Category Archives: Innovation

Innovation Friction Risks and Pitfalls

Innovation Friction Risks and Pitfalls

GUEST POST from Howard Tiersky

There’s a lot to be learned about innovation by looking at good ideas that just didn’t make it. We’d all like to believe that if we have an idea that genuinely improves upon something, and if we execute that idea correctly, the idea will be successful. But there is another factor to consider:

Here’s today’s example:

Back in the early 2000’s, I was running part of the eCommerce practice for Ernst & Young. Around 2003 we moved into a shiny new building at 5 Times Square in New York City, right next to where the ball drops on New Year’s Eve. The building was the first place I had ever seen with a keypad-controlled elevator. Instead of pushing an up or down button, the elevator is called by a numerical pad. You type in the number of the floor you are going to and receive a response from the keypad with a letter (such as D.) That letter corresponds to the elevator that you have been assigned to. You go to “your” elevator and, when it arrives, it automatically takes you to your floor.

This innovation delivers several benefits that improve the elevator experience:

1. It makes the elevators more efficient.

People going to lower floors are clustered together, as are people going to higher floors, and people going to the same floors are put on the same elevator. This allows more elevators to run express. Fewer stops. Less waiting for an elevator and a faster trip in the elevator.

2. It reduces “clicks.”

In a traditional system, an elevator user has to “call” the elevator and indicate their desire to go up or down. Once in the elevator, the user has to pick a floor. The old system was not a massive amount of effort, but the new system reduces two interactions to one. Presumably an improvement.

(Plus there’s no worrying about the kid in the elevator who decides to push all the buttons — there aren’t any!)

Is there a downside to this innovation?

Well, if you’re already in the elevator, there’s no opportunity to change your mind without getting off on the wrong floor and repeating the whole process. The biggest downside of this innovation is simply that it requires users to learn something new. In fact, when I moved into 5 Times Square, I found that when people came to meet with me for the first time, the first 10 minutes of our meeting was inevitably focused on their need to vent their reactions to our crazy elevators and how they couldn’t figure out how to use them!

Truthfully, the elevators were easy to use. Clear instructions were printed above the keypad, and the system worked very well. The problem was that it required users to relearn a skill they had fully and completely mastered (i.e., using an elevator) and start over at a beginner level — even if it only took 30 seconds to learn how to use the new elevator system.

I’ve watched with interest over the years to see if these types of elevators would take off. It turns out they didn’t. Very recently, I was visiting a client in Houston. The building had actually spent money to remove the keypad system and replace it with the traditional 2-step process. Wow. You know your innovation is not doing well when your customers are willing to invest tens of thousands of dollars to get rid of it and go back to the old way.

After much thought, I believe it’s all because of the friction of asking people to re-learn how to push an elevator button. Some innovations don’t require this. The new Boeing 787s have substantial innovation, but from a passenger standpoint, they work in basically the same way as the last round of airplanes. The innovations improve comfort, fuel efficiency, and other factors, but you recline the seat and return your tray table to an upright position in pretty much the same old way. Other innovations require learning: ATMs, DVRs, electric cars. All of these innovations have been successful, despite their learning requirements. However, the need for users to learn new behavior did slow their adoption. Innovation friction slows down adoption of innovations that require substantial behavior change, and even more so if it requires learning. This is especially true if the innovation requires un-learning an old way of doing something. If the friction is greater than the momentum of the benefit of overcoming it, the innovation stops dead in its tracks.

An example of this friction is the metric system, which has made only a very small amount of progress in adoption over the last 50 years, despite being clearly superior to the “English system.” It’s just too darn much trouble to change.

One last story about innovation friction from early in my career.

At that time, I was working with a lot of insurance companies creating web-based interfaces to replace traditional “green screen” systems used by insurance agents to quote and initiate new policies for auto and home insurance. It typically took a new hire 4-5 months to learn the system well enough to complete a policy quote — and well over a year to become truly proficient with it! We proudly designed replacement systems that anyone with basic computer skills could learn in a day or two at most, but found that some users were quite hostile to our efforts. They already knew how to use the green screen systems, and they were pretty darn fast with them. One Customer Support Agent even quoted Charlton Heston to me, saying I would only be able to take away her green screen if I pried it from her “cold dead hands.” Creepy? Yes. But also telling. Those old systems are gone now, because of the huge benefit of being able to train people on the new system so quickly. This benefit put the companies that used the new system in a position to more or less force that innovation onto other users.

Many successful innovations have required change and learning — automobiles, indoor toilets, smartphones. With all of these examples, we’ve seen many people willing to learn, for whom the “pain” of change was outweighed by the perceived benefit. But we also see a substantial number of users who resisted for years, saying, “No thanks, I like my outhouse (or horse and buggy or bank teller) just fine.” When conceiving or launching an innovation that requires learning, it’s important to consider the role innovation friction will play in adoption, where you can reduce it, and where you can increase the user’s willingness to accept it as the cost of the greater benefit.

