Author Archives: John Bessant

About John Bessant

Originally a chemical engineer, John Bessant has been active in the field of research and consultancy in technology and innovation management for over 40 years. He is Emeritus Professor of Innovation and Entrepreneurship at the University of Exeter and also has visiting appointments at the universities of Stavanger, Norway and Erlangen-Nuremburg, Germany. Author of over 30 books and 200 articles, you can find out more here: www.johnbessant.org

Our Innovation is All on Tape

1 Reply

Why Old Technologies Are Sometimes Still the Best Ones

Our Innovation is All on Tape

GUEST POST from John Bessant

Close your eyes and imagine for a moment a computer room in the early days of the industry. Chances are you’ll picture large wardrobe-sized metal cabinets whirring away with white-coated attendants tending to the machines. And it won’t be long before your gaze lands on the ubiquitous spools of tape being loaded and unloaded.

Which might give us a smug feeling as we look at the storage options for our current generation of computers — probably based on some incredibly fast access high-capacity solid state flash drive. It’s been quite a journey — the arc stretches a long way back from the recent years of USB sticks and SD cards, external HDDs and then the wonderful world of floppy discs, getting larger and more rigid as we go back in time. The clunky 1980s when our home computers rode on cassette drives, right back to the prehistoric days where the high priests of mini and mainframes tended their storage flock of tapes.

Ancient history — except that the tape drive hasn’t gone away. In fact it’s alive and well and backing up our most precious memories. Look inside the huge data farms operated by Google, Apple, Amazon, Microsoft Azure or anyone else and you’ll find large computers — and lots of tape. Thousands of kilometres of it, containing everything from your precious family photos to email backups to data from research projects like the Large Hadron Collider.

It turns out that tape is still an incredibly reliable medium — and it has the considerable advantage of being cheap. The alternative would be buying lots of hard drives — something which increasingly matters as the volume of data we are storing is growing. Think about the internet of things — all those intelligent devices, whether security cameras or mobile phones, manufacturing performance data loggers or hospital diagnostic equipment, are generating data which needs secure long-term storage. We’ve moved long past the era of measuring storage in kilobytes or megabytes; now we’re into zettabytes, each one the equivalent of to 250billion DVDs. In 2020 estimates suggest we produced close to 59Zb of data, projected to rise to 175zb by 2025! Fortunately IBM scientist Mark Lantz , an expert in storage, suggests that we can keep scaling tape and doubling capacity every 2.5 years for the next 20 years.

Plus tape offers a number of other advantages, not least in terms of security. Most of the time a tape cartridge is not plugged in to a computer and so is pretty immune to visiting viruses and malware.

In fact the market for magnetic tape storage is in robust health; it’s currently worth nearly $5bn and is expected to grow to double that size by 2030. Not bad for a technology coming up on its hundredth anniversary. Making all of this possible is, of course, our old friend innovation. It’s been a classic journey of incremental improvement, doing what we do but better, punctuated with the occasional breakthrough.

It started in 1877 when “Mary Had a Little Lamb” was recorded and played on Thomas Edison’s first experimental talking machine called a phonograph; the sounds were stored on wax cylinders and severely limited in capacity. The first tape recorder was developed in 1886 by Alexander Graham Bell in his labs using paper with beeswax coated on it. This patented approach never really took off because the sound reproduction was inferior to Edison’s wax cylinders.

Others soon explored alternatives; for example Franklin C. Goodale adapted movie film for analogue audio recording, receiving a patent for his invention in 1909. His film used a stylus to record and play back, essentially mimicking Edison’s approach but allowing for much more storage.

But in parallel with the wax-based approach another strand emerged in 1898, with the work of Voldemar Poulsen, a Danish scientist who built on an idea originally suggested ten years earlier by Oberlin Smith. This used the concept of a wire (which could be spooled) on which information was encoded magnetically. Poulsen’s model used cotton thread, steel sawdust and metal wire and was effectively the world’s first tape recorder; he called it a ‘telegraphone’.

Which brings us to another common innovation theme — convergence. If we fast forward (itself a term which originated in the word of tape recording!) to the 1930s we can see these two strands come together; German scientists working for the giant BASF company built on a patent registered to Fritz Pfleumer in 1928. They developed a magnetic tape using metal oxide coated on plastic tape which could be used in recording sound on a commercial basis; in 1934 they delivered the first 50,000 metres of it to the giant electronics corporation AEG.

The big advantage of magnetic recording was that it didn’t rely on a physical analogue being etched into wax or other medium; instead the patterns could be encoded and read as electrical signals. It wasn’t long before tape recording took over as the dominant design — and one of the early entrants was the 3M company in the USA. They had a long history of coating surfaces with particles, having begun life making sandpaper and moved on to create a successful business out of first adhesive masking tape and then the ubiquitous Scotch tape. Coating metal oxide on to tape was an obvious move and they quickly became a key player in the industry.

Innovation is always about the interplay between needs and means and the tape recording business received a fillip from the growing radio industry in the 1940s. Tape offered to simplify and speed up the recording process and an early fan was Bing Crosby. He’d become fed up with the heavy schedule of live broadcasting which kept him away from his beloved golf course and so was drawn to the idea of pre-recording his shows. But the early disc-based technology wasn’t really up to the task, filled with hisses and scratches and poor sound quality. Crosby’s sound engineer had come across the idea of tape recording and worked with 3M to refine the technology.

The very first radio show, anywhere in the world, to be recorded directly on magnetic tape was broadcast on 1 October 1947 featuring Crosby. It not only opened up a profitable line of new business for 3M, it also did its bit for changing the way the world consumed entertainment, be it drama, music hall or news. (It was also a shrewd investment for Crosby who became one of the emerging industry’s backers)

Which brings us to another kind of innovation interplay, this time between different approaches being taken in the worlds of consumer entertainment and industrial computing. Ever since Marconi, Tesla and others had worked on radio there had been a growing interest in consumer applications which could exploit the technology. And with the grandchildren of Edison’s gramophone and in the 1940s the work on television, the home became an increasingly interesting space for electronics entrepreneurs.

But as the domestic market for fixed appliances grew saturated so the search began for mobile solutions. Portability became an important driver for the industry and gave rise to the transistor radio; it wasn’t long before the in car entertainment market began to take off. An early entrant from the tape playback side was the 8-track cartridge in the mid-1960s which allowed you to listen to your favorite tracks without lugging a portable gramophone with you. Philips’ development of the compact cassette (and its free licensing of the idea to promote rapid and widespread adoption) led to an explosion in demand (over 100 billion cassette tapes were eventually sold worldwide) and eventually to the idea of the Walkman as the first portable personal device for recorded and recording music.

Without which we’d be a little less satisfied. Specifically we’d never been introduced to one of the Rolling Stones’ greatest hits; as guitarist Keith Richards explained in his 2010 autobiography:

“I wrote the song ‘Satisfaction’ in my sleep. I didn’t know at all that I had recorded it, the song only exists, thank God, to the little Philips cassette recorder. I looked at it in the morning — I knew I had put a new tape in the night before — but it was at the very end. Apparently, I had recorded something. I rewound and then ‘Satisfaction’ sounded … and then 40 minutes of snoring!”

Meanwhile back in the emerging computer industry of the 1950s there was a growing demand for storage media for which magnetic tape seemed well suited. Cue the images we imagined in the opening paragraph, acolytes dutifully tending the vast mainframe machines.

Early computers had used punched cards and then paper tape but these soon reached the limit of their usefulness; instead the industry began exploring magnetic audio tape.

IBM’s team under the leadership of Wayne Winger developed digital tape-based storage; of particular importance was finding ways to encode the 1s and 0s of binary patterns onto the tape. They introduced the commercial digital tape recorder in 1952, and it could store what was (for its time) an impressive 2mB of data on a reel.

Not everyone was convinced; as Winger recalled, “A white-haired IBM veteran in Poughkeepsie pulled a few of us aside and told us, ‘You young fellows remember, IBM was built on punched cards, and our foundation will always be punched cards.’ Fortunately Tom Watson Jnr, son of the company founder became a champion and the project went ahead.

But while tape dominated in the short term another parallel trajectory was soon established, replacing tapes and reels with disc drives whose big advantage was the ability to randomly access data rather than wait for the tape to arrive at the right place on the playback head. IBM once again led the way with its launch in 1956 of the hard disc drive and began a steady stream of innovation in which storage volumes and density increased while the size decreased. The landscape moved through various generations of external drives until the advent of personal computers where the drives migrated inside the box and became increasingly small (and floppy).

These developments were taken up by the consumer electronics industry with the growing use of discs as an alternative recording and playback medium, spanning various formats but also decreasing in size. Which of course opened the way for more portability with Sony and Sharp launching mini-disc players in the early 1980s.

All good news for the personal audio experience but less so for the rapidly expanding information technology industry. While new media storage technology continued to improve it came at a cost and with the exponential increase in volumes of data needing to be stored came a renewed interest in alternative (and cheaper) solutions. The road was leading back to good old-fashioned tape.

Its potential was in long-term storage and retrieval of so-called ‘cold data’. Most of what is stored in the cloud today is this kind — images, emails, all sorts of backup files. And while these need to be around they don’t have to be accessed instantly. And that’s where tape has come back into its own. Today’s tapes have moved on somewhat from IBM’s 1952 limited 2mB of capacity version. They are smaller on the outside but their capacity has grown enormously — they can now hold 20Tb or even if compressed 60pTb — that’s a 10 millionfold increase in 70 years. The tapes are not wound by hand on to capstans but instead loaded into cartridges, each of which hold around a kilometer of tape; companies use libraries containing tens of thousands of these cartridges which can be mounted via automated systems deploying robots. This process takes around 90 seconds to locate a cartridge and access and load the tape, so you could be forgiven for thinking that it’s a bit slow compared to your flash drive which has an access time measured in milliseconds.

There’s a pattern here — established and once important technologies giving way to the new kids on the block with their apparently superior performance. We’ve learned that we shouldn’t necessarily write the old technologies off — at the minimum there is often a niche for them amongst enthusiasts. Think about vinyl, about the anti-mp3 backlash from hi-fi fans or more recently photography using film and plates rather than their digital counterparts.

But it’s more than just nostalgia which drives this persistence of the old. Sometimes — like our magnetic tape — there are performance features which are worth holding on to — trading speed for security and lower storage cost, for example. Sometimes there is a particular performance niche which the new technology cannot enter competitively — for example the persistence of fax machines in healthcare where they offer a secure and reliable way of transmitting sensitive information. At the limit we might argue that neither cash nor physical books are as ‘good’ as their digital rivals but their persistence points to other attributes which people continue to find valuable.

And sometimes it is about the underlying accumulated knowledge which the old technology represents — and which might be redeployed to advantage in a different field. Think of Fujifilm’s resurgence as a cosmetics and pharmaceuticals company on the back of its deep knowledge of emulsions and coatings. Technologies which it originally mastered in the now largely disappeared world of film photography. Or Kodak’s ability to offer high speed high quality printing on the back of knowledge it originally acquired in the same old industry — that of accurately spraying and targeting millions of droplets on to a surface. And it was 3M’s deep understanding of how to coat materials on to tapes gained originally from selling masking tape to the paint shops of Detroit which helped it move so effectively into the field of magnetic tape.

Keeping these technologies alive isn’t about putting them on life support; as the IBM example demonstrates it needs a commitment to incremental innovation, driving and optimising performance. And there’s still room for breakthroughs within those trajectories; in the case of magnetic tape storage it came in 2010 in the form of the Linear Tape File System (LTFS) open standard. This allowed tape drives to emulate the random access capabilities of their hard disk competitors, using metadata about the location of data stored on the tapes.

Whichever way you look at it there’s a need for innovation, whether bringing a breakthrough to an existing field or helping sustain a particular niche for the long haul. And we shouldn’t be too quick to write off ‘old’ technologies as new ones emerge which appear superior. It’s worth remembering that the arrival of the steamship didn’t wipe out the shipyards building sailing ships around the world; it actually spurred them on to a golden era of performance imporvement which it took steampships a long time to catch up with.

So, there’s often a lot of life left in old dogs, especially when we can teach them some new innovative tricks.

You can find a podcast version of this here and a video version here

And if you’d like to learn with me take a look at my online course here

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Innovating Innovation

1 Reply

How Machine Learning is Transforming the Innovation Game

Innovating Innovation

GUEST POST from John Bessant

One of the difficult parts of being a parent is when your kids grow up and you lose the excuse to play with their toys. And in my case one that I particularly miss is the Transformer series. Originally developed in the 1980s and accompanied by a TV spin-off these robots could masquerade as ordinary vehicles like cars and oil tankers. And then, at a crucial moment, they could reassemble themselves into well-armed fighting robots able to save mankind on a weekly basis from all sorts of alien threats. The toys were masterpieces of engineering; the underlying story clearly had staying power since there is a new generation of transformers (and video /movie accompaniment) today.

For their time they were symbols of the power of transformation, being able to adapt and repurpose to deal with new challenges. And these days we have a much more powerful and real example of such power in the form of a new generation of machine learning models.

Machine learning has its roots back in experiments with ‘artificial intelligence’ in the 1970s but has come to represent a powerful technological trajectory as the idea of mimicking human neural networks and their learning capabilities has been explored. We’ve seen with increasing frequency many bastions fall to these models; it seems a lifetime ago (1996 actually) that IBM’s Deep Blue beat chess champion Gary Kasparov deploying something of a brute force approach. But by 2016 Google’s Alpha Go model managed to beat the world champion Lee Se-Dol at the much more complex game of ‘Go’. And recent contests at which machine learning seems to have ‘beaten’ human opponents include those like poker which involve not only strategy but the ability to bluff — essentially requiring computer models to imagine what an opponent is thinking and then generate a diversionary move.

