Category Archives: Entrepreneurship

The Larger-Than-Life Story of Isaac Merritt Singer

Sewing up the Competition

The Larger-Than-Life Story of Isaac Merritt Singer

GUEST POST from John Bessant

‘To be or not to be…. ?’

Sooner or later an actor will find themselves declaiming those words – whether delivering Hamlet’s soliloquy or reflecting on the precarious career prospects of the thespian calling. If the answer turns out to be in the ‘not to be…’ direction then the follow-up question is what else might you be. And if you have a leaning towards high risk options you might select ‘become an entrepreneur’ as an alternative choice.

Torquay is a drama queen of a town. Displaying itself in the summer for the tourists who flock to the English Riviera, attracted by its mild weather and (occasionally) sparkling blue bay. Full of larger-than-life characters, birthplace and home of Agatha Christie and still hosting plenty of theaters to add to the offstage stories playing out in the streets. And tucked away in the town cemetery is the last resting place of one of the largest of characters, an actor and entrepreneur to the end. Isaac Merritt Singer, father of the sewing machine and responsible for much more besides.

Born in 1811 in Pittstown, New York, Singer was youngest of eight children, and from an early age learned to hustle, taking on various odd jobs including learning the skills of joinery and lathe turning. His passion for acting emerged early; when he was twelve he ran away to join an acting troupe called the Rochester Players. Even in those days acting was not a reliable profession and so when he was nineteen he worked as an apprentice machinist. A move which helped support his early days of family life; he married fifteen year old Catherine Haley and had two children with her before finally succumbing once again to the siren call of the stage and joining the Baltimore Strolling Players.

His machinist studies paid off however, when in 1839 he patented a rock-drilling machine.

He’d been working with an older brother to help dig the Illinois waterway and saw how he could improve the process; it worked and he sold it for $2,000 (around $150,000 in today’s money). This windfall gave him the chance to return to the dramatic world and he formed a troupe known as the “Merritt Players”.

On tour he appeared onstage under the name “Isaac Merritt”, with a certain Mary Ann Sponsler who called herself “Mrs. Merritt”; backstage they looked after a family which had begun growing in 1837 and had swollen to what became eight children, The tour lasted about five years during which time he became engaged to her (neglecting to mention that he was already married).

Fortunately he’d kept up his craftsman skills interests and developed and patented a “machine for carving wood and metal” on April 10, 1849. Financially struggling once again he moved the family back to New York City, hoping to market his machine. He built a prototype and more important, met a bookseller, G. B. Zieber who was to become his partner and long-suffering financier.

Unfortunately the prototype was destroyed in a fire; Zieber persuaded Singer to make a new start in Boston in 1850 using space kindly offered by Orson Phelps who ran a small machine shop. Orders for his wood cutting machine were not, however, forthcoming so he turned his inventive eye to the world of sewing machines.

Singer Sewing Machine

A short history of sewing machines…

People started sewing by hand some 20,000 years ago, where the first needles were made from bones or animal horns and the thread made from animal sinew. But it remained a largely manual process until the Industrial Revolution in the 18th century and the growing demand for clothing which manual labor couldn’t really meet. Demand pull innovation prompted plenty of entrepreneurs to try their hand at improving on the basic manual process.

Their task wasn’t easy; sewing is a complex task involving different materials whose shape isn’t fixed in the way that wood or metal can be. And manual labor was still cheaply available so the costs of a machine to replace it would also need to be low. Not surprisingly many of the early inventors died in straitened circumstances.

A German-born engineer working in England, Charles Fredrick Wiesenthal, can lay claim to one of the first patents, awarded in Britain for a mechanical device to aid the art of sewing, in 1755. But this was more of a mechanical aid; it wasn’t until 1790 that an English cabinet maker by the name of Thomas Saint was granted a patent for five types of varnishes and their uses, a machine for ‘spinning, twisting, and doubling the thread’, a machine for ‘stitching, quilting, or sewing’, and a machine for ‘platting or weaving’. A specification which didn’t quite include the kitchen sink but came pretty close to covering it!

His very broad-ranging patent somewhat obscured its real value – the machine for ‘stitching, quilting, or sewing’. (So much so that when the Patent Office republished older patents and arranged them into new classes, it was placed into ‘wearing apparel’ rather than ‘sewing and embroidering’).

But his machine brought together several novel features including a mechanism for feeding material into the machine and a vertical needle. It was particularly designed for working with leather to make saddles and bridles but it was adapted for other materials like canvas to make ship sails.

Saint’s vision somewhat outstripped his ability to make and sell the machine but his underlying model introduced the key elements of what became the basic configuration – the ‘dominant design’ – for sewing machines. Much later, in 1874, a sewing machine manufacturer, William Newton Wilson, found Saint’s drawings in the UK Patent Office, made a few adjustments and built a working machine, which is still on display today on the Science Museum in London).

Saint wasn’t alone in seeing the possibilities in mechanization of sewing. Innovation often involves what’s called ‘swarming’ – many players see the potential and experiment with different designs, borrowing and building on these as they converge towards something which solves the core problem and eventually becomes the ‘dominant design’.

In the following years various attempts were made to develop a viable machine, some more successful than others. In 1804, two Englishmen, Thomas Stone and James Henderson, built a simple sewing device and John Duncan in Scotland offered an embroidery machine. An Austrian tailor, Josef Madersperger, presented his first working sewing machine publicly in 1814. And in 1818 John Doge and John Knowles invented America’s first sewing machine, but it could only sew a few bits of fabric before breaking.

But wasn’t until 40 years after Saint’s patent that a viable machine emerged. Barthelemy Thimonnier, a French tailor, invented a machine that used a hooked needle and one thread, creating a chain stitch. The patent for his machine was issued on 17 July 1830, and in the same year, he and his partners opened the first machine-based clothing manufacturing company in the world to create uniforms for the French Army.

(Unfortunately sewing machine inventors seem to have a poor track record as far as fire risk is concerned; Thimonnier’s factory was burned down, reportedly by workers fearful of losing their livelihood, following the issuing of the patent).

Over in America Walter Hunt joined the party bringing his contribution in 1832 in the form of the first lock-stitch machine. Up till then machines had used a simple chain stitch but the lock stitch was a big step forward since it allowed for tighter more durable seams of the kind needed in many clothes. It wasn’t without its teething troubles and Hunt only sold a handful of machines, he only bothered to patent his idea much later in 1854.

