Tag Archives: Prototyping

The Fail Fast Fallacy

The Fail Fast Fallacy

GUEST POST from Rachel Audige

The Fail Fast Fallacy is that while we speak of failing fast, many corporate executives are not going to do so successfully because a) corporates continue to expect success b) the highest achievers are the ones being asked to fail at what they are best at and c) we are injecting perfectionism into our testing and prototyping.

As lean and agile methods have permeated businesses both large and small, the notions of excellence and success have been replaced by catch cries around ‘failing fast, often and cheap’.

There is a certain amount of what we call ‘innovation theatre’ (the speak but not the action) around failure awards and risk rewards in the innovation ecosystem. It sounds brilliant but I am yet to walk into a room of employees that does not speak of ‘fear of failure’ in the organisation.

Notwithstanding some excellent programs around experimentation, with most clients, I hear:

  • “You are not paid to experiment. You are paid to know.”
  • “You can’t take risks or try anything new.”
  • “There is no way that I will agree to take on a big project like that. I’d rather play it safe.”
  • “We lose so much time getting all the boxes ticked.”

I now work with a variety of engineering companies and most not only encounter this issue but also cannot afford to fail. The investments are too substantial or the safety risks too great.

As a general rule, perfectionism is rewarded. Mistakes are not. The corporate paradigm is predicated on providing shareholder value and this does not leave a lot of wriggle room for mistakes.

There also seems to be a dissonance in organisations between what is preached and what is practiced or, in other words, what is promised and what is, in fact, punished. We hear the leadership talk about taking risks but, in parallel, see a colleague fired when an initiative fails.


Not only is the general idea of failing an issue but, specifically, the ones most expected to ‘fail fast’ and be experimental are often those least willing to. Let me illustrate what I mean:

You know when you are amongst the high potentials in a corporate. You tend to get the ‘good gigs’. You are sent to head office for special training programs. You get more opportunities. You get more time with the boss. You also have more occasions to get involved in special projects.

One of the projects I was interested in when I was in a corporate role was part of a four-pillar strategy and the one I was dying to lead was around building an innovation culture. My title didn’t include the word ‘innovation’—no one had that in the organisation at the time—but I was given the green light to drive an innovation community, train coaches and inject innovative thinking across the business. I received the flack, but I also enjoyed the buzz. I did this alongside other ‘high potentials’. Anyone who was given permission to step outside their objectives and spend time helping others to solve their problems tended to be perceived as being excellent at their day job and was encouraged to do more.

Most of the people in the project were high performers in their roles and, ironically—unless an exceptional growth mindset prevails— probably the least likely people to want to be seen making mistakes or failing.

This is entirely consistent with research performed by Liz Wiseman who identified how our expertise and expectation of excellence when working in our area of expertise prevents us from exposing ourselves to less than excellent work.

A similar desire for excellence and perfectionism creeps into prototyping and the way we test out ideas. There is a tendency to overwork the prototype, to create something fully-functioning. As Alberto Savoia—who coined the word ‘pretotyping’—said: “The tough part is getting over our compulsion for premature perfectionism and our desire to add more features, or content, before releasing the first version.”

A pretotype is a stripped-down version of a product, used to merely validate interest. For your restaurant with delivery service, a pretotype could be a simple website that tracks how many visitors come to your page, giving you an idea as to whether or not people would be interested in ordering food from you.

Melbourne-based thought leader, Steve Glaveski, writes that corporate executives have understood the need to prototype but are not tending to ‘fail fast’ because “What they create is too often fully-functioning concepts which cost thousands of dollars and take months to develop.”

This may be particularly true for the technology providers. “People get stuck into tech too quickly,” explains Streicher Louw, Behavioural Strategist and former Innovation Lead at NBN. “They try to build the prototype in too high a fidelity. The moment you start carving that prototype into tech, it is less malleable.”

Not only is it ‘less malleable’ but the teams behind the prototyped concept have a strong incentive to ‘prove’ the value of the investment. The more you invest in it, the harder it will be to let it go and admit it was the wrong thing. You are likely to add more bells and whistles and expect that this will win the customer over.

Louw, who spent seven years in Australia’s National Broadband Network (NBN) where there is a strong culture of Human Centred Design and experimentation, says: “We get so much more for our efforts if we take the time to work out what problem a product solves for a customer and how he intends to use it before we start to build it.”

