Design Comes First for High Tech Entrepreneurs: Quantum Computing, Robotics, Artificial Intelligence, BioComputing, Machine Learning

While the design is increasingly central to the operations of established technology companies, it remains overlooked within research teams pursuing initial research of those same technologies. This is due to the lack of understanding held by high tech entrepreneurs about the role of design. They think it is decoration - when in truth - it is the fundamental process to transform abstract ideas into new realities. Design is the cornerstone to successful entrepreneurship.

In the last few weeks, I have had conversations with dozens of high tech startups that have one foot in the lab and one foot on the path to a new market. Interplanetary robotics, quantum computing, and super intelligent machines are exciting new domains described at length in business and technology magazines alike, yet these ventures struggle to overcome the leap from research in unstructured domains to generating meaningful human-product experiences and viable companies. They struggle to think and work like designers.

How Design Serves Advanced Technology Companies

Computers are everywhere. As more of our lives are inundated with computers - cars, planes, banks, security, government - the software on those computers is getting very sophisticated and difficult to test. It is also difficult to build to ensure that it is testable. Think of the complexity to manage all air flights in a country, the testing of that software is critical to everyone's safety - but with so many airplanes, airports, satellites and so on - how do you test it?

In my engagement with machine learning companies throughout Silicon Valley and Pittsburgh, I found one that has solved this problem. They build tools to help other big companies build reliable software for complex systems.  They can even predict if your company is going to create a bug before it happens. It is incredible.

The company does amazing work and is profitable. Yet they have a terrible website. They know it. Their tools do not really have a user interface that users enjoy using or easily understand. Their software is very advanced and difficult to communicate.  It is challenging to hire for this company. They see every market as possible and yet are not sure how to access them. Their work is in such high demand they are doing well... but will this always be the case?  What are the limits of their current market? How do they know?

Upon offering to help with the website, I've since had multiple conversations with the founder of this company. I understand the technical details of their software. As a researcher, I am equipped to study and understand the problems they face. I also am a designer so I am able to communicate it to people who do not understand.  Consequently, as a designer, I also have methods to rapidly TEST & LEARN from the range of possible consumers on how to tailor the language, the product, and the transaction. We do not, consequently, have to worry about marketing or even business development. With design, we can KNOW and VALIDATE our language, our image, our transaction, and our team to transform machine learning into highly needed customer solutions. With Design, we can better engineer success, not just software.

This is Different than The Current Business Models and Operations

At each startup, the conversation unfolds the same way every time.  I ask about the product and the CEO demonstrates or describes the product. I ask about the business and they display a prepared document or slide deck on the business strategy and organizational shape.  I ask about financing and they tell me about early mistakes made impacting future financing for the negative. I ask about the distinction between the market they set out to pursue vs the market opportunity they have discovered - and they start to get depressed.  I ask about new market or growth opportunities, and they say "we hope to figure that out soon... " and perhaps "if only we could hire the right person for business development."

Hire the right person for business development?

Certainly, the most important aspect of building a company is the team. Yet to assume that the success of the business - to align internal operations to market demand - is the job of a solo individual is misguided.  MBA programs tout the ability to transform graduates into such beings - and there are many times this person can hold an instrumental role - but for highly sophisticated technologies, there is no evidence that a traditional business approach will always work.  To make the assumption is high risk.

In addition, when I ask "do you have a designer?" - the CEO confuses my question, thinking I asked: "Do you have someone to make this pretty?"  They say no or "that is important, but we aren't there yet" or "we know it needs to be attractive so we outsource that, we have someone make it look good." The worst ones point to their current success and say "we don't need a designer, we are doing just fine" and months later are panicking because they had all along been meeting the needs of only one or two clients and could not actually scale their business. By their definition, to have a designer on the team is expensive and the person would sit around most the time with nothing to do. If they learn their lesson - it is often too late.

Design Driven Business is an Optimized Transaction

Every company requires some basic components - they need the product, they need an efficient way to generate the product, they need a clear path to connect to the consumers for the product, they need a very simple mechanism to exchange the product for capital, and they need the ability to do this over and over again.  If this process is well tuned, the capital acquired will outweigh the capital exhausted and the company can flourish.