Meanwhile British inventors Newton and Archibold improved on the emerging technology with a better needle and the use of two pressing surfaces to keep the pieces of fabric in position, in 1841. And John Greenough registered a patent for the first sewing machine in the United States in 1842.

Each of these machines had some of the important elements but it was only in 1844 that they converged in the machine built by English inventor John Fisher. All should have been well – except that the apparent curse of incomplete filing (which seems to have afflicted many sewing machine inventors) struck him down. His patent was delayed and he failed to get the recognition he probably deserves as the architect of the modern sewing machine.

Instead it was Elias Howe from America with his 1845 machine (which closely resembled Fisher’s) who took the title. His patent was for “a process that uses thread from 2 different sources….” building on the idea of a lockstitch which William Hunt had actually developed thirteen years earlier. Hunt’s failure to patent this meant that Howe could eventually reap the not inconsiderable rewards, earning him $5 for every sewing machine sold in America which used the lockstitch principle.

Howe’s machine was impressive but like all the others was slow to take off and he decided to try and market it in Europe, sailing for England. Leaving the American market open for other entrants, Including one Isaac Merritt Singer who patented his machine in 1851.

Singer Sewing Table

Image: Public domain, via Wikimedia Commons

Singer’s machine

Singer became interested in sewing machines by trying to make them better. Orson Phelps (in whose machine shop Singer was working) had recently started making sewing machines for the modestly successful Lerow and Blodgett Company. Zieber and Phelps convinced Singer to take a look at the machine to see if he could improve upon its design.

Legend has it that Singer was sceptical at first, questioning its market potential. “You want to do away with the only thing that keeps women quiet?” But they managed to persuade him and in 1850, the three men formed a partnership, with Zieber putting up the money, Singer doing the inventing, and Phelps the manufacturing.

Instead of repairing the machine, Singer redesigned it by installing a treadle to help power the fabric feed and rethinking the way the shuttle mechanism worked, replacing the curved needle with a straight one.

Like Henry Ford after him Singer’s gift was not in pure invention but rather in adapting and recombining different elements. His eventual ddesign for a machine combined elements of Thimonnier, Hunt and Howe’s machines; the idea of using a foot treadle to leave both hands free dated back to the Middle Ages.

Importantly, the new design caused less thread breakage with the innovation of an arm-like apparatus that extended over the worktable, holding the needle at its end. It could sew 900 stitches per minute, a dramatic improvement over an accomplished seamstress’s rate of 40 on simple work. On an item as complex as a shirt the time required could be reduced from fifteen hours to less than one.

Singer obtained US Patent number 8294 for his improvements on August 12, 1851.

But having perfected the machine there were a couple of obstacles in the way of their reaping the rewards from transforming the market. First was the problem of economics; their machine (and others like it) opened up the possibility of selling for home use – but at $125 each ($4,000 in 2022 dollars) the machines were expensive and slow to catch on.

And then there was the small matter of sorting out the legal tangles involved in the intellectual property rights to sewing machinery.

Climbing out of the patent thicket

Elias Howe had been understandably annoyed to find Singer’s machine using elements of his own patent and duly took him to court for patent infringement. Singer tried to argue that Howe had actually infringed upon William Hunt’s original idea; unfortunately for him since Hunt hadn’t patented it that argument failed. The judge ruled that Hunt’s lock-stitch idea was free for anyone – including Howe – to use. Consequently, Singer was forced to pay a lump sum and patent royalties to Howe.

(Interestingly if John Fisher’s UK patent hadn’t have been filed wrongly, he too would have been involved in the law suit since both Howe and Singer’s designs were almost identical to the one Fisher created).

Sounds complicated? It gets worse, mainly because they weren’t the only ones in the game. Inventors like Allen B. Wilson were slugging it out with others like John Bradshaw; both of them had developed and patented devices which improved on Singer and Howe’s ideas. Wilson partnered up with Nathaniel Wheeler to produce a new machine which used a hook instead of a shuttle and much quieter and smoother in operation. That helped the Wheeler & Wilson Company to make and sell more machines in the 1850s and 1860s than any other manufacturer. Wilson also invented the feed mechanism that is still used on every sewing machine today, drawing the cloth through the machine in a smooth and even fashion. Others like Charles Miller patented machinery to help with accessories like buttonhole stitching.

The result was that in the 1850s a rapidly increasing number of companies were vying with each other not only to produce sewing machines but also to file lawsuits for patent infringement by the others. It became known as the Sewing Machine War – and like most wars risked ending up benefiting no-one. It’s an old story and often a vicious and expensive one in which the lawyers end up the only certain winners.

Fortunately this one, though not without its battles, was to arrive at a mutually successful cease-fire. In 1856, the major manufacturers (including Singer, Wheeler & Wilson) met in Albany, New York and Orlando Potter, president of the Grover and Baker Company, proposed that, rather than squander their profits on litigation, they pool their patents.

They agreed to form the Sewing Machine Combination, merging nine of the most important patents; they were able to secure the cooperation of Elias Howe by offering him a royalty on every sewing machine manufactured. Any other manufacturer had to obtain a license for $15 per machine. This lasted until 1877 when the last patent expired.

Singing the Singer song

So the stage was finally set for Isaac Singer to act his most famous role – one which predated Henry Ford as one of the fathers of mass production. In late 1857, Singer opened the world’s first facility for mass producing something other than firearms in New York and was soon able to cut production costs. Sales volume increased rapidly; in 1855 he’d sold 855 machines, a year later over 2500 and in 1858 his production reached 3,591 and he opened three more New York-based manufacturing plants.

Efficiency in production allowed the machines to drop in price to $100, then $60, then $30, and demand exploded. By 1860 and selling over 13,000 machines Singer became the largest manufacturer of sewing machines in the world. Ten years later and that number had risen tenfold; twenty years on they sold over half a million machines a year.

Like Ford he was something of a visionary, seeing the value of a systems approach to the problem of making and selling sewing machines. His was a recombinant approach, taking ideas like standardised and interchangeable parts, division of labour, specialisation of key managerial roles and intensive mechanisation to mass produce and bring costs down.

His thespian skills were usefully deployed in the marketing campaign; amongst other stunts he staged demonstrations of the sewing machine in city centre shop windows where bystanders could watch a (skilled) young woman effortlessly sewing her own creations. And he was famous for his ‘Song of the Shirt’ number which he would deliver as background accompaniment in events at which, once again, an attractive and accomplished seamstress would demonstrate the product.