In many cases, it leads to months of wasted time and large sums of money, energy, hopes and dreams.

So how can we do a better job of failing fast?


The challenge is to make failing more palatable, more tenable. To do that we need to get comfortable with feeling uncomfortable. How? There are a number of steps organisations can take and environments they can create.

1. Don’t just tell them, enable them.

At innovation conferences, we occasionally hear from corporate intrapreneurs who have instigated failure awards or CEOs who have learned to be vulnerable with their organisation and share mistakes. Both remain the exception. The most realistic initiatives I have encountered do not overplay the tolerance for risk taking or mistakes, rather, they remove obstacles for doing things differently and invest in the enablers (robust methods, resources, skilled experts). People are rewarded for working with other teams to help solve their problems. More focus is put on the work that goes on behind the scenes to get to a result (good or bad). People are not simply told to ‘go and innovate’; they are offered solid training in methods that will help with the full innovation journey. People are not fired for trying.

2. High performers should work outside their comfort zone to free them up to make mistakes.

Have you noticed how people feel safer asking what they think is a ‘stupid question’ in contexts where they are not expected to know better? In my innovation lead role, the workshops I ran for product managers in divisions outside mine were probably where I could bring most value. I was expert in a method but knew nothing about their business and felt entirely free to ask the naive and pointy questions. The participants were also more receptive to my input because I was not invested in the project; I wasn’t perceived as having an agenda.

“Put your staff in situations where they can’t help but make mistakes. Position them at the bottom of a learning curve where they’ll need to scramble back to the top by taking small steps, making mistakes, and getting fast feedback. Do more than make failure an option, make it inevitable,” advises Liz Wiseman.

This realisation is apparent in the Wiseman Group’s research which suggests that we should deliberately put people outside their area of expertise so that they give themselves permission to produce the minimum viable product, “not because they are told to, but because that’s all they know how to do”.

When we work in this ‘rookie mode’, as Wiseman calls it, we approach things in surprisingly productive and innovative ways.

Many of us have experienced this and I use this when assigning innovation champions outside their area of expertise. When we step out of our comfort zone and are not expected to be experts, we are less weighed down by expectations. Our novice state makes us more curious, we listen better and we are more humble and receptive to others. When I have managed others or observed myself in this mode, I find that I am more likely to make mistakes but I get over them faster. You tend to chunk things down and check on how you are doing and learn and adjust in a more agile way.

3. Run thought experiments that embrace ambiguity (with constraints).

One of the safest ways of testing what you know before talking with the customer is to run ‘safe’ thought experiments. Some simple yet robust approaches I have used include:

▶ Mapping exercises where you walk in the shoes of your customer and explore the ‘so what?’ of the key features of a given offering. SIT calls this ‘Attribute Value Mapping’ and it’s a great way of not only identifying sticky value propositions but unearthing what you need to improve to make the proposition all the more true!

▶ Bias-busting exercises using tools to scan for any mental fixedness that might have undermined the concept—before you move to testing it.

▶ Asking the ‘empty chair’. It is an established practice to include empty chairs for absent stakeholders. You then check your idea from their perspective.

4. Get used to working with a range of low-fidelity prototypes.

Try to use the fastest method of validation that you think is reasonable. The cadence of business is increasing. Cycles have to decrease. Fast prototyping is crucial. The term comes from the Greek word prōtotypon meaning ‘a first or primitive form’. It is just that.

“You will uncover the product you are supposed to make by prototyping the one you thought you should make,” says Streicher Louw.

If we are truly going to ‘fail fast’, we need to avoid falling in love with our idea and move quickly and cheaply. Be experimental and document both your hypotheses and what you learn. Teams should be created that enable effective experimentation and include a copywriter, a graphic artist, a data specialist and someone well versed in the products.

In my experience, there is tremendous value in rough concepts as a quick and easy way of testing functionality rather than a more polished visual representation of a product or service. The more finished it is, the less they engage and feel they can contribute. It feels done and dusted.

“When the first person you give it to uses it differently to how you intended, rather than educating the user you adapt the design,” says Louw.

The mindset needs to be one that is totally geared towards adapting to the user. It should be rough and approximate so that it is as flexible as possible, meaning that you can learn and change it quickly and for zero cost as you do.