Nothing in this product demands marketing, or branding, or financial planning. There is no need to hire agile coaches or communications consultants. I also mentioned nothing about aesthetic design. These things - these tasks - are simply tools to help solve the core problem: the material transaction. The material transaction is made possible by the optimized movement of information. It is possible to invest in these things to make that transaction happen, but within an unknown market for an untested product, operating on the thresholds of possibility... it is difficult to measure the effectiveness of these tools. Marketing language, targetted advertising, and agile product development are all just attempts to optimize the movement of information. Yet with advanced technologies, early wins are just as likely events of luck. There is no way to know.

Design is the Process to Optimize Transactions

Designers are specialized in the art of communication. This communication may take place through graphic text, plastic form, or even through the process of a work itself (this is where the post-its come into use). Optimized communication within a team will increase efficiency, to transform a team from thinkers to doers but with less technical debt.  Optimized communication to external consumers will get the two groups together faster for the transaction to take place.

Optimized design of the transaction itself - a form of communication - will result in high satisfaction for everyone.  If transactions are fast and positive, and communication from the buyer can connect to the team (who internally is optimized to leverage it by communicating), the transaction will take place again.

You can call this stuff strategy, marketing, team building, communications, - whatever you want. But to get from lab to market - you are better off to optimize your business by starting with Design.

Unlocking Machine Learning with Human Centered Design

Human Centered Design (HCD) is at its core, a process for eliciting the practice of human values and embedding those new values into a new artifact, process, or service.  It is perhaps challenging to consider how HCD may have a role within something as quantitative as Machine Learning, yet HCD can be an important component to the formation downstream benefit for machine learning services. The value to injecting HCD into the design and use of machine learning solutions is valuable to contribute to unknown opportunities and risk the threat of future-tense machine learning decisions.

Quick Intro to Machine Learning
Machine learning is best described as a methodology for a computer program to learn to write new programs without human assistance.  There are two primary domains of machine learning - supervised and unsupervised. Unsupervised machine learning utilizes various statistical functions to identify patterns in information and to extrapolate decisions from that information.  The reference to Big Data is often a use of unsupervised algorithms that are, at their core, a regression analysis. Supervised Machine Learning also leverages statistics, but to do so, relies upon a body of data curated by an individual.  The computer program builds an understanding of the data to replicate it or to use that understanding as a filter on other data.  For example, if 2000 images of a tree are shared with the program, it will use those 2000 images to build a concept of a tree, and will then be able to identify a tree from a suite of images which may or may not contain trees.

Of Machine Models and Human Cognition
To build an HCD approach to Machine Learning, it is important to first distinguish how machine learning is similar and different from Human Learning.  First, both the human brain and the ML program do have a core similarity - they are massive engines of statistical pattern recognition.  Our brains engage and understand the world through pattern recognition. To see an apple, know it is an apple, and understand what an apple does is in fact a massively complex undertaking.  The connection between sight and concept requires our brain to internalize and make sense of about 8,000 different points of information... how light bounces off the surface into our eyes to inform geometry, color, shape, texture, and subtle visual implications of weight, density, and so on that are difficult to articulate.  When our brain has sufficiently identified enough information bits to create a pattern - the apple pattern - it has built a conceptual model.  Our brains are massive repositories of conceptual models, and we use these models to discover new ones. "That is not an apple but it looks like an apple."

Machine learning programs are similar (as most are based on our brain's neural architecture). They use many different methods to analyze information and identify a pattern, like "tree."  Yet their methods are various different from ours, such as rapid quantitative measurements between corners, measurements of curvature, measurement of gaps - and thus generate a very different kind of model.  The ML model is not interpretable to the human - nor does it need to be. The result is the total information required by a computer to make inferences about other possible models.  A computer, also, has limits and it is difficult to make inferences. Thus a human can rapidly intuit a new situation "apple by a tree suggests apple tree," the computer will only see an apple and see a tree,  unless enough information is presented about apple trees to make the leap.

