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 Corporate DAOs Are Rewriting the Rules of Governance

The Code of Consensus

LAST UPDATED: November 14, 2025 at 2:43 PM

How Corporate DAOs Are Rewriting the Rules of Governance

GUEST POST from Art Inteligencia

In our increasingly Agile World, the pace of decision-making often determines the pace of innovation. Traditional hierarchical structures, designed for stability and control, frequently become bottlenecks, slowing progress and stifling distributed intelligence. We’ve previously explored the “Paradox of Control,” where excessive top-down management inhibits agility. Now, a new organizational model, emerging from the edges of Web3, offers a powerful antidote: the Decentralized Autonomous Organization (DAO).

For most, DAOs conjure images of cryptocurrency projects and esoteric online communities. However, the underlying principles of DAOs — transparency, automation, and distributed governance — are poised to profoundly impact corporate structures. This isn’t about replacing the CEO with a blockchain; it’s about embedding a new layer of organizational intelligence that can accelerate decision-making, empower teams, and enhance trust in an era of constant change.

The core promise of a corporate DAO is to move from governance by committee and bureaucracy to governance by consensus and code. It’s a human-centered change because it redefines power dynamics, shifting from centralized authority to collective, transparent decision-making that is executed automatically.

What is a Decentralized Autonomous Organization (DAO)?

At its heart, a DAO is an organization governed by rules encoded as a computer program on a blockchain, rather than by a central authority. These rules are transparent, immutable, and executed automatically by smart contracts. Participants typically hold “governance tokens,” which grant them voting rights proportionate to their holdings, allowing them to propose and vote on key decisions that affect the organization’s operations, treasury, and future direction.

Key Characteristics of Corporate DAOs

  • Transparency: All rules, proposals, and voting records are visible on the blockchain, eliminating opaque decision-making.
  • Automation: Decisions, once approved by the community (token holders), are executed automatically by smart contracts, removing human intermediaries and potential biases.
  • Distributed Governance: Power is spread across many participants, rather than concentrated in a few individuals or a central board.
  • Immutability: Once rules are set and decisions made, they are recorded on the blockchain and cannot be arbitrarily reversed or altered without further community consensus.
  • Meritocracy of Ideas: Good ideas, regardless of who proposes them, can gain traction through transparent voting, fostering a more inclusive innovation culture.

Key Benefits for Enterprises

While full corporate adoption is nascent, the benefits of integrating DAO principles are compelling for forward-thinking enterprises:

  • Accelerated Decision-Making: Bypass bureaucratic bottlenecks for specific types of decisions, leading to faster execution and greater agility.
  • Enhanced Trust & Accountability: Immutable, transparent records of decisions and resource allocation build internal and external trust.
  • Empowered Workforce: Employees or specific teams can be granted governance tokens for defined areas, giving them real, verifiable influence over projects or resource allocation. This boosts engagement and ownership.
  • De-risked Innovation: DAOs can manage decentralized innovation funds, allowing a wider array of internal (or external) projects to be funded based on collective intelligence rather than a single executive’s subjective view.
  • Optimized Resource Allocation: Budgets and resources can be allocated more efficiently and equitably through transparent, community-driven proposals and votes.

Case Study 1: Empowering an Internal Innovation Lab

Challenge: Stagnant Internal Innovation Fund

A large technology conglomerate maintained a multi-million-dollar internal innovation fund, but its allocation process was notoriously slow, biased towards executive favorites, and lacked transparency. Project teams felt disempowered, and many promising ideas died in committee.

DAO Intervention:

The conglomerate implemented a “shadow DAO” for its innovation lab. Each internal project team and key R&D leader received governance tokens. A portion of the innovation fund was placed into a smart contract governed by this internal DAO. Teams could submit proposals for funding tranches, outlining their project, milestones, and requested budget. Token holders (other teams, R&D leads) would then transparently vote on these proposals. Approved proposals automatically triggered fund release via the smart contract once specific, pre-agreed milestones were met.

