Tag Archives: innovation skills

Don’t ‘Follow the Science’, Follow the Scientific Method

Don't 'Follow the Science', Follow the Scientific Method

GUEST POST from Pete Foley

The scientific method is probably the most useful thing I’ve learnt in my life. It is a near universal tool that can be used in so many ways and so many places.  It unleashes a whole world of assisted critical thinking that is invaluable to innovators, but also in our personal lives.  Teaching it to individuals or teams who are not trained as scientists is one of the most powerful and enabling things we can do for them.  And teaching it to kids, as opposed to endless facts and data that they can easily access from the web is something we should do far more of.  

Recruiting Skills not Expertise:  When I was involved in recruiting, I always valued PhD’s and engineers.  Sometimes that was for their unique, specialized knowledge.  But more often than not it was more for the critical thinking skills they had acquired while gaining that specialized knowledge. In today’s rapidly evolving world, specific knowledge typically has a relatively short shelf-life.  But the cognitive framework embodied by the scientific method is a tool for life, and one that can be reapplied in so many ways.  .

Don’t Follow the Science, Follow the Process:  All too often today the scientific method gets confused with ‘following the science’.  The scientific process is almost infinitely useful, but blindly ‘following the science’ is often far less so, and can be counter productive.  The scientific method is a process that helps us to evaluate information, challenge assumptions, and in so doing, get us closer to truth.  Sometimes it confirms our existing ideas, sometimes it improves them, and sometimes it completely replaces them.  But it is grounded in productive and informed skepticism, and this is at the heart of why science is constantly evolving.

‘Follow the Science’ in many ways the opposite.  It assumes someone in a position of power already has the right answer.  At it’s best it means blindly follow the consensus of today’s experts.  All too often it really means ‘do as you are told’.   Frequently the people saying this are not the experts themselves, but are instead evoking third party expertise to support their viewpoint.  That of course is the opposite of science.  It’s often well intended, but not always good advice.

Science is not a Religion:  At the heart of this is a fundamental misunderstanding of science, and scientists. In today’s media and social media, all too often science and scientists are presented with a quasi-religious reverence, and challenging the current view is framed as heretical.  How often do you here the framing ‘scientists tell us… ‘ as a way of validating a position?   

This is understandable.  The sheer quantity and complexity of information we are faced with in our everyday lives is increasingly unmanageable, while big challenges like climate unimaginably complex.  I find it almost impossible to keep up with my own interests, let alone everything that is happening.  And some topics are so technical that they simply require translation by experts.  When someone announces they’ve discovered the Higgs boson particle, it’s not really practical for any of us to pop over to the local particle accelerator and check for ourselves.  So expertise is clearly an important part of any decision chain. But experts come with their own biases. An engineer naturally tends to see problems and through, an engineering lens, a chemist through a chemical one.

Science in Support of an Agenda:  One danger with the ‘follow the science’ mantra is that it is often used to reinforce a belief, opinion, or even agenda.  I’ve seen this all too often in my work life, with the question, ‘can you find me a paper that supports ‘x’.  This is often benign, in that someone passionately believes something, and wants to find evidence to support it.   But this is fundamentally the wrong question, and of course, completely ‘unscientific’.

The scientific literature is filled with competing theories, disproven or outdated ideas, and bad science.   If you look for literature to support an idea you can usually find it, even if it’s wrong.   Scientists are not gods.  They make mistakes, they run poor experiments, and they are subject to confirmation biases, ego, and other human frailties. There is a good reason for the phrase that science evolves one death at a time. Science, like virtually every human organization is hierarchical, and a prestigious scientist can advance a discipline, but can also slow it down by holding onto a deeply held belief. And mea culpa, I know from personal experience that it’s all too easy to fall in love with a theory, and resist evidence to the contrary. 

Of course, some theories are more robust than others.   Both consensus and longevity are therefore important considerations.  Some science is so well established, and supported by so much observation that it’s unlikely that it will fundamentally change.  For example, we may still have a great deal to learn about gravity, but for practical purposes, apples will still drop from trees.    