As Jang Dae-Ik, a science philosopher at Seoul National University, told The Korea Herald after AlphaGo’s victory ‘This is a tremendous incident in the history of human evolution — that a machine can surpass the intuition, creativity and communication, which has previously been considered to be the territory of human beings…..Before, we didn’t think that artificial intelligence had creativity…..Now, we know it has creativity — and more brains, and it’s smarter’.

At heart these developments reflect a fundamental shift in machine learning applications and models. In the early days models were used to help with highly focused activities — for example applied in data mining where they might be searching for something specific. But now we have generative AI, which does what it says on the tin — generates something new. And this brings the uncomfortable challenge to our perception of ourselves as the only ones capable of creativity — generating novel and useful solutions to challenges.

A quick review of the growing literature on ‘artificial creativity’ shows that there are grounds for worrying. Machine learning models can now ‘create’ music, literature or visual art to a standard which makes it increasingly difficult to detect its non-human origin.

For example, the Next Rembrandt project was an attempt by a team of art historians, data scientists and engineers to teach a machine to think, act and paint like Rembrandt. The documentary film of this venture highlights the challenges and complexities involved in producing a painting which convinced many — 347 years after the painter’s death!

In similar fashion, there are a number of websites featuring music composed by AI in the style of — and often hard to distinguish from — the original composer. And in 2016 IBM’s Watson AI engine produced a trailer for the horror movie ‘Morgan’. This involved Watson ‘watching’ and analyzing hundreds of examples of trailers and then selecting scenes for editors to patch together into their film. This cut the time for the process from over a week to less than a day.

Which brings us to Chat — GPT and the explosion of interest in this particular model. It was launched by the OpenAI company in November 2022 as the latest in a series of generative models with the capability to come up with its own answers to questions posed to it. (Amongst its predecessors is Dall-E, a powerful image generator). The GPT stands for Generative Pre-Trained Transformer, a class of model which they have been working on for some time.

At its heart the Chat GPT model (and its equivalents in the labs of Google/Alphabet, Meta and many other companies) is a machine learning model trained on billions of facts. It has the ability to explore and analyse those and ‘learn’ how to synthesis coherent and credible answers to questions posed by a very wide and diverse audience. Within two weeks of its launch Chat GPT had attracted over a million users, and the demand is now so high there is a waiting list to access it. People have been experimenting with its capabilities to create songs and poems, write newspaper articles, answer exam questions and even to enter and pass the preliminary tests for people wishing to qualify as medical professionals in the USA!

Not surprisingly OpenAI it has seen its valuation rapidly escalate to around $29bn with Microsoft taking a significant share in the business. It’s likely that the next year will see an explosion of interest in such models with new and better variants and increasing competition from other players.

One area where such models may well have a significant impact is in the field of innovation itself. In an excellent article Frank Piller and colleagues explore this — and the implications for innovation management. They point out that there is already increasing use of generative machine learning models in innovation; these include searching large data sources to identify insights around customer needs and using generative models to create marketing and advertising copy for new products and services.

They map their analysis of where and how such models might be used on to a typical representation of the innovation process — the so-called ‘double diamond’ linked to ‘design thinking’. Here there is a front end concerned with exploring the ‘problem space’ — understanding user needs and potential opportunities. Work at this stage involves divergent exploration followed by convergence, closing in on promising directions. It is linked to a second divergent/convergent diamond linked to exploring the ‘solution space’ and then closing in on projects to be taken further.

Double Diamond Deisgn Process Model

What they were interested in was the ways in which features of machine learning might help with these activities and the possible impact on how innovation is undertaken — and by whom.

A fascinating feature of their research is that they do so not just on the basis of informed speculation but by putting the Chat-GPT model to the test, giving it some innovation challenges to work on. Thinking about the possibilities for new products in the field of camping and outdoor activity they designed three questions to put to the model, looking for whether and how new insights might be generated to help with:

  • Searching through large data sets containing information about potential new directions and trajectories
  • Exploring data on customer experience and searching for new insights into potential needs
  • Helping create new concepts around which innovations might be developed

All of these are typical tasks which innovation teams undertake in organizations; for example they spend a lot of time at the front end researching what has already been done, drawing in knowledge and building a picture of possible problem and solution space. They deploy a wide range of market research tools including various forms of trend analysis. And they work with a range of creativity tools to generate possible solution options for further progression.

It’s early days but the performance of the machine learning model was instructive. In exploring what is known about camping gear a Google search identified 299 million results which certainly exceeds the capacity of even a small army of human researchers to analyze! The Chat-GPT model did a good job in analyzing and pulling out results of possible relevance, providing at least a powerful first-pass filter.

In its second task the Chat-GPT model managed to make sense of a wide range of customer reviews to generate insights into trends and possible needs — so-called ‘sentiment analysis’. Once again its skill in sifting through the text of thousands of reviews showed potential for providing new insights into emerging and hidden customer needs.

And in the field of creating potential solutions the researchers set the model a brainstorming kind of task — to come jup with novel and useful ideas for new camping products. The strategy here is to prompt the model with some examples of typical brainstorming insights and then allow it to learn how to generate its own. Once again the performance on the task was impressive; not only did it come up with plausible incremental innovation ideas, it also generated some radically new ones which opened up new solution space.

At first glance this kind of performance across several areas of the innovation process might seem worrying. Even though there has been a backlash to the wave of enthusiasm around generative machine learning models the overall trajectory looks ominous in terms of its implications for ‘creative’ tasks in organizations. If machine learning continues to improve how long might it be before we no longer need human beings to work in the innovation process?

The reality seems to point more towards a hybrid model in which AI supports human activity — for example by using it to sift through enormous amounts of data and extract potentially relevant information which its human counterparts can then work with. As the researchers conclude, ‘….by expanding the problem and solution spaces in which NPD (new product development) teams can operate, language models create an opportunity to access and generate larger amounts of knowledge, which in turn results in more possible connections of problems and solutions. This should ultimately lead to qualitatively superior solutions and higher innovation performance.’

So can we relax and not worry about the machines taking over our innovation role? Not really —  if we want to take advantage of the powerful hybrid approach which Frank PIller and his colleagues point towards then we need to start learning some new skills and developing some new working arrangements to capitalise on it.

We’re going to need a lot of innovation model innovation.

P.S. In writing this piece I did NOT make use of Chat-GPT, though I was tempted to try! Researching this piece inspired me to write another innovation song:


Image credit: Wikipedia

You can find a podcast version of this here and a video version here

And if you’d like to learn with me take a look at my online course here

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Delivering Innovation

Leave a reply

How the History of Mail Order Can Help Us Manage Innovation at Scale

Delivering innovation

GUEST POST from John Bessant

2022 was a record year for home delivery of parcels and packages. After the Covid-19 lockdowns the idea of remote shopping became an even bigger reality and changed the behavior patterns of millions. It’s a habit which is hard to break — even when there are increasing disturbances in the delivery end of things like strikes and negative publicity surrounding how packages are actually handled and delivered. Estimates of the market size for this activity vary widely but suggest that it is worth close to $500bn worldwide.

But where did this revolution begin and what’s the innovation history behind remote retailing? For that we need to go back a couple of hundred years and locate ourselves in the beautiful hills of Powys in Wales. In the valley alongside the river Severn is the small town of Newtown, a market center since the 13th century. And in 1856 the home of Pryce Jones, a draper’s assistant who rose to take over the business in which he worked. And for which he had big plans.

He renamed the company the Royal Welsh Warehouse and specialized in selling Welsh flannel. His vision grew out of a belief in the wonderful powers of the soft warm fabric crafted from wool from the sheep he could see on the hillsides all around him. But it was also sharply focused on the potential size of that market — if he could only grow it. Which he did courtesy of two key enabling innovations which reached sufficient maturity to give him the channels to reach his imagined global market.

The channels were the postal system and the railways. Neither was new by this time but they were now coming of age — and enabling hitherto unrealizable dreams to take shape. Back in 1654 Oliver Cromwell had established the idea of a state postal system but it was another 30 years before a reliable system began to operate around the city of London. And another hundred years before Parliament authorized the creation of ‘Penny Posts’ in any town or city; while the idea grew in popularity it was still expensive and local in impact. It wasn’t until the major reforms of the Post Office in 1840 that the idea of a Uniform Penny Post was established, facilitating the safe, speedy and cheap conveyance of letters. With it came the first pre-payment in the form of postage stamps (beginning with the famous Penny Black).

Pryce Jones was quick to spot the possibilities in the newly-emerging postal system and began offering his wares via mail order. The offer was simple; place your order via mail and it will be delivered the next day (effectively anticipating Amazon’s Prime service by 150 years and offering faster delivery!). To explain to his market what he had to offer he developed an illustrated catalogue from which they could choose what they wanted; he launched this in 1861.

He was able to fulfil this delivery promise because the railways had also come of age; from the ‘Rocket’ which George Stephenson demonstrated in 1829 the idea of modern railway network had developed rapidly. The railway came to Newtown and Jones was quick to exploit its possibilities, building a warehouse next to the station and opening his mail order business alongside the post office. He expanded several times and in 1879, he built the Royal Welsh Warehouse, a tall red brick building in the centre of Newtown which still stands today.

His idea paid off; within months his business had started to grow and by the 1880s he had an international operation, counting amongst his patrons included the royal houses of Austria, Britain, Denmark, Germany, Hanover, Italy, Naples, and Russia. Valuable customers not only for their purchases but also for their implicit endorsement. Because Jones wasn’t just skilled at utilising new channels; he also played the role of ‘conveyor’, someone actively encouraging and promoting the use of the new business model along these channels. His mail order catalogue wasn’t simply a price list of items, it was a form of storytelling, complete with pictures and expansive descriptions. He understood the principles of marketing, the need to get consumers to buy into a vision of something which they wanted — and then he was able to fulfil that demand.

(He was also a gifted product innovator; amongst other things he is credited with the invention of the sleeping bag which he patented in 1876 under the name of the Euklisia Rug. He exported the product around the world, at one point landing a contract with the Russian Army for 60,000 rugs.)

Pryce Jones wasn’t alone; like so many innovations the idea of mail order retailing came to several people independently and around the same time, reflecting the changing environment and the enabling technologies. For example in Austria the Thonet family began selling their furniture in 1859 using a mail order catalogue and taking advantage of postal and transport innovations. In fact Pryce Jones’ model was predated by the US luxury goods company Tiffany’s who in 1845 launched their ‘Blue Book’ — arguably the world’s first mail order catalogue though targeted at a very small, select (and wealthy) market.

It wasn’t long before other entrepreneurs began to see the possibilities beyond extending the reach into new markets for particular products. They realised that there was a second side to the new market-place — the suppliers. These days we’re used to seeing examples of ‘platform’ businesses everywhere we look — just glance at your smart-phone to see the array of apps (representing goods and services) being offered across the platform of its shiny screen. But it was 150 years ago that this kind of business model first emerged.

In 1872, Aaron Montgomery Ward from Chicago started his own single-page mail order catalogue; it listed 163 items for sale. He’s credited amongst other things with coining the sales slogan ‘satisfaction guaranteed or your money back!’ The model worked; ten years later the ‘Wish book’ catalogue listed over 10,000 items. Most important was the fact that Ward didn’t manufacture many of these; he effectively created the platform across which the market in multiple goods and services could operate.

In doing so he paved the way for many others spotting and exploiting a similar opportunity. For example in Canada one of the largest department stores was the Eaton Company originally founded in 1869 to sell dry goods, backed by a growing network of factories.

Eaton Company Catalog

Timothy Eaton saw the possibilities in mail order and in 1884 released its 32 page catalogue. He expressed his vision of a network stretching across the sub-continent of Canada in a note accompany the catalogue; “This catalogue is destined to go wherever the maple leaf grows, throughout the vast Dominion. We have the facilities for filling mail orders satisfactorily, no matter how far the letter has to come and the goods have to go.”

And down in North Redwood, Minnesota Richard Warren Sears , a railroad services agent. began a sideline business by purchasing a batch of watches which had been refused delivery and selling them on to local people. In 1886, he used the profits he earned from it to set up a mail-order business selling watches as R.W. Sears Watch Company. That year he met a watch repairman named Alvah Curtis Roebuck and in 1887 the two of them relocated their business to Chicago. In 1888 they launched a printed catalogue offering a range of luxury goods like watches and jewelry; by 1892 this had grown to a 322 page catalogue which included sewing machines, sporting goods, musical instruments, saddles, firearms, buggies, bicycles, baby carriages, and some clothing.

Sears Roebuck

What Sears and Roebuck (and a growing number of others) were doing was developing the new business model of a platform, using the catalogue as the focal point across which remote retailing could expand. But this wasn’t simply a matter of printing and distributing a catalogue; what they were doing was mastering the art of building an ecosystem for retail innovation. They recognized that simply advertising a wide range of products and services to an expectant public would be a very fast way of losing money and reputation. In order to make the system work they needed to pull together a network and get it working to deliver ‘emergent properties’ — where the whole offered more than the sum of the parts.

Making remote retailing work meant finding ways to procure (or manufacture) a wide range of products and then holding them in a warehouse so they are available for quick delivery. But holding stock takes up space and costs money so the trick is to manage the logistics of sales forecasting, order processing and stockholding, plus being able to ensure rapid and reliable delivery. Which places emphasis on reliable channels — as Pryce Jones discovered.

And underneath this web of suppliers and deliverers is the challenge of cash flow — how to ensure enough money comes back into the system fast enough to cover costs and return a profit which helps keep the supply side engaged. New models for financing and payment began to emerge — not least the concept of paying cash on delivery.

The model expanded throughout the world and was often at the heart of a move from remote shopping to direct retail. The origins of the 20th century department store include a sizeable crossover — for example Kastner & Öhler was the first mail order business in Central Europe. The company was founded in 1873 in Austria, releasing its first mail order catalogue in 1885; as it grew it opened its first department store in 1894 and went on the become one of the household names in European retailing.