This article originally appeared on the Howard Tiersky blog
Image Credits: Unsplash, Howard Tiersky

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

Five Lessons from the Apple Car’s Demise

Five Lessons from the Apple Car's Demise

GUEST POST from Robyn Bolton

In 2014, rumors started to circulate that Apple was developing a self-driving autonomous car to compete with Tesla.  At the end of February 2024, rumors circulated that Apple was shutting down “Project Titan,” its car program. According to multiple media outlets, the only logical conclusion from the project’s death is that this decision signals the beginning of the end of Apple.

As much as I enjoy hyperbole and unnecessary drama, the truth is far more mundane.

The decision was just another day in the life of an innovation.

As always, there is a silver lining to this car-shaped cloud: the lessons we can learn from Apple’s efforts.

Lesson 1: Innovation isn’t all rainbows and unicorns

People think innovation is fun.  It is.  It is also gut-wrenching, frustrating, and infuriating.  Doing something new requires taking risks, which is uncomfortable for most people.  Even more challenging is that, more often than not, when you take a risk, you “fail.” (if you learned something, you didn’t fail, but that’s another article). 

What you can do: Focus on the good stuff – moments of discovery, adventures when experimenting, signs that you’re making life better for others – but don’t forget that you’re defying the odds.

Lesson 2: More does not mean success

It’s been reported that Apple spent over ten billion dollars on Project Titan and that over 2000 people were working on it before it was canceled. With a market cap of over two trillion dollars, a billion dollars a year isn’t even a rounding error. But it’s still an eye-popping number, which makes Apple’s decision to cut its losses downright courageous.

What you can do: Be on guard for the sunk-cost fallacy.  It’s easy to believe that you’ll eventually succeed if you keep working and pouring resources into a project.  That’s not true, as Apple experienced.  And in the rare cases when it is, executives are often left wondering if the success was worth the cost.

Lesson 3: Pivot based on data, not opinions

At least four different executives led Project Titan during its decade in development, and each leader brought their own vision for what the Apple Car should be.  First, it was an electric vehicle with driver assistance that would compete with Tesla.  Next, it was a self-driving car to compete with Google’s WayMo.  Then, plans for fully autonomous driving were canceled. Finally, the team returned to its original target of matching Tesla’s Level 2 automation.  

Changes in project objectives, strategies, and execution plans are necessary for innovation, so there’s nothing obviously wrong with these pivots.  But the fact that they tended to happen when a new leader was appointed (and that Jony Ive caused an 18-month hiring freeze simply by expressing “displeasure”) makes me question how data-based these pivots actually were

What you can do: Be willing to change but have a high standard for what is required to cause a change.  Data, even qualitative and anecdotal data, should be seriously considered.  The opinion of a single executive, not so much.

Lesson 4: Dream big, build small

Apple certainly dreamed big with its aspirations to build a fully semi-autonomous vehicle and it poured billions into developing and testing the sensors, batteries, and partnership required to make it a reality.  But it was never all-or-nothing in its pursuit of the automotive industry.  Apple introduced CarPlay the same year it kicked off Project Titan, and it continues to offer regular updates to the system.  Car Key was announced in 2020 and is now offered by BMW, Genesis, Hyundai, and Kia.

What you can do: Take a portfolio approach towards your overall innovation portfolio (Apple kept working on the iPhone, iPad, Apple Watch, and Vision Pro) and within each project.  It’s not unusual that a part of the project turns out to be more valuable than the whole project.

Lesson 5: ___________________________

Yes, that is a fill-in-the-blank because I want to hear from you. What lesson are you taking away from Project Titan’s demise, and how will it make you a better innovator?

Image credit: Dall-E via Bing

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.

Top 10 Human-Centered Change & Innovation Articles of April 2024

Top 10 Human-Centered Change & Innovation Articles of April 2024Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are April’s ten most popular innovation posts:

  1. Ignite Innovation with These 3 Key Ingredients — by Howard Tiersky
  2. What Have We Learned About Digital Transformation? — by Geoffrey A. Moore
  3. The Collective Growth Mindset — by Stefan Lindegaard
  4. Companies Are Not Families — by David Burkus
  5. 24 Customer Experience Mistakes to Stop in 2024 — by Shep Hyken
  6. Transformation is Human Not Digital — by Greg Satell
  7. Embrace the Art of Getting Started — by Mike Shipulski
  8. Trust as a Competitive Advantage — by Greg Satell
  9. 3 Innovation Lessons from The Departed — by Robyn Bolton
  10. Humans Are Not as Different from AI as We Think — by Geoffrey A. Moore

BONUS – Here are five more strong articles published in March that continue to resonate with people:

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last four years:

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






London Calling

London Calling Braden Kelley

by Braden Kelley

I will be in London attending a reunion soon and have some availability May 15-17, 2024 if anyone would like to book a keynote, workshop, or advisory session while I’m there.