The Learning and Reproduction of Human Values
Machine Learning asserts a transactional value by  using found pattern within a set of information to predict how that pattern is extended against a new inflow of information.  We can use ML to facilitate market analysis, reduce risk in complex situations, and optimize organizations. Yet what does this ability inform... optimize how? Reduce risk to do what?  HCD is a process that requires the designer also conducts pattern analysis - but a key distinction is in the domain of qualitative patterns.  How does a user feel about a given situation over time?  How can that situation shift over time to elicit other feelings?  The intersection of opportunities between HCD and Machine Learning are vast when you consider the bigger context of the data.

Within supervised machine learning, the assembly of training data could be done in bulk or it can be curated.  A designer can pay close attention to the data... what kind of trees are presented? What health are they in?  Can multiple sets of training data be presented that contain healthy trees, sick trees, and variations in environmental conditions?  By curating the data, the human agent can tune the specific types of patterns to be determined, and thus generate greater value later.  Not all data is equal.

Likewise, as the outputs are generated from an ML program - such as new sets of data (often relayed in a dashboard or a customized suggestion), the human experience of that new data point can be catalogued and leveraged to tune the ML program.  While there is such a thing of "Human in the Loop," to aid the precision of the algorithm, another way to thin about this is "Value in the Loop," to reward the program for generating particular kinds of experiences. While abstract, the program does not need to understand the human value - it will build its own conceptual model to make sense of the behavior.  These values can tune the algorithm to new directions over time, wherein we can expect the algorithm to generate new data points very much unlike the original yet of great use.

Geographic Variance in Modelling
A critical aspect of being human is the variations that exists between humans for perception in the world.  Within small groups, language can take on a range of meanings, while across geographies, the variance of meaning ascribed to concrete things can be quite large. Machine learning generates one kind of solution for one kind of person and then generate a new solution for another kind of person... it can be granular, like Netflix, to suggest customized outputs.  Yet what about machine learning applications for broad data sets that are irregular?

Within the design community, I advocate close consideration of the role of place and data. There is a tendency for developers to look to large open data sets. Vast databases exist with training data (like this popular one at MIT).  Yet consistent with my above comments, what are the values which drive the labelling of this data?  For example, if you apply this training data to images of Mogadishu, it will generate results like "earthquake."  Not only is this inaccurate - but the correct label will vary depending on who and where the labeler resides.  A Somali in Nairobi will give a different answer than a design student in Boston to describe a set of data.  Yet most Somalis in Nairobi will supply more similar labels.

Consequently, it is not enough to parse data by meaning.  One must also ground the meaning, and to do this, I suggest the use of classic lat/long coordinates.  If we can build rich databases of place-based data and place-based identification, we can do more than build intelligent softwares, we can build softwares that are flexible to the global shifts in meaning and identity which are traditionally at odds with the demands of computation.  To fuse machine learning with the vast ocean of human value creation and reproduction is a great opportunity for the future.

The Execution Gap : Bottom Up and Top Down is a False Paradigm

Over the last 16 years I have spent a lot of time with people who have committed their lives toward social change.  Historically these people were architects in rust-belt cities and then aid workers in refugee camps.  Later I worked with lawyers and government officials in government offices. Today I continue the same work, but work with electrical engineers and data scientists. As I continue to engage the same kinds problems but across wildly different disciplines, it has become clear that many of the deeply entrenched beliefs of one group are completely foreign or irrelevant to another. 

For most of the 100+ year history of urban planning, the cannon of planning theory describes top-down vs bottom-up processes. Various terms are used to describe this such as "centralized vs decentralized" or "rationalist and grass roots." A few other sub-models of planning have emerged, such as Advocacy Planning and Participatory Action models. What has been less dominant is a recognition that our technologies are a big part of the planning process. Consideration of the technology as an equal force throughout the process opens many new directions for exploration. More importantly, technologies such as machine learning, when applied to dormant low-tech economic sectors, are far more powerful change agents than people. 

A critical question that has been consistently absent from planning theory concerns use of the appropriate technology relative to the complexity of the problem and idea. Why spend a million dollars, engage hundreds of people, conduct dozens of technical studies, and then document a robust vision for the future in only a book or a website? How do these technologies advance the implementation of the vision? These technologies increase risk because the translation of a complex vision from written text into built form or economic process is a massive leap.