The Human-Centered Lesson:

This shift democratized innovation. It moved from a subjective, top-down funding model to an objective, peer-reviewed, and code-governed system. It fostered a meritocracy of ideas, boosted team morale and ownership, and significantly accelerated the time-to-funding for promising projects. The “Not Invented Here” syndrome diminished as teams collectively invested in each other’s success.

Case Study 2: Supply Chain Resilience through Shared Governance

Challenge: Fragmented, Inflexible Supplier Network

A global manufacturing firm faced increasing supply chain disruptions (geopolitical, natural disasters) and struggled with a rigid, centralized supplier management system. Changes in sourcing, risk mitigation, or emergency re-routing required lengthy contracts and approvals, leading to significant delays and losses.

DAO Intervention:

The firm collaborated with key tier-1 and tier-2 suppliers to form a “Supply Chain Resilience DAO.” Participants (the firm and its trusted suppliers) were issued governance tokens. Critical, pre-agreed operational decisions — such as activating emergency backup suppliers, re-allocating shared logistics resources during a crisis, or approving collective investments in new sustainable sourcing methods — could be proposed and voted upon by token holders. Once consensus was reached, the smart contracts could automatically update sourcing agreements or release pre-committed funds for contingency plans.

The Human-Centered Lesson:

This created a robust, transparent, and collectively governed supply network. Instead of bilateral, often adversarial, relationships, it fostered a collaborative ecosystem where decisions impacting shared risk and opportunity were made transparently and efficiently. It transformed the human element from reactive problem-solving under pressure to proactive, consensus-driven resilience planning.

The Road Ahead: Challenges and Opportunities

Adopting DAO principles within a traditional corporate environment presents significant challenges: legal recognition, integration with legacy systems, managing token distribution fairly, and overcoming deep-seated cultural resistance to distributed authority. Yet, the opportunities for enhanced agility, transparency, and employee empowerment are too compelling to ignore.

For human-centered change leaders, the task is clear: begin by experimenting with “shadow DAOs” for specific functions, focusing on clearly defined guardrails and outcomes. It’s about taking the principles of consensus and code and applying them to solve real, human-centric organizational friction through iterative, experimental adoption.

“The future of corporate governance isn’t just about better software; it’s about better social contracts, codified for trust and agility.”

Your first step toward exploring DAOs: Identify a specific, low-risk internal decision-making process (e.g., allocating a small innovation budget or approving a new internal tool) that currently suffers from slowness or lack of transparency. Imagine how a simple, token-governed voting system could transform it.

Image credit: Google Gemini

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Why Dissatisfaction is Awesome

Why Dissatisfaction is Awesome

GUEST POST from Mike Shipulski

If you’re dissatisfied, there’s a reason.

If you’re dissatisfied, there’s hope for us all.

If you’re not dissatisfied, there’s no forcing function for change.

If you’re not dissatisfied, the status quo will carry the day.

If you’re not dissatisfied, innovation work is not for you.

If you’re dissatisfied, you know it could be better next time.

If you’re dissatisfied, your insecure leader will step on your head.

If you’re dissatisfied, there’s a reason and that reason is real.

If you’re dissatisfied, follow your dissatisfaction.

If you’re dissatisfied, I want to work with you.

If you’re dissatisfied, it’s because you see things as they are.

If you’re dissatisfied, your confident leader will ask how things should go next time.

If you’re dissatisfied, it’s because you want to make a difference.

If you’re dissatisfied, look inside.

If you’re dissatisfied, there’s a reason, the reason is real and it’s time to do something about it.

If you’re dissatisfied, you’re thinking for yourself.

If you’re so dissatisfied you openly show anger, thank you for trusting me enough to show your true self.

If you’re dissatisfied, it’s because you know things should be better than they are.

If you’re dissatisfied, do something about it.

If you’re dissatisfied, thank you for thinking deeply.

If you’re dissatisfied, it’s because you’re not asleep at the wheel.

If you’re dissatisfied, it’s because your self-worth allows it.

Thank you for caring enough to be dissatisfied.