Are you looking to build a continuous innovation infrastructure in your organization?

Would you like to learn more about the Change Planning Toolkit?

Want to learn how to become your own Futurist using the FutureHacking™ suite of tools?

I’m also open to helping promote a get together if someone has a space in central London to offer up for hosting a Human-Centered Change and Innovation community meetup.

Contact me if you have interest in any or all of these!

p.s. Be sure and follow both my personal account and the Human-Centered Change and Innovation community on LinkedIn.

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






Learning About Innovation – From a Skateboard?

How learning about innovation can come from unlikely directions…

Learning about innovation - from a skateboard?

GUEST POST from John Bessant

What have ollies, decks, trucks, popsicles, cruisers and kicktails got in common? If you’d asked me that back in December I would have quietly assumed you were from another planet. But now I’m happy to say I’m in a good position to enlighten you…

It seemed like a good idea for a Christmas present; Lara had trailed it enough to give us a clue that Father Christmas’s arrival with a skateboard in his sack would be a welcome surprise. And the initial impact was decidedly so; unwrapping a bright blue machine with impressive decoration and wheels which lit up as she sped along the corridor in the first test flights.

So it was a good job that the present included a full safety kit of helmet, knee and elbow pads, etc. because she spent much of the next day crashing into bookcases, doors and anything else unfortunate enough to be in the way of her hurtling progress. And that was just inside the house!

Cue the need for some training in the art of riding a skateboard. Since this is a skillset which I am most certainly not in possession of this led me to outsource the problem to our local indoor skateboard park where I booked a couple of lessons. Turned out to be an education for me too; while sheltering from the noise of kids shouting encouragement and challenge at each other and the rumble of wheels over plywood ramps and chicanes I sipped my coffee and thought about some of the innovation lessons it was demonstrating….

Not least the power of innovation to create a whole new market. I had no idea just how big a business skateboarding had become but a quick dive into my phone revealed some impressive facts. Estimates suggest the market size is of the order of $3.2bn in 2022 and forecast to keep growing at 3.5% over the next ten years. That’s a lot of skateboards on the streets and in specialist parks like the one I’m in. Extending my research I came to realise what a sheltered life I’ve been leading — there’s a whole world out there beyond the simple piece of wood on wheels that I thought it was.

And it’s an industry full of innovation. I use something called the ‘4Ps framework’ to explain the different ways you can innovate to my classes — it’s a sort of ‘innovation compass’ that points the different directions you might explore. Skateboarding’s got all of these and plenty of examples.

First there’s product innovation — from core components like wheels and bearings, boards and safety equipment through to fashion and merchandise. The early days saw the emergence of what I now know is called a ‘streetboard’ as a basic design; it still represents nearly half the market in terms of boards sold and has standardised around some core dimensions. They are often called ‘popsicle boards’ (don’t ask me why) and they are the kind of board you’ll typically see out in the streets.

But there’s also the longboard variant which is growing in significance. As its name suggests it’s longer but also lighter and faster on account of its wheel size and construction materials. This — as Lara points out to me, already dissatisfied with her glamourous but un-tricksy popsicle board — is the kind of board you use for performing tricks, racing, freestyling and even dancing. Plus we’ve now got the new wave of electric boards which bring another dimension to the sport, potentially opening up the personal mobility market.

And each of those boards involves multiple contributing streams of innovation around components. Take something as mundane as wheels; naively I assumed they were just roller skate or suitcase wheels attached to a piece of wood. Nope. The early days of metal wheels were fraught with poor performance, high friction and a lot of collateral damage to the surfaces being ridden on. So non-metallic alternatives emerged, from simple clay versions in the 1960s through to today’s polyurethane variants based on Frank Nasworthy’s 1972 invention. This latest iteration also provides an opportunity to build in some of the fancy display electronics which Lara finds such a draw on her board.

Even the humble bearings inside the wheels turn out to be a key innovation step, moving from simple loose ball bearings to sophisticated engineered precision bearings which last longer and give a smooth ride even under the tortuous twists and turns of a ride round the skateboard park.

Or look at the trucks on which the wheels are fitted. They’ve moved from being simple cross-over fixtures taken from roller skating to becoming a high tech branch of the component world, continuously refined to give better turning radius, stability, and control allowing for more complex manoeuvres and tricks. Classic need pull innovation.