It’s often easy to overlook the contribution of others in the innovation story – not least when the chief protagonist is an actor with a gift for self-publicity. Much of the development of the Singer business was actually down to the ideas and efforts of his partner at the time Edward Cabot Clark. It was Clark, for example, who came up with the concept of instalment purchasing plans which literally opened the door to many salesmen trying to push their product. He also suggested the model of trading in an older model for one with newer features – something enthusiastically deployed a century later in the promotion of a host of products from smart-phones to saloon cars.

Singer and Clark worked to create the necessary infrastructure to support scaling the business. They opened attractive showrooms, developed a rapid spare parts distribution system and employed a network of repair mechanics.

This emerging market for domestic sewing machines attracted others; in 1863 an enterprising tailor, Ebenezer Butterick, began selling dress patterns and helped open up the home dressmaking business. Magazines, pattern books and sewing circles emerged as women saw the opportunities in doing something which could bring both social and economic benefit to their lives. Schools and colleges began offering courses to teach the required skills, many of them helpfully sponsored by the Singer Sewing Machine Company.

It wasn’t just a new business opportunity; this movement provided important impetus to a redefinition of the role of women in the home and their access to activity which could become more than a simple hobby. Singer’s advertising put women in control with advertisements suggesting that their machine was ‘… sold only by the maker directly to the women of the family’. Charitable groups such as the Ladies Work Society and the Co-operative Needlewoman’s Society emerged aimed at helping poorer women find useful skills and respectable employment in sewing.

By 1863 Singer’s machine had become America’s most popular sewing machine and was on its way to a similar worldwide role. They pioneered international manufacturing, particularly in their presence in Europe having first tried to enter the overseas market through licensing their patents to others. Quality and service problems forced them to rethink and they moved instead to setting up their own facilities.

Their Clydebank complex in Scotland, opened in 1885, became the world’s largest sewing machine factory with two main manufacturing buildings on three levels. One made domestic machines, the other industrial models; the whole was overseen by a giant 60 metre high tower with the name ‘Singer ‘ emblazoned on it and with four clock faces, the world’s largest. Employing over 3500 people it turned out 8000 sewing machines a week. By the 1900s, it was making over 1.5 million machines to be sold around the world.

Estimates place Singer’s market share at 80% of global production, from 1880 through at least 1920 and beyond. Over one thousand different models for industrial and home use were offered. Singer had 1,700 stores in the United States and 4,300 overseas, supported by 60,000 salesmen.

Singer Sewing Machine Two

Image: Public domain via Wikimedia Commons

Off-stage activities

Singer was a big man with a commanding presence and a huge appetite for experiences. But he had no need of a Shakespeare to conjure up a plot for his own dramatic personal life, his was quite rich enough. The kind where it might help to have a few thousand miles of Atlantic Ocean to place between you and what’s going on when your past is suddenly and rapidly catching up with you…

(Pay attention, this gets more complicated than the patent thicket).

Catherine, his first wife, had separated from him back in the 1830s but remained married to him, benefitting from his payments to her. She finally agreed to a divorce in 1860 at which point his long-suffering mistress and mother of eight of his children, Mary Ann believed Isaac was free to marry her. He wasn’t keen to change his arrangements with her b ut in any case the question became somewhat academic.

In 1860 she was riding in her carriage along Fifth Avenue in New York when she happened to see Isaac in another carriage seated alongside Mary McGonigal. One of Isaac’s employees about whom Mary Ann already had suspicions. Confronting him she discovered that not only had he fathered seven children with McGonigal but that he had also had an affair with her sister Kate!

Hell hath no fury like a woman scorned and Mary Ann really went for Isaac, having him arrested and charged with bigamy; he fled to London on bail taking Mary McGonigal with him. But leaving behind even more trouble; further research uncovered a fourth ‘wife’, one Mary Walters who had been one of his glamorous sewing machine demonstrators. She also added another child to the list of his offspring. The final tally of his New York wives netted a total of four families, all living in Manhattan in ignorance of each other with a total of sixteen of his children!

Isaac’s escape to England allowed him enough breathing space to pick up on another affair he had started in France the previous year with Isabella Boyer, a young Frenchwoman whose face had been the model for the Statue of Liberty. He’d managed to leave her pregnant and so she left her husband and moved to England to join Isaac, marrying him in 1863. They settled down to life on their huge estate in Devon where they had a further six children.

Legacy

Singer left behind a lot – not least a huge fortune. On his death in 1871 he was worth around $13m (which would be worth close to $400billion today). From considerably humbler beginnings he’d managed to make his way to a position where he was able to buy a sizeable plot of land near Torquay and build a grand 110 room house (Oldway) modeled on the royal palace at Versailles complete with a hall of mirrors, maze and grotto garden.

And when he was finally laid to rest it was in a cedar, silver, satin and oak-lined marble tomb in a funeral attended by over 2000 mourners.

His wider legacy is, of course, the sewing machine which formed the basis of the company he helped found and which became such a powerful symbol of industrial and social innovation. He reminds us that innovation isn’t a single flash of inspiration but an extended journey and he deployed his skills at navigating that journey in many directions. He’s of course remembered for his product innovations like the sewing machine but throughout his life he developed many ideas into serviceable (and sometimes profitable) ventures.

But he also pioneered extensive process innovation, anticipating Henry Ford’s mass production approach to change the economics of selling consumer goods and rethinking the ways in which his factories could continue to develop. He had the salesman’s gift, but his wasn’t just an easy patter to persuade reluctant adopters. Together with Edward Clark he pioneered ways of targeting and then opening up new markets, particularly in the emerging world of the domestic consumer. And he was above all a systems thinker, recognizing that the success or failure of innovation depends on thinking around a complete business model to ensure that good ideas have an architecture through which they can create value.

Isaac Singer retained his interest in drama up to his death, leaving his adopted home of Torbay with a selection of imposing theaters which still offer performances today. It can only be a matter of time before someone puts together the script for a show based on this larger than life character and the tangled web that he managed to weave.


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How to Survive the Next Decade

The Not So Obvious or Easy Answer

How to Survive the Next Decade

GUEST POST from Robyn Bolton

Last week, I shared that 74% of executives believe that their organizations will cease to exist in ten years. They believe that strategic transformation is required, but cite the obvious problem of organizational  inertia and the easy scapegoat of people’s resistance to change.

Great.  Now we know the problem.  What’s the solution?

