative functions, product development/engineering, sales and marketing, and operational departments can all be working on their part of the execution plan at the same time. Thus, AVID and a streamlined phase/stage gate component decrease the time to market for new products/services by compressing the total lead time of the process.
In summary, using AVID on the front-end of a traditional phase/stage gate methodology eliminates many of the steps and decision gates in the execution phase, dramatically reducing the risks, costs, and time-to-market for developing new products/services.
Up until now, a rigorous and comprehensive methodology for structuring the discovery phase has not been proposed in our opinion (in the context of new product/service development within established organizations). What are generally used in lieu of the AVID methodology are piecemeal methods and tools that fail to address all the critical aspects of the discovery phase. The result of such an ad hoc approach is the selection of fictitious or marginal growth opportunities where innovation risks go unresolved as the project moves into the execution phase.
Alternatively, the AVID methodology offers companies an orderly and repeatable best practice for managing the discovery process. The effective use of the AVID methodology will dramatically increase the chances that a new product/service innovation will succeed. Ultimately, AVID enables companies to systematically create profitable growth engines in less time and with less risk.
AVID is ideal for companies of all sizes that want to develop innovative products and services that will help the company achieve its strategic growth objective. The input into the AVID methodology is a growth target for a yet-to-be identified new product/service. The growth target specifies the desired amount of net profit that a new product/service needs to generate over some time period to support the company’s strategic growth plan.
The AVID methodology is the best practice for identifying a new product/service that is capable of achieving this growth target. Specifically, the AVID methodology delivers:
- A new product/service capable of achieving a desired growth target
- A business model that is capable of creating and delivering a compelling value proposition to customers while generating the required net profit for the company
- An optimal product/service design that is capable of fulfilling the customer value proposition via the business model.
All assumptions associated with the critical outputs are validated via empirical testing. The validation of assumptions resolves all innovation risks prior to the execution phase. The final deliverable of the AVID methodology includes execution strategies for implementing the critical outputs in the execution phase.
AVID Leverages the Core Innovation Technologies
Over the years, a variety of innovation theories, concepts, principles, methods, tools, and techniques have been introduced that can be very helpful in the discovery phase. Collectively, we call these the core innovation technologies (see table below). Each of these innovation technologies focuses on a certain aspect of innovation discovery or enables a specific approach to innovation. While some of the core innovation technologies cover more ground than others, all of them are limited to specific contexts, domains, or circumstances of innovation.
The problem is that even though the core innovation technologies are very helpful in the innovation process, using one or a combination of these technologies is generally not sufficient to produce a successful innovation. Innovation projects can fail because teams miss or neglect critical aspects of the discovery phase. By doing so, they unknowingly increase the risk of their value innovation projects, which is not realized until it’s too late.
For example, Blue Ocean Strategy offers effective concepts, methods, and tools to help companies identify lucrative growth opportunities in new markets. However, Blue Ocean Strategy does not address the specifics of how to design a good product/service, business model structure and dynamics, assumption testing procedures, operational execution, ecosystem risks, and other things.
Disruptive Innovation offers powerful strategies for driving new products/services into markets without eliciting incumbent response. Yet disruptive Innovation suffers from the same gaps as Blue Ocean Strategy.
The Customer Development Model and the Lean Startup methodologies are great for startups, but are less useful for established companies. Open Services Innovation focuses on the flexible sourcing of the raw ingredients of innovation, but does not deal with other important aspects of the innovation process. Design Thinking offers a great creative process for innovating via rapid prototyping, but it ignores many other aspects of creating customer demand.
Discovery-driven Innovation provides powerful innovation management methods, but it has little to do with the actual innovation process itself. In short, every innovation technology has its strengths within a certain area/context of innovation, but none of them address all the critical aspects of the discovery phase. Critical aspects of the discovery phase that are not effectively dealt with will significantly increase the risk of innovation failure.
The Agile Value Innovation Discovery (AVID) methodology is not a new innovation technology, per se. Rather, it is a composite of all the aforementioned core innovation technologies. This enables companies to leverage any and all the innovation technologies that are useful during the discovery phase.
In fact, the innovation technologies provide the means of executing steps in the AVID methodology. Going forward, we refer to an innovation technology as a “lens” and/or a tool. A lens provides a conceptual/theoretical view of a particular aspect of innovation. A tool structures the activity associated with executing steps.