Reid Hoffman, founder of LinkedIn, warns that: “If you are not embarrassed by the first version of your product, you’ve launched too late.” And nobody wants to do that.

Most of us cannot afford to be playing it safe. We need to accelerate the learning cycle. If we want this idea of failing fast to be meaningful, we need to give people the frameworks to innovate, the space to run safe thought experiments, to build iteratively and the opportunity to work outside their area of expertise to free them up from their own aversion to failure.


In most cases, these should be shared with target customers or users to have them interact with it, respond, hone and, if possible, co-create.

1. Diagrams & Maps

Any sort of diagram or map can be a prototype. That includes stakeholder, process, customer journey, jobs to be done, UX maps. Work through what the customer is seeking to do and explore current and proposed solutions to see where they fit along the customer journey or on a simple map.

2. Stories

News of the Future: Tell the story of your idea and describe what the experience will be like. Letter to Grandma: Would she understand your concept?

3. Cardboard

Create low fidelity prototypes; simply mock up a concept using cardboard, sticky tape, bluetack and imagination and see people interacting with it. This way they can very rapidly work out how people use it. Build the next iteration incorporating user interaction with a first level of technology but with a human behind it, the processing is still simulated. Once the cardboard has done the job, you may want to move to prototyping tools such as POP or Invision to build an app that people can play with.

4. Sketches

We all know that a picture tells a thousand words.

5. Lego prototyping

Bring in some customers and describe your product. Have them build it with Lego while your model remains hidden. Bring yours out and discuss only once you have gleaned insights from their models.

6. Storyboarding

This is a visualisation of the complete experience over time.

Break it into scenes to make sense of interactions. Invite your customers to react and adapt.

7. Wizard of Oz pretotypes

This is rather artful deception in that the MVP is an illusion. There is nothing behind it. Zappos is known for having started with no store or inventory of their own; they simply had a web page. Dropbox was launched on the back of a simple three-minute video on ‘Hacker News’ which gave the founder immediate, high quality feedback.

It is a clever approach but should not feel like false advertising as that will quickly erode trust.

8. Social media ads, eDMs and landing pages

Eventbrite, Google, LinkedIn, Facebook—all these platforms enable you to cheaply test a concept and, based on click-rate, decide if there is a market. This is a good way to test purchase intent. It is also a good way to test two different campaigns with distinct value propositions.

9. Crowdfunding

The beauty of this approach is that it gives you the ability to test the market while raising funds to build it. In 2012 in what was then the most successful Kickstarter (crowdfunding platform) campaign in history, Pebble Technology Corporation was able to prove a market for wearable tech long before any of the tech giants moved in that direction.

10. 3D prototypes

Most of us have now seen a 3D printer in action. They are astonishing. They are also a relatively cheap way of testing the look and feel—as opposed to the functionality—of a concept.

11. Pilot Simulations

This is simply small scale testing of an experience. It is possible to create a different experience in a single store, for example, without generalising across all stores.

12. Run ECHO sessions

Use very rough sketches of concepts to enable clients to Engage, Co-create and HOne the solution.

Image credit: Rachel Audige

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The Surprising Power of Business Experiments

The Surprising Power of Business ExperimentsInterview with Stefan H. Thomke

I had the opportunity recently to interview fellow author Stefan H. Thomke, the William Barclay Harding Professor of Business Administration at Harvard Business School to talk with him about his new book Experimentation Works: The Surprising Power of Business Experiments, to explore the important role that experimentation plays in business and innovation.

1. Why is there a business experimentation imperative?

My book Experimentation Works is about how to continuously innovate through business experiments. Innovation is important because it drives profitable growth and creates shareholder value. But here is the dilemma: despite being awash in information coming from every direction, today’s managers operate in an uncertain world where they lack the right data to inform strategic and tactical decisions. Consequently, for better or worse, our actions tend to rely on experience, intuition, and beliefs. But this all too often doesn’t work. And all too often, we discover that ideas that are truly innovative go against our experience and assumptions, or the conventional wisdom. Whether it’s improving customer experiences, trying out new business models, or developing new products and services, even the most experienced managers are often wrong, whether they like it or not. The book introduces you to many of those people and their situations—and how business experiments raised their innovation game dramatically.