Skateboard product innovation also provides an excellent example of the idea of dominant designs and technological trajectories. The classic pattern in which different designs compete in the early days of an innovation before a dominant version emerges which sets the path for further incremental development. Which is periodically interrupted by a radical shift enabled by technology or different market demand.

In the case of skateboards the simple plank of wood on wheels which dominated the early days 1930s was upended (pardon the pun) by Larry Stevenson’s rethinking of the deck to include a kicktail in 1969. This is the upturned rear end of a board which gives better control and crucially enables many of those fancy tricks. It wasn’t long before the double kicktail — a turn-up at each end — became the standard for modern boards.

And we’re not just talking about the boards themselves; there is matching innovation right across the ecosystem which has emerged. Take the case of footwear; the increasing profile of the sector attracted both specialist sports suppliers and also fashion brands and has led to another interactive set of pulls and pushes to create innovation. For example Vans, in the 1970s, introduced the waffle-cup, diamond shoe pattern, offering better grip and foot protection. Since then, skate shoe companies have continuously innovated, focusing on preventing bruises, increasing flexibility, and enhancing grip.

What about process innovation? Watching Lara on the wooden ramps and slopes showed how much this has changed since the early days of trying to travel down a road in a straight line. Now we’re in a world of complex spins and runs, jumps and curves. And supporting them are specialist architects and engineers designing ever more complex experiences across which boards can travel, swivel, twist, turn and jump in multiple ways. Not surprising that in 2016, the International Olympic Committee announced the inclusion of skateboarding in the 2020 Summer Olympics.

There’s also scope for what we’ call ‘position innovation’ — expanding the market to whom the innovation is targeted and changing story being told about it. Skateboarding is no longer the province of hardcore fans — it’s grown to be a mainstream sport, drawing in enthusiasts of an increasingly wide age range. According to a recent survey, skateboarding is marked at the third position in the most popular sports category after football and basketball in the USA. Europe has been catching on fast and Asia now has the highest growth rates — not least China.

It’s also another world, full of fans connected online and physically, creating a sub-culture around the sport. Since the first skateboarding magazine was published back in 1964 a whole media system has emerged spanning print, video, podcasting and films and populated by real life and fictional heroes. With my newly-acquired skateboarding vocabulary even I am able to make more sense of the arcane worlds of Mutant Turtles, Shredders and their like.

And it’s a powerful force in fashion now, and not just as a fan-based marketplace. In 2016, at Paris Fashion Week, Dior launched its winter collection on a neon-coloured skateboard catwalk ramp. The icons are everywhere — and so too are the sponsors, riding their own market waves with their brands plastered all over boards, apparel and other merchandise.

From time to time there is also an opportunity for what we call ‘paradigm innovation’ — changing the way we think about what it is we are doing and how it creates value. It can refer to the ‘business model’ being used or changed — but it can also get right to the heart of what we consider the boundaries and shape of our activity.

We’ve seen this before in then world of sport. Just as Dick Fosbury shifted the way high jump athletes think and move with his famous Fosbury Flop, so skateboarding has had its turning points. I’m reliably assured from my instant education via Wikipedia that Alan Gelfand invented a move in 1978 called the Ollie, which revolutionized the field. It’s the basic move in which the rider leaps in the air without using their hands — and it enabled all those amazing twirls, somersaults and assorted gravity-defying manoeuvres which make the sport so exciting. (You’ll be glad to learn that there are now at least 15 variations on the Ollie including the nollie (nose ollie), the switch ollie, the half-can, the pop-shuvit, and the fakie ollie).

Skateboard Business

So there’s plenty in the current innovation space around skateboarding. But where did it come from? Its origins lie in the 1930s, not surprisingly in the surf-rich worlds of Hawaii and California. The idea of attaching roller skate wheels to a wooden board gave us the ‘skate-scooter’ but it wasn’t until the 1950s that the crossover from the world of surfing really took hold. Standing on a board rather than scooting along with one foot on it led to the need to find ways to balance, turn and move in ways analogous to riding the ocean swell.

Going back through its history you quickly find yourself facing a classic example of user-led innovation. Thanks to the pioneering studies of Eric von Hippel and colleagues we know a lot about this type of innovation. It doesn’t begin with the design office in a company or in a marketing agency. Instead it starts with users who face a challenge, feel a frustration or generally seek something different, perhaps new thrills or experiences. They have a high incentive to innovate but this primarily comes from within — it’s not about growing a market or commercial expansion. They have a challenge and they want to deal with it.

In addition to this high incentive to innovate they are tolerant of failure and prepared to experiment and prototype. So many user innovations emerge from this type of experimenting — think of the first pickup trucks which were not designed in Detroit but were the result of farmers modifying the Model Ts and other cars they bought to make them more suitable for farm use.