The AVID methodology resolves three important issues. First, it enables teams to more effectively leverage more innovation technologies across a wide spectrum of innovation contexts, circumstances, and conditions. Second, it addresses all critical aspects of the discovery phase of innovation, significantly decreasing the risk of innovation failure. Third, it provides a common language around which teams can ideate, collaborate, and work to maximize knowledge creation and workflow productivity during the innovation discovery process.
Why AVID is Called “Agile”
The AVID methodology is called “agile” because it incorporates some of the general principles of Agile Methodology, an iterative (non-linear) approach to software development that has been evolving since the 1980s. The agile approach uses empirical evidence captured throughout the discovery phase to generate customer value insights rather than a fixed execution plan that assumes knowledge of customer needs. Because of this, customer needs do not need to be known ahead of time.
Assumptions regarding value creation, delivery, and capture are tested along the way to determine their validity before they are accepted. Agile methodology is a more effective way to work when the project requirements cannot be known in advance due to uncertainties. An Agile approach provides opportunities to assess the direction of a value innovation project throughout the discovery phase as new knowledge is acquired.
The agile approach is “iterative” because teams are required to cycle back to previous steps to challenge flawed assumptions when they fail the reality test. The agile approach is also “incremental” because stages and/or steps provide the structure that moves a project steadily towards completion. By contrast, teams using a conventional linear-sequential product development model have only one chance to get each aspect of a project right.
With a linear approach, the efficacy of the project is not known until the project is complete. In the agile paradigm, every aspect of development — customer needs, product/service design, business model, risks, and assumptions — is continually revisited throughout development.
A Systems Perspective of Innovation
Many believe that the discovery phase for product/service innovation cannot be structured because it is too amorphous and dynamic. They say that there are just too many scenarios, approaches, contexts, and that no one methodology can take all of these possibilities into account. They assert that structuring the discovery phase will stifle creativity and will slow down the process with bureaucracy and controls; that innovation is a serendipitous phenomenon that depends on having just the right people and conditions to make it happen.
The fact is that innovation is a very complex phenomenon — it is the ultimate multi-disciplinary sport. Innovation involves all aspects of business — competitive strategy, operations, marketing, finance, supply chain, leadership, culture, organizational capabilities, ecosystems, and other things. Who is comfortable claiming that they are competent in all of these business disciplines? Not many. Quite simply, innovation is overwhelming for most of us mortals.
Then there are the core innovation technologies like Blue Ocean Theory, Design Thinking, etc. Although each of these technologies is very useful, none of them individually encompasses all the aspects and contexts of the discovery phase. It’s like having bits and pieces of a large map with lots of chunks missing. Companies do their best with what they have. They gather a few pieces of the map and proceed through an ad hoc innovation process. Sometimes they develop a winner.
However, all indications are that only one out of five product/service innovations succeed. These are terrible odds! What most people do not appreciate is just how many ways a product/service innovation can fail. The truth is that no amount of creativity, bravado, or wishful thinking will surmount these pitfalls. The only way to increase the odds of success is to use a best practice for the discovery phase that enables a company to consistently develop successful innovations via an orderly and repeatable process.
A best practice provides the foundation for continual learning and the development of organizational capabilities. It is through a best practice that all employees rise to the occasion to contribute to the innovation efforts, not just the few gifted or the most influential.
The dynamics inside the discovery phase include interactions between various aspects of the business, interactions with customers, interactions between people, interactions between a company and its business ecosystem, and interactions with competitors and the market. The discovery phase is too complex to comprehend from a linear perspective, which conceptualizes a phenomenon as a collection of discrete causes and effects. The discovery phase is too complex to be reduced in this way.
However, the dynamics of the discovery phase can be understood from a systems thinking perspective. When viewed through the lens of systems thinking, it becomes clear that certain patterns of interactions produce certain outcomes. Because the various aspects of a system are interdependent, interactions are subject to tradeoffs and constraints. In the world of systems, interactions produce feedbacks. These feedbacks then inform more interactions.
In the discovery phase, interactions and feedbacks iterate to produce knowledge which then informs decisions. These decisions determine the success or failure of outcomes. This is why conventional linear methods like phase/stage gate do not manage the discovery phase well when uncertainty is high. As the name suggests, “discovery” is the result of multiple iterations of interactions, feedbacks, and learning over the course of time.
The best growth opportunities cannot be ascertained by conventional analysis methods like SWOT, which assumes everything there is to know is already out there. Instead, the best opportunities are discovered through interactions. As these opportunities are developed into business models and product/service designs, no assumption can be taken for granted. The discovery phase requires empirical validation rather than analytical verification.