There are other ways of course. Through small scale pilots, rapid trials, online Wikis, collaborative mapping sessions, partnerships with local technology companies (big and small), one can always find a way to create new experiences.  A classic example is from the Czech Republic; after the fall of communism a process was needed to rapidly educate the public on the history of their nation which had been suppressed under totalitarian rule. Rather than merely release a book, the city of Prague presented a living timeline of Czech history. Maps, photos, documents, and didactic panels lined the main streets so that citizens can walk through the history of their nation and learn how one era informed the next.  Woven into the architecture, the city itself functioned as the core technological platform to engage citizens and enhance thinking on the needs and futures of the nation-state.  As a robust experience, it also created stakeholder buy-in for the next logical step.

Design as a Process of Optimization

To develop a concept and not implement as part of the ideation process is high risk. Working at the EPA, for example, I find it very hard to convince leadership and scientists that a particular organizational or technical change is possible. For many decades, people have made suggestions and introduced new visions, but rarely have these been successful.  In contrast, I find a way to rapidly prototype every idea. Should we create a new division? Lets get some people to volunteer and test how this division might operate. Should we purchase an enterprise solution?  Lets get the 30 day free trial and see how we might need to also change if we buy it. Rapid prototyping any kind of solution will always give more information and reduce risk.

Planning and Design as Synthetic Futures

As Herbert Simon pointed out, the scientific method is an excellent way to understand the natural world that is around us, yet when faced with creating something new in the world - and thus artificial - we turn to design. The more we understand the design process, and how it is similar or different from the scientific method, the better we can design and introduce new things into the the world.

The role of design as an additional demand - to not only stretch and reveal what is possible in the future - but to help us get to what is preferable future. Empowered with the ability to make something new, design is a means to consider multiple future realities, and equipped with the ability to implement them, each idea on a  possible future exists in a state between idea and reality. Consequently, mindfully experimenting with a material process to test implementation measures and reduce the risk, one develop synthetic futures and transform them into new realities.

Impact Any Problem Like a Designer

This morning I was asked if I approach design management (the emergent term for the application of design to organizations to engage complex problems) as an architect or as a communications designer. It was a little hard to answer is because the answer squarely falls into the domain of neither and both. While I believe whole heartedly in a non-disciplinary approach to design, if it is necessary to specify a form of design practice and theory, it is important to recognize that these fields exist on a gradient. Over the last 15 years of research and practice in design and urban planning, I have developed a systematic approach to structure problems and interventions across this gradient and have developed a simplified conceptual model in response to demands.

Illustrated above, I look at all problems as fitting somewhere within the above structure - wherein a problem might be defined by thought and language (sign), by tangible products and interfaces (object), by spatial context (environment) or by large scale invisible systems such as formal law and culture (culture can be considered another expression of law).  So for example, if you are attempting to solve a big problem like poverty, it exists in all sections because poverty is contextual, has artifacts, and there are many existing specific words and images that are used to communicate the idea of poverty. Whereas a problem that is very well defined, like the design of a toaster, will most likely sit squarely in the domain of objects.

At Carnegie Mellon University, I was introduced to Richard Buchanon's theory on the Four Orders of Design, which was very similar to my own model, but we maintain very different objectives and I found his model is harder to operationalize.  Buchanon does have other variations,  and additional work on operationalization has been pursued by Golsby-Smith.  There are additional models out there and while I find it validating and interesting to look at their models, my own approach emerged from the field. It is not informed by these other works, I point them out merely because they exist, and I find these other frameworks are missing a critical component, the people.

Within my framework, the most important characteristic is the recognition of dispositions held by people who occupy each conceptual frame. Without people - there is no framework.  There are no objects or contexts without people - there is also no design or strategy - people are the scaffolding of everything.  Consequently, I do not consider this framework as universal, but is thus far, a model that has arisen organically through various design interactions with people, technologies, and spaces.

Yet people are highly complex. I cannot manage to engage all people in every project on every level, and therefore I have created over the years a simple heuristic to note critical attributes of people within a project which will determine the project outcome.  All stakeholders in a project have, want, or lack resources (for their interest or mine), they likewise all hold a unique vision for their lives and the project outcome, along with specific objectives, beliefs, expectations, and baggage from prior experiences. I cannot juggle all these balls for every person at one time, but I do attempt to establish an sense of organizational structure between different actors and their unique attributes.