2. What makes a good business experiment, and what are some of the keys to successful experiment design?

In an ideal experiment, testers separate an independent variable (the presumed cause) from a dependent variable (the observed effect) while holding all other potential causes constant. They then manipulate the former to study changes in the latter. The manipulation, followed by careful observation and analysis, yields insight into the relationships between cause and effect, which ideally can be applied and tested in other settings. To obtain that kind of learning—and ensure that each experiment contains the right elements and yields better decisions—companies should ask themselves seven important questions: (1) Does the experiment have a testable hypothesis? (2) Have stakeholders made a commitment to abide by the results? (3) Is the experiment doable? (4) How can we ensure reliable results? (5) Do we understand cause and effect? (6) Have we gotten the most value out of the experiment? And finally, (7) Are experiments really driving our decisions? Although some of the questions seem obvious, many companies conduct tests without fully addressing them.

Here is a complete list of elements that you may find useful:


  • Is the hypothesis rooted in observations, insights, or data?
  • Does the experiment focus on a testable management action under consideration?
  • Does it have measurable variables, and can it be shown to be false?
  • What do people hope to learn from the experiments?


  • What specific changes would be made on the basis of the results?
  • How will the organization ensure that the results aren’t ignored?
  • How does the experiment fit into the organization’s overall learning agenda and strategic priorities?


  • Does the experiment have a testable prediction?
  • What is the required sample size? Note: The sample size will depend on the expected effect (for example, a 5 percent increase in sales).
  • Can the organization feasibly conduct the experiment at the test locations for the required duration?


  • What measures will be used to account for systemic bias, whether it’s conscious or unconscious?
  • Do the characteristics of the control group match those of the test group?
  • Can the experiment be conducted in either “blind” or “double-blind” fashion?
  • Have any remaining biases been eliminated through statistical analyses or other techniques?
  • Would others conducting the same test obtain similar results?


  • Did we capture all variables that might influence our metrics?
  • Can we link specific interventions to the observed effect?
  • What is the strength of the evidence? Correlations are merely suggestive of causality.
  • Are we comfortable taking action without evidence of causality?


  • Has the organization considered a targeted rollout—that is, one that takes into account a proposed initiative’s effect on different customers, markets, and segments—to concentrate investments in areas when the potential payback is the highest?
  • Has the organization implemented only the components of an initiative with the highest return on investment?
  • Does the organization have a better understanding of what variables are causing what effects?


  • Do we acknowledge that not every business decisions can or should be resolved by experiments? But everything that can be tested should be tested.
  • Are we using experimental evidence to add transparency to our decision-making process?

Experimentation Works3. Is there anything special about running online experiments?

In an A/B test, the experimenter sets up two experiences: the control (“A”) is usually the current system—considered the champion—and the treatment (“B”) is some modification that attempts to improve something—the challenger. Users are randomly assigned to the experiences, and key metrics are computed and compared. (A/B/C or A/B/n tests and multivariate tests, in contrast, assess more than one treatment or modifications of different variables at the same time.) Online, the modification could be a new feature, a change to the user interface (such as a new layout), a back-end change (such as an improvement to an algorithm that, say, recommends books at Amazon), or a different business model (such as an offer of free shipping). Whatever aspect of customer experiences companies care most about—be it sales, repeat usage, click-through rates, or time users spend on a site—they can use online A/B tests to learn how to optimize it. Any company that has at least a few thousand daily active users can conduct these tests. The ability to access large customer samples, to automatically collect huge amounts of data about user interactions on websites and apps, and to run concurrent experiments gives companies an unprecedented opportunity to evaluate many ideas quickly, with great precision, and at a negligible cost per additional experiment. Organizations can iterate rapidly, win fast, or fail fast and pivot. Indeed, product development itself is being transformed: all aspects of software—including user interfaces, security applications, and back-end changes—can now be subjected to A/B tests (technically, this is referred to as full stack experimentation).