Image credit: Pixabay

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Don’t Adopt Artificial Incompetence

Don't Adopt Artificial Incompetence

GUEST POST from Shep Hyken

I’ve been reviewing my customer experience research, specifically the section on the future of customer service and AI (Artificial Intelligence). A few findings prove that customers are frustrated and lack confidence in how companies are using AI:

  • In general, 57% of customers are frustrated by AI-fueled self-service options.
  • 49% of customers say technologies like AI and ChatGPT scare them.
  • 51% of customers have received wrong or incorrect information from an AI self-service bot.

As negative as these findings sound, there are plenty of findings that point to AI getting better and more customers feeling comfortable using AI solutions. The technology continues to improve quickly. While it’s only been five months since we surveyed more than 1,000 U.S. consumers, I bet a new survey would show continued improvement and comfort level regarding AI. But for this short article, let’s focus on the problem that needs to be resolved.

Upon reviewing the numbers, I realized that there’s another kind of AI: Artificial Incompetence. That’s my new label for companies that improperly use AI and cause customers to be frustrated, scared and/or receive bad information. After thinking I was clever and invented this term, I was disheartened to discover, after a Google search, that the term already exists; however, it’s not widely used.

So, AI – as in Artificial Incompetence – is a problem you don’t want to have. To avoid it, start by recognizing that AI isn’t perfect. Be sure to have a human backup that’s fast and easy to reach when the customer feels frustrated, angry, or scared.

And now, as the title of this article implies, there’s more. After sharing the new concept of AI with my team, we brainstormed and had fun coming up with two more phrases based on some of the ideas I covered in my past articles and videos:

Feedback Constipation: When you get so much feedback and don’t take action, it’s like eating too much and not being able to “go.” (I know … a little graphic … but it makes the point.) This came from my article Turning Around Declining Customer Satisfaction, which teaches that collecting feedback isn’t valuable unless you use it.

Jargon Jeopardy: Most people – but not everyone – know what CX means. If you are using it with a customer, and they don’t know what it means, how do you think they feel? I was once talking to a customer service rep who kept using abbreviations. I could only guess what they meant. So I asked him to stop with the E-I-E-I-O’s (referencing the lyrics from the song about Old McDonald’s farm.) This was the main theme of my article titled Other Experiences Exist Beyond Customer Experience (EX, WX, DX, UX and more).

So, this was a fun way at poking fun of companies that may think they are doing CX right (and doing it well), but the customer’s perception is the opposite. Don’t use AI that frustrates customers and projects an image of incompetence. Don’t collect feedback unless you plan to use it. Otherwise, it’s a waste of everyone’s time and effort. Finally, don’t confuse customers – and even employees – with jargon and acronyms that make them feel like they are forced to relearn the alphabet.

Image Credits: 1 of 950+ FREE quote slides available at http://misterinnovation.com

This article originally appeared on Forbes.com

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1,000+ Free Innovation, Change and Design Quotes Slides

LAST UPDATED: November 12, 2025 at 10:21AM
1,000+ Free Innovation, Change and Design Quotes Slides

Spice Up Your Meetings, Presentations, Keynotes and Workshops

I’m flattered that people have been quoting my keynote speeches and my first two books Stoking Your Innovation Bonfire and Charting Change (now in its Second Edition).

So, I’m making some of my favorite quotes available from myself and other thought leaders in a fun, visual, easily shareable format.

I’ve been publishing them on Instagram, LinkedIn, Facebook, and Twitter.

Find a compelling quote for a meeting, presentation, workshop or keynote speech on any of these topics:

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Print them, share them on social media, or use them in your presentations, keynote speeches or workshops.

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Re-engineering Trust and Retention in the AI Contact Center

The Empathy Engine

LAST UPDATED: November 9, 2025 at 1:36PM
Re-engineering Trust and Retention in the AI Contact Center

by Braden Kelley

The contact center remains the single most critical point of human truth for a brand. It is where marketing promises meet operational reality. The challenge today, as highlighted by leaders like Bruce Gilbert of Young Energy at Customer Contact Week(CCW) in Nashville recently, is profound: Customers expect friction-less experiences with empathetic responses. The solution is not merely throwing technology at the problem; it’s about strategically weaving automation into the existing human fabric to create an Empathy Engine.