Mail order was a powerful business model which worked well during most ot the 20th century — but as we’ve learned so often about innovation, nothing lasts forever. New developments opened up new possibilities and it is not always the existing players who are best placed or able to exploit them. In the early 1980s a new channel began to appear — the internet. It opened up not only new opportunities in terms of potential reach, mirroring what Pryce Jones had seen in the emergence of uniform postal systems a century before. But it also changed the underlying thinking behind some of the core warehousing and logistics underpinning the mail order model.

Jeff Bezos was aware of the opportunity and had created a list of possible sectors to target with an internet-based model. He chose books, and quickly realized that he could not only reach a huge market via this new channel but he could also service it without the high costs of actually warehousing and distributing the books. He recognized the ‘long tail’ possibilities; with his model he could reach people with highly specific needs and connect them to suppliers who could meet that need. He also saw that the underlying business model was available to anyone — the advantages would come to those who could scale early and build a platform. As the major bookseller Barnes and Noble pointed out in their submission to legal authorities in their lawsuit of 1997Amazon was not a bookseller at all, it was a book broker.

Where Amazon and others paved the way for a new model to emerge, putting the platform kind of business on steroids, others were slower to recognise and adapt. The German firm Quelle had grown since its founding in 1927 to become one of the biggest mail order operations in Europe, with a dedicated logistics and warehousing operation near the town of Fürth in Bavaria. It was, along with Tempelhof airport in Berlin, one of the largest industrial sites in Europe stretching over nearly 7 hectares. But a failure to adapt fast enough to the rapid changes being brought about through internet retailing meant that by 2006 it collapsed into bankruptcy. All that remains today is the 90m high Quelle-Turm (Quelle Tower) built in 1964 and now preserved as a landmark to a different industrial era.

One of the features of the model Pryce Jones developed was the stimulus it gave to local producers, enabling the region around Newtown to prosper with new businesses. And something very similar has happened with the internet-driven mail order business built across the huge Alibaba platform in China. In 2003 Jack Ma launched the idea of a Taobao marketplace where people could trade goods and services using the ability of the platform to reach a large and distributed market and display content in rich and interesting formats.

This model is comprised primarily of small businesses but has grown to be the largest digital retail platform in the country and has spawned many ‘Taobao villages’ — areas where over 10% of the population is engaged in online retailing. It has had a huge impact on the rural economy; by August 2019 there were nearly 4500 Taobao villages in 25 provinces and estimates suggest up to half of the rural population has benefitted from this. It is equivalent to around 600,000 small shops and trading businesses employing around 10 million people with an economic value of around $195 billion worth of e-commerce sales.

The story is of course not over. With the rising expectations of a growing market for instant delivery has come a challenge and opportunity around the ‘last mile’ challenge — how to move from the digital world to physical delivery of products. And whilst there are many major traditional logistics players now operating in this space there are challenges on the horizon — for example drone delivery or even 3-D printing of a growing range of physical products. The virtualisation process has only just begun though it may still be a while before the Welsh flannel beloved of Pryce Jones emerges spinning out of a 3-D wool printer in our homes.

But perhaps the best kept secret is the one shrouded in Arctic mists and dating back hundreds of years. Somehow a single enterprise (the mysterious S. Claus operation) has managed the challenge of reliable overnight delivery on a global basis to millions of expectant children; there are clearly lessons still to be learned around wish fulfilment innovation.

You can also hear this as a podcast or watch it as a video.

If you’d like more songs, stories and other resources on the innovation theme, check out my website or listen to my podcast. And if you’d like to learn with me take a look at my online course here

Image credit: Wikimedia Commons, Unsplash

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

A Letter to Innovation Santa

1 Reply

Ten Essentials for an Innovator’s Christmas List

A Letter to Innovation Santa

GUEST POST from John Bessant

Christmas, as my 6-year old never tires of reminding me, is coming. Never mind that technically it’s a month away and forget the efforts I make, Scrooge-like, not to allow any trace of the season to cross our threshold until at least 1st December. She excitedly points out that everywhere — the TV and online adverts, the shop decorations, even some of our early bird neighbors with their flashing light displays — everywhere the signals are unmistakable. ‘It’s nearly here!’

Which prompts her to write lists, long and getting longer, of gift ideas in case Santa is short of the relevant information about this year’s must-have items without which a six-year old’s world can never be complete. I feel like King Canute, water lapping around my ankles as I desperately try to stem the tide, inevitability fast approaching on my horizon.

In a desperate attempt to distract myself from these seasonal waves I began thinking about the kind of list I might put together for a would-be innovating organization. Assuming there was an innovation equivalent of the old gentleman at the North Pole what might he be working on with his elves right now? What are his stock-pickers pulling off the warehouse shelves and loading up on the sleigh? What might be on the must-have list for an innovation Christmas?

Turns out to be a useful exercise in choice editing. Just like my daughter, my early lists grew and grew like Topsy, sprawling over several pages. And containing lots of items in the ‘Motherhood and apple pie’ class — things which were unarguably ‘good’ for innovation but a bit vague in how they might actually be implemented. ‘Make innovation happen every day in every way’ or ‘Put innovation at the heart of everything you do’, or ‘Keep reinventing the organization’ kind of thing — nice sentiments but not exactly helpful.

Back to the drawing board, trying to focus on things which not only represent important elements but also have some specific tools to help put them into practice. So here’s the result. If you’re looking for inspiration for your seasonal innovation shopping here’s a few ideas that might help. (And if you believe in an Innovation Santa they might be useful items to add to the ‘what I’d really like’ list you’re about to send up the chimney)

First the wrapping paper.

Innovation matters. If we don’t change what we offer the world (our products and services) and the ways we create and deliver them (our processes) then there’s a good chance that we won’t survive long in today’s turbulent market-place. The issue isn’t about whether or not to innovate but how?

The good news is that we’ve learned a lot about this challenge; whilst innovation still remains a risky business there are some key insights which can help stack the deck in our favour. Over a hundred years of research consistently shows that successful innovators …

1. Manage innovation as a process.

Innovation isn’t like the cartoon moment with a light bulb flashing on above someone’s head. It’s a journey, involving key steps (search, select, implement) to create value from ideas. Anyone might get lucky once but in order to repeat the innovation trick we need a process for managing this; it doesn’t have to be bureaucratic, but it does have to be systematic. So, to help you work with this one there are plenty of frameworks which you can adapt, checklists to make sure you’ve got a system for innovation — you could do a lot worse than start with the framework which the ISO launched this year for an Innovation Management Standard …

2. Explore all the innovation space available.

There are many different ways to innovate, from changing our offering, updating our processes, exploring new market contexts and even switching our underlying business model. It’s a little like an innovation compass and the challenge is to make sure we explore the full 360 degrees of opportunity. Once again there are plenty of tools to help with this — try looking at Doblin’s ten types of innovation, play with the 4Ps innovation compass or explore Blue Ocean thinking

3. Have an innovation strategy

A clear roadmap of where and how innovation will take the business forward. It’s easy to wave the flag and shout about how important innovation is; serious players think through their strategy for dealing with it, share the roadmap and make sure that people buy into it. And there are plenty of stocking fillers here to help with strategic positioning and analysis, from good old PEST and SWOT through to more thorough future scanning, scenarios and road mapping and discovery-driven planning.

4. Pay attention to the small stuff.

Although radical changes are the ones which hit the headlines the underlying economic evidence is clear; most innovation, most of the time, is about doing what we do a little better. Incremental innovation of this kind adds up and has the additional advantage that it is much lower in risk, advancing slowly along well-known frontiers. This is where the lean toolkit becomes a must-have — whether it’s tools like fishbones and process maps for continuous improvement of processes or value analysis and product feature maps for our offerings.

5. Mobilize the mainstream.

Many organizations have specialists who are given the responsibility for innovation — a bit like James Bond, they have the ‘license to innovate’. But every human being comes fitted with the standard equipment to enable us to be creative, finding solutions to problems and coming up with new ideas. Smart innovators mobilize this creativity across the entire organization. And there’s a rich toolbox to help with this one, from simple variants on the humble suggestion scheme to powerful collaboration platforms to ensure voices get heard, ideas get shared and collective intelligence puts its weight behind the big strategic challenges facing the organization.

6. Make connections.

Innovation has always been a multi-player game rather than a solo act and these days the talk is all about ‘open innovation’. Simply put, in a world rich in knowledge even the largest organization has to recognize that ‘not all the smart people work for us’. The game has shifted from one where knowledge creation and ownership is key to one where managing knowledge flow is the critical ingredient. The good news for smaller firms is that this levels the playing field; you don’t have to have all the resources for innovation as long as you know where they are and how to connect to them. By now some version of ‘open innovation’ should be at the heart of your strategy and there are plenty of tools and frameworks to help you work out what connections you need and how to build them. It might also be worth looking at your absorptive capacity — how well placed are you to take advantage of all the rich knowledge that’s out there, making sure you don’t get a kind of ‘knowledge indigestion’ as a result of gorging yourself on everything that’s on offer!

7. Build an innovative organization.

Companies like 3M and Google are famous for giving their staff time and space to explore and experiment, not just because they are generous employers trying to attract and retain talented employees. What they’re really doing is actively trying to recreate the entrepreneurial spirit which began their businesses. They believe that embedding that spirit in ‘the way we do things round here’ gives them a real long-term edge — everyone is an entrepreneur. But they also know that creating that kind of climate needs work — on the physical layout (to make sure people have the chance to creatively collide) on time (to allow ideas to emerge and incubate), on support and space (to provide fertile environments for creativity) and on their approach to ‘failure’ (not punishing people when things don’t work but encouraging an experimental learning approach). Maybe take a look at some of the tools available to help you assess how much of a creative climate you have — and focus on what you might usefully work on to develop it further.

8. Co-create with users.

Learning from markets has always been important but customers aren’t passive, they can also be a rich source of ideas for innovation. Finding ways to tap into user innovation not only generates more diverse ideas, it also helps create a partnership with the marketplace which improves adoption of innovation. People will use things, work with processes, feel a sense of ‘ownership’ if they’ve been involved in the innovation process. And the good news here is that we have plenty of tools and frameworks to help — starting with design thinking and embracing approaches like lead user methods …

9. Accept failure

Innovation is omelet territory and the odd broken egg is an important part. The key is to learn from failures and use the information to build and strengthen capability for the future. That’s been the big lesson coming out of the whole lean -start-up’ model for developing new ventures and it sits just as well inside established organizations who make use of agile approaches. Once again there are plenty of tools which capture this ‘build-measure-learn — pivot’ approach which builds on ‘intelligent failure’ …

10. Build dynamic capability.

Innovation involves a moving target — constant changes in technologies, markets, competition, regulation and a host of other variables. Successful innovators build on the above principles, but they also keep checking and updating their innovation management capabilities, learning new tricks and discarding old ones which no longer work. Innovation model innovation. Having a commitment to structured and constructively critical reflection is a key to this ‘metacognition’ approach — and there are plenty of helpful frameworks to enable and support that process. Check out the Innovation Fitness Test as an example …

One last seasonal thought. Innovation, like a puppy, is for life, not just for Christmas. It’s something we need to think about all year round. So, it might be worth recycling your Christmas innovation list into something you could use as a set of New Year resolutions …

You can find a podcast version of this here

If you’d like more songs, stories and other resources on the innovation theme, check out my website here, or listen to my podcast here, and if you’d like to learn with me take a look at my online course here

Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Ice Cream Dreams

Leave a reply

The surprising innovation stories behind that sunny afternoon delight

Ice Cream Dreams

GUEST POST from John Bessant

Season of mists, mellow fruitfulness — and those rare but wonderful days when the sun smiles down benignly. Strolling in the park, absorbing the warmth my attention was taken by an ice cream.

Or rather, to the face of a toddler who was very happily getting himself around an eminently lickable cone, with the usual results. We probably don’t really have to worry too much about the dietary impact of ice cream in situations like these because 80% of the foodstuff was being liberally spread around his face, across his clothes or dripping sadly to the floor. Which prompted the idle thought (it was a very warm and lazy afternoon) about the possibility of non-melting ice cream and from there to reflections on the general pattern of ice cream innovation.

It’s been with us a long time; the origins of ice cream are shrouded in the usual temporal mists but it’s generally thought to have emerged from eating snow and then someone having the bright idea (in China around 200BCE) of mixing in some milk and rice. Great if you happen to have nearby mountains to provide the necessary cold stuff but if not you need some way of making or at least preserving ice. Which is where the Persians came in with the necessary engineering; around 400 BCE, they developed an early concept for the refrigerator, a large pyramidal structure called a yakhchal that used evaporation and insulation to keep things cool.

Armed with this process innovation and after a few hundred more years they developed a delicacy called a sharbat — an ice-based fusion of various flavourings and a magic ingredient — sugar — which trade with India had given them access to. It’s not a huge stretch of the imagination to think that Xanadu (in Coleridge’s famous poem Kubla Khan) — his ‘…. miracle of rare device, A sunny pleasure-dome with caves of ice’ was populated by people happily eating these central Asian delights.

Not surprisingly the idea of ice cream spread across Europe though the pace of innovation slackened somewhat. It took another couple of centuries before ships began returning from the exciting exploratory voyages of the 16th century bringing with them a wonderful range of new flavours and additives — sugar, chocolate, vanilla and many more exotic spices. This kick-started a new phase of product innovation which placed the delicacy firmly on the tables of those people wealthy enough to afford it. Experiments proliferated and it was in England that the idea of mixing in milk was developed; in her cookery book published in 1718 Mrs Mary Eales wrote the first recipe down, based on her experience working as confectioner to Queen Anne:

Take Tin Ice-Pots, fill them with any Sort of Cream you like, either plain or sweeten’d, or Fruit in it; shut your Pots very close…

Lay a good deal of Ice on the Top, cover the Pail with Straw, set it in a Cellar where no Sun or Light comes, it will be froze in four Hours.