Importantly because user innovators aren’t primarily motivated for commercial market expansion they are often happy to share and so communities form around promising new ideas. And these communities can significantly accelerate the rate of innovation by pooling both ideas and the results of their experiences.

Which is the classic pattern in many new sports — things like kitesurfing, windsurfing and mountain biking — and certainly characterises skateboarding. Originally developed as a street alternative to riding the waves early prototypes were shared, modified and developed in a co-laboratory on the streets and in communities prepared to share ideas (and how they got those bumps and bruises). Many of the early companies which emerged in this field were founded by users — and a good few failed to grow partly because their founders were more interested in riding their boards than in selling them!

The model persists; much of the move to tricks and through them to specialist materials, safety equipment and even the parks in which such tricks are enabled owe their genesis to user innovators. People like Rodney Mullen, who invented numerous foundational tricks used in modern street skating, and Tony Hawk, who not only pushed the boundaries of skateboarding tricks but also contributed to the design and material innovations through his company, Birdhouse Skateboards.

Skateboard Girl

Watching a seven year old try to master a complex physical skill requiring considerable co-ordination also provided me with another angle on innovation. There’s a strong vision of what she wants to achieve and periodically she’ll pause to watch one of the older kids running through their repertoire of tricks, culminating with jumps, back flips and other seriously cool manoeuvres. But there’s also a determination allied to acceptance of the multiple falls and collisions which go with the learning process. Plus, from time to time a sideways approach to the whole thing.

You can almost see her thinking that these wonderfully sculpted slopes and cliffs and jumps are OK for riding your board but they are also pretty interesting as a surface to be clambered over and played on in their own right. So why not combine the two? Within half an hour she was pretty adept at lying prone on her board and hurtling up and down in a new variant of the sport with more in common with bobsleigh riding. Perhaps I’ve just witnessed the birth ofyet another offshoot innovation?

At the end of the session one of the organizers came up and asked if I fancied having a go. My instincts were all around self-preservation reinforced by the important principle about the impossibility of teaching old dogs new tricks. But a voice was also whispering in my ear that innovation is, of course, all about learning new tricks. Dynamic capability and all that. Time to practice what I preach?

So maybe the next time I write this blog it will be from my hospital bed, having crocked myself up in spectacular fashion but with the satisfied smile of my face that comes to those who finally execute their first ollie….

You can find my podcast here and my videos here

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

Image credits: Dall-E via Microsoft CoPilot

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.

How I Use AI to Understand Humans

(and Cut Research Time by 80%)

How I Use AI to Understand Humans

GUEST POST from Robyn Bolton

AI is NOT a substitute for person-to-person discovery conversations or Jobs to be Done interviews.

But it is a freakin’ fantastic place to start…if you do the work before you start.

Get smart about what’s possible

When ChatGPT debuted, I had a lot of fun playing with it, but never once worried that it would replace qualitative research.  Deep insights, social and emotional Jobs to be Done, and game-changing surprises only ever emerge through personal conversation.  No matter how good the Large Language Model (LLM) is, it can’t tell you how feelings, aspirations, and motivations drive their decisions.

Then I watched JTBD Untangled’s video with Evan Shore, WalMart’s Senior Director of Product for Health & Wellness, sharing the tests, prompts, and results his team used to compare insights from AI and traditional research approaches.

In a few hours, he generated 80% of the insights that took nine months to gather using traditional methods.

Get clear about what you want and need.

Before getting sucked into the latest shiny AI tools, get clear about what you expect the tool to do for you.  For example:

  • Provide a starting point for research: I used the free version of ChatGPT to build JTBD Canvas 2.0 for four distinct consumer personas.  The results weren’t great, but they provided a helpful starting point.  I also like Perplexity because even the free version links to sources.
  • Conduct qualitative research for meI haven’t used it yet, but a trusted colleague recommended Outset.ai, a service that promises to get to the Why behind the What because of its ability to “conduct and synthesize video, audio, and text conversations.”
  • Synthesize my research and identify insights: An AI platform built explicitly for Jobs to be Done Research?  Yes, please!  That’s precisely what JobLens claims to be, and while I haven’t used it in a live research project, I’ve been impressed by the results of my experiments.  For non-JTBD research, Otter.ai is the original and still my favorite tool for recording, live transcription, and AI-generated summaries and key takeaways.
  • Visualize insights:  MuralMiro, and FigJam are the most widely known and used collaborative whiteboards, all offering hundreds of pre-formatted templates for personas, journey maps, and other consumer research templates.  Another colleague recently sang the praises of theydo, an AI tool designed specifically for customer journey mapping.

Practice your prompts

“Garbage in.  Garbage out.” Has never been truer than with AI.  Your prompts determine the accuracy and richness of the insights you’ll get, so don’t wait until you’ve started researching to hone them.  If you want to start from scratch, you can learn how to write super-effective prompts here and here.  If you’d rather build on someone else’s work, Brian at JobsLens has great prompt resources. 