The strategic error most organizations make is starting with the technology’s capability rather than the human need. The conversation must start with empathy not the technology — focusing first on the customer and agent pain points. AI is not a replacement for human connection; it is an amplification tool designed to remove friction, build trust, and elevate the human agent’s role to that of a high-value relationship manager.

The Trust Imperative: The Cautious Adoption Framework

The first goal when introducing AI into the customer journey is simple: Building trust. The consumer public, after years of frustrating Interactive Voice Response (IVR) systems and rigid chatbots, remains deeply skeptical of automation. A grand, “all-in” AI deployment is often met with immediate resistance, which can manifest as call abandonment or increased churn.

To overcome this, innovation must adhere to a principle of cautious, human-centered rollout — a Cautious Adoption Framework: Starting small and starting with simple things can help to build this trust. Implement AI where the risk of failure is low and the utility is high — such as automating password resets, updating billing addresses, or providing initial diagnostics. These are the repetitive, low-value tasks that bore agents and frustrate customers. By successfully automating these simple, transactional elements, you build confidence in the system, preparing both customers and agents for more complex, AI-assisted interactions down the line. This approach honors the customer’s pace of change.

The Agent Retention Strategy: Alleviating Cognitive Load

The operational cost of the contact center is inextricably linked to agent retention. Finding and keeping high-quality agents remains a persistent challenge, primarily because the job is often highly stressful and repetitive. AI provides a powerful retention tool by directly addressing the root cause: cognitive load.

Reducing the cognitive load and stress level on agents is a non-negotiable step for long-term operational health. AI co-pilots must be designed to act as true partners, not simply data overlays. They should instantly surface relevant knowledge base articles, summarize the customer’s entire history before the agent picks up the call, or even handle real-time data entry. This frees the human agent to focus entirely on the empathetic response — active listening, problem-solving, and de-escalation. By transforming the agent’s role from a low-paid data processor into a high-value relationship manager, we elevate the profession, directly improving agent retention and turning contact center employment into an aspirational career path.

The Systemic Challenge: Orchestrating the AI Ecosystem

A major limiting factor in today’s contact center is the presence of fragmented AI deployments. Many organizations deploy AI in isolated pockets — a siloed chatbot here, a transcription service there. The future demands that we move far beyond siloed AI. The goal is complete AI orchestration across the enterprise, requiring us to get the AIs to talk to each other.

A friction-less customer experience requires intelligence continuity: a Voice AI must seamlessly hand off its collected context to a Predictive AI (which assesses the call risk), which then informs the Generative AI (that drafts the agent’s suggested response). This is the necessary chain of intelligence that supports friction-less service. Furthermore, complexity demands a blended AI approach, recognizing that the solution may involve more than one method (generative vs. directed).

For high-compliance tasks, a directed approach ensures precision: for instance, a flow can insert “read as is” instructions for regulatory disclosures, ensuring legal text is delivered exactly as designed. For complex, personalized problem-solving, a generative approach is vital. The best systems understand the regulatory and emotional context, knowing when to switch modes instantly and without customer intervention.

The Strategic Pivot: Investing in Predictive Empathy

The ultimate strategic advantage lies not in reacting to calls, but in preventing them. This requires a deeper investment in data science, moving from descriptive reporting on what happened to predictive analytics to understand why our customers are calling in before they dial the number.

This approach, which I call Predictive Empathy, uses machine learning to identify customers whose usage patterns, payment history, or recent service interactions suggest a high probability of confusion or frustration (e.g., first-time promotions expiring, unusual service interruptions). The organization then proactively initiates a personalized, AI-assisted outreach to address the problem or explain the confusion before the customer reaches the point of anxiety and makes the call. This shifts the interaction from reactive conflict to proactive support, immediately lowering call volume and transforming brand perception.

The Organizational Checkpoint: Post-Deployment Evolution

Once you’ve successfully implemented AI to address pain points, the work is not finished. A crucial strategic question must be addressed: What happens after AI deployment? What’s your plan?