Across the pond it was the same story. A confectioner called Philip Lenzi was the first to announce publicly the sale of ice cream, advertising in the New York Gazette in May, 1777 and George Washington indulged his presidential weakness for the delicacy to the tune of a $200/day habit during the summer of 1790. It was one of his chefs, Augustus Jackson, who came up with the valuable process innovation of adding salt to the ice mixture to lower its freezing point.

The only problem with all of this was that the cost of the key ingredient — ice — was so high that ice cream remained firmly at the luxury end of the market.

We can use another innovation lens to help understand what happened next. Abernathy and Utterback’s valuable model of innovation dynamics suggests that emphasis shifts during an innovation’s life cycle; in its early days the attention is on experimenting with the core product idea until a ‘dominant design’ emerges which captures the attributes the market values. This is followed by a shift of interest towards the process innovation side — how to make this cheaper or more reliably.

And in the case of ice cream this shone a spotlight on the core problem. If ice cream were ever to shift from being the exclusive luxury consumed by French aristocrats, US presidents or English monarchs then someone needed to do something about the chilling side of the equation.

That someone was a 23 year-old Boston merchant named Fredric Tudor who in 1806 hit upon the idea of harvesting ice from his father’s farm and shipping it to the (relatively) nearby islands of the West Indies. His ship, the Favorite, made the 1,500 mile journey in three weeks carrying its precious cargo in holds lined with sawdust to act as an insulator. It half-worked; he was able to sell on the half of his cargo which hadn’t melted in Cuba, albeit incurring a significant loss. Following the idea of ‘fail fast’ he followed up on this venture with three more voyages during the following year, all of which compounded his losses.

His business model wasn’t bad; shipping costs were low (because most made the journey to the islands empty to return with cargoes of sugar and fruit) and sawdust was free as a by-product of the timber industry. But it took him 4 years to turn a profit from the venture and his cash flow worsened to the point that he spent several stretches in debtor’s prison during 1812 and 1813. He struggled on and eventually he was able to open up the ice market in cities across the southern states of America.

His gradual success encouraged others to work on the process side; one of his suppliers, Nathaniel Wyeth, developed a horse-drawn plough for cutting huge blocks of ice, opening the door to large-scale harvesting. Others worked on the logistics and insulation side; by 1833 it was possible to sail the 16,000 miles from Boston to Calcutta with a cargo of 180 tons of ice and land over 100 of them on the dockside, ready for sale at a huge mark-up. The increasingly profitable ice trade flourished; by 1886 the industry employed over 40,000 people and cut a record 25 million tons of ice to ship as far afield as Hong Kong or Rio de Janeiro.

It’s at this point that we see another familiar innovation face — disruptive innovation. In 1834 Jacob Perkins had been granted a patent for his “Apparatus and means for producing ice, and in cooling fluids” with which he effectively demonstrated that vaporizing and condensing a volatile liquid in a closed system would do the job. In doing so he outlined the basic architecture which underpins today’s refrigerators; his work influenced a generation of researchers like the young Carl von Linde who beavered away in their laboratories to explore the approach. It wasn’t long before artificial ice making became a reality; by 1873 a patented commercial refrigeration system was on the market. In the years which followed the industry grew — in 1879 there were 35 plants and ten years later 222 making artificial ice.

Effectively this development sounded the death knell for the ice-harvesting industry, although it took a long time to go under. For a while both industries grew alongside each other, learning and innovating along their different pathways and expanding the overall market for ice — for example, by feeding the growing urban demand to fill domestic ‘ice boxes.’ But inevitably the new technology took over as the old harvesting model reached the limits of what it could achieve in terms of technological efficiencies. Significantly most of the established ice harvesters were too locked in to the old model to make the transition and so went under — to be replaced by the new refrigeration industry dominated by new entrant firms.

Ice Cream ConesAll of which was good news for the ice cream side of things. The stage was set now for another kind of innovation — market positioning. Anticipating Henry Ford by decades the next wave of innovation was all about turning a luxury product into one for mass consumption. With the falling cost and rising availability of ice the entrepreneurial opportunities became increasingly apparent, not least to Signor Carlo Gatti, a native of the Italian corner of Switzerland who moved to England in 1847. He started out with a small street stall selling roasted nuts and waffles in London and was successful enough to be able, two years later, to open a small café in Holborn selling a variety of coffee, chocolate and confectionery — including ice cream.

His ice came from the nearby Regent Canal via the Regent Canal Company who had followed Tudor’s ideas and diversified into ice harvesting. With them as partners Gatti was able to expand, exhibiting at the Great Exhibition of 1851 and in the same year opening another outlet in Charing Cross, a stand from which people could buy various drinks and confections, including ice cream. He’d got the economics down to the point where he could sell a portion served in a glass shell for one penny — something which became known as a ‘penny lick’.

It helped bring ice cream to the attention of a wide population though it didn’t do much for public health. His imitators (in a classic example of what Joseph Schumpeter called ‘swarming’) soon began offering ice cream everywhere but it was often served under questionable sanitary conditions. Essentially when you had finished your penny lick you handed the glass shell back to the vendor who would give it a perfunctory rinse in what was increasingly dirty water, wipe it with a rag — and then use it for their next sale!

Gatti’s efforts on the supply side to bring ice cream to the masses were matched by those of a cookery writer, Agnes Marshall, whose books jostled with those of Mrs Beeton for a place in the kitchens of a growing number of Victorian households. Her 1888 edition included a recipe for ‘cornets with cream’ which was perhaps the first published version of what became the ubiquitous ice cream cone. It did her reputation no harm; she became known as ‘the Queen of ices’. She helped position ice cream as a standard dish on the menu of households who could increasingly afford to buy ice from a local icehouse and store it in their own ice box.

These developments were mirrored in other countries; Manufacturing ice cream was pioneered in in America in 1851 by a Baltimore milk dealer named Jacob Fussell. Another company called Bassetts began making ice cream in 1861, and then opened theiir own shop in 1885; it’s still available today.

Gatti didn’t stop with selling ice cream. He understood the challenge of scaling innovations and the importance of building a system, a network which could deliver value at scale. He used his early profits to buy into ice storage, opening in 1857 an ‘ice well’ next to the Regent’s Canal where he could store ice for use all year round — and also sell it to others. It was so successful that he built a second in 1862 and also began importing ice from Norway, shipping it up the river Thames, unloading and transferring to barges and then moving it by canal to his warehouses. He quickly became the largest ice dealer in the country and completed his network with the other half of the logistics equation, a fleet of handcarts which took the ice to private houses in the better-off streets of London. And he consolidated his original distribution channel, opening a series of restaurants, cafes and even a music hall in the city.

His ice warehouses also supplied the growing number of small vendors who would make and sell ice cream from stalls and shops, opening up the market on the back of a plentiful supply of the cold stuff. And they also enabled a distribution network for the finished product; by the 1890s ice cream stalls were springing up everywhere and the increasing availability of ice enabled enterprising vendors to take the ice cream where it was needed — in parks on sunny afternoons, outside the opera at night, to the crowds gathering for public festivals and so on.

This trend towards portability of sales outlet led to another example of a common innovation phenomenon — peripheral innovation. In this case it involved the invention, often by small scale user innovators, of a variety of solutions to the sales and distribution problem. People began improvising refrigerated handcarts which could be pushed around, or attaching them to bicycles. And one of them, Italo Marchiony, was doing so in the streets of New York in 1896 He was particularly frustrated with the problem of what to serve his ice cream in; the glass containers which he used needed cleaning before re-use, they were prone to breakage and not a few of them wandered off in the hands of Wall St traders out for a lunchtime stroll and never returned.

So, he began experimenting with an edible container, based on making waffles and then folding them before they cooled into small cups. The idea worked and people began to enjoy the additional taste experience as well as the contents; his business boomed and by 1902 he was running a fleet of 45 ice cream carts, now horse-drawn. He couldn’t keep up with demand for his cups using his family’s kitchen and so developed and patented (in 1903) a machine for making ice cream cups. With the increasing volume he was able to build a successful business, setting up a factory in 1904 to produce cups and later wafers to enable him to sell an ice cream sandwich as an alternative delivery option.

That same year at the St Louis World’s Fair saw ice cream seller Arnold Fornachou running short of paper cups and increasingly desperate to find an alternative. The next-door concession was a stall run by Ernest Hamwi selling a crisp waffle called zalabis. He quickly saw a solution, rolling the waffles into a cone shape (a cornucopia) and in the process solving the problem and inventing a new form for eating ice cream. It caught on and prompted Hamwi to set up in the business of making cones, establishing the Cornucopia Waffle Company and in 1910 founding the Missouri Cone Company.

(This appears to be another case of simultaneous innovation although according to his daughter, Marchiony also exhibited his waffle cups at the same World’s Fair and it was he who invented the cone).

Ice Cream Boat

It didn’t really matter; the market grew fast enough to accommodate both of them. By 1924 annual production in the USA reached 245million cones and the idea had spread around the world. Ice cream had become big business and it drew in a number of other players including one of the largest butchers in the UK, the Wall’s company. They saw the potential in diversifying into ice cream since sales of meat traditionally slumped in the summer, and they also had extensive investments and experience in refrigeration. They began experimenting in 1913 and went into full-scale production after the First World War in 1922.

They sold their ice cream in their shops and even going door-to-door and they also mobilised a fleet of bicycles to distribute during the summer of 1923; by 1924 they’d expanded the business with new manufacturing facilities and a new fleet of 50 specially-designed tricycles. Their efforts paid off; by 1927 sales had increased from £13,719 to £444,000.

Ice cream delivery vans were a next obvious step since they could extend the range of coverage and carry more stock on board. Equipped with loudspeakers to replace the bicycle bell they became a feature of every summertime street across the country. They also opened up an interesting sideline in what we might call ‘pirate innovation’ — using a novel idea in unexpected ways.

The city of Glasgow in Scotland became notorious during the 1980s for what were termed the ‘ice cream wars’ in which there was increasing violence between ice cream van salesmen — a classic case of gangland turf wars. These weren’t fuelled by a particularly strong appetite amongst the local population for ice cream; the problem arose because the vans (being highly mobile and working as cash-based businesses) offered an excellent base for illegal trafficking of drugs and stolen goods!

Back to our Abernathy/Utterback model of the innovation life cycle which also points us towards the next innovation wave which occurred in the 1970s. Once a dominant deisgn has been established and process innovation takes over there’s a drift towards maturity — which opens up the possibility of new growth coming as the cycle repeats. In the case of ice cream this was through marketing innovation — repositioning the product.

This may involve significant storytelling, weaving a new narrative around an old idea. In the case of ice cream it changed perception of the product from a simple treat to be enjoyed by children and their indulgent parents on hot days to something which was a much more adult-focused luxury experience. Exotic flavours proliferated and advertising stressed the sophisticated aspect; brands like Haagen Dazs were created which emphasised the sensual pleasures of consuming frozen milk.

Of course, this effectively returned ice cream to where it had started — as something which only the wealthy could afford. Only this time its luxury appeal was to everyone; the rise of such specialist ice cream can be seen today in the amount of refrigerated cabinet space now devoted to it in supermarkets.

Today’s market for ice cream is vast; estimates suggest it will reach $97.85 billion in 2027, up from $71.52 billion in 2021. And that’s without taking the potential demand increase which might come if global warming continues! It also provides further incentive for innovation, with increasing investment into advanced R&D to try and understand things like the micro-crystalline structures of ice cream or the key parameters involved in stimulating taste and texture receptors inside the mouth. So maybe somewhere in a laboratory right now someone is working on my non-melting ice cream idea.

Image credits: Pixabay

You can find a podcast version of this here

If you’d like more songs, stories and other resources on the innovation theme, check out my website here

And if you’d like to learn with me take a look at my online course here

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Kicking the Copier Won’t Fix Your Problem

Leave a reply

Kicking the Copier Won't Fix Your Problem

GUEST POST from John Bessant

Have you ever felt the urge to kick the photocopier? Or worse? That time when you desperately needed to make sixty copies of a workshop handout five minutes before your session begins. Or when you needed a single copy of your passport or driving license, it’s the only way you can prove your identity to the man behind the desk about not to approve your visa application? Remember the awful day when you were struggling to print your boarding passes for the long-overdue holiday; that incident meant you ended up paying way over the odds at the airport?

The copiers may change, the locations and contexts may differ but underneath is one clear unifying thread. The machines are out to get you. Perhaps it’s just a random failure and you are just the unlucky one who keeps getting caught. Or maybe it’s more serious, they’ve started issuing them with an urgency sensor which detects how critical your making a copy is and then adjusts the machine’s behavior to match this by refusing to perform.

Whatever the trigger you can be sure that it won’t be a simple easy to fix error like ‘out of paper’ which you just might be able to do something about. No, the kind of roadblock these fiendish devices are likely to hurl on to your path will be couched in arcane language displayed on the interface as ‘Error code 3b76 — please consult technician’.

Given the number of photocopiers in the world and the fact that we are still far from being a paperless society in spite of our digital aspirations, it’s a little surprising that the law books don’t actually contain a section on xeroxicide — the attempt or execution of terminal damage to the lives of these machines.

Help is at hand. Because whilst we may still have the odd close and not very enjoyable encounter with these devices the reality is that they are getting better all the time. Not only through adding a bewildering range of functionality so that you can do almost anything with them apart from cook your breakfast, but also because they are becoming more reliable. And that is, in large measure, down to something called a community of practice. One of the most valuable resources we have in the innovation management toolkit.

The term was originally coined by Etienne Wenger and colleagues who used it to describe “groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly.” Which is where the idea of communities of practice comes in. It’s a simple enough idea, based on the principle that we learn some things better when we act together.

Shared learning helps, not least in those situations where knowledge is not necessarily explicit and easily available for the finding. It’s a little like mining for precious metals; the really valuable stuff is often invisible inside clumps of otherwise useless rock. Tiny flecks on the surface might give us the clue to something valuable being contained therein but it’s going to take quite a lot of processing to extract it in shiny pure form.