The Obvious: Put the Right People in Leadership Roles

Flipping through the report, the obvious answers (especially from an executive search firm) were front and center:

  • Build a top team with relevant experience, competencies, and diverse backgrounds
  • Develop the team and don’t be afraid to make changes along the way
  • Set a common purpose and clear objectives, then actively manage the team

The Easy: Do Your Job as a Leader

OK, these may not be easy but it’s not that hard, either:

  • Relentlessly and clearly communicate the why behind the change
  • Change one thing at a time
  • Align incentives to desired outcomes and behaviors
  • Be a role model
  • Understand and manage culture (remember, it’s reflected in the worst behaviors you tolerate)

The Not-Obvious-or-Easy-But-Still-Make-or-Break:  Deputize the Next Generation

Buried amongst the obvious and easy was a rarely discussed, let alone implemented, choice – actively engaging the next generation of leaders.

But this isn’t the usual “invite a bunch of Hi-Pos (high potentials) to preview and upcoming announcement or participate in a focus group to share their opinions” performance most companies engage in.

This is something much different.

Step 1: Align on WHY an “extended leadership team” of Next Gen talent is mission critical

The C-Suite doesn’t see what happens on the front lines. It doesn’t know or understand the details of what’s working and what’s not. Instead, it receives information filtered through dozens of layers, all worried about positioning things just right.

Building a Next Gen extended leadership team puts the day-to-day realities front and center. It brings together capabilities that the C-Suite team may lack and creates the space for people to point out what looks good on paper but will be disastrous in practice.

Instead, leaders must commit to the purpose and value of engaging the next generation, not merely as “sensing mechanisms” (though that’s important, too) but as colleagues with different and equally valuable experiences and insights.

Step 2: Pick WHO is on the team without using the org chart

High-potentials are high potential because they know how to succeed in the current state. But transformation isn’t about replicating the current state. It requires creating a new state.  For that, you need new perspectives:

  • Super connecters who have wide, diverse, and trusted relationships across the organization so they can tap into a range of perspectives and connect the dots that most can barely see
  • Credible experts who are trusted for their knowledge and experience and are known to be genuinely supportive of the changes being made
  • Influencers who can rally the troops at the beginning and keep them motivated throughout

Step 3: Give them a clear mandate (WHAT) but don’t dictate HOW to fulfill it

During times of great change, it’s normal to want to control everything possible, including a team of brilliant, creative, and committed leaders. Don’t involve them in the following steps and be open to being surprised by their approaches and insights:

  • At the beginning, involve them in understanding and defining the problem and opportunity.
  • Throughout, engage them as advisors and influencers in decision-making (
  • During and after implementation, empower them to continue to educate and motivate others and to make adaptations in real-time when needed.

Co-creation is the key to survival

Transforming your organization to survive, even thrive, in the future is hard work. Why not increase your odds of success by inviting the people who will inherit what you create to be part of the transformation?

Image credit: Pixabay

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74% of Companies Will Die in 10 Years Without Business Transformation

According to Executives

74% of Companies Will Die in 10 Years Without Business Transformation

GUEST POST from Robyn Bolton

One day, an architect visited the building site of his latest project. There he saw three people all laying bricks. He asked each what they were doing. “I’m laying bricks,” the first responded. “I’m building a wall,” said the second.  “I’m building a cathedral,” exclaimed the third.

The parable of the Three Bricklayers is a favorite amongst motivational speakers, urging their audiences to think beyond today’s tasks and this quarter’s goals to commit to a grandiose vision of eternal success and glory.

But there’s a problem.

The narrative changed

The person who had a vision of building a cathedral? They now believe they’re building ruins.

Is the C-Suite Quietly Quitting?

Recently published research found that three out of four executives believe that “without fundamental transformation* their organization will cease to exist” in ten years. That’s based on data from interviews with twenty-four “current or former CEOs who have led successful transformations” and 1,360 survey responses from C-Suite and next-generation leaders.

And, somehow, the news gets worse.

While 77% of C-suite executives report that they’re committed to their companies’ transformation efforts, but 57% believe their organization is taking the wrong approach to that transformation. But that’s still better than the 68% of Next-Gen executives who disagree with the approach.

So, it should come as no surprise that 71% of executives rate their companies’ transformation efforts as not at all to moderately successful. After all, it’s hard to lead people along a path you don’t agree with to a vision you don’t believe in.

Did they just realize that “change is hard in human systems?”

We all fall into the trap of believing that understanding something results in commitment and change.

But that’s not how humans work.

That’s definitely not how large groups of humans, known as organizations, work.

Companies’ operations are driven only loosely by the purpose, structures, and processes neatly outlined in HR documents. Instead, they are controlled by the power and influence afforded to individuals by virtue of the collective’s culture, beliefs, histories, myths, and informal ways of working.

And when these “opaque dimensions” are challenged, they don’t result in resistance,

They result in inertia.

“Organizational inertia kills transformations”

Organizations are “complex organisms” that evolve to do things better, faster, cheaper over time. They will continue doing so unless changed by an external force (yes, that’s Newton’s first law of motion).

That external force, the drive for transformation, must be strong enough to overcome:

  1. Insight Inertia stops organizations from getting started because there is a lack of awareness or acceptance amongst leaders that change is needed.
  2. Psychological Inertia emerges when change demands abandoning familiar success strategies. People embrace the idea of transformation but resist personal adaptation, defaulting to comfortable old behaviors.
  3. Action Inertia sets in and gains power as the long and hard work of transformation drags on. Over time, people grow tired. Exhausted by continuous change, teams progressively disengage, becoming less responsive and decisive.

But is that possible when 74% of executives are simply biding their time and waiting for failure?

“There’s a crack in everything, that’s how the light gets in.”

Did you see the crack in all the doom and gloom above?

  • 43% of executives believe their organizations are taking the right approach to transformation.
  • 29% believe that their organizations’ transformations have been successful.
  • 26% believe their company will still be around in ten years.

The majority may not believe in transformation but only 33% of bricklayers believed they were building a cathedral, and the cathedral still got built.

Next week, we’ll explore how.

Image credit: Pixabay

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Reduce Innovation Risk with this Nobel Prize Winning Formula

Reduce Innovation Risk with this Nobel Prize Winning Formula

GUEST POST from Robyn Bolton

As a kid, you’re taught that when you’re lost, stay put and wait for rescue. Most executives are following that advice right now—sitting tight amid uncertainty, hoping someone saves them from having to make hard choices and take innovation risk.

