The Collective Growth Mindset

The Collective Growth Mindset

GUEST POST from Stefan Lindegaard

What makes a team great? It’s a loaded question. Let’s dive in: you’re a team player, yes? But does your team prioritize collective growth and psychological safety? If so, there’s always room for further enhancement.

Here’s my perspective, based on interacting with teams globally:

1. Collective Growth Mindset: Teams thrive with curious learners, not just know-it-alls.

2. Psychological Safety: Embrace constructive feedback, hard conversations, and risk-taking in a secure environment.

3. Clear Purpose: Ensure team objectives resonate personally, answering “what’s in it for me?”

4. Trust and Transparency: Despite potential risks, mutual trust, dependability, and transparency yield substantial rewards.

5. Execution: All the above mean nothing without effective execution. Support and mandate are crucial.

6. Have Fun: A joyful environment can enhance productivity and team spirit.

Which of these elements resonates most with you? Is something missing in this list? I’m curious on your thoughts and open for a discussion on how your team can get even better.

The Collective Growth Mindset Stefan Lindegaard

Image Credit: Pexels

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3 Innovation Lessons from The Departed

3 Innovation Lessons from The Departed

GUEST POST from Robyn Bolton

It’s award season, which means that, as a resident of Boston, I have the responsibility and privilege to talk about The Departed (pronounced: The Dep-ah-ted).  The film won the Oscar for Best Picture in 2007 and earned Martin Scorsese his first, and to date only, Academy Award for Best Director.  It is also chock-full of great lessons for corporate innovators.

Quick Synopsis

If you’ve seen The Departed, you can skip this part.  If you haven’t, why not and read on.

The Departed is loosely based on notorious Boston crime boss Whitey Bulger and features three main characters:

  1. Frank Costello (Jack Nicholson), a vicious and slightly unhinged Irish mob boss
  2. Colin Sullivan (Matt Damon), a Massachusetts State Trooper in the Special Investigation Unit (SIU) formed to catch Costello, who, in his spare time, is a spy for Costello.
  3. Billy Costigan (Leonardo DiCaprio), a police academy recruit who goes undercover to infiltrate Costello’s organization

But wait!  There’s more.  Alec Baldwin plays Colin’s SIU boss, George Ellerby.  Martin Sheen and Mark Wahlberg (who received an Oscar nomination for this role) play Billy’s Mass State Police (MSP) bosses, Captain Queenan and Staff Sergeant Dignam, respectively.  Completing the chaos is Vera Farmiga, who plays Madolyn Madden, Colin’s girlfriend and Billy’s court-ordered psychiatrist.

There’s a lot of other stuff going on, but that gives you enough context for the following quotes to hopefully make sense.

Listen to the words people use.

Colin (after Dignam refuses to hand over undercover files): I need those passwords.

Ellerby: No, you want those passwords

It’s not often that Ellerby says something useful, let alone wise, but he nails it with this one.  Colin wants the passwords to Dignam’s files on undercover agents because it will make both Colin’s official job of finding Costello’s rat in the MSP and his unofficial job of finding the MSP officer in Costello’s crew easier.  He doesn’t need the passwords, however, because, with enough time and effort, he can find the rats he’s looking for.

When we hear from customers that they want something, it’s tempting to run off and create it.  But as Ellerby points out, wants and needs are different.  Just because customers want something doesn’t mean they are willing to pay for or change their behavior to get and use it. 

Figuring out what a customer needs is difficult because it requires them to trust you enough to admit they have a problem they can’t solve.  It’s also difficult because most of us have access to solutions to our functional needs (think the bottom few layers of Maslow’s hierarchy).  As a result, the needs consumers grapple with tend to be emotional and social, and it’s far more challenging to admit those to a stranger, especially in a focus group or product-focused interview.

How you feel impacts everyone around you

Madolyn (after a counseling session): Why is the last patient of the day always the hardest?

Billy: Because you’re tired, and you don’t give a sh*t.  It’s not super-natural.

Billy and Madolyn get off to a rough start in their first counseling session, culminating in Billy asking for a prescription for Valium.  Madolyn calls him out for “drug-seeking behavior” and throws two Valiums across the desk before Billy storms out.  A few minutes later, Madolyn catches up with Billy, hands him a prescription for Valium, and asks the above question.

Being a corporate innovator can be difficult, sometimes soul-crushing work (ask the good people at Store 8).  It can also be thrilling and inspiring.  It can even be all those things in one day.  That’s what makes it tiring, even when you give a sh*t. 