Knowledge is the same; it’s often not available in easy reach or plain sight. Instead it’s what Michael Polanyi called tacit as opposed to explicit. We sometimes can’t even speak about it, we just know it because we do it.

Which brings us back to our photocopiers. And to the work of Julian Orr who worked in the 1990s as a field service engineer in a large corporation specializing in office equipment. He was an ethnographer, interested in understanding how communities of people interact, rather as an anthropologist might study lost tribes in the Amazon. Only his research was in California, down the road from Silicon Valley and he was carrying out research on how work was organized.

He worked with the customer service teams, the roving field service engineers who criss-cross the country trying to fix the broken machine which you’ve just encountered with its ‘Error code 3b76 — please consult technician’ message. Assuming you haven’t already disassembled the machine forcibly they are the ones who patiently diagnose and repair it so that it once again behaves in sweetly obedient and obliging fashion.

They do this through deploying their knowledge, some of which is contained in their manuals (or these days on the tablets they carry around). But that’s only the explicit knowledge, the accumulation of what’s known, the FAQs which represent the troubleshooting solutions the designers developed when creating the machines. Behind this is a much less well-defined set of knowledge which comes from encountering new problems in the field and working out solutions to them — innovating. Over time this tacit knowledge becomes explicit and shared and eventually finds its way into an updated service manual or taught on the new version of the training course.

Orr noticed that in the informal interactions of the team, the coming together and sharing of their experiences, a great deal of knowledge was being exchanged. And importantly that these conversations often led to new problems and solutions being shared and solved. These were not formal meetings and would often happen in temporary locations, like a Monday morning meet-up for breakfast before the teams went their separate ways on their service calls.

You can imagine the conversations taking place across the coffee and doughnuts, ranging from catching up on the weekend experience, discussing the sports results, recounting stories about recalcitrant offspring and so on. But woven through would also be a series of exchanges about their work — complaining about a particular problem that had led to one of them getting toner splashed all over their overalls, describing proudly a work-around they had come up with, sharing hacks and improvised solutions.

There’d be a healthy skepticism about the company’s official repair manual and a pride in keeping the machines working in spite of their design. More important the knowledge each of them encountered through these interactions would be elaborated and amplified, shared across the community. And much of it would eventually find its way back to the designers and the engineers responsible for the official manual.

Orr’s work influenced many people including John Seely Brown (who went on to be Chief Scientist at Xerox) and Paul Duguid who explored further this social dimension to knowledge creation and capture. Alongside formal research and development tools the storytelling across communities of practice like these becomes a key input to innovation, particularly the long-haul incremental improvements which lie at the heart of effective performance.

Tacit Explicit KnowledgeAn important theme which Japanese researchers Ikujiro Nonaka and Hirotaka Takeuchi were aware of and formalised in their seminal book about ‘the knowledge creating company’. They offered a simple model through which tacit knowledge is made explicit, shared and eventually embedded into practice, a process which helped explain the major advantages offered by engaging a workforce in high involvement innovation. Systems which became the ‘lean thinking’ model which is in widespread use today have their roots in this process, with teams of workers acting as communities of practice.

Their model has four key stages in a recurring cycle:

  • Socialization — in which empathy and shared experiences create tacit knowledge (for example, the storytelling in our field service engineer teams)

  • Externalization — in which the tacit knowledge becomes explicit, converted into ideas and insights which others can work with

  • Combination — in which the externalized knowledge is organized and added to the stock of existing explicit knowledge — for example embedding it in a revised version of the manual

  • Internalization — in which the new knowledge becomes part of ‘the way we do things around here’ and the platform for further journeys around the cycle

CoPs are of enormous value in innovation, something which has been recognized for a long time. Think back to the medieval Guilds; their system was based on sharing practice and building a community around that knowledge exchange process. CoPs are essentially ‘learning networks’. They may take the form of an informal social group meeting up where learning is a by-product of their being together; that’s the model which best describes our photocopier engineers and many other social groups at work. Members of such groups don’t all have to be from the same company; much of the power of industrial clusters lies in the way they achieve not only collective efficiency but also the way they share and accumulate knowledge.

Small firms co-operate to create capabilities far beyond the sum of their parts — and communities of practice form an excellent alternative to having formal R&D labs. John Seely Brown’s later research looked at, for example, the motorcycle cluster around the city of Chongquing in China whose products now dominate the world market. Success here is in no small measure due to the knowledge sharing which takes place within a geographically close community of practice.

CoPs can also be formally ‘engineered’ created for the primary purpose of sharing knowledge and improving practice. This can be done in a variety of ways — for example by organizing sector level opportunities and programs to share experience and move up an innovation trajectory. This model was used very successfully in, for example, the North Sea oil industry first to enable cost-reduction and efficiency improvements over a ten-year period in the CRINE (Cost reduction for a new era) program. It resulted in cumulative savings of over 30% on new project costs and as a result a similar model was deployed to explore new opportunities to deploy the sector’s services elsewhere in the world as the original North Sea work ran down.

It can work inside a supply network where the overall performance on key criteria like cost, quality and delivery time depends on fast diffusion of innovation amongst all its members. One of Toyota’s key success factors has been in the way in which it mobilizes learning networks across its supplier base and the model has been widely applied in other sectors, using communities of practice as a core tool.

CoPs have been used to help small firms share and learn around some of the challenges in growth through innovation — for example in the highly successful Profitnet program in the UK. It’s a model which underpins the start-up support culture where expert mentoring can be complemented by teams sharing experiences and trying to help each other in their learning journeys towards successful launch. And it’s being used extensively in the not-for-profit sector where working at the frontier of innovation to deal with some of the world’s biggest humanitarian and development challenges can be strengthened by sharing insights and experiences through formal communities of practice.

At heart the idea of a community of practice is simple though it deals with a complex problem. Innovation is all about knowledge creation and deployment and we’ve learned that this is primarily a social process. So, working with the grain of human interaction, bringing people together to share experiences and build up knowledge collectively, seems an eminently helpful approach.

Which suggests that next time you are thinking of taking a chainsaw to the photocopier you might like to pause — and maybe channel your energies into thinking of ways to innovate out of the situation. A useful first step might be to find others with similar frustrations and mobilize your own community of practice.

You can find a podcast version of this here

If you’d like more songs, stories and other resources on the innovation theme, check out my website here

And if you’d like to learn with me take a look at my online course here

Image credit: FreePik

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Is Digital Different?

2 Replies

Is Digital Different?

GUEST POST from John Bessant

‘Now the chips are down…’

‘The robots are coming…’

‘Digitalize or die!’

There’s no shortage of scary headlines reminding us of the looming challenge of digital transformation. The message is clear. On the one hand if we don’t climb aboard the digital bandwagon we’ll be left behind in a kind of late Stone Age, slowly crumbling to dust while the winds of change blow all around us. On the other we’re facing some really big questions — about employment, skills, structures, the whole business model with which we compete. If we don’t have a clear digital strategy to deal with these we’re going to be in trouble.

And it’s not just the commercial world which is having to face up to these questions; the same is true in the public sector and in the not-for-profit world. The digital storm has arrived.

There aren’t any easy solutions to this which explains why so many conferences now have the digital word scrawled across their strap-lines. They provide focal points, create tents within which people can huddle and talk together, trying to work out exactly how they are going to manage this challenge. I’ve spent the past couple of weeks attending a couple — ‘Innovating in the digital world’ was the banner under which the ISPIM (the International Society for Professional Innovation Management) community gathered while ‘Leading digital transformation’ brought EURAM (the European Academy of Management) together. Close to a thousand people gathering for more than just a welcome post-Covid reunion; conferences like these are a good indication of the scale of the questions which digital transformation raises.

A Pause for Thought

But look again at those headlines at the start of this piece. They were actually newspaper cuttings from the 1980s, close on fifty years ago. Anxiety about the transformative potential of digital technology was running pretty high back then and for similar reasons. And yet their dire predictions of disaster and massive structural upheaval haven’t quite emerged. Somehow, we’ve made it through, we haven’t had mass unemployment, we haven’t been replaced by intelligent machines, and while income distribution remains very unequal the causes of that are not down to technological change.

Which is not to say that nothing has changed. Today’s world is radically different along so many dimensions, and not everyone has made it through the digital crisis. Plenty of organizations have failed, unable to come to terms with the new technology whilst others have emerged from nowhere to dominate the global landscape. (It’s worth reflecting that none of the FAANGS corporations (Facebook/Meta, Amazon, Apple, Netflix and Google were even born when those headlines were written). So, we’ve had change, yes, but it’s not necessarily been destructive or competence-destroying change.

If we’re serious about managing the continuing challenge then it’s worth taking a closer look at just what digital innovation involves. Is it really so revolutionary and transformative? The answer is a mixture. In terms of speed of arrival it’s been a very-slow paced change. Digital innovation isn’t new. Despite the hype around the disruptive potential of this technological wave the reality is that it’s been building for at least 70 years, ever since the invention of the transistor back in Bell Labs in 1947. And there’s a good argument for seeing it date back fifty years before that to when John Fleming and Lee DeForest began playing around with valves and enabling simple electronic circuits.

The idea of programmable control was around another hundred years before that; early on in the Industrial Revolution we saw mechanical devices increasingly substituting for human skill and intervention. Textile manufacturers were able to translate complex designs into weaving instructions for their looms through the use of punched card systems, an innovation pioneered by Joseph Marie Jaquard. Not for nothing did the Luddites worry about the impact technology might have on their livelihoods. And we should remember that it was in the nineteenth, not the twentieth century that the computer first saw the light of day in the form of the difference and analytical engines developed by Charles Babbage and Ada Lovelace.

In fact although there has been rapid acceleration in the application of digital technology over the past thirty years in many ways it has more in common with a number of other ‘revolutions’ like steam power or electricity where the pattern is what Andrew Hargadon calls ‘long fuse, big bang’. That is to say the process towards radical impact is slow but when it converges there can be significant waves of change flowing from it.

Riding the Long Waves of Change

Considerable interest was shown back in the 1980s (when the pace of the ‘IT revolution’ appeared to be accelerating) in the ideas of a Russian economist, Nikolai Kondratiev. He had observed patterns in economic activity cycles which seemed to have a long period (long waves) and which were linked to major technological shifts. The pattern suggested that major enabling technologies like steam power or electricity which had widespread application potential could trigger significant movements in economic growth. The model was applied to the idea of information technology and in particular Chris Freeman and Carlota Perez began developing the approach as a lens through which to explore major innovation-led changes. They argued that the role of technology as a driver had to be matched by a complementary change in social structures and expectations, a configuration which they called the ‘techno-economic paradigm’ .

Importantly the upswing of such a change would be characterised by attempts to use the new technologies in ways which mainly substituted for things which already happened, improving them and enhancing productivity. But at a key point the wave would break and completely new ways of thinking about and using the technologies would emerge, accelerating growth.

A parallel can be drawn to research on the emergence of electricity as a power source; for a sustained period it was deployed as a replacement for the large central steam engines in factories. Only when smaller electric motors were distributed around the factory did productivity growth rise dramatically. Essentially the move involved a change in perspective, a shift in paradigm.

Whilst the long wave model has its critics it offers a helpful lens through which to see the rise of digital innovation. In particular the earlier claims for revolutionary status seemed unfounded, reflecting the ‘substitution’ mode of an early TEP. Disappointment with the less than dramatic results of investing in the new wave would slow its progress — something which could be well-observed in the collapse of the Internet ‘bubble’ around 2000. The revolutionary potential of the underlying technologies was still there but it took a while to kick the engine back into life; this time the system level effects are beginning to emerge and there is a clearer argument for seeing digital innovation as transformative across all sectors of the economy.

This idea of learning to use the new technology in new ways underpins much of the discussion of what is sometimes called the ‘productivity paradox’ — the fact that extensive investment in new technologies does not always seem to contribute to expected rises in productivity. Over time the pattern shifts but — as was the case with electric power — the gap between introduction and understanding how to get the best out of new technology can be long, in that case over fifty years.

Surfer

Strategy Matters

This model underlines the need for strategy — the ability to ride out the waves of technological change, using them to advantage rather than being tossed and thrown by them, finally ending up in pieces on a beach somewhere. Digital technology is like any other set of innovations; it offers enormous opportunities but we need to think hard about how we are going to manage them. Riding this particular wave is going to stretch our capabilities as innovation managers, staying on the board will take a lot of skill and not a little improvisation in our technique.

It’s easy to get caught up in the flurry of dramatic words used to describe digital possibilities but we shouldn’t forget that underneath them the core innovation process hasn’t changed. It’s still a matter of searching for opportunities, selecting the most promising, implementing and capturing value from digital change projects. What we have to manage doesn’t change even though the projects may themselves be significant in their impact and scalable across large domains. There’s plenty of evidence for that; whilst there have been notable examples of old guard players who have had to retire into bankruptcy or disappearance (think Kodak, Polaroid, Blockbuster) many others continue to flourish in their new digital clothes. Their products and services enhanced, their processes revived and revitalised through strategic use of digital technologies.

If the conferences I’ve been attending are a good barometer of what’s happening then there’s a lot behind this. Organizations of all shapes and sizes are now deploying new digitally driven product and service models and streamlining their internal operations to enable efficient and effective global reach. If anything the Covid-19 pandemic has forced an acceleration in these trends, pushing us further and faster into a digital world. And it’s working in the public and third sector too; for example the field of humanitarian innovation has been transformed by the use of mobile apps, Big Data and maker technologies like 3D printing. Denmark even has a special ministry within government tasked with delivering digitally-based citizen innovation.

Digital Innovation Management

Perhaps what’s really changing — and challenging — is not the emerging set of innovations but rather the way we need to approach creating and delivering them — the way we manage innovation. And here the case for rethinking is strong; continuing with the old tried and tested routines may not get us too far. Instead we need innovation model innovation.