As AI absorbs simple transactions, the nature of the calls that reach the human agent becomes disproportionately more complex, emotional, and high-value. This creates a skills gap in the remaining human workforce. The organization must plan for and fund the Up-skilling Initiative necessary to handle these elevated interactions, focusing on conflict resolution, complex sales, and deep relationship management. The entire organizational structure — training programs, compensation models, and career paths — must evolve to support this higher-skilled human workforce. By raising the value of the human role, the contact center transitions from a cost center into a profit-generating Relationship Hub.

Conclusion: Architecting the Human Layer

The goal of innovation in the contact center is not the elimination of the human, but the elevation of the human. By using AI to build trust, reduce cognitive load, enable predictive empathy, and connect disparate systems, we free the human agent to deliver on the fundamental customer expectation: a friction-less experience coupled with an empathetic response. This is how we re-engineer the contact center from a cost center into a powerful engine for talent retention and customer loyalty.

“AI handles the transaction. The human handles the trust. Design your systems to protect both.” — Braden Kelley

Your first step into the Empathy Engine: Map the single most stressful task for your top-performing agent and commit to automating 80% of its cognitive load using a simple AI co-pilot within the next 90 days.

What is that task for your organization?

Image credits: Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, insights captured from the Customer Contact Week session, panelists to mention, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article.

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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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Four Signs of an Industry Disruption

Four Signs of an Industry Disruption

GUEST POST from Greg Satell

In his book, Thinking, Fast and Slow, Nobel laureate Daniel Kahneman explained that there are two modes of thinking that we use to make decisions, which he calls “System 1” and “System 2.” The first is more instinctual and automatic, the second more rational and deliberative. We need to use both to make good decisions.

Businesses also have two systems, which can sometimes conflict. One is immediate and operational. It seeks to optimize processes, gain market share and maximize profitability. The second builds capacity for the long term, by investing in employees, building trustful partnerships and creating new markets to compete for the future.

Obviously, these are not mutually exclusive. Just as we can step back and think rationally about instinctual urges, we can invest for both the short and the long term. Yet given that every business eventually matures and needs to renew itself, many end up taking the wrong path. Here are four signs that your industry might be in the process of being disrupted.

1. Maturing Technology

Fifteen years ago hardly anyone had a smartphone. Social media was in its infancy. Artificial intelligence was still science fiction. Yet today all of those things are somewhat mature technologies that have become an integral part of everyday life. Anywhere you go you see people using them as a matter of habit.

It’s become conventional wisdom to look at these developments and say that technology is accelerating. It certainly seems that way. Nevertheless, look a little closer and it becomes clear that’s not really true. Buy a computer or smartphone today and its capabilities are not that different to those that came out five years ago.

The truth is that every major technology has a similar life cycle called an S-curve. It emerges weak, buggy and flawed. Adoption is slow. In time, it hits its stride and enters a period of rapid growth until maturity and an inevitable slowdown. That’s what’s happening now with digital technology and we can expect many areas to slow down in the years to come.

In the 1920s and 30s there was a time of explosive growth in the automobile industry and electronic appliances. The 1950s and 60s was a golden age for antibiotics, with a number of life-saving new drugs being discovered every year. The 1970s were considered the heyday for airlines and the past few decades have been focused on digital technology.

Yet every technology matures and every S-curve flattens, which is exactly what we’re seeing with digital technology today. Moore’s Law, the consistent doubling of transistors we can cram on a silicon wafer, is ending, and the digital era will end with it. Once opportunities to innovate narrow, firms look to other avenues to increase profits.

2. Consolidation

One of the key tools in any strategist’s toolbox is Michael Porter’s five forces analysis. The basic idea is that to compete effectively, you need to focus not just on the key competitors in your industry, but also customers, suppliers, new market entrants and substitutes. To build competitive advantage, you need to increase your bargaining power against all five.

Yet when an industry is in decline, the forces external to the industry get the upper hand. With new market entrants and substitutes becoming more attractive, customers and suppliers are in a position to negotiate better deals, margins get squeezed and profits come under pressure.

That’s why a lot of consolidation in an industry is usually a bad sign. It means that firms within the industry don’t see enough opportunities to improve their business by serving their customers more effectively, through innovating their products or their business models. To maintain margins, they need to combine with each other to control supply.