Spend time testing and refining your prompts by using a previous project as a starting point.  Because you know what the output should be (or at least the output you got), you can keep refining until you get a prompt that returns what you expect.    It can take hours, days, or even weeks to craft effective prompts, but once you have them, you can re-use them for future projects.

Defend your budget

Using AI for customer research will save you time and money, but it is not free. It’s also not just the cost of the subscription or license for your chosen tool(s).  

Remember the 80% of insights that AI surfaced in the JTBD Untangled video?  The other 20% of insights came solely from in-person conversations but comprised almost 100% of the insights that inspired innovative products and services.

AI can only tell you what everyone already knows. You need to discover what no one knows, but everyone feels.  That still takes time, money, and the ability to connect with humans.

Run small experiments before making big promises

People react to change differently.  Some will love the idea of using AI for customer research, while others will resist with.  Everyone, however, will pounce on any evidence that they’re right.  So be prepared.  Take advantage of free trials to play with tools.  Test tools on friends, family, and colleagues.  Then under-promise and over-deliver.

AI is a starting point.  It is not the ending point. 

I’m curious, have you tried using AI for customer research?  What tools have you tried? Which ones do you recommend?

Image credit: Unsplash

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.

Why Quiet Geniuses Excel at Breakthroughs

Why Quiet Geniuses Excel at Breakthroughs

GUEST POST from Greg Satell

When you think of breakthrough innovation, someone like Steve Jobs, Jeff Bezos or Elon Musk often comes to mind. Charismatic and often temperamental, people like these seem to have a knack for creating the next big thing and build great businesses on top of them. They change the world in ways that few can.

Yet what often goes unnoticed is that great entrepreneurs build their empires on the discoveries of others. Steve jobs didn’t invent the computer or the mobile phone any more than Jeff Bezos discovered e-commerce or Elon Musk dreamed up electric cars. Those things were created by scientists and engineers that came long before.

In researching my book, Mapping Innovation, I got to know many who truly helped create the future and I found them to be different than most people, but not in a way that you’d expect. While all were smart and hardworking, the most common trait among them was their quiet generosity and that can teach us a lot about how innovation really works.

How Jim Allison Figured it All Out

At least in appearance, Jim Allison is a far cry from how you would normally picture a genius to look like. Often disheveled with a scruffy beard, he kind of mumbles out a slow Texas drawl that belies his amazingly quick mind. Unassuming almost to a fault, when I asked him about his accomplishments he just said, “well, I always did like figuring things out.”

When Jim was finishing up graduate school, scientists had just discovered T-cells and he told me that he was fascinated by how these things could zip around your body and kill things for you, but not actually hurt you. The thing was, nobody had the faintest idea how it all worked. So Jim decided to become an immunologist and devote his life to figuring it all out.

Over the next few decades, he and his colleagues at other labs did indeed do much to figure it out. They found one receptor, called B-7, which acts like an ignition switch that initiates the immune response, another, CD-28, that acts like a gas pedal and revs things up into high gear and a third, called CTLA-4, that puts on the brakes so things don’t spin out of control.

Jim played a part in all of this, but his big breakthrough came from the work of another scientist in his lab, which made him suspect that the problem with cancer wasn’t that our immune system can’t fight it, but that it puts the brakes on too soon. He thought that if he could devise a way to pull those brakes off, we could cure cancer in a new and different way.

As it turned out, Jim was right. Today, cancer immunotherapy has become a major field unto itself and, in October 2018, he won the Nobel Prize for his discovery of it. Yet the truth is that it wasn’t one major breakthrough, but a decades-long process of slowly putting the pieces together that made it all possible.

How Gary Starkweather Went From Blowup To Breakthrough

Gary Starkweather is every bit as quiet and unassuming as Jim Allison. Yet when I talked to him a few years ago, I could still hear the anger in his voice as he told me about an incident that happened almost 50 years before. In the late 60s, Gary had an idea to invent a new kind of printer, but his boss at Xerox was thwarting his efforts.

At the time, Gary was one of the few experts in the emerging field of laser optics, so there weren’t many others who could understand his work, much less how it could be applied to the still obscure field of computers. His boss was, in fact, was so hostile to Gary’s project that he threatened to fire anyone who worked with him on it.

Furious, the normally mild mannered Gary went over his boss’s head. He walked into the Senior Vice President’s office and threatened, “Do you want me to do this for you or for someone else?” For the stuffy, hierarchical culture of Xerox, it was outrageous behavior, but as luck would have it, the stunt paid off. News of Gary’s work made it across the country to the fledgling computer lab that Xerox had recently established in California, the Palo Alto Research Center (PARC).