Take the challenge of search — how do we find opportunities for innovation in a vast sea of knowledge? Learning the new skills of ‘open innovation’ has been high on the innovation management agenda for organizations since Henry Chesbrough first coined the term nearly twenty years ago. We know that in a knowledge-rich world that ‘not all the smart people work for us’ and we’ve developed increasingly sophisticated and effective tools for helping us operate in this space.

Digital technologies make this much faster and easy to do. Internet searches allow us to access rich libraries of knowledge at the click of a mouse, social media and networks enable us to tap into rich and varied experience and to interact with it, co-creating solutions. ‘Recombinant’ innovation tools fuelled by machine learning algorithms scour the vast mines of knowledge which the patent system represents and dig out unlikely and fruitful new combinations, bridging different application worlds.

Broadcast search allows us to crowdsource the tricky business of sourcing diverse ideas from multiple different perspectives.  And collaboration platforms allow us to work with that crowd, harnessing collective intelligence and draw in knowledge, ideas, insights from employees, customers, suppliers and even competitors.

Digital innovation management doesn’t stop there; it can also help with the challenge of selection as well. We can use that same crowd to help focus on interesting and promising ideas, using idea markets. Think Kickstarter and a thousand other crowdfunding platforms and look at the increasing use of such approaches within organizations trying to sharpen up their portfolio management. Simulation and exploration technologies enable us to delay the freeze — to continue exploring and evaluating options for longer, assembling useful information on which to base our final decision about whether or not to invest.

And digital techniques blur the lines around implementation, bringing ideas to life. Instead of having to make a once for all commitment and then standing back and hoping we open up a range of choice. We can still kill off the project which isn’t working and has no chance — but we can also adapt in real time, pivoting around an emerging solution to sharpen it, refine it, help it evolve. Digital twins enable us to probe and learn, stress testing ideas to make sure they will work. And the whole ‘agile innovation’ philosophy stresses early testing of simple prototypes — ‘minimum viable products’ — followed by pivoting. Innovation becomes less dependent on a throw of the dice and a lot of hope; instead it is a guided series of experiments hunting for optimum solutions.

Capturing value is all about scale and the power of digital technologies is that they enable us to ‘turbocharge’ this phase. The physical limits on expansion and access are removed for many digital products and services and even physical supply chains and logistics networks can be enhanced with these approaches. Networks allow us not only to spread the word via multiple channels but also enable us to tap into the social processes of influence which shape diffusion. Innovation adoption is still heavily influenced by key opinion leaders but now those influencers can be mobilised much more rapidly and extensively.

The story of Tupperware is a reminder of this effect; it took a passionate woman (Brownie Wise) building a social system by herself in the 1950s to turn a great product into one of the most recognised in the world. Today’s social marketing technologies can draw on powerful tools and infrastructures from the start.

In the same way assembling complementary assets is essential — the big question is one of ‘who else/what else do we need to move to scale? In the past this was a process of finding and forming a series of relationships and carefully nurturing them to create an ecosystem. Today’s platform architectures and business models enable such networks to be quickly assembled and operated in digital space. Amazon didn’t invent remote retailing; that model emerged a century ago with the likes of Sears and Roebuck painstakingly building their system. But Amazon’s ability to quickly build and scale and then to diversify across to new areas deploying the same core elements depends on a carefully thought-out digital architecture.

Digital is Different?

So yes, digital is different in terms of the radically improved toolkit with which we can work in managing innovation. Central to this is a strategy — being clear where and why we might use these tools and what kind of organization we want to create. And being prepared to let go of our old models; even though they are tried and tested and have brought us a long way the reality is that we need innovation model innovation. That’s at the heart of the concept of ‘dynamic capability’ — the ability to configure and reconfigure our processes to create value from ideas.

The idea of innovation management routines is a double-edged sword. On the one hand routines enable us to systematise and codify the patterns of behaviour which help us innovate — how we search, select , implement and so on. That helps us repeat the innovation trick and means that we can build structures and processes and policies to strengthen our innovation capability. But we not only need to review and hone these routines, we also need the capacity to step back and challenge them and the courage to change or even abandon them if they are no longer appropriate. That’s the real key to successful digital transformation.

If you’re interested in more innovation stories please check out my website here
And if you’d like to listen to a podcast version you can find it here
Or follow my online course here

Image credits: FreePik

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Managing Knowledge Spaghetti

Leave a reply

How collaboration platforms can help turbocharge your innovation efforts

Managing Knowledge Spaghetti

GUEST POST from John Bessant

Say the word ‘innovation’ and many people quickly conjure in their mind the wonderful ‘lightbulb moment’. But of course, innovation isn’t like this — that flash of inspiration is only the start of what will be a long journey trying to create value from that initial idea. It’s all about navigating our way through a landscape of uncertainty, learning to deal with a variety of roadblocks, potholes and other unexpected barriers.

And if we want to be able to repeat the trick, to give our ideas a fighting chance then the evidence is clear; we need some kind of a process. Over a hundred years of research has fed our understanding to the point where the kind of system we need to make innovation happen can be specified as an international standard. And in terms of pictures what we’re really looking for is less a lightbulb moment than a reproduceable process, something like this.

Clear Innovation Strategy Bessant

Which is fine, as long as we bear in mind one important truth. Innovation doesn’t happen like that.

Back in 1931 the mathematician Alfred Korzybski presented a paper to a meeting in New Orleans on mathematical semantics. It was pretty complex stuff but one phrase which he used has stuck in the wider popular memory. He pointed out that ‘the map is not the territory…’ In other words, a description of something is not the thing itself. The model is not reality. Which has some pretty important implications for the way we work with innovation.

Process models, however detailed, are simplifications, ways of representing how innovation might take place. But — like any map, be it a crumpled sketch someone has drawn or a sophisticated Google Maps picture — it is a guide, it isn’t the place itself. The map is, by its nature, a reduction of the process and in reducing it we lose some important information.

The reality, of course, is that innovation is more complex. And it’s all about knowledge spaghetti.

Just like a plate of pasta innovation involves many different strands. Only this time we are talking about knowledge — technical knowledge, market knowledge, legal knowledge, financial knowledge and so on. They need to be woven together to create value.

And these knowledge strands are held by different people, inside and outside the organization. We have to find them and connect them, link them together to enable us to innovate. Whilst we can superimpose structures on it to help us with this task, we shouldn’t forget that we’re really working with knowledge spaghetti.

Spaghetti solutions

So how do we work with it? Just like recipes for spaghetti there are many variations. One approach is to employ specialists and create cross-functional teams which bring together the relevant strands and align them towards a focused target. That’s proved to be a good model for developing new products and services, especially if we can find ways to bring in all the relevant players including users.

We can use a similar cross-functional approach to the design and implementation of major internal process innovations — things like introducing a new IT system or reorganizing to become more customer-focused. And a third approach involves carefully constructed strategic collaborations, bringing knowledge partners together with complementary strands of knowledge spaghetti.

One very powerful model is based on the idea that everyone in the organization has something to contribute to the innovation story — high involvement innovation. Here we’re working on the belief that even small strands of knowledge can be important and if we could bring them in to the story, we’d make significant progress.

Which history tells us we can. In countless embodiments the principle of high involvement has been shown to pay dividends. Ask people for what they know that might help solve problems around quality, cost, delivery, etc — and there’s no shortage of good ideas in response. The challenge has, historically, been one of working with such high volumes of knowledge and keeping the flywheel going by responding to employee suggestions and giving feedback on progress towards their implementation.

Suggestion boxes and schemes work but until recently had their limitations. Two recent trends have changed all that. The first, borne on the waves of total quality thinking and then the whole ‘lean’ movement, has shown us that in any context people are very effective innovators, well able to improve on what they are doing on a continuing basis.

And the second has been the emergence of collaboration platforms on which they can deploy their innovation skills. Today’s collaborative innovation platform resembles its suggestion box predecessor in outline only; it’s still a way of collecting ideas from employees. But it does so in an interactive space in which challenges can be posed, ideas suggested, comments added and shaping, and welding together multiple knowledge sets and experience enabled. And in doing so they open up the very real possibilities of high involvement innovation — getting everyone to contribute to the innovation story.

Emergent properties

But it’s not just the raw return on investment which collaboration platforms offer — though these benefits are impressive. Their real value lies in the way they enable ‘emergent properties’ — the innovation whole becomes much greater than the sum of its parts.

They give us new and powerful ways of working with the knowledge spaghetti. Not only can we handle the sheer scale of the knowledge challenge and focus it towards key objectives but we can do so in ways which yield surprising additional benefits. They effectively turbocharge our innovation system.

In particular they contribute in the following ways:

  • Reach — one of the obvious ways in which platforms can help is that they create a network which even remote users can connect to. We can spread the innovation net far and wide, can reach the parts other innovation approaches don’t. For example, recruiting ideas from people on ships at sea or working on an off-shore oil platform would have been impossible until recently. Now they can join the innovation conversation as simply as placing a phone call. Working under extreme conditions like in a humanitarian disaster area can now also be a space for crowdsourcing new and urgent solutions to problems. (We’ve seen this in Ukraine where the problems of getting urgent supplies in and vulnerable people out of a war zone are being addressed by many people sharing ideas across makeshift collaboration platforms based on mobile phone networks). We’re now able to involve people in innovation anywhere on the planet and on a 24/7 timescale.

    • High involvement innovation has always worked well in teams — that’s been at the heart of the success of lean approaches. But until recently that depended on the team being physically together, exploring and co-creating solutions — not easy if you’re working with a distributed team. Platforms solve this challenge, enabling virtual team meetings and collaboration and asynchronous collaboration.

    Organizations like Conoco-Philips employ around 10,000 people, globally distributed and often in hard to access places like off-shore oil platforms. Airbus has around 130,000, again globally distributed and engaged in multiple activities. And Bombardier have over 15,000 ‘knowledge workers’ around the world with whom they want to engage. Through the use of collaboration platforms organizations like these are able to achieve sustained high involvement and significant traction on their innovation challenges.

    • Richness — successful high involvement innovation isn’t just about assembling lots of people. By their nature people are different and diversity matters in innovation. They bring different perspectives, different ways of framing and working with the problem being explored. Plus they are not just cardboard cut-outs, they have a rich history of different experiences — their origins, their education, their work experience. All of this represents potentially useful strands of knowledge spaghetti, and platforms help us draw on this.

    Subsea7, a major player in the world of offshore services for the oil and other industries has used a platform approach to great effect. In one example a long-running concern with turnaround times for fitting out ships was solved when someone on the platform identified a solution which he had originally seen in action at a previous employer. The resulting savings ran into millions of dollars.

    People also bring with them networks of connections; knowledge is socially distributed and connecting to these networks can yield surprising possibilities. It means the innovating organization can access different skills and specialized knowledge inside and outside the organization. It’s classic open innovation, building on the idea that in even the largest organization ‘not all the smart people work for you’.

    • Refining — one of the powerful features of collaboration platforms is that they enable — well, collaboration! They make it possible to comment, criticize (constructively), modify and refine ideas, setting up a process of true co-creation. This fits well with recent research which argues that there’s a fundamental flaw in the model of ‘brainstorming’ used by many organizations to source ideas. The principle of postponing judgment has been replaced by a ‘no criticism’ approach in which every idea is accepted. But the reality is that good ideas need to be tempered, hammered into shape, worked on — and processes of constructive criticism are really important. Pixar, for example, has made this a core feature of its daily ideation process.

    And having access to the diversity of perspectives which platforms allow means that there is real potential for shaping and developing interesting ideas into great and value-adding ones. They provide a way of creating those magical ‘water-cooler’ moments in an online and distributed world.

    • Requiring — using focused campaigns to draw out ideas in particular directions. One of the limits of the old model of suggestion schemes is that they operate in ‘bottom up’ fashion, solving problems which are important and visible at a local level. But the real power of HII lies in mobilizing it to work on ‘top-down’ strategic challenges. The campaign model sits at the heart of many collaboration platforms and allows short intense ‘sprints’ focusing the innovation energy on a key problem area, rather like a laser beam.

    Conoco Philips Alaska have been using a process targeted at continuous improvement of their extensive operations; they run between 6 and 8 campaigns every year, involve around 1500 employees and generate savings running into millions of dollars annually.

    But it depends on several things — not least spending time to ensure the ‘right’ question is being asked. Simply setting ‘how can we improve productivity’ as a target is too vague, a bit like using that medieval weapon, the blunderbuss. Chances are some of your shots will hit the target but there’s an awful lot of waste involved.

    So it’s important to ensure we’re asking the ‘right’ question; a key feature of successful collaboration platforms is the amount of effort which goes in to this kind of front-end problem exploration. The sharper the question the better the quality of answers and the chance that new creative pathways can be opened up.

    • Recombination — ‘ if only our organization knew what it knows’ is a source of concern for anyone concerned with innovation. So much knowledge which might be useful is locked up inside silos and not shared. Worse, we don’t always know what’s inside those silos or whether and how it could be relevant to someone else. Platforms have the power to make this visible, not least by drawing it out in response to focused and challenging campaigns.

    There’s also the possibility that someone else in the organization may have experienced a similar type of problem even if they don’t recognize the relevance of their experience. A powerful principle in creativity is looking for analogous solutions — for example, the challenge of cutting turnaround times in airports for low-cost carriers was solved by applying principles originally developed for Formula 1 pitstops. And the same approach was then adopted by surgeons in London looking to improve the utilization of operating theatres.

    • Reverse reinvention — lots of effort is often wasted by reinventing wheels, solving the same problem in different places. Platforms offer a way of reversing this process, highlighting solutions which have been tried elsewhere and also inviting creative improvisation around those solutions, extending their applicability and effectiveness. A kind of creative re-iteration.

    The Canadian engineering company Bombardier have been using a collaboration platform approach for over ten years and one of the biggest benefits they have seen is a significant increase in the amount of knowledge being shared across their organization.