The Difference of Design in Organizations
Lets imagine an international company hires me with a big problem phrased as a simple request, "how do we become the leader in our industry?"  When companies have approached me before, they have already conducted many of the preliminary SWOT assessments and strategy planning sessions. Perhaps they have utilized a more traditional business management strategy, but found the problem too sprawling to meet the discrete demands... for example, it is impossible to identify and validate appropriate benchmarks if the problem itself is poorly defined. Driven by market research, they believe they should offer the same technologies or assets as their competitors. Yet it makes no logical sense to mirror competitor if you want to be the industry leader. It is important to do something new - but what and how?

Using the Framework to Generate the Big Picture
As a complex problem, I will work at all levels of the framework. In the case of robotics, I will take this problem and build a detailed understanding of their robots (the object).  I will look at all documentation, branding, communications, and language used in relation to their robots (sign). I will go into the facilities where the robots are used and spend time understanding the relationship between the robots and the Environment. I will also look at sales trends, labor laws, social movements, international trade agreements, and latent technology trends (perhaps also concerning language, objects, environments) to capture a big picture understanding of the robots in relation to some invisible systems that shape the future of the company.

Digging Deep into the Social Terrain
In this process, however, I have left out the most important component: the people.  Who is talking about the robots? Who is listening? Where are they? When customers purchase the robots, what are they saying? How do they represent their needs?  In the environmental context, who works with the robots and how?  How do those people exchange information about the robot in that context?  More importantly, how does the robot relate (or not) to the resources, objectives, histories and so on, of every person at every level?  If I go to the capital and talk to the people shaping policies that inform the outcome of robotics markets - congressmen and lobbyists for example - what can I learn from them?

Insight by Emergence
Working through this framework to understand the problem is only the first step. Yet the more I can build knowledge at each level of interaction, the more flexibility I have to craft and test interventions. Perhaps the corporate strategy is something simple like a branding campaign or promoting a national policy - yet perhaps it also requires manipulation to the technology to better facilitate how other companies train their employees? If that is the case, what language should be used and by what device should it be communicated? By means of this approach, the key insights and opportunities will emerge and do not need to be invented - nor can they be predicted.

Impact by Design
The final outcome of such a problem will rarely consist of one single action.  Rather, it will require many small interventions choreographed across the system.  Some interventions are more important than others. To describe the processes on design for wicked problems deserves more attention than I can provide right, yet with this framework, one is equipped to better understand any kind of problem to get going in the right direction by doing the following:

1. Get away from the tunnel vision of a personal discipline or expertise
2. Build an integrated and fluid systems understanding of a problem 
3. Identify many points of intervention across scale/scope and points of view
4. Leverage the most powerful yet high-risk asset of any problem, the people.
5. Uncover new opportunities for exploration and testing

Beyond Ideological Innovation - Into the Methods, Concept and Experience

I just spent the last two days observing members of an entrenched government and business community learning about principals of lean startup, design thinking, and agile processes.  That was a good thing and I'm proud of this community for walking down these roads.  By taking these steps they will be better equipped to produce strong results and different kinds of outcomes. Yet we should not confuse this set of practices as innovation.  There is a distinction between innovation methods, innovation as a concept, and innovation as an experience.

Methods

Innovation, as a term, has come to describe a set of methods to drive new forms of socio-cultural and economic production.  This brand of innovation, like all other ideologies, was borne of necessity within particular economic conditions. The rise of the merchant class advanced Capitalism, the Industrial Revolution pushed forth Marxism, and Silicon Valley delivered The Lean Startup. Like any ideology - complexity, nudge, sustainability to name a few others - innovation has been appropriated by corporations to benefit their bottom lines and advance their missions.