This year’s Nobel Prize winners in Economics have bad news: there is no rescue coming. Joel Mokyr, Philippe Aghion, and Peter Howitt demonstrated that disruption happens whether you participate or not. Freezing innovation investments doesn’t reduce innovation risk.  It guarantees competitors destroy you while you stand still.

They also have good news: innovation follows predictable patterns based on competitive dynamics, offering a framework for making smarter investment decisions.

How We Turned Stagnation into a System for Growth

For 99.9% of human history, economic growth was essentially zero. There were occasional bursts of innovation, like the printing press, windmills, and mechanical clocks, but growth always stopped.

200 years ago, that changed. Mokyr identified that the Industrial Revolution created systems connecting two types of knowledge: Propositional knowledge (understanding why things work) and Prescriptive knowledge (practical instructions for how to execute).

Before the Industrial Revolution, these existed separately. Philosophers theorized. Artisans tinkered. Neither could build on the other’s work. But the Enlightenment created feedback loops between theory and practice allowing countries like Britain to thrive because they had people who could translate theory into commercial products.

Innovation became a system, not an accident.

Why We Need Creative Destruction

Every year in the US, 10% of companies go out of business and nearly as many are created. This phenomenon of creative destruction, where companies and jobs constantly disappear and are replaced, was identified in 1942. Fifty years later, Aghion and Howitt built a mathematical model proving its required for growth.

Their research also lays bare some hard truths:

  1. Creative destruction is constant and unavoidable. Cutting your innovation budget does not pause the game. It forfeits your position. Competitors are investing in R&D right now and their innovations will disrupt yours whether you participate or not.
  2. Competitive position predicts innovation investments. Neck-to-neck competitors invest heavily in innovation because it’s their only path to the top. Market leaders cut back and coast while laggards don’t have the funds to catch-up. Both under-invest and lose.
  3. Innovation creates winners and losers. Creative destruction leads to job destruction as work shifts from old products and skills to new ones. You can’t innovate and protect every job but you can (and should) help the people affected.

Ultimately, creative destruction drives sustained growth. It is painful and scary, but without it, economies and society stagnate. Ignore it at your peril. Work with it and prosper.

From Prize-winning to Revenue-generating

Even though you’re not collecting the one million Euro prize, these insights can still boost your bottom line if you:

  • Connect your Why teams with your How teams. Too often, Why teams like Strategy, Innovation, and R&D, chuck the ball over the wall to the How teams in Operations, Sales, Supply Chain, and front-line operations. Instead, connect them early and often and ensure the feedback loop that drives growth
  • Check your R&D and innovation investments. Are your R&D and innovation investments consistent with your strategic priorities or your competitive position? What are your investments communicating to your competitors? It’s likely that that “conserving cash” is actually coasting and ceding share.
  • Invest in your people and be honest with them. Your employees aren’t dumb. They know that new technologies are going to change and eliminate jobs. Pretending that won’t happen destroys trust and creates resistance that kills innovation. Tell employees the truth early, then support them generously through transitions.

What’s Your Choice?

Playing it safe guarantees the historical default: stagnation. The 2025 Nobel Prize winners proved sustained growth requires building innovation systems and embracing creative destruction.

The only question is whether you will participate or stagnate.

HALLOWEEN BONUS: Save 30% on the eBook, hardcover or softcover of Braden Kelley’s latest book Charting Change (now in its second edition) — FREE SHIPPING WORLDWIDE — using code HAL30 until midnight October 31, 2025

Image credit: Wikimedia Commons

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How Compensation Reveals Culture

Five Questions with Kate Dixon

How Compensation Reveals Culture

GUEST POST from Robyn Bolton

It’s time for your company’s All-Hands meeting. Your CEO stands on stage and announces ambitious innovation goals, talking passionately about the importance of long-term thinking and breakthrough results. Everyone nods enthusiastically, applauds politely, and returns to their desks to focus on hitting this quarter’s numbers.  After all, that’s what their bonuses depend on.

Kate Dixon, compensation expert and founder of Dixon Consulting, has watched this contradiction play out across Fortune 500 companies, B Corps, and startups. Her insight cuts to the heart of why so many innovation initiatives fail: we’re asking people to think long-term while paying them to deliver short-term.

In our conversation, Kate revealed why most companies are inadvertently sabotaging their own innovation efforts through their compensation structures—and what the smartest organizations are doing differently.


Robyn Bolton: Kate, when I first heard you say, “compensation is the expression of a company’s culture,” it blew my mind.  What do you mean by that?

Kate Dixon: If you want to understand what an organization values, look at how they pay their people: Who gets paid more? Who gets paid less? Who gets bigger bonuses? Who moves up in the organization and who doesn’t? Who gets long-term incentives?

The answers to these questions, and a million others, express the culture of the organization.  How we reward people’s performance, either directly or indirectly, establishes and reinforces cultural norms.  Compensation is usually the biggest, if not the biggest, expenses that a company has so they’re very thoughtful and deliberate about how it is used.  Which is why it tells you what the company actually does value.

RB: What’s the biggest mistake companies make when trying to incentivize innovation?

KD: Let’s start by what companies are good at when it comes to compensations and incentives.  They’re really good about base pay, because that’s the biggest part of pay for most people in an organization. Then they spend the next amount of time and effort trying to figure out the annual bonus structure. After that comes other benefits, like long term incentives, assuming they don’t fall by the wayside.

As you know, innovation can take a long time to payout, so long-term incentives are key to encouraging that kind of investment.  Stock options and restricted shares are probably the most common long-term incentives but cash bonuses, phantom stock, and ESOP shares in employee-owned companies are also considered long term incentives.

Large companies are pretty good using some equity as an incentive, but they tie it t long term revenue goals, not innovation. As you often remind us, “innovation is a means to the end, which is growth,” so tying incentives to growth isn’t bad but I believe that we can do better. Tying incentives to the growth goals and how they’re achieved will go a long way towards driving innovation.

RB: I’ve worked in and with big companies and I’ve noticed that while they say, “innovation is everyone’s job,” the people who get long-term incentives are typically senior execs.  What gives?

Long-term incentives are definitely underutilized, below the executive level, and maybe below the director level. Assuming that most companies’ innovation efforts aren’t moonshots that take decades to realize, it makes a ton of sense to use long-term incentives throughout the organization and its ecosystem.  However, when this idea is proposed, people often pushback because “it’s too complex” for folks lower in the organization, “they wouldn’t understand.” or “they won’t appreciate it”. That stance is both arrogant and untrue.  I’ve consistently seen that when you explain long-term incentives to people, they do get it, it does motivate them, and the company does see results.