Gary thrived in the freewheeling, collaborative culture at PARC. The researchers there had developed a graphical technology called bitmapping, but had no way to print the images out until he showed up. His development of the laser printer was not only a breakthrough in its own right, but with the decline of Xerox’s copier business, it actually saved the company.

The Wild Ideas Of Charlie Bennett

Charlie Bennett is one of those unusual minds that amazes everyone he meets. He told me that when he was growing up in the quiet Westchester village of Croton-on-Hudson he was a “geek before geeks were cool.” While the other kids were playing sports and trading baseball cards, what really inspired Charlie was Watson and Crick’s discovery of the structure of DNA.

So he went to college and majored in biochemistry and then went on to Harvard to do his graduate work, where he served as James Watson’s teaching assistant. Yet it was an elective course he took on the theory of computation that would change his fate. That’s where he first encountered the concept of a Turing Machine and he was amazed how similar it was to DNA.

So Charlie never became a geneticist, but went to work for IBM as a research scientist. It proved to be just the kind of place where a mind like his could run free, discussing wild ideas like quantum cryptography with colleagues around the globe. It was one of those discussions, with Gilles Brassard, that led to his major breakthrough.

What the two discussed was the wildest idea yet. They proposed to transfer information by quantumly entangling photons, something that Einstein had derisively called “spooky action at a distance” and was adamant couldn’t happen. Yet the two put a team together and, in 1993, successfully completed the quantum teleportation experiment.

That, in turn, led Charlie just a few months later to write down his four laws of quantum information, which formed the basis for IBM’s quantum computing program. Today, in his eighties, Charlie is semi-retired, but still goes into the labs at IBM research to quietly discuss wild ideas with the younger scientists, such as the quantum internet that’s continuing to emerge now.

For Innovation, Generosity Is A Competitive Advantage

My conversations with Jim, Gary, Charlie and many others made an impression on me. They were all giants in their fields (although Jim hadn’t won his Nobel yet) and I was a bit intimidated talking to them. Yet I found them to be some of the kindest, most generous people I ever met. Often, they seemed as interested in me as I was in them.

In fact, the behavior was so consistent that I figured it couldn’t be an accident. So I researched the matter further and found a number of studies that helped explain it. One, at Bell Labs, found that star engineers had a knack for “knowing who knows.” Another at the design firm IDEO found that great innovators essentially act as “knowledge brokers.“

A third study helps explain why knowledge brokering is so important. Analyzing 17.9 million papers, the researchers found that the most highly cited work tended to be mostly rooted within a traditional field, with just a smidgen of insight taken from some unconventional place. Breakthrough creativity occurs at the nexus of conventionality and novelty.

So as it turns out, generosity is often a competitive advantage for innovators. By actively sharing their ideas, they build up larger networks of people willing to share with them. That makes it that much more likely that they will come across that random piece of information and insight that will help them crack a really tough problem.

So if you want to find a truly great innovator, don’t look for the ones that make the biggest headlines are that are most inspiring on stage. Look for those who spend their time a bit off to the side, sharing ideas, supporting others and quietly pursuing a path that few others are even aware of.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Pixabay

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.






Iterate Your Thinking

Iterate Your Thinking

GUEST POST from Dennis Stauffer

One of the things that all sound innovation processes have in common is some way to iterate. To repeatedly work through a process that allows you to refine whatever you’re trying to create.

That might be building a prototype, testing it and building another version based on what you’ve learned. It might be gathering customer feedback and making adjustments that are more appealing or solve a problem more effectively. It might be exploring more than one business model or marketing strategy until you find one that works.

We tend to think of those iterations as making refinements to a product or strategy, but more than anything, it’s refining your own thinking. It’s being willing to change how you understand the world, by challenging your assumptions and beliefs—your mindset.

We’ve grown accustomed to thinking of learning as mastering a set of already well-defined concepts, like how to solve a math problem or memorizing facts from history. But innovation—and life in general—requires a different kind of learning. More like gradually mastering how to play a sport or musical instrument, or drive a car. This kind of learning is a more incremental process. One that prompts questions like:

  • How might I be wrong, and need to correct myself?
  • What do I not understand as well as I could?
  • What are some alternative beliefs and opinions, to the ones I have?
  • How might someone else see things differently and what could I learn from them?

The ability to iterate your own thinking, by being open to new interpretations of what you experience, is crucial to innovation. It’s also a good strategy for ordering your life, so you don’t lock onto a mindset that may not be the most effective for you.

Mental iteration is a powerful life skill—and healthy innovation habit—that also helps you innovate yourself.

View this post as a video 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.

Problems are Required for Progress to Occur

Problems are Required for Progress to Occur

GUEST POST from Mike Shipulski

Without a problem, there can be no progress.
And only after there’s too much no progress is a problem is created.
And once the problem is created, there can be progress.