Peer Review:  Policing the literature is hard.  Consensus is right until its not. Another phrase I often hear is ‘peer reviewed’, in the context that this makes the paper ‘right’.  Of course, peer review is valuable, part of the scientific process, and helps ensure that content has quality, and has been subject to a high level of rigor.   If one person says it, it can be a breakthrough or utter nonsense.  If a lot of smart people agree, it’s more likely to be ‘right’.  But that is far from guaranteed, especially if they share the same ingoing assumptions. Scientific consensus has historically embraced many poor theories; a flat earth, or the sun revolving around the earth are early examples. More tragically, I grew up with the thalidomide generation in Europe.  On an even bigger scale, the industrial revolution gave us so much, but also precipitated climate change.  And on a personal level, I’ve just been told by my physician to take a statin, and I am in the process of fighting my way through rapidly growing and evolving literature in order to decide if that is the right decision.  So next time you see a scientist, or worse, a politician, journalist, or a random poster on Twitter claim they own scientific truth, enjoin you to ‘follow the science’, or accuse someone else of being a science denier, treat it with a grain of sodium chloride.

They may of course be right, but the more strident they are, or the less qualified, the less likely they are to really understand science, and hence what they are asking you to follow.  And the science they pick is quite possibly influenced by their own goals, biases or experience. Of course, practically we cannot challenge everything. We need to be selective, and the amount of personal effort we put into challenging an idea will depend upon how important it is to us as individuals.      

Owning your Health:  Take physicians as an example.  At some time or other, we’ve all looked to a physician for expert advise.  And there is a good reason to do so.  They work very hard to secure deep knowledge of their chosen field, and the daily practice of medicine gives then a wealth of practical as well as well as theoretical knowledge.  But physicians are not gods either.  The human body is a very complex system, physicians have very little time with an individual patient (over the last 10 years, the average time a physician spends with a patient has shrunk to a little over 15 minutes), the field is vast and expanding, and our theories around how to diagnose and treat disease are constantly evolving.  In that way, medicine is a great example of the scientific method in action, but also how transient ‘scientific truths’ can be.  

I already mentioned my current dilemma with statins.   But to give an even more deeply personal example, neither my wife or I would be alive today if we’d blindly followed a physicians diagnosis.

I had two compounding and comparatively rare conditions that combined to appear like a more common one.  The physician went with the high probability answer.  I took time to dig deeper and incorporate more details.  Together we got to the right answer, and I’m still around!

This is a personal and pragmatic example of how valuable the scientific process can be.  My health is important, so I chose to invest considerable time in the diagnosis I was given, and challenge it productively, instead of blindly accepting an expert opinion. My physicians had far more expertise than I did, but I had far more time and motivation.  We ultimately complemented each other by partnering, and using the scientific method both as a process, and as a way to communicate.   

The Challenge of Science Communication:  To be fair, science communication is hard.   It requires communicating an often complex concept with sufficient simplicity for it to be understandable, often requires giving guidance, while also embracing appropriate uncertainty. Nowhere was this more evident than in the case of Covid 19, where a lot of ‘follow the science’, and ‘science denier’ language came from.  At the beginning of the pandemic, the science was understandably poorly developed, but we still had to make important decisions on often limited data.  At first we simply didn’t understand even the basics like the transmission vectors (was it airborne or surface, how long did it survive outside of the body, etc).  I find it almost surreal to think back to those early months, how little we knew, the now bizarre clean room protocols we used on our weekly shopping, and some of the fear that has now faded into the past.  

But because we understood so little, we made a lot of mistakes.  The over enthusiastic use of ventilators may have killed some patients, although that is still a hotly debated topic. Early in the pandemic masks, later to become a controversial and oddly politically charged topic, masks were specifically not recommended by the US government for the general public. Who knows how many people contracted the disease by following this advice?   It was well intentioned, as authorities were trying to prevent a mask shortage for health workers. But it was also mechanistically completely wrong.