    • Retaining and recording — making sure ideas are retained even if they can’t be applied right now. One of the challenges of mobilizing collective intelligence is that we may well attract thousands of ideas. Some can be shaped and refined for immediate implementation, some require further work and investment. And for some there is the problem of being the right idea at the wrong time. In the past organizations hitting this problem would probably lose sight of the idea, leaving it buried in a file somewhere or gathering dust. But platforms allow for effective curation of ideas, not only tracking and recording all suggestions but also retaining them to match against future campaigns and challenges.
    • Rewiring — organizations are like people — they have ‘predictive minds’ . They are inclined to take a lazy approach, picking tried and tested solutions off the shelf when they confront a problem. But being forced to redefine, to reframe, can trigger a search for new approaches to those old problems. We see this effect often under crisis conditions where traditional solution pathways may not be available and we have to think differently — to make new neural connections across the collective mind. Creating novel campaigns to provide this challenge can open up new idea space — they can help us ‘get out of the box’.
    • Refreshing — at heart high involvement innovation is about people and the key ingredient to its long term success is finding ways to keep the motivation high. People are brilliant problem solvers but they’re only going to give their ideas if they see some benefit. Research has shown that money isn’t a strong motivator — but having your voice heard and having the opportunity to create the change you’d like to see around your organization is. There’s a wealth of research to support this going right back to the early years of organization studies; the message on employee engagement remains the same but the question is then raised about how to achieve this. Collaboration platforms by their inclusive and open nature offer a powerful new tool to help and organizations like Liberty Global consider this motivational aspect to be a key factor in helping build a culture of innovation across a large organization.

    Knowledge Spaghetti Success

    So whether it is an upgrade to continuous improvement activity, harvesting employee suggestions for doing what we do but better, or pushing the frontiers to create novel products and services, there’s real scope for using this turbocharged approach.

    But powerful though they are, collaboration platforms are at heart still software. It’s not a case of ‘plug and play’ — getting the best out of these systems requires hands-on management, something we’ll look at in a future blog.

    For more on innovation-related themes like this please visit my website

    And if you’d like to listen to this as a podcast please visit my site here

    Image credits: Pexels, John Bessant

    Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

    The Suggestion Box Strikes Back!

    1 Reply

    How collaboration platforms can turbocharge your innovation efforts

    The Suggestion Box Strikes Back!

    GUEST POST from John Bessant

    Organizations need to innovate. So far, so blindingly obvious. But they also need to innovate their innovation approaches; the best recipes may no longer work in a context which is continually changing. Smart players recognize that they need to add innovation model innovation to their repertoire — constantly reviewing what they do to organize and manage the process of creating value from ideas and, if necessary, adapting it.

    Sometimes this will involve dramatic change. Think, for example, of the way Procter and Gamble have been re-engineering their whole business over the past twenty years from a model dominated by internal R&D to an open approach based on ‘Connect and develop’. Or how capital goods giants like Caterpillar and Rolls-Royce have shifted the entire basis of their business towards ‘servitization’, no longer developing and selling new products but rather renting out capabilities like ‘power by the hour’. This has required them to rethink the entire innovation model, putting customer focus much more center stage and involving extensive partnerships and strategic alliance to deliver the whole new package of service.

    But often it’s a quieter revolution, a gradual change in which new routines emerge or existing ones are upgraded to work in new ways, deploying new mechanisms to make them work better. That’s been the story of the suggestion box.

    Smart People Don't Always Have Smart Ideas

    ‘….the beauty of it is that with every pair of hands I get a free brain!’

    It’s a very old idea, and an obvious one. People are smart so why not tap into their ideas to help with the innovation agenda? Ask them, and you might be surprised at what they have to offer. Elements of this approach can be found in the medieval guild system where it was used to help develop and improve craft skills and practices. It was an idea which the eighth shogun of Japan, Yoshimuni Tokugawa tried out in 1721 with his ‘Meyasubako’, a box placed at the entrance of the Edo Castle for written suggestions from his subjects. And the British navy pioneered a similar scheme in 1770, asking its sailors and marines for their ideas — significantly reassuring them that such suggestions would not carry the risk of punishment!

    By the time of the Industrial Revolution innovation was recognized as a powerhouse — and not everyone thought that ideas should be confined to specialists with the workers simply employed as pairs of hands. In 1871 Denny’s shipyard on the banks of the Clyde began operating a suggestion scheme amongst its 350 employees; it enabled them to cut the time to build a warship from six months to four whilst contributing a variety of other quality and productivity improvements. And in 1892 John Paterson at the National Cash Register company in the USA began exploring ways of tapping into ‘the hundred-headed brain’ of his workforce; his success led the Eastman Kodak company to implement a similar scheme in 1896.

    It wasn’t just innovation rates which improved; a growing number of studies, not least in the famous Western Electric research at the Hawthorne plant, found that asking people for their ideas and enrolling them in workplace productivity improvements had the by-product effect of better motivation and employee satisfaction. The great quality management writer Joseph Juran talked about ‘the gold in the mine’, describing how unlocking the potential of employees to add their mental weight to the innovation problem could dramatically improve quality.

    By the late twentieth century these ideas were widespread; the 1980s total quality revolution gave birth to lean thinking and with it the core recognition that asking people for their ideas was a pretty smart way of driving up productivity, whatever the setting.

    Thinking Inside the Box

    Thinking inside the box

    So a great idea — but not without its limitations. The trouble with suggestion boxes and schemes is that they bump up against some unfortunate logistical challenges. Even in the best-intentioned companies, with enlightened leadership supporting the concept and innovation facilitators trying to make it happen the idea of high involvement quickly runs aground on some simple arithmetic around idea management.

    Suppose you have a workforce of 100 people and you convince them to join in the innovation effort and suggest improvements. By the end of week one you have 100 ideas, by week four 400 and pretty soon you can get into thousands of ideas. Lots of enthusiasm and there’s no shortage of good ideas — people generally have them and have probably been carrying a backlog around with them for some time. And when they tell their friends there’s an accelerator effect; the innovation wave starts to build.

    Your problem is most certainly not going to be a shortage of ideas — quite the reverse. But what do you do with them? Of course, a good percentage will be simple things which people can implement for themselves — and your job is to encourage them (and also to track the changes they’re making to ensure they don’t end up conflicting with your established operating procedures).

    But a lot more will need thinking about. They may need modifying and developing from a germ of a possibility into something polished. Juran’s raw gold ore doesn’t glisten straight away, it needs processing. And some of them will need quite a lot of effort and specialist input to yield eventually valuable results.

    All the while you are working on these the inflow pipeline is filling up, hundreds of ideas every week. But with the ideas also comes expectation — people not unreasonably asking ‘what are you doing with my idea?’ So you need to spend your precious time not only processing the ideas but also feeding back; sometimes this means saying no to unworkable ideas or those which don’t fit, and doing so in a way which doesn’t discourage people from suggesting further new ideas.

    Walt Disney Fantasia

    It doesn’t take long before you’re like Mickey Mouse in the wonderful Disney film ‘Fantasia’ where he plays the Sorcerer’s Apprentice. He tries to improve the way he deals with his household chores by a little magic spell which at first helps him out with brooms, buckets and scrubbing cloths working hard on his behalf. But pretty soon things get out of control, there’s water everywhere and an army of brushes and mops threatening to take over his world. The resulting chaos is only halted by the arrival of the master Sorcerer who magically puts things back to how they were.

    It’s potentially the same with your magic spell of high involvement innovation. What began as a great movement towards innovation from everyone soon becomes a nightmare precisely because people are volunteering ideas. There isn’t the capacity to deal with them, they’re coming at you thick and fast but you can’t handle them all. And then things take a turn for the worse. People keep asking you what’s happening to their idea and when they get no response they start to grumble. They get fed up with seeing their great thoughts disappear into what seems to them to be a black hole. Nothing seems to happen and so they stop bothering to make suggestions and slip back into simply doing what they are told, albeit with a bad grace. And they tell their friends who nod their heads and agree that the system simply isn’t working, so why bother with it?

    Pretty soon you’re back to where you started. Not only has the flow if ideas dried up but now people are resentful and suspicious. They won’t get fooled again; next time you come around asking for their suggestions they’re not going to give them up so easily.

    Sadly, that kind of story is typical; the limitation of suggestion schemes is that they aren’t well-equipped to deal with a high volume of ideas or high levels of participation. There’s nothing wrong with the model which is why employee engagement can work so well in teams. Where the focus is local, based around workplace teams working on quality or lean six sigma a trained team can keep chipping away at its productivity improvement goals very effectively. There’s shared motivation, clear local targets and high visibility of the results. Getting everyone involved in innovation works and if you keep it going it delivers consistent bottom line benefits. The only trouble is that it’s hard to scale it.

    Doctor Superhero

    Technology to the rescue…

    Fortunately, around the turn of the millennium things began to change. Faced with the problem of idea management a number of people began working on IT-based solutions. Their earliest attempts were little more than electronic versions of the old physical suggestion box and they had limited success. Feeding ideas into complicated spreadsheets wasn’t particularly exciting or motivating even if it was now possible to do some rudimentary tracking of those ideas.

    But gradually things improved. The interface became more friendly and, with the growth of internal networks, the possibility of accessing a terminal and logging on to a screen became available to many more people. Instead of being a one-way posting process the beginnings of visibility emerged; people could see what happened to their ideas and get some feedback on them.

    It wasn’t just the technology which was getting better and offering a closer match to the needs which organizations had for effective idea management. The wider context was changing too, undergoing a revolution at scale. Social networking began to emerge and quickly caught on, offering new ways to interact with people in an online space. By 2008 close to 120 million people were using the MySpace platform every day and by 2012 Facebook had a user base in excess of 1bn and growing.

    This external shift opened up huge new possibilities for the ways in which interaction could happen across an innovation platform. People could not only connect but also share, like, comment, build the conversation — all features which developers of collaborative innovation platforms saw as rich in possibilities for their offering. User companies began to sit up and take notice as a new way of engaging employees emerged — one which offered the twin advantages of richness and reach. Through such platforms a high volume of people could be connected, forming the ‘neurons’ in a potentially giant innovation brain. And their activity could be extended way beyond simply posting up a suggestion; they could comment on other people’s ideas, like or suggest modifications, join into virtual teams building and shaping ideas into real innovation possibilities.

    As if that wasn’t a strong enough impulse to regenerate interest in high involvement innovation we also discovered ‘crowdsourcing’ as an approach to collecting ideas. This wasn’t a new concept; back in 1714 the idea of taking a big and apparently intractable problem and asking a lot of people for their help with solving it had been deployed to great effect. Faced with the growing crisis in navigation caused by ship’s captains being unable to calculate their longitude accurately because they lacked a reliable portable timepiece the British Admiralty launched what we would recognize today as an innovation contest. With the support of the king and with the attraction of a significant financial prize the challenge was taken up and solved very effectively; the winning (and wonderful) design by John Harrison was soon being fitted to all the ships in the British navy as standard equipment.

    ‘Broadcast search’ of this kind undoubtedly works — the trouble was that in those days it was a difficult process to organize and manage. But with today’s powerful communications infrastructure it’s possible to set up and run an innovation contest in an afternoon and reach out to the whole world for answers. Idea marketplaces have sprung up all over the internet, connecting seekers of solutions with potential solvers; one such platform, Innocentive.com currently has a population of regular solvers over half a million strong offering their input to the various challenges posted on the site.

    Tapping into such ‘collective intelligence’ in this way isn’t just about increasing the volume of ideas coming into the system. Its real value is in extending the reach, drawing in ideas from across the ‘long tail’ of different perspectives on the problem you’re trying to solve. Karim Lakhani and colleagues highlighted this effect in their detailed studies of traffic across the innocentive.com platform; the benefits came not from having tens of thousands of people working on your problem but from the diversity in approaches which they brought. Fresh minds, new insights, alternative ways of framing the problem.

    Collaboration platforms 2022

    Today’s collaborative innovation platform resembles its suggestion box predecessor in outline only; it’s still a way of collecting ideas from employees. But it does so in an interactive space in which challenges can be posed, ideas suggested, comments added and shaping and welding together multiple knowledge sets and experience enabled. And in doing so they open up the very real possibilities of high involvement innovation — getting everyone to contribute to the innovation story.

    And it works. There are countless case studies drawn from contexts as different as aerospace and agriculture, medicine to microelectronics manufacture. High involvement works in in the public and not-for-profit world as well as in the commercial one, and the targets for such innovation range from straightforward cost-savings and productivity improvements to creating new crisis responses in the world of humanitarian aid or finding ways to improve access to shelter, health care and basic needs in the world of international development.

    Where it was once the exception to find firms like Toyota reporting high levels of participation and harvesting the benefits emerging from millions of suggestions, it is now commonplace to find benefits reported in terms of million dollar savings. One of the founder companies in the idea management field, Imaginatik, has a running total on its website suggesting that their platform has enabled over 2bn ideas to be suggested, generating $1.1bn of savings; similar data emerges from other suppliers of the technology.

    But it’s not just the raw return on investment which collaboration platforms offer — thought these benefits are impressive. Their real value lies in the way they have matured to enable systematic innovation routines to work at scale and across the entire process of innovation, not just the front-end idea generation.

    Idea Plugin
    Image: @studiogstock on Freepik

    Plug’n’play?

    So you might think that the answer is simple — invest in a platform if you want to turbocharge your innovation activities. But you’d be wrong, and for several reasons. First we need to remind ourselves that platforms are simply tools. They may be significantly more powerful than their predecessors but just like a power drill with lots of shiny new attachments, in the hands of an inexperienced amateur it will not deliver — and may leave you with a series of unsightly holes and marks on your wall!