RB: Are there any examples of organizations that are getting this right?

We’re seeing a lot more innovative and interesting risk-taking behaviors in companies that are not primarily focused on profit.

Our B Corp clients are doing some crazy, cool stuff.  We have an employee-owned company that is a consulting firm, but they had an idea for a software product.  They launched it and now it’s becoming a bigger and bigger part of their business.

Family-owned or public companies that have a single giganto shareholder are also hotbeds of long-term thinking and, therefore, innovation.  They don’t have that same quarter to quarter pressure that drives a relentless focus on what’s happening right now and allows people to focus on the future.

What’s the most important thing leaders need to understand about compensation and innovation?

If you’re serious about innovation, you should be incentivizing people all over the organization.  If you want innovation to be a more regular piece of the culture so you get better results, you’ve got to look at long term incentives.  Yes, you should reward people for revenue and short-term goals.  But you also need to consider what else is a precursor to our innovation. What else is makes the conditions for innovating better for people, and reward that, too.


Kate’s insight reveals the fundamental contradiction at the heart of most companies’ innovation struggles: you can’t build long-term value with short-term thinking, especially when your compensation system rewards only the latter.

What does your company’s approach to compensation say about its culture and values?

Image credit: Pexels

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Three Steps from Stuck to Success

Managing Uncertainty

Three Steps from Stuck to Success

GUEST POST from Robyn Bolton

When a project is stuck and your team is trying to manage uncertainty, what do you hear most often:

  1. “We’re so afraid of making the wrong decision that we don’t make any decisions.”
  2. “We don’t have time to explore a bunch of stuff. We need to make decisions and go.”
  3. “The problem is so multi-faceted, and everything affects everything else that we don’t know where to start.”

I’ve heard all three this week, each spoken by teams leads who cared deeply about their projects and teams.

Differentiating between risk and uncertainty and accepting that uncertainty would never go away, just change focus helped relieve their overwhelm and self-doubt.

But without a way to resolve the fear, time-pressure, and complexity, the project would stay stuck with little change of progressing to success.

Turn Uncertainty Into an Asset

It’s a truism in the field of innovation that you must fall in love with the problem, not the solution. Falling in love with the problem ensures that you remain focused on creating value and agnostic about the solution.

While this sounds great and logically makes sense, most struggle to do it. As a result, it takes incredible strength and leadership to wrestle with the problem long enough to find a solution.

Uncertainty requires the same strength and leadership because the only way out of it is through it. And, research shows, the process of getting through it, turns it into an asset.

Three Steps to Turn Uncertainty Into an Asset

Research in the music and pharmaceutical industries reveals that teams that embraced uncertainty engaged in three specific practices:

  1. Embrace It: Start by acknowledging the uncertainty and that things will change, go wrong, and maybe even fail. Then stay open to surprise and unpredictability, delving into the unknown “by being playful, explorative, and purposefully engaging in ventures with indeterminate outcome.”
  2. Fix It: Especially when dealing with Unknowable Uncertainty, which occurs when more info supports several different meanings rather than pointing to one conclusion, teams that succeed make provisional decisions to “fix” an uncertain dimension so they can move forward while also documenting the rationale for the fix, setting a date to revisit it, and criteria for changing it.
  3. Ignore It: It’s impossible to embrace every uncertainty at once and unwise to fix too many uncertainties at the same time. As a result, some uncertainties, you just need to ignore. Successful teams adopt “strategic ignorance” “not primarily for purposes of avoiding responsibility [but to] allow postponing decisions until better ideas emerge during the collaborative process.

This practice is iterative, often leading to new knowledge, re-examined fixes, and fresh uncertainties. It sounds overwhelming but the teams that are explicit and intentional about what they’re embracing, fixing, and ignoring are not only more likely to be successful, but they also tend to move faster.

Put It Into Practice

Let’s return to NatureComp, a pharmaceutical company developing natural treatments for heart disease.

Throughout the drug development process, they oscillated between addressing What, Who, How, and Where Uncertainties. They did that by changing whether they embraced, fixed, or ignored each type of uncertainty at a given point:

As you can see, they embraced only one type of uncertainty to ensure focus and rapid progress. To avoid the fear of making mistakes, they fixed uncertainties throughout the process and returned to them as more information came available, either changing or reaffirming the fix. Ignoring uncertainties helped relieve feelings of being overwhelmed because the team had a plan and timeframe for when they would shift from ignoring to embracing or fixing.

Uncertainty is Dynamic – You Need to Be Dynamic, Too

You’ll never eliminate uncertainty. It’s too dynamic to every fully resolve. But by dynamically embracing, fixing, and ignore it in all its dimensions, you can accelerate your path to success.

Image credit: Pexels

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Don’t Fall for the Design Squiggle Lie

Don't Fall for the Design Squiggle Lie

Editor’s Note — Braden Kelley

What is the Design Squiggle?

The Design Squiggle is a simple illustration created by designer Damien Newman that depicts the design process as a chaotic, messy squiggle on the left that gradually straightens into a clean, focused line on the right. The left side represents the early stages of design — research, uncertainty, ambiguity, and exploration. The right side represents the outcome — a clear, refined solution. The squiggle has become one of the most widely shared visuals in design thinking and innovation communities because it captures something intuitively true: creative problem-solving is messy before it’s clear.

The squiggle resonates because it validates the discomfort of the early design process — the feeling that nothing is clear yet, that you’re exploring without direction, that the problem itself keeps shifting. For designers and innovation leaders, it’s a reassuring reminder that this messiness is not a sign of failure but a necessary phase of the work.

But does the Design Squiggle tell the whole truth? Robyn Bolton, a leading innovation practitioner, argues below that the squiggle — however appealing — contains a dangerous lie that sends innovation teams in the wrong direction. It’s a perspective worth taking seriously.

GUEST POST from Robyn Bolton

Last night, I lied to a room full of MBA students. I showed them the Design Squiggle, and explained that innovation starts with (what feels like) chaos and ends with certainty.

The chaos part? Absolutely true.

The certainty part? A complete lie.

Nothing is Ever Certain (including death and taxes)

Last week I wrote about the different between risk and uncertainty.  Uncertainty occurs when we cannot predict what will happen when acting or not acting.  It can also be broken down into Unknown uncertainty (resolved with more data) and Unknowable uncertainty (which persists despite more data).