I think it’s clear that Silicon Valley is going through some version of this today. With Moore’s Law ending, the opportunities to innovate are narrowing and acquisitions are accelerating. The last breakthrough product, arguably, was the iPhone launched in 2007. Startups, don’t try to upend incumbents anymore, they sell to them.

3. Rent Seeking & Regulatory Capture

The goal of every business is to defy markets. Any firm at the mercy of supply and demand will find itself unable to make an economic profit—that is profit over and above its cost of capital. In other words, unless a firm can beat Adam’s Smith’s invisible hand, investors would essentially be better off putting their money in the bank.

That leaves entrepreneurs and managers with two viable strategies. The first is innovation. Firms can create new and better products that produce new value. The second, rent seeking, is associated with activities like lobbying and regulatory capture, which seeks to earn a profit without creating added value. In fact, rent seeking often makes industries less competitive.

There is abundant evidence that over the last 20 years, American firms have shifted from an innovation mindset to one that focuses more on rent seeking. First and foremost, has been the marked increase in lobbying expenditures, which have more than doubled since 1998, especially in the tech industry. Firms invest money for a reason. They expect a return.

It seems like they are getting their money’s worth. Corporate tax rates in the US have steadily decreased and are now among the lowest in the developed world. Occupational licensing, often the result of lobbying by trade associations, has increased five-fold since the 1950s. These restrictions have coincided with a decrease in the establishment of new firms.

If your industry is more focused on protecting existing markets than creating new ones, that is one sign that it is vulnerable to disruption.

4. The Inevitable Scandals

In the 1920s the Teapot Dome scandal rocked Washington. The Secretary of the Interior, Albert Bacon Fall, was found to have corruptly leased Navy petroleum reserves to private companies. In response, Congress was given the right to subpoena any US citizen’s tax records as well as increased regulation of campaign finance.

In the century since, we have had continuous cycles of largesse and reform. The savings and loan crisis in the 1980s led to the FIRREA Act to increase oversight. Accounting scandals, like those involving Enron and WorldCom, led to Sarbanes Oxley. The Financial Crisis led to Dodd-Frank.

More recently, tens of billions of dollars were plowed into WeWork before it was exposed as little more than a Ponzi scheme. The Theranos fraud went on for more than a decade before its board realized that its product was an elaborate ruse. FTX was valued at $32 billion but turned out to be worthless. Yet there has been no reform.

As Bain pointed out a decade ago, the extreme measures taken after the Great Recession led to a superabundance of capital, which paved the way for the highest profit margins in half a century. Now it seems that the era of easy money and easy regulation is ending, making it a near certainty that more frauds will be exposed.

We need to learn the telltale signs that an industry is being disrupted. Once technology begins to mature, we can expect consolidation, rent-seeking and regulatory capture to follow. After that, it’s just a matter of how much time—and how big the bubble gets—before everything bursts.

— Article courtesy of the Digital Tonto blog
— Image credit: Gemini

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Are We Suffering from AI Confirmation Bias?

Are We Suffering From AI Confirmation Bias?

GUEST POST from Geoffrey A. Moore

When social media first appeared on the scene, many of us had high hopes it could play a positive role in community development and civic affairs, as indeed it has. What we did not anticipate was the long-term impact of the digital advertising model that supported it. That model is based on click-throughs, and one of the most effective ways to increase them was to present content that reinforces the recipient’s existing views.

Statisticians call the attraction to one’s existing point of view confirmation bias, and we all have it. As individuals, we believe we are in control of this, but it is obvious that at the level of populations, we are not. Confirmation bias, fed first by social media, and then by traditional media once it is converted to digital, has driven political and social polarization throughout the world. It has been further inflamed by conspiracy theories, malicious communications, fake news, and the like. And now we are faced with the advent of yet another amplifier—artificial intelligence. A significant portion of the fears about how AI could impact human welfare stem from how easily it can be put to malicious use through disinformation campaigns.