    In particular they need embedding in a culture which supports the underlying values and behaviors associated with high involvement innovation. If you don’t actually believe that everyone can contribute, or if you believe it but don’t commit the resources to train and enable people to deliver their ideas, then your investment in a platform will simply be a white elephant. What makes it work is a culture, an integrated suite of behaviors which are articulated, supported, reinforced until they become ‘the way we do things around here

    For more on innovation-related themes like this please visit my website

    And if you’d like to listen to this as a podcast please visit my site here

    Image credits: Pixabay, Wikimedia Commons, Pexels, Freepik

    Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

    Not Invented Here

    1 Reply

    Sometimes Letting Go is the Hardest Part of Innovation

    Not Invented Here

    GUEST POST from John Bessant

    (You can find a podcast version of this story here)

    The Welsh valleys are amongst the most beautiful in the world. Lush green hills steeply falling into gorges with silver water glistening below. It’s a place of almost perfect peace, the only movement the gentle trudge of sheep grazing safely, shuffling across the jagged landscape the way they’ve done for thousands of years. And amongst the most scenic and peaceful of these valleys are those situated between Dolgellau in the north, and Machynlleth in the south.

    Except when there’s traffic in the ‘Mach loop’ — which is what the region is known as in military circles. It’s the place where young men and women from a variety of international air forces hone their skills at high-speed low-level flying, often as low as 75 meters from the ground. At any moment your peaceful walk may be rudely interrupted by the roar of afterburners, your view across the green hillsides suddenly broken by the nose of an F16 or Typhoon poking its way up from one of the gorges below.

    Your reaction may be mixed; annoyance at the interruption or admiration for the flying skills of those pilots giving you a personal air display. But it’s certainly impossible to ignore. And it does raise an interesting question — despite the impressive skills being demonstrated, do we actually need pilots flying the planes? Is there an alternative technology which allows low level high precision flying but which can be carried out by an operator sitting far away in a remote location? After all we’ve become pretty good at controlling devices at a distance, can even land them on distant planets or steer a course through the furthest reaches of our universe.

    UAVs — unmanned aerial vehicles — are undoubtedly changing the face of aviation. But are they also a disruptive innovation, particularly in the military world where the heroic tradition of those magnificent men (and women) in their flying machines is still so strong?

    A brief history of drones

    The idea of using unmanned flying vehicles isn’t new; back in 1839 Austrian soldiers attacked the city of Venice with unmanned balloons filled with explosives. During the early years following the Wright brothers successful flight researchers began looking at the possibilities of unmanned aircraft. The first prototype took off in 1916 in the form of the Ruston Proctor Aerial Target; as its name suggests it was a pilotless machine designed to help train British aircrew in dogfighting. Importantly it drew on early versions of radio control and was one the many brainchildren of Nikolai Tesla but its early performance was unremarkable and the British military chose to scrap the project, believing that unmanned aerial vehicles had limited military potential.

    A year later, an American alternative was created: the Hewitt-Sperry Automatic Airplane and successful trials led to the development of a production version, the Kettering Bug in 1918. Although its performance was impressive it arrived too late to be used in the war and further development was shelved.

    By the time of the Second World War the enabling technologies around control and navigation had improved enormously; whilst still crude the German V1 and V2 rockets and flying bombs provided a powerful demonstration of what could be achieved at scale. Emphasis was placed on remote delivery of explosives — using UAVs as flying bombs or aerial torpedoes — but the possibilities of using them in other applications such as reconnaissance were beginning to be explored.

    The Vietnam war saw this aspect come to the fore; the difficulties of operating in remote jungle and mountain zones made reconnaissance flying hazardous and the risks to aircrew who were shot down led to extensive use of UAVs. The Ryan Firebee drone flew over 5000 surveillance missions, controlled by a ground operator using a remote camera. Its versatility meant that it could be used for surveillance, delivery of supplies and as a weapon; UAVs began to be viewed as an alternative to manned aircraft. But despite their success and promise it was not until the 1990s that they began to occupy an increasingly significant role.

    Early Drone - Wikimedia Commons

    The technology found more support in Israel and during the 1973 Yom Kippur war UAVs were used in a variety of ways, as part of an integrated approach alongside piloted aircraft. A great deal of learning in this context meant that for a while Israel became the key source of UAV technology with the US acquiring and deploying this knowledge to improve its own capabilities, leading to the new generation deployed in the Gulf War. UAVs emerged as a critical tool for gathering intelligence at the tactical level. These systems were employed for battlefield damage assessment, targeting, and surveillance missions, particularly in high-threat airspace.

    Fast forward to today. There’s been an incredible acceleration in the key enabling technologies which has helped UAVs established themselves as serious contenders for many aerial roles. For example GPS has moved from its early days in 1981 where a unit weighed 50kg and cost over $100k to a current cost of less than $5 for a chip-based unit weighing less than a gram. The Internal Measurement Unit (IMU) which measures a drone’s velocity, orientation and accelerations has followed a similar trajectory; in the 1960s an IMU weighed several kilograms and cost several million dollars but today the chipset which puts these features on your phone costs around $1. Kodak’s 1976 digital camera could only manage a 0.1 megapixel image from a unit weighing 2kg and costing over $10,000. Today’s digital cameras are approximately a billion times better (1000x resolution, 1000x smaller and 100x cheaper). And (perhaps most important) the communications capabilities now offered by Wi-Fi and Bluetooth enable accurate and long-range communication and control.

    With an improvement trajectory like this you might be forgiven for assuming that UAVs would have largely replaced manned flying in most applications. It’s a cheap technology, versatile and (in military terms) expendable — losing a drone doesn’t carry with it the tragic costs of losing a trained pilot. Yet the reality is that the Mach Loop continues to reverberate with the sound of fast jets and their pilots practicing high-speed low-level maneuvers.

    Not invented here?

    Continuing to rely on manned aircraft is also a costly option — when a British F-35 Lightning crashed after take-off from an aircraft carrier in 2021 it represented over £100m sinking beneath the waves. So why is the adoption of UAV technology still problematic within major established air forces? It almost looks like another case of ‘not invented here’ — that strange innovation phenomenon in which otherwise smart organizations reject or bury promising new ideas.

    At first sight it fits into a pattern which has been around a long time. Take the case of continuous aim gunfire at sea. Sounds rather dry and technical but what it boils down to is that 19th century naval warfare was not a very accurate game. Trying to shoot at something a long way away whilst perched on a ship which is rocking and rolling unexpectedly isn’t easy; most ships firing at other ships missed their targets. A study commissioned by the US Bureau of Ordnance in the late 1890s found an accuracy rate of less than 3%; in one test in 1899 five ships of the British North Atlantic Squadron fired for five minutes each at an old hulk at a range of 1600 yards; after 25 minutes only 2 hits had been registered on the target.

    Clearly there was scope for innovation and it took place in 1898 on the decks of a British navy frigate called HMS Scylla, under the command of Percy Scott. He’d noticed that one of his gun crews was managing a much better performance and began studying and exploring what they were doing with a view to developing it into a system. By the time he was in command, two years later, of a squadron in the South China Sea he had refined his methods and equipped his flagship, HMS Terrible with new equipment and trained his gun crews.

    Image: Painting by Christoffer Wilhelm Eckersberg, public domain

    The improvements were significant and importantly influenced a young US lieutenant on secondment to the squadron. William Sims learned about the new system and applied it on his own ship with remarkable results; convinced of the power of this innovation he decided it was his mission to carry the news to Washington and change naval practice. What followed is a fascinating story for what it reveals about NIH and the many ways in which it can be deployed.

    In his fascinating account Elting Morison highlights three strategies used by the US military to defend against the new idea. The first was simply to bury the idea; Sims’ reports to the Bureau of Ordnance and the Bureau of Navigation were simply filed away and forgotten. The second was to try and rebut the information; the response from the Bureau of Ordnance was along the lines of claiming that US equipment was as good as the British so any differences in firing performance must be due to the men involved. More important was their argument that continuous-aim firing was impossible; when that failed they conducted experiments to try to show there was no significant benefit from the approach. By running them on dry land they were able to cast doubt on the relative advantage of the new approach.

    And their third strategy was to try and sideline Sims, painting him as an eccentric troublemaker, stressing his youth and lack of experience, highlighting the fact that he’d spent too long with the British navy and in other ways undermining his credibility. Needless to say this only strengthened Sims’ resolve and he duly went over the heads of the senior staff and appealed to President Roosevelt himself. He finally ‘won’; he remained as unpopular as ever but the new approach was grudgingly adopted and quickly became the dominant design for future naval gunnery.

    Image: UK HMSO Public Domain

    On dry land and a decade later a similar outsider — Major J. C. Fuller — was working with the British Army. He’d seen the possibilities in using tanks as a fast mobile strike capability and his ideas were eventually borne out, briefly in the latter part of the First World War when they were used to good effect in Cambrai and Amiens. But despite being given responsibility for introducing the new technology he met with resistance (not helped by his abrasive nature); there were many who saw tanks as an unwelcome diversion. It didn’t help that the organizational location in the command structure was in the Cavalry Corps — the very group most threatened by the change to tanks. Their post-war strategy was to continue to rely on the equine model; ‘more hay, more horses’ rather than investment in tanks or learning about tank warfare. Elsewhere though his ideas found fertile soil and he was credited by Adolf Hitler himself as the architect of the idea of ‘blitzkrieg’ — the fast mobile warfare which helped overrun France and much of Europe within a few weeks at the start of World War 2.

    Drones as disruptive innovation?

    Of course it’s complicated but could the case of drone adoption be history repeating itself? One explanation for why NIH happens in this fashion can be found in what we’ve learned about disruptive innovation. When it was published twenty five years ago Clayton Christensen’s classic book exploring the phenomenon ‘The innovator’s dilemma’ had the intriguing subtitle ‘When new technologies cause great firms to fail’. His core argument was that the organizations which were affected by disruptive innovations were not stupid but rather selectively blind, a consequence of their very market success and the organizational arrangements which had grown up over a long period of time to support that success.

    For him the challenge wasn’t the old one of balancing radical and incremental change with the losing firms being too cautious. Rather it was about trajectories; whether a new technology was sustaining — reinforcing the existing trajectory — or disrupting, offering a new trajectory. As we’ve come to realize the core issue is about business models; disruption occurs when someone frames the new trajectory as something which can create value under different conditions.

    The search for such a new business model doesn’t take place in the mainstream as a direct challenge; instead it emerges in different markets which are unserved or underserved but where the new features offer potential value (often good enough performance at much lower cost). These fringe markets provide the laboratory in which learning and refinement of the new technology and development of the business model can take place.

    The problem arises when the new business model built on a new trajectory begins to appeal to the old mainstream market. At this point it’s a challenge to existing incumbents to let go of their old business model and reconfigure a new one. Jumping the tracks to a new trajectory is risky anyway but when you carry the baggage of years, perhaps decades or even centuries of the old model it becomes very hard. That’s when NIH rears its head and it can snap and bite at the new idea with surprising defensive vigor.

    There’s almost a cycle to it like that developed by Elizabeth Kubler-Ross to explain the grieving process. First there’s denial — ignore it and it will go away, it’s not relevant to us, it won’t work in our industry, it’s not our core business, etc. Then there’s a period of rationalization, accepting the new idea but dismissing it as not relevant to the core business, followed by experimentation designed not so much to learn as to demonstrate why and how the new model offers little advantage. Variations on this theme include locating the experiments in the very part of the organization which has the most to lose (think about giving tank development to the Cavalry Corps).

    Only when the evidence becomes impossible to ignore (often as a clear shift in the market towards the new trajectory and a significant competitive threat) comes the moment of acceptance. But even then commitment is often slow and lukewarm and the opportunity to get on the bus may have been missed.

    Meanwhile in another part of the galaxy…

    It’s not easy for the innovators trying to introduce the change. They struggle to break into the mainstream because they have no presence in that market and they are up against established interests and networks. Their best strategy is to continue to work with their fringe markets who do see the value in their model and to hope that eventually a cross-over to the mainstream takes place. Which is what has happened in the world of drone technology.

    Demanding users in fringe application markets have provided the laboratory for fast learning. Early markets were in aerial photography where the cost of hiring planes and pilots could be cut significantly but where challenges around stability and development of lightweight equipment forced rapid innovation. Or mapping and surveying where difficult and sometimes inaccessible territory could be explored remotely. Once drones were able to carry specialized lightweight tools they could be used for remote repair and maintenance on oil platforms and other difficult or dangerous locations. Their capabilities in transportation opened up new possibilities in logistics, especially in challenging areas like delivering humanitarian aid. Significantly the demands of these fringe markets drove innovation around stability, payload, propulsion and other technologies, reinforcing and widening the appeal.

    Estimates suggest the 2021 drone services market is worth $9 billion with predictions of growth rates as high as 45% per year. Application sectors outside mainstream aviation include infrastructure, agriculture, transport, security, media and entertainment, insurance, telecommunication and mining.

    Holding the horses?

    These days UAVs can do a lot for low price. Just like low-cost flying, mini mill steelmaking, and earthmoving equipment they represent a technology which has already changed the game in many sectors. They qualify as a disruptive innovation but they also trigger some interesting NIH behavior amongst the established incumbents. ‘We’ve always done it this way’ is particularly powerful when ‘this way’ has been around a long time and is associated with a history of past success.

    Elting Morison has another story which underlines this challenge. Once again it concerns gunnery, this time the firing performance of mobile artillery crews in the British army during World War 2. A time and motion study was carried out using photographs of the procedure; the researcher was increasingly puzzled by the fact that at a certain point just before the gun was fired two men would peel away and stand several meters distant. It wasn’t until he discussed his findings with a retired colonel from the First World War that the mystery was solved. He was able to explain that the move was perfectly clear — the men were holding the horses. Despite the fact that the 1942 artillery was transported by truck the procedures for horse-drawn guns still remained in place.

    Something worth reflecting on when you are walking in those Welsh hills…

    Image: Pixabay

    Image credits: as captioned, Wikimedia Commons, Pixabay

    If you’re interested in more innovation stories please check out my website here

    Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.