But no matter how we slice, dice, and define uncertainty, it never goes away.

It may be higher or lower at different times,

More importantly, it changes focus.

Four Dimensions of Uncertainty

Something new that creates value (i.e. an innovation) is multi-faceted and dynamic. Treating uncertainty as a single “thing”  therefore clouds our understanding and ability to find and addresses root causes.

That’s why we need to look at different dimensions of uncertainty.

Thankfully, the ivory tower gives us a starting point.

WHAT: Content uncertainty relates to the outcome or goal of the innovation process. To minimize it, we must address what we want to make, what we want the results to be, and what our goals are for the endeavor.

WHO: Participation uncertainty relates to the people, partners, and relationships active at various points in the process. It requires constant re-assessment of expertise and capabilities required and the people who need to be involved.

HOW: Procedure uncertainty focuses on the process, methods, and tools required to make progress. Again, it requires constant re-assessment of how we progress towards our goals.

WHERE: Time-space uncertainty focuses on the fact that the work may need to occur in different locations and on different timelines, requiring us to figure out when to start and where to work.

It’s tempting to think each of these are resolved in an orderly fashion, by clear decisions made at the start of a project, but when has a decision made on Day 1 ever held to launch day?

Uncertainty in Pharmaceutical Development

 Let’s take the case of NatureComp, a mid-sized company pharmaceutical company and the uncertainties they navigated while working to replicate, develop, and commercialize a natural substance to target and treat heart disease.

  1. What molecule should the biochemists research?
  2. How should the molecule be produced?
  3. Who has the expertise and capability to synthetically poduce the selected molecule because NatureComp doesn’t have the experience required internally?
  4. Where to produce that meets the synthesization criteria and could produce cost-effectively at low volume?
  5. What target disease specifically should the molecule target so that initial clincial trials can be developed and run?
  6. Who will finance the initial trials and, hopefully, become a commercialization partner?
  7. Where would the final commercial entity exist (e.g. stay in NatureComp, move to partner, stand-alone startup) and the molecule produced?

 And those are just the highlights.

It’s all a bit squiggly

The knotty, scribbly mess at the start of the Design Squiggle is true. The line at the end is a lie because uncertainty never goes away. Instead, we learn and adapt until it feels manageable.

Next week, you’ll learn how.

Image credit: The Process of Design Squiggle by Damien Newman, thedesignsquiggle.com

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Mismanaging Uncertainty & Risk is Killing Our Businesses

Mismanaging Uncertainty & Risk is Killing Our Businesses

GUEST POST from Robyn Bolton

During September 2011, the English language officially died.  That was the month that the Oxford English Dictionary, long regarded as the accepted authority on the English language published an update in which “literally” also meant figuratively. By 2016, every other major dictionary had followed suit.

The justification was simple: “literally” has been used to mean “figuratively” since 1769. Citing examples from Louisa May Alcott’s Little Women, Charles Dickens’ David Copperfield, Charlotte Bronte’s Jane Eyre, and F. Scott Fitzgerald’s The Great Gatsby, they claimed they were simply reflecting the evolution of a living language.

What utter twaddle.

Without a common understanding of a word’s meaning, we create our own definitions which lead to secret expectations, and eventually chaos.

And not just interpersonally. It can affect entire economies.

Maybe the state of the US economy is just a misunderstanding

Uncertainty.

We’re hearing and saying that word a lot lately. Whether it’s in reference to tariffs, interest rates, immigration, or customer spending, it’s hard to go a single day without “uncertainty” popping up somewhere in your life.

But are we really talking about “uncertainty?”

Uncertainty and Risk are not the same.

The notion of risk and uncertainty was first formally introduced into economics in 1921 when Frank Knight, one of the founders of the Chicago school of economics, published his dissertation Risk, Uncertainty and Profit.  In the 114 since, economists and academics continued to enhance, refine, and debate his definitions and their implications.

Out here in the real world, most businesspeople use them as synonyms meaning “bad things to be avoided at all costs.”

But they’re not synonyms. They have distinct meanings, different paths to resolution, and dramatically different outcomes.

Risk can be measured and/or calculated.

Uncertainty cannot be measured or calculated

The impact of tariffs, interest rates, changes in visa availability, and customer spending can all be modeled and quantified.

So it’s NOT uncertainty that’s “paralyzing” employers.  It’s risk!

Not so fast my friend.

Not all Uncertainties are the same

According to Knight, Uncertainty drives profit because it connects “with the exercise of judgment or the formation of those opinions as to the future course of events, which…actually guide most of our conduct.”

So while we can model, calculate, and measure tariffs, interest rates, and other market dynamics, the probability of each outcome is unknown.  Thus, our response requires judgment.

Sometimes.

Because not all uncertainties are the same.

The Unknown (also known as “uncertainty based on ignorance”) exists when there is a “lack of information which would be necessary to make decisions with certain outcomes.”

The Unknowable (“uncertainty based on ambiguity”) exists when “an ongoing stream [of information]  supports several different meanings at the same time.”

Put simply, if getting more data makes the answer obvious, we’re facing the Unknown and waiting, learning, or modeling different outcomes can move us closer to resolution. If more data isn’t helpful because it will continue to point to different, equally plausible, solutions, you’re facing the Unknowable.

So what (and why did you drag us through your literally/figuratively rant)?

If you want to get unstuck – whether it’s a project, a proposal, a team, or an entire business, you first need to be clear about what you’re facing.

If it’s a Risk, model it, measure it, make a decision, move forward.

If it’s an uncertainty, what kind is it?

If it’s Unknown, decide when to decide, ask questions, gather data, then, when the time comes, decide and move forward

If it’s Unknowable, decide how to decide then put your big kid pants on, have the honest and tough conversations, negotiate, make a decision, and move on.

I mean that literally.

Image credit: Pixabay

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McKinsey is Wrong That 80% Companies Fail to Generate AI ROI

McKinsey is Wrong That 80% Companies Fail to Generate AI ROI

GUEST POST from Robyn Bolton

Sometimes, you see a headline and just have to shake your head.  Sometimes, you see a bunch of headlines and need to scream into a pillow.  This week’s headlines on AI ROI were the latter:

  • Companies are Pouring Billions Into A.I. It Has Yet to Pay Off – NYT
  • MIT report: 95% of generative AI pilots at companies are failing – Forbes
  • Nearly 8 in 10 companies report using gen AI – yet just as many report no significant bottom-line impact – McKinsey

AI has slipped into what Gartner calls the Trough of Disillusionment. But, for people working on pilots,  it might as well be the Pit of Despair because executives are beginning to declare AI a fad and deny ever having fallen victim to its siren song.