It is easy to identify when the concept of innovation has been repackaged for consumption. If you participate in a workshop concerning Innovation as Design, you will likely have to do something with post-it notes and white boards, and maybe have to participate in low-fidelity rapid prototyping exercises.  If it is concerned with Lean Startup, you might take a standard idea shared with your team then quickly go call a few customers to ask if they like it or make a mockup for them to test.  This is also the general description of many UX Design education programs today.  These strategies at their core attempt to reconcile the simplicity of the scientific method with the irrationality of social behavior.

These procedures are all good things to do. I do these things and teach them to others. More people should try them.  Is this innovation? Sometimes, but not necessarily. These processes can provide pathways to innovation, but more acutely, do more to provide pathways to success according to an already existing - but perhaps unarticulated - definition of success that is situated within the minds of the participants.  To satisfy the demands of a  shared disposition is not the same thing as realized innovation.

Concept

We must consider innovation as something far more powerful, a force at work within a paradigm shift.  When Thomas Kuhn wrote on the structure of scientific revolutions, he described the paradigm shift as a social process, in which an accumulation of outlier evidence - over time - suddenly sways social beliefs to then become the new normative reality. When the earth was believed to be the center of the universe, attempts to research the universe often validated this belief or were built off of the assumption. It was held as fundamentally true, and to simplify history, Galileo was executed because his evidence contradicted the belief. Over time, enough evidence accumulated in favor of Galileo's argument and the community changed their belief.  In consequence, a new era for the intersection of science, religion, and society was borne.

There are things like religion in which one must work to hold a belief - to have faith - and there are things we just believe outright, such as the shared experience of a color or the weather. Within this distribution of beliefs, science is held as objective and the scientific research method is void of human error, but we fail to consider how science or faith is founded on a widespread predisposition.   In theory we eventually learn to identify our collective errors and we pivot or manage the constraints. When that happens, the school text books are rewritten as the sun becomes the center of the solar system, the universe grows in size, and space/time is a fluid dimensional fabric we believe in but struggle to understand, because the evidence is at odds with day-to-day human experience.

Innovation is not about design, science, or lean frameworks. It is the distribution of events that brushed up against each other so as to transform the entire normative experience of reality. These moments could be anything - scientific evidence, a new idea,  an observation, or a conversation. Some of these moments might be brilliant and profound, but many are just outlier fragments and glitches of daily experience.  We like to imagine innovation as a singular act, but singular acts have limited force, and thus the power of innovation relies upon loose configurations.

These individual events have limited power as a singular instances, but in coordination, can become a fulcrum of radical difference. A strange turn of phrase, uttered at the moment a butterfly lands on your arm, might unlock the gateway to a new personal ontology.  When this moment happens en masse, all possible roads into the future shift toward a new direction of possibility.  When Latour wrote Artemis, the failure of the high-tech transportation system was described as a network effect of many flickering and semi-related life moments. Latour wrote this to prove that social systems do not exist but are merely perceived... yet when these flickers do align into a system?  That is innovation.

Obviously this approach to innovation is too complex for a corporation to adapt because it cannot be packaged as a playbook or a method.  It cannot be entirely designed and it cannot be diffused or appropriated with ease. The Cult of Innovation fills much of the demand for change but in 10 years, our corporations and governments will look to another trend for answers because the more tightly packaged a concept for distribution, the less that concept can satisfy complex organizational needs. A truer path of innovation will not be appropriated because it is a plurality of outliers, and the core of its value is a contradiction to what we hold correct. Design or lean tactics may bring us to innovation - but I suspect this happens with less frequency than we believe.

Experience

Often if something is innovative, we do not like it. We dismiss it.  It pushes against our values, rubs us the wrong way, and introduces friction into our lives. We can adopt methods to reduce pain or mitigate risks, but ultimately, change has a cost, and that cost is at times the very foundation of whatever we believe to be real and true. Innovation is painful because it forces our brains to work differently.  If you witness a singular event and consider it brilliant, it is only a good according to subscribed preconditions.  By this definition, Elon Musk's Hyperloop is a good idea, not innovation. It might be an innovative act with in lattice of other acts, but we will need to stand back and observe.  

We usually only know if something was innovative in the past tense.  We believe in historical periods and future epochs such as the renaissance, the 20th century, and the information age.  We do not know how to experience time in other ways and yet we also have no ability to determine if something is ending an era or creating a new one.  When a radical disruption creates discomfort and only appears to be situated within the broader trends of the present tense - then we do not call it innovation.  We expect dramatic paradigm shifts to be immediate and identifiable - but this is misguided thinking.