Managing your energy and monitoring your behavior are leadership qualities we don’t discuss often enough.  It’s okay to be exhausted after a day of facilitating ideation sessions or intense strategic meetings.  It’s normal to be frustrated after a contentious conversation or demotivated when you get bad news.  But leaders usually find a way to not take those emotions out on their teams.  And, in the rare instance when they punish the team for someone else’s sin, they apologize and explain. 

Your job is not your identity.

Billy: Look, I just want my identity back, all right?  That’s all.

Colin: All right, I understand.  You want to be a cop again.

Billy: No, no, being a cop’s not an identity.  I want my identity back.

Towards the end of the film, Billy is tired of working undercover and reports to MSP headquarters to complete the paperwork required to expunge his criminal record and get his identity back.  That’s when Colin makes the same mistake most of us make and confuses Billy’s job with his identity.

We spend so much time at work.  We rely on our paychecks for so much.  We even introduce ourselves to new people using our job titles.  It’s easy for your job to feel like your identity, especially when your job aligns so closely with your deeply held beliefs and values.  But your job is not your identity.  You are still a Tempered Radical, even without your corporate title.   You are still an optimistic problem-solver, even when it’s been months since your last brainstorming session. 

You are an innovator, even if you don’t have a business card to prove it.

Image credit: RadioTimes.com

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Don’t Blame Technology When Innovation Goes Wrong

Don't Blame Technology When Innovation Goes Wrong

GUEST POST from Greg Satell

When I speak at conferences, I’ve noticed that people are increasingly asking me about the unintended consequences of technological advance. As our technology becomes almost unimaginably powerful, there is growing apprehension and fear that we will be unable to control what we create.

This, of course, isn’t anything new. When trains first appeared, many worried that human bodies would melt at the high speeds. In ancient Greece, Plato argued that the invention of writing would destroy conversation. None of these things ever came to pass, of course, but clearly technology has changed the world for good and bad.

The truth is that we can’t fully control technology any more than we can fully control nature or each other. The emergence of significant new technologies unleash forces we can’t hope to understand at the outset and struggle to deal with long after. Yet the most significant issues are most likely to be social in nature and those are the ones we desperately need to focus on.

The Frankenstein Archetype

It’s no accident that Mary Shelley’s novel Frankenstein was published at roughly the same time as the Luddite movement was in full swing. As cottage industries were replaced by smoke belching factories, the sense that man’s creations could turn against him was palpable and the gruesome tale, considered by many to be the first true work of science fiction, touched a nerve.

In many ways, trepidation about technology can be healthy. Concern about industrialization led to social policies that helped mitigate its worst effects. In much the same way, scientists concerned about the threat of nuclear Armageddon did much to help establish policies that would prevent it.

Yet the initial fears almost always prove to be unfounded. While the Luddites burned mills and smashed machines to prevent their economic disenfranchisement, the industrial age led to a rise in the living standards of working people. In a similar vein, more advanced weapons has coincided with a reduction of violent deaths throughout history.

On the other hand, the most challenging aspects of technological advance are often things that we do not expect. While industrialization led to rising incomes, it also led to climate change, something neither the fears of the Luddites nor the creative brilliance of Shelley could have ever conceived of.

The New Frankensteins

Today, the technologies we create will shape the world as never before. Artificially intelligent systems are automating not only physical, but cognitive labor. Gene editing techniques, such as CRISPR, are enabling us to re-engineer life itself. Digital and social media have reshaped human discourse.

So it’s not surprising that there are newfound fears about where it’s all going. A study at Oxford found that 47% of US jobs are at risk of being automated over the next 20 years. The speed and ease of gene editing raises the possibility of biohackers wreaking havoc and the rise of social media has coincided with a disturbing rise of authoritarianism around the globe.

Yet I suspect these fears are mostly misplaced. Instead of massive unemployment, we find ourselves in a labor shortage. While it is true that the biohacking is a real possibility, our increased ability to cure disease will most probably greatly exceed the threat. The increased velocity of information also allows good ideas to travel faster and farther.

On the other hand, these technologies will undoubtedly unleash new challenges that we are only beginning to understand. Artificial intelligence raises disturbing questions about what it means to be human, just as the power of genomics will force us to grapple with questions about the nature of the individual and social media forces us to define the meaning of truth.