Because they’re listening to the NYT, Forbes, and McKinsey.

And they’re wrong.

ROI Reality Check

In 20205, private investment in generative AI is expected to increase 94% to an estimated $62 billion.  When you’re throwing that kind of money around, it’s natural to expect ROI ASAP.

But is it realistic?

Let’s assume Gen AI “started” (became sufficiently available to set buyer expectations and warrant allocating resources to) in late 2022/early 2023.  That means that we’re expecting ROI within 2 years.

That’s not realistic.  It’s delusional. 

ERP systems “started” in the early 1990s, yet providers like SAP still recommend five-year ROI timeframes.  Cloud Computing“started” in the early 2000s, and yet, in 2025, “48% of CEOs lack confidence in their ability to measure cloud ROI.” CRM systems’ claims of 1-3 years to ROI must be considered in the context of their 50-70% implementation failure rate.

That’s not to say we shouldn’t expect rapid results.  We just need to set realistic expectations around results and timing.

Measure ROI by Speed and Magnitude of Learning

In the early days of any new technology or initiative, we don’t know what we don’t know.  It takes time to experiment and learn our way to meaningful and sustainable financial ROI. And the learnings are coming fast and furious:

Trust, not tech, is your biggest challenge: MIT research across 9,000+ workers shows automation success depends more on whether your team feels valued and believes you’re invested in their growth than which AI platform you choose.

Workers who experience AI’s benefits first-hand are more likely to champion automation than those told, “trust us, you’ll love it.” Job satisfaction emerged as the second strongest indicator of technology acceptance, followed by feeling valued.  If you don’t invest in earning your people’s trust, don’t invest in shiny new tech.

More users don’t lead to more impact: Companies assume that making AI available to everyone guarantees ROI.  Yet of the 70% of Fortune 500 companies deploying Microsoft 365 Copilot and similar “horizontal” tools (enterprise-wide copilots and chatbots), none have seen any financial impact.

The opposite approach of deploying “vertical” function-specific tools doesn’t fare much better.  In fact, less than 10% make it past the pilot stage, despite having higher potential for economic impact.

Better results require reinvention, not optimization:  McKinsey found that call centers that gave agents access to passive AI tools for finding articles, summarizing tickets, and drafting emails resulted in only a 5-10% call time reduction.  Centers using AI tools to automate tasks without agent initiation reduced call time by 20-40%.

Centers reinventing processes around AI agents? 60-90% reduction in call time, with 80% automatically resolved.

How to Climb Out of the Pit

Make no mistake, despite these learnings, we are in the pit of AI despair.  42% of companies are abandoning their AI initiatives.  That’s up from 17% just a year ago.

But we can escape if we set the right expectations and measure ROI on learning speed and quality.

Because the real concern isn’t AI’s lack of ROI today.  It’s whether you’re willing to invest in the learning process long enough to be successful tomorrow.

Image credit: Microsoft CoPilot

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This AI Creativity Trap is Gutting Your Growth

This AI Creativity Trap is Gutting Your Growth

GUEST POST from Robyn Bolton

“We have to do more with less” has become an inescapable mantra, and goodness, are you trying.  You’ve slashed projects and budgets, “right-sized” teams, and tried any technology that promised efficiency and a free trial.  Now, all that’s left is to replace the people you still have with AI creativity tools.  Welcome to the era of the AI Innovation Team.

It sounds like a great idea.  Now, everyone can be an innovator with access to an LLM.  Heck, even innovation firms are “outsourcing” their traditional work to AI, promising the same radical results with less time and for far less money.

It sounds almost too good to be true.

Because it is too good to be true.

AI is eliminating the very brain processes that produce breakthrough innovations.

This isn’t hyperbole, and it’s not just one study.

MIT researchers split 54 people into three groups (ChatGPT users, search engine users, and no online/AI tools using ChatGPT) and asked them to write a series of essays.  Using EEG brain monitoring, they found that the brain connectivity in networks crucial for creativity and analogous thinking dropped by 55%.

Even worse? When people stopped using AI, their brains stayed stuck in this diminished state.

University of Arkansas researchers tested AI against 3,562 humans on a series of four challenges involving finding new uses for everyday objects, like a brick or paperclip.   While AI scored slightly higher on standard tests, when researchers introduced a new context, constraint, or modification to the object, AI’s performance “collapsed.” Humans stayed strong.

Why? AI relies on pattern matching and is unable to transfer its “creativity” to unexpected scenarios. Humans use analogical reasoning so are able to flex quickly and adapt.

University of Strasbourg researchers analyzed 15,000 studies of COVID-19 infections and found that teams that relied heavily on AI experts produced research that got fewer citations and less media attention. However, papers that drew from diverse knowledge sources across multiple fields became widely cited and influential.

The lesson? Breakthroughs require cross-domain thinking, which is precisely what diverse human teams provide, and, according to the MIT study, AI is unable to produce.

How to optimize for efficiency AND impact (and beat your competition)

While this seems like bad news if you’ve already cut your innovation team, the silver lining is that your competition is probably making the same mistake.

Now that you know better, you can do better, and that creates a massive opportunity.

Use AI for what it does well:

  • Data analysis and synthesis
  • Rapid testing and iteration to refine an advanced prototype
  • Process optimization

Use humans for what we do well:

  • Make meaningful connections across unrelated domains
  • Recognize when discoveries from one field apply to another
  • Generate the “aha moments” that redefine industries

Three Questions to Ask This Week

  1. Where did your most recent breakthroughs come from? How many came from connecting insights across different domains? If most of your innovations require analogical leaps, cutting creative teams could kill your pipeline.
  2. How are teams currently using AI tools? Are they using AI for data synthesis and rapid iteration? Good. Are they replacing human ideation entirely? Problem.
  3. How can you see it to believe it? Run a simple experiment: Give two teams an hour to solve a breakthrough challenge. Have one solve it with AI assistance and one without.  Which solution is more surprising and potentially breakthrough?

The Hidden Competitive Advantage

As AI commoditizes pattern recognition, human analogical thinking and creativity become a competitive advantage.

The companies that figure out the right balance will eat everyone else’s lunch.

Image credit: Gemini

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