The Big Picture
The AVID methodology is a systems approach for managing the discovery phase of product/service innovation. The structure of AVID facilitates rapid learning and knowledge creation through cycles of interactions and feedback which systematically reveal critical outputs for lucrative growth opportunities. The AVID methodology enables teams to quickly refine critical outputs and resolve associated innovation risks through empirical procedures.
The AVID methodology is a hybrid framework that incorporates certain structural features, functions, and logic from other development methodologies — Agile Methodology, Phase/Stage Gate methodology, the Spiral Lifecycle Model, Kline’s Chain-Linked Model, and the New Concept Development Model.
The AVID methodology is both incremental and iterative. It incorporates theories, concepts, strategies, principles, techniques, methods, and tools from many of the core innovation technologies — Theory of Disruptive Innovation, Customer Jobs Theory, Blue Ocean Theory, the Customer Development Model, Lean Startup, Discovery-driven Growth, the Business Model Canvas, the Value Proposition Canvas, Open Services Innovation, Innovation Ecosystems, Design Thinking, and TRIZ. The AVID methodology is a contemporary best practice for structuring the discovery phase of value innovation projects.
External Inputs and Outputs
The AVID process begins with a high potential opportunity that is identified via the Entrepreneurial Insight Generator (indicated by the inward pointing arrow labeled “Hi Potential Opportunity”). The final outputs of the AVID methodology are execution strategies for a validated opportunity that is capable of achieving certain profit-revenue targets over time (indicated by the outward pointing arrow labeled “Execution Phase”).
AVID Stages, Action Steps, and Critical Stage Outputs
AVID structures the value innovation discovery phase into four incremental stages of development: (Stage 1) verify opportunity, (Stage 2) design job solution, (Stage 3) validate opportunity, and (Stage 4) develop execution strategies. Each stage consists of a number of action steps that are required to complete that stage in the discovery process. This results in a total of 11 action steps through four stages of development. The 11 action steps are iterative while the four stages are incremental.
A team will need to cycle back to previous steps when:
- Assumptions fail the reality test
- New knowledge is acquired in later steps that impacts earlier assumptions
- It becomes clear that the pieces or logics of the formative critical outputs do not fit together into a coherent whole.
The four stages represent the incremental development of the critical outputs and systematic resolution of innovation risks.
Each stage involves a critical output which serves as the input into the subsequent stage. The critical output from Stage 1 is a viable business model; the critical output from Stage 2 and part of Stage 3 (test differential value) is the best job solution fit possible for the target customers relative to competing solutions; the critical output from Stage 3 is evidence that the job solution prototype is the best value for the target customers relative to competitive solutions; and the critical output from Stage 4 are execution strategies (as articulated in a demand creation plan), which are passed to the execution phase of the project.
Critical outputs are expected to change when new knowledge is acquired as a result of iterations between action steps. The changing of these four critical outputs through iteration is represented by the four large, red, circular arrows positioned between each of the four stages. The text inside each iteration arrow is the critical output that is passed on from the proceeding stage to the subsequent stage. The circular arrows move backwards indicating that a team will need to cycle back to the preceding stage if that critical output is not achieved.
AVID Decision Gates
Decision gate 1 requires a “go forward” or “go back” decision on critical output 1 (viable business model) from Stage 1 before proceeding to Stage 2. This decision involves evaluating the business model to determine its viability and whether it is capable of meeting the profit-revenue target for the product/service based on what is known at that point (traditionally called a “proof of concept”).
Decision gate 2 requires a go forward or go back decision on critical output 2 (job solution fit) from Stage 2 and part of stage 3 (test differential value) before proceeding to validating the business model in Stage 3.
Decision gate 3 requires a go forward or go back decision on critical output 3 (best value) from Stage 3 before proceeding to stage 4.
Decision gate 4 requires a go forward or go back decision on critical output 4 (effectual plan) from Stage 4 before proceeding to the execution phase of a value innovation project.
Again, multiple iterations between action steps will be required to evolve the critical outputs to the point where they are capable of passing through the decision gates. That is, a team should know before presenting to decision makers if a critical output will meet the decision criteria for its respective gate.
With a go back decision on any gate, a team will need to iterate through the previous action steps until the gate criteria can be met. If the gate criteria cannot be met, the value innovation opportunity is abandoned, preventing the company from developing a new product/service that will likely disappoint. However, even when an innovation opportunity is abandoned, the learning acquired from the effort is cumulative and benefits future innovation efforts.