As innovation is ambiguous in time, diffused in activity, and dissonant in experience, we would benefit to stop repeating our expectations of innovation as 'sudden, concrete, satisfying and specific.' Changing our perception of innovation might give us a better path to embrace it. And later, when the dust has settled, we can look back to say innovation has happened, though we may not be able to repeat it. 

Finding Zero Gravity in Big Messy Social Problems

500 volunteers "move a mountain" 10 cm with spades, art performance by Francis Alys

I recently worked with a team who has been tasked with a massive problem in federal government. The problem is so huge, if you ask every person on the team to explain it, they will each give you a different answer.  They have less than a year to somehow tame this wicked problem.  I was brought in because with a problem so big and ugly, no one could agree on the team how to start dealing with it.

We all know that whenever someone says "well I'll tell you the real problem," you might as well stop listening to him because that person has no idea whats going on. First, there is no 'real' problem. There are only perceptions of problems, evidence to be found, and theories of how those perceptions and evidence match or do not match.  To define a big problem is hard, and sometimes impossible, but one needs a path forward and isolating everything through one concept is not the way.  To find the best path forward requires discovering the most opportune point of entry into the problem and the only way to find this is for everyone in the room to stop thinking.

In the past, I've written about the necessity to have clarity of your personal values regarding the work that you do and also the necessity to not inject those values in the work.  These statements may appear to be contradictory - but they are not.  You need to stop thinking about the problem, and identify where you stand, so as to better separate yourself and engage it on its own terms. Clarity of values is essential to ensure they are not integrated into your thought process and avoiding integration is essential to inform better thinking about the problem.

Its like when you buy a new car, and suddenly you see the same car on every street, but before you bought this car, you never saw it anywhere.  When you let personal values exist with your attempt to engage a problem - that x is good or bad, that x is desirable or not, that x is the right solution - that value is surrounded with a gravitational force that will pull other ideas and ways of working near it. Like just like seeing your new car over and over, you will see things that align with that value, and everything else will be less obvious. You might see every Black Toyota Corolla on the street and fail to see the Black Prius.  If you approach the problem stripped of values, it will be necessary to construct strategies to observe and measure ideas/insights, and these strategies will exist only in relation to the problem - not the other stuff. You will develop a better way to see cars.

Another thing that can derail the ability to deal with a big problem is when the problem/solution is to advance alongside a desired side-benefit.  If I approach a problem with the goal "this solution will be so excellent it will impress lots of people, generate a 10 million dollar contract, and therefore advance my career" then every idea and option will be weighed against that 10 million dollar contract.  A lot of people make this mistake and I used to do it all the time which led to more  frustration than happiness.  Approaching a problem in this way, you forget that there are a million external unknown factors which also will determine the acquisition of the reward, and if it doesn't happen, you will still not see them - believing that your solution was somehow a failure but unsure why.

The bigger problem with this approach, is that the solution generated is not likely to be the ideal solution for the problem because it was affected by the gravitational pull of the reward.  With the reward in the review mirror, I will lose sight of the fast and simple solutions in front of me. I will get distracted. I will lose others on the road. Under the stress of all these new conditions, I will generate an output that meets the reward criteria, but the output will not structurally align to the demands of the problem. It will only create a new set of problem conditions, eventually passing the problem to someone else, and potentially making the problem bigger.

There a place where your values come back into the problem - when the job is finished. The reward of this process does satisfy personal values, but on account that the process has generated the best viable solution that checks as many boxes as possible.  Since these boxes are determined by the demands of the problem, the problem has been crushed, and even if it continues, it is only a whisper compared to the previous chaos.  To watch something terrible change from chaos to whisper is truly satisfying.

To work in zero gravity is to be liberated.  Solve the problem according to the demands of the problem and the other things will likely happen anyway. Success, however you measure it, because you will leave a path of crushed problems behind you and others will eventually notice. And if they don't, it doesn't really matter because you've done something extraordinary that speaks for itself.