At the time I used simple scientific reasoning, and realized this made little sense.  If the virus was transmitted via an airborne vector, a mask would help.  If it wasn’t, it would do no harm, at least as long as I didn’t subtract from someone with greater need. By that time the government had complete control of the mask supply chain anyway, so that was largely a moot point. Instead I dug out a few old N95 masks that had been used for spray painting and DIY, and used them outside of the house (hospitals would not accept donations of used masks). I was lambasted with ‘follow the science’ by at least one friend for doing so, but followed an approach with high potential reward and virtually zero downside. I’ll never know if that specifically worked, but I didn’t get Covid, at least not until much later when it was far less dangerous.

Science doesn’t own truth: Unlike a religion, good science doesn’t pretend to own ultimate truths.  But unfortunately it can get used that way.  Journalists, politicians, technocrats and others sometimes weaponize (selective) science to support an opinion. Even s few scientists who have become frustrated with ‘science deniers’ can slip into this trap.

Science is a Journey: I should clarify that the scientific method is more of a journey, not so much a single process. To suggest is is a single ‘thing’ so is probably an unscientific simplification in its own right. It’s more a way of thinking that embraces empiricism, observation, description, productive skepticism, and the use of experimentation to test and challenge hypothesis. It also helps us to collaborate and communicate with experts in different areas, creating a common framework for collaboration, rather than blindly following directions or other expert opinions.    

It can be taught, and is incredibly useful.  But like any tool, it requires time and effort to become a skilled user.   But if we invest in it, it can be extraordinarily valuable, both in innovation and life. It’s perhaps not for every situation, as that would mire us in unmanageable procrastination.  But if something is important, it’s an invaluable tool. 

Image credits: Pixabay

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The Innovation Talent Stack

Skills for the Next Decade of Change

The Innovation Talent Stack

GUEST POST from Chateau G Pato

For decades, companies searched for the elusive “Chief Innovation Officer” — the singular genius tasked with pulling the organization into the future. That era is dead. Today’s pace of change is too rapid, and the challenges too complex, for innovation to reside in a single silo or department. The modern competitive advantage belongs to organizations that have successfully distributed innovation capabilities across their workforce, creating an Innovation Talent Stack.

The Talent Stack is not a list of job requirements; it is a layered framework of meta-skills — mindsets, methodologies, and technological fluencies — that collectively enable continuous change and disruption. When these three layers are strong and interconnected, the organization transforms from being merely adaptive to becoming inherently resilient and generative. We must shift our focus from finding the singular “T-shaped employee” to building an organization where T-shaped skills are the standard.

The Three Layers of the Innovation Talent Stack

To prepare your workforce for the next decade, training must move beyond basic technical skills and build these three integrated layers:

1. The Foundation: Mindset and Attitude

This is the cultural operating system. Without it, methodologies and tools become fragile or threatening. This layer focuses on the individual’s approach to complexity and failure.

  • Adaptability Quotient (AQ): The capacity to recognize and thrive in an environment of constant change. This means teaching employees to unlearn old rules and embrace ambiguity.
  • Cognitive Empathy: The ability to step into a user’s world and understand their pain points and motivations — not just emotionally, but analytically — to accurately frame the problem that needs solving.
  • Tolerance for Ambiguity: The mental fortitude to operate without a defined outcome, focusing on the quality of the process and the learning derived from failure, not just success.

2. The Mid-Layer: Methodology and Process

These are the structured tools that translate the innovative mindset into repeatable, de-risked action. They enforce human-centered principles and drive efficiency in exploration.

  • Human-Centered Design (HCD): Deep proficiency in observing, ideating, prototyping, and testing solutions with the user at the center. This is the antidote to internal bias and the primary tool for generating market value.
  • Lean Experimentation: The skill of designing minimal-cost tests (MVPs, prototypes) to prove or disprove core assumptions. This includes mastery of metrics that measure learning speed and validated assumptions, not just immediate revenue.
  • Systems Thinking: The ability to trace the downstream effects of any single change. Innovation leaders must see their product or service as one node in a vast, interconnected ecosystem, anticipating ripple effects on regulation, supply chain, and culture.

3. The Top Layer: Technological Fluency and Acceleration

This is not about coding; it’s about strategic literacy. It’s the ability to speak the language of the machine to accelerate speed and scale across the organization.