4. What are some of the keys to building a culture of large-scale experimentation?

Shared behaviors, beliefs, and values (aka culture) are often an obstacle to running more experiments in companies. For every online experiment that succeeds, nearly 10 don’t—and in the eyes of many organizations that emphasize efficiency, predictability, and “winning,” those failures are wasteful. To successfully innovate, companies need to make experimentation an integral part of everyday life—even when budgets are tight. That means creating an environment in which employees’ curiosity is nurtured, data trumps opinion, anyone (not just people in R&D) can conduct or commission a test, all experiments are done ethically, and managers embrace a new model of leadership. More specifially, companies have addressed some of these obstacles in the following ways:

They Cultivate Curiosity

Everyone in the organization, from the leadership on down, needs to value surprises, despite the difficulty of assigning a dollar figure to them and the impossibility of predicting when and how often they’ll occur. When firms adopt this mindset, curiosity will prevail and people will see failures not as costly mistakes but as opportunities for learning. Many organizations are also too conservative about the nature and amount of experimentation. Overemphasizing the importance of successful experiments may inadvertently encourage employees to focus on familiar solutions or those that they already know will work and avoid testing ideas that they fear might fail.

They Insist That Data Trump Opinions

The empirical results of experiments must prevail when they clash with strong opinions, no matter whose opinions they are. But this is rare among most firms for an understandable reason: human nature. We tend to happily accept “good” results that confirm our biases but challenge and thoroughly investigate “bad” results that go against our assumptions. The remedy is to implement the changes experiments validate with few exceptions. Getting executives in the top ranks to abide by this rule is especially difficult. But it’s vital that they do: Nothing stalls innovation faster than a so-called HiPPO—highest-paid person’s opinion. Note that I’m not saying that all management decisions can or should be based on experiments. Some things are very difficult, if not impossible, to conduct tests on—for example, strategic calls on whether to acquire a company. But if everything that can be tested online is tested, experiments can become instrumental to management decisions and fuel healthy debates.

They Embrace a Different Leadership Model

If most decisions are made through experiments, what’s left for managers to do, beyond developing the company’s strategic direction and tackling big decisions such as which acquisitions to make? There are at least three things:
Set a grand challenge that can be broken into testable hypotheses and key performance metrics. Employees need to see how their experiments support an overall strategic goal.

Put in place systems, resources, and organizational designs that allow for large-scale experimentation. Scientifically testing nearly every idea requires infrastructure: instrumentation, data pipelines, and data scientists. Several third-party tools and services make it easy to try experiments, but to scale things up, senior leaders must tightly integrate the testing capability into company processes.

Be a role model. Leaders have to live by the same rules as everyone else and subject their own ideas to tests. Bosses ought to display intellectual humility and be unafraid to admit, “I don’t know…” They should heed the advice of Francis Bacon, the forefather of the scientific method: “If a man will begin with certainties, he shall end in doubts; but if he will be content to begin with doubts, he shall end in certainties.”

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Examining the Role of Virtual Reality in Futurology

Examining the Role of Virtual Reality in Futurology

GUEST POST from Chateau G Pato

Virtual Reality (VR) has become a major part of futurology, which is the study of predicting the future of technology. In recent years, VR has been used to explore potential future scenarios, to understand how technology might impact our lives, and to identify potential opportunities and challenges. Through the use of VR, futurists can gain a better understanding of how technology may shape the world of the future.

Simulations of Potential Futures

One way that VR is being used in futurology is to develop simulations of potential futures. By running simulations in a virtual environment, futurists can explore different scenarios and identify potential opportunities and challenges. For example, researchers at the University of Southern California are using VR to create simulations of future cities. By allowing users to explore these virtual cities, researchers can gain insights into how different technologies and trends may shape the future of urban living.

Creating Immersive Experiences

Another way that VR is being used in futurology is to create immersive experiences. Through the use of VR, users can experience a potential future in a way that would not be possible in the real world. For example, researchers at Microsoft are using VR to create immersive experiences that explore potential future scenarios. By allowing users to explore and interact with a virtual world, researchers can gain insights into how different technologies may shape our lives.

Virtual Prototypes

Finally, VR is being used in futurology to create virtual prototypes. By using virtual prototypes, futurists can gain insights into how a technology might function in the future. For example, researchers at Google are using VR to create virtual prototypes of autonomous cars. By allowing users to explore and interact with a virtual car, researchers can gain insights into how autonomous cars might function in the future.

Overall, VR is playing an important role in futurology. By using VR, futurists can gain a better understanding of how different technologies may shape the world of the future. Through the use of simulations, immersive experiences, and virtual prototypes, futurists can explore potential future scenarios and identify potential opportunities and challenges. As VR technology continues to develop, it is likely that it will become an increasingly important tool in futurology.

Bottom line: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Unsplash

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