Revealing And Building

Clearly, Shelly and the Luddites were very different. While Shelley was an aristocratic intellectual, the Luddites were working class weavers. Yet both saw the rise of technology as the end to a way of life and, in that way, both were right. Technology, if nothing else, forces us to adapt, often in ways we don’t expect.

In his 1954 essay, The Question Concerning Technology the German philosopher Martin Heidegger sheds some light on these issues. He described technology as akin to art, in that it reveals truths about the nature of the world, brings them forth and puts them to some specific use. In the process, human nature and its capacity for good and evil is also revealed.

He gives the example of a hydroelectric dam, which reveals the energy of a river and puts it to use making electricity. In much the same sense, Mark Zuckerberg did not “build” a social network at Facebook, but took natural human tendencies and channeled them in a particular way. After all, we go online not for bits or electrons, but to connect with each other.

Yet in another essay, Building Dwelling Thinking, he explains that building also plays an important role, because to build for the world, we first must understand what it means to live in it. The revealing power of technology forces us to rethink old truths and re-imagine new societal norms. That, more than anything else, is where the challenges lie.

Learning To Ask The Hard Questions

We are now nearing the end of the digital age and entering a new era of innovation which will likely be more impactful than anything we’ve seen since the rise of electricity and internal combustion a century ago. This, in turn, will initiate a new cycle of revealing and building that will be as challenging as anything humanity has ever faced.

So while it is unlikely that we will ever face a robot uprising, artificial intelligence does pose a number of troubling questions. Should safety systems in a car prioritize the life of a passenger or a pedestrian? Who is accountable for the decisions an automated system makes? We worry about who is teaching our children, but scarcely stop to think about who is training our algorithms.

These are all questions that need answers within the next decade. Beyond that, we will have further quandaries to unravel, such as what is the nature of work and how do we value it? How should we deal with the rising inequality that automation creates? Who should benefit from technological breakthroughs?

The unintentional consequences of technology have less to do with the relationship between us and our inventions than it does between us and each other. Every technological shift brings about a societal shift that reshapes values and norms. Clearly, we are not helpless, but we are responsible. These are very difficult questions and we need to start asking them. Only then can we begin the cycle of revealing truths and building a better future.

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

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Humans Are Not as Different from AI as We Think

Humans Are Not as Different from AI as We Think

GUEST POST from Geoffrey A. Moore

By now you have heard that GenAI’s natural language conversational abilities are anchored in what one wag has termed “auto-correct on steroids.” That is, by ingesting as much text as it can possibly hoover up, and by calculating the probability that any given sequence of words will be followed by a specific next word, it mimics human speech in a truly remarkable way. But, do you know why that is so?

The answer is, because that is exactly what we humans do as well.

Think about how you converse. Where do your words come from? Oh, when you are being deliberate, you can indeed choose your words, but most of the time that is not what you are doing. Instead, you are riding a conversational impulse and just going with the flow. If you had to inspect every word before you said it, you could not possibly converse. Indeed, you spout entire paragraphs that are largely pre-constructed, something like the shticks that comedians perform.

Of course, sometimes you really are being more deliberate, especially when you are working out an idea and choosing your words carefully. But have you ever wondered where those candidate words you are choosing come from? They come from your very own LLM (Large Language Model) even though, compared to ChatGPT’s, it probably should be called a TWLM (Teeny Weeny Language Model).

The point is, for most of our conversational time, we are in the realm of rhetoric, not logic. We are using words to express our feelings and to influence our listeners. We’re not arguing before the Supreme Court (although even there we would be drawing on many of the same skills). Rhetoric is more like an athletic performance than a logical analysis would be. You stay in the moment, read and react, and rely heavily on instinct—there just isn’t time for anything else.

So, if all this is the case, then how are we not like GenAI? The answer here is pretty straightforward as well. We use concepts. It doesn’t.

Concepts are a, well, a pretty abstract concept, so what are we really talking about here? Concepts start with nouns. Every noun we use represents a body of forces that in some way is relevant to life in this world. Water makes us wet. It helps us clean things. It relieves thirst. It will drown a mammal but keep a fish alive. We know a lot about water. Same thing with rock, paper, and scissors. Same thing with cars, clothes, and cash. Same thing with love, languor, and loneliness.

All of our knowledge of the world aggregates around nouns and noun-like phrases. To these, we attach verbs and verb-like phrases that show how these forces act out in the world and what changes they create. And we add modifiers to tease out the