  • AI Co-Pilot Literacy (Prompt Crafting): The skill of giving generative AI tools high-quality strategic direction and constraints, transforming the interaction from a simple query into a genuine co-creation partnership that dramatically compresses time-to-insight.
  • Data Storytelling and Visualization: The ability to use complex data insights (from predictive analytics, for example) to craft compelling narratives that drive organizational consensus and action, making the unseen risks and opportunities visible.
  • Ecosystem Mapping: Utilizing digital tools to visualize market structures, competitor moves, and partner potential in real-time, allowing for rapid strategic pivots based on external shifts.

Case Study 1: The Legacy Manufacturer’s Mindset Shift

Challenge: Product Failure due to Internal Bias

A large industrial equipment manufacturer, steeped in a culture of engineering perfection, consistently failed to launch new products successfully. Their design process was entirely internal, based on what their engineers thought the customer needed, demonstrating a critical lack of Cognitive Empathy and a low Tolerance for Ambiguity (they demanded perfect V1 launches).

Talent Stack Intervention:

The firm invested heavily in the Mindset and Methodology layers. They mandated Human-Centered Design (HCD) training for all product and sales teams, forcing them into the field to observe customer workflows. They deliberately celebrated small, cheap product failures within the innovation lab as “Learned Lessons,” directly improving Tolerance for Ambiguity. This cultural shift led to their next generation of heavy machinery being co-designed with operators. The result was a 25% decrease in post-launch support costs and a 40% increase in market adoption for the new line, proving that a methodology-driven mindset change is the necessary prerequisite for market success.

The Cognitive Gap: Where Talent Stacks Collapse

The biggest threat to this model is the Cognitive Gap — the chasm that exists when a technologically fluent team delivers a brilliant solution, but the rest of the organization lacks the mindset (AQ) or the methodology (HCD) to adopt it. When a data scientist uses complex visualization (Top Layer) but the leadership team only measures short-term ROI (Foundation Layer deficiency), the innovation dies on the vine. The Talent Stack demands horizontal fluency to bridge this gap.

Bridging this gap requires the Chief HR Officer to think like the Chief Innovation Officer. They must design training pathways that are non-linear, forcing employees to develop skills across all three layers simultaneously. A successful innovator today must be an empathetic explorer (Mindset), a structured experimenter (Methodology), and a strategically-literate technologist (Fluency).

Case Study 2: The Financial Service Firm and Accelerated Fluency

Challenge: Stagnant Idea Flow and Risk Aversion

A major bank had a strong HCD practice but its experimentation cycle was painfully slow due to regulatory and technical complexity. They could generate great ideas, but struggled with execution and de-risking, creating a backlog of ideas that never reached the market.

Talent Stack Intervention:

The bank focused on strengthening the Technological Fluency layer, particularly AI Co-Pilot Literacy and Data Storytelling. They established a “Regulatory Sandbox” where teams, using generative AI co-pilots, could draft, test, and vet new product disclosures and compliance documentation at 10x speed. This allowed them to simulate regulatory outcomes and quickly de-risk new financial products. By cutting the compliance review cycle from six weeks to three days using AI tools, they accelerated their Lean Experimentation cycle (Methodology) dramatically. This immediate acceleration of speed allowed the bank to launch a new consumer loyalty product eight months ahead of their main competitor, directly proving the return on investment from strategic technological fluency.

Conclusion: Building the Portfolio of Capabilities

The Innovation Talent Stack represents the new strategic map for organizational development. It is a Portfolio of Capabilities that guarantees relevance in the face of continuous disruption. Your company is only as innovative as its least adaptive layer. If your people have the tools but lack the empathy, they will build solutions no one wants. If they have the mindset but lack the methodology, they will remain stuck in perpetual brainstorming.

The time for focusing on single-skill specialists is over. We must cultivate T-shaped innovators — deep in a core function, but broadly fluent across the entire Talent Stack.

“Innovation is not an event, but a culture. And culture is simply the cumulative effect of the skills and mindsets you choose to reward.” — Braden Kelley

Your first step toward building the stack: Identify the top five functional leaders in your organization and assess which of the nine skills listed above they are weakest in. Then, design cross-functional immersion training to plug those specific gaps.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pixabay

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