When you know there’s a problem just over the horizon, you have a problem.
Your problem is that no one else sees the future problem, so they don’t have a problem.
And because they have no problem, there can be no progress.
Progress starts only after the calendar catches up to the problem.

When someone doesn’t think they have a problem, they have two problems.
Their first problem is the one they don’t see, and their second is that they don’t see it.
But before they can solve the first problem, they must solve the second.
And that’s usually a problem.

When someone hands you their problem, that’s a problem.
But if you don’t accept it, it’s still their problem.
And that’s a problem, for them.

When you try to solve every problem, that’s a problem.
Some problems aren’t worth solving.
And some don’t need to be solved yet.
And some solve themselves.
And some were never really problems at all.

When you don’t understand your problem, you have two problems.
Your first is the problem you have and your second is that you don’t know what your problem by name.
And you’ve got to solve the second before the first, which can be a problem.

With a big problem comes big attention. And that’s a problem.
With big attention comes a strong desire to demonstrate rapid progress. And that’s a problem.
And because progress comes slowly, fervent activity starts immediately. And that’s a problem.
And because there’s no time to waste, there’s no time to define the right problems to solve.

And there’s no bigger problem than solving the wrong problems.

Image credit: Pexels

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.






Predicting Unintended Consequences

The 93% Rule

Predicting Unintended Consequences

GUEST POST from Robyn Bolton

Unintended consequences often catch us off guard despite their predictability.  The moment they occur, we gasp in shock, shake our heads, and look at each other in wide-eyed horror at this thing that just happened that we could never ever ever have anticipated. 

Yet, when (if) we do an After-Action Review, we often realize that these consequences were not entirely unforeseeable. In fact, had we anticipated them, we might have made different decisions.

The Unintended Consequences of Spreadsheets

In 1800 BCE, ancient Babylonians started recording data by scratching grids and columns onto clay tablets, and the spreadsheet was born.  Over the millennia, we went from clay tablets to papyrus to parchment and then paper. 

Fast forward to 1963 when R. Brian Walsh of Marquette University ported the Business Computer Language (BCL) program to an IBM 7040, and electronic spreadsheets became a reality.  The introduction of VisiCalc by Apple in 1979 revolutionized spreadsheet capabilities, followed by Lotus 123 and Microsoft Excel. Today, spreadsheets are ubiquitous in education, business operations, financial markets, budgeting, and even personal inventories.

Unintended yet predictable consequences

While spreadsheets have undoubtedly enhanced efficiency and accuracy compared to traditional methods like clay tablets or hand-drawn tables on parchment, their ease of use has inadvertently led to complacency.

We stopped engaging in a multi-millennial habit of discussing, debating, and deciding before making a spreadsheet. We started flippantly asking people to create spreadsheets and providing little, if any, guidance because “it’s easy to make changes and run scenarios.”

This shift resulted in a reliance on automated models and a lack of shared assumptions or analytical rigor in decision-making processes.

Of course, these behaviors were never intended.  They were, however, very predictable.

93% of Human Behavior is predictable.

Research spanning disciplines as varied as network scientists, anthropology, neuropsychology, and paleontology shines a light on how truly predictable we are.

Here are some examples:

Emotions before Reason: Ask someone if they make decisions based on their motivations, aspirations, and fears and use data to justify the decisions, and they’ll tell you no. Ask them the last time someone else made a decision that “made no sense,” and you’ll listen to a long list of examples.

Small gains now are better than big gains later: Thoughtfully planning before using solutions like spreadsheets, word processing, email, and instant messaging could save us time at work and help us get home 30 minutes earlier or work a few hours less on the weekend.  But saving a few seconds now by brain-dumping into Word, setting up a “flexible” spreadsheet, and firing off a text feels much better.

Confidence > Realism: We’ve all been in meetings where the loudest voice or the most senior person’s opinion carried the day.  As we follow their lead, we ignore signs that we’re wrong and explain away unexpected and foreboding outcomes until we either wake up to our mistakes or adjust to our new circumstances.

Predict the 93%. Create for the 7%

Acknowledging the predictability of human behavior is not an endorsement of stereotypes but a recognition of our innate cognitive processes. By incorporating this understanding into design, innovation, and decision-making processes, we better anticipate potential outcomes and mitigate unintended consequences.

While 93% of human behavior may follow predictable patterns rooted in evolutionary instincts, focusing on the remaining 7% allows for the exploration of unique behaviors and novel solutions.  By embracing both aspects of human nature, we can navigate challenges more effectively and anticipate a broader range of outcomes in our endeavors, leading to informed decision-making and value creation.

Now, if I could only get Excel to stop auto-converting numbers into date/time format.

Image credit: Pixabay

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.