The impact of all this on our political life is chilling. Polarized media amplifies the impact of extremism and dampens the impact of moderation. This has most obviously been seen in primary elections, but it has now carried over into general elections to the point where highly unqualified individuals who have no interest in public service hold some of the most important roles in state and federal government. The resulting dysfunction is deeply disturbing, but it is not clear if and where a balance can be found.

Part of the problem is that confirmation bias is an essential part of healthy socialization. It reflects the impact that narratives have on our personal and community identities. What we might see as arrant folly another person sees as a necessary leap of faith. Our founding fathers were committed to protecting our nation from any authority imposing its narratives on unwilling recipients, hence our Constitutional commitment to both freedom of religion and freedom of speech.

In effect, this makes it virtually impossible to legislate our way out of this dilemma. Instead, we must embrace it as a Darwinian challenge, one that calls for us as individuals to adapt our strategies for living to a dangerous new circumstance. Here I think we can take a lesson from our recent pandemic experience. Faced with the threat of a highly contagious, ever-mutating Covid virus, most of the developed economies embraced rapid vaccination as their core response. China, however, did not. It embraced regulation instead. What they and we learned is that you cannot solve problems of contagion through regulation.

We can apply this learning to dealing with the universe of viral memes that have infected our digital infrastructure and driven social discord. Instead of regulation, we need to think of vaccination. The vaccine that protects people from fake news and its many variants is called critical thinking, and the healthcare provider that dispenses it is called public education.

We have spent the past several decades focusing on the STEM wing of our educational system, but at the risk of exercising my own confirmation bias, the immunity protection we need now comes from the liberal arts. Specifically, it emerges from supervised classroom discussions in which students are presented with a wide variety of challenging texts and experiences accompanied by a facilitated dialog that instructs them in the practices of listening, questioning, proposing, debating, and ultimately affirming or denying the validity of the argument under consideration. These discussions are not about promoting or endorsing any particular point of view. Rather, they teach one how to engage with any point of view in a respectful, powerful way. This is the intellectual discipline that underlies responsible citizenship. We have it in our labs. We just need to get it distributed more broadly.

That’s what I think. What do you think?

Image Credit: Pixabay

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The AI Agent Paradox

How E-commerce Must Proactively Manage Experiences Created Without Their Consent

LAST UPDATED: November 7, 2025 at 4:31 PM

The AI Agent Paradox

GUEST POST from Art Inteligencia

A fundamental shift is underway in the world of e-commerce, moving control of the customer journey out of the hands of the brand and into the hands of the AI Agent. The recent lawsuit by Amazon against Perplexity regarding unauthorized access to user accounts by its agentic browser is not an isolated legal skirmish; it is a red flag moment for every company that sells online. The core challenge is this: AI agents are building and controlling the shopping experience — the selection, the price comparison, the checkout path — often without the e-commerce site’s knowledge or consent.

This is the AI Agent Paradox: The most powerful tool for customer convenience (the agent) simultaneously poses the greatest threat to brand control, data integrity, and monetization models. The era of passively optimizing a webpage is over. The future belongs to brands that actively manage their relationship with the autonomous, agentic layer that sits between them and their human customers.

The Three Existential Threats of the Autonomous Agent

Unmanaged AI agents, operating as digital squatters on your site, create immediate systemic problems for e-commerce sites:

  1. Data Integrity and Scraping Overload: Agents typically use resource-intensive web scraping techniques that overload servers and pollute internal analytics. The shopping experience they create is invisible to the brand’s A/B testing and personalization engines.
  2. Brand Bypass and Commoditization: Agents prioritize utility over loyalty. If a customer asks for “best price on noise-cancelling headphones,” the agent may bypass your brand story, unique value propositions, and even your preferred checkout flow, reducing your products to mere SKU and price points. This is the Brand Bypass threat.
  3. Security and Liability: Unauthorized access, especially to user accounts (as demonstrated by the Amazon-Perplexity case), creates massive security vulnerabilities and legal liability for the e-commerce platform, which is ultimately responsible for protecting user data.

The How-To: Moving from Resistance to Proactive Partnership

Instead of relying solely on defensive legal action (which is slow and expensive), e-commerce brands must embrace a proactive, human-centered API strategy. The goal is to provide a superior, authorized experience for the AI agents, turning them from adversaries into accelerated sales channels — and honoring the trust the human customer places in their proxy.

Step 1: Build the Agent-Optimized API Layer

Treat the AI agent as a legitimate, high-volume customer with unique needs (structured data, speed). Design a specific, clean Agent API separate from your public-facing web UI. This API should allow agents to retrieve product information, pricing, inventory status, and execute checkout with minimal friction and maximum data hygiene. This immediately prevents the resource-intensive web scraping that plagues servers.

Step 2: Define and Enforce the Rules of Engagement

Your Terms of Service (TOS) must clearly articulate the acceptable use of your data by autonomous agents. Furthermore, the Agent API must enforce these rules programmatically. You can reward compliant agents (faster access, richer data) and throttle or block non-compliant agents (those attempting unauthorized access or violating rate limits). This is where you insert your brand’s non-negotiables, such as attribution requirements or user privacy protocols, thereby regaining control.

Step 3: Offer Value-Added Agent Services and Data

This is the shift from defense to offense. Give agents a reason to partner with you and prefer your site. Offer exclusive agent-only endpoints that provide aggregated, structured data your competitors don’t, such as sustainable sourcing information, local inventory availability, or complex configurator data. This creates a competitive advantage where the agent actually prefers to send traffic to your optimized channel because it provides a superior outcome for the human user.

Case Study 1: The Furniture Retailer and the AI Interior Designer

Challenge: Complex, Multivariable E-commerce Decisions

A high-end furniture and décor retailer struggled with low conversion rates because buying furniture requires complex decisions (size, material, delivery time). Customers were leaving the site to use third-party AI interior design tools.

Proactive Partnership:

The retailer created a “Design Agent API.” This API didn’t just provide price and SKU; it offered rich, structured data on 3D model compatibility, real-time customization options, and material sustainability scores. They partnered with a leading AI interior design platform, providing the agent direct, authorized access to this structured data. The AI agent, in turn, could generate highly accurate virtual room mock-ups using the retailer’s products. This integration streamlined the complex path to purchase, turning the agent from a competitor into the retailer’s most effective pre-visualization sales tool.

Case Study 2: The Specialty Grocer and the AI Recipe Planner

Challenge: Fragmented Customer Journey from Inspiration to Purchase

An online specialty grocer, focused on rare and organic ingredients, saw their customers using third-party AI recipe planners and shopping list creators, which often failed to locate the grocer’s unique SKUs or sent traffic to competitors.

Proactive Partnership:

The grocer developed a “Recipe Fulfillment Endpoint.” They partnered with two popular AI recipe apps. When a user generated a recipe, the AI agent, using the grocer’s endpoint, could instantly check ingredient availability, price, and even offer substitute suggestions from the grocer’s unique inventory. The agent generated a “One-Click, Fully-Customized Cart” for the grocer. The grocer ensured the agent received a small attribution fee (a form of commission), turning the agent into a reliable, high-converting affiliate sales channel. This formalized partnership eliminated the friction between inspiration and purchase, driving massive, high-margin sales.

The Human-Centered Imperative

Ultimately, this is a human-centered change challenge. The human customer trusts their AI agent to act on their behalf. By providing a clean, transparent, and optimized path for the agent, the e-commerce brand is honoring that trust. The focus shifts from control over the interface to control over the data and the rules of interaction. This strategy not only improves server performance and data integrity but also secures the brand’s place in the customer’s preferred, agent-mediated future.

“The AI agent is your customer’s proxy. If you treat the proxy poorly, you treat the customer poorly. The future of e-commerce is not about fighting the agents; it’s about collaborating with them to deliver superior value.” — Braden Kelley

The time to move beyond the reactive defense and into proactive partnership is now. The e-commerce leaders of tomorrow will be the ones who design the best infrastructure for the machines that shop for humans. Your essential first step: Form a dedicated internal team to prototype your Agent API, defining the minimum viable, structured data you can share to incentivize collaboration over scraping.

Image credit: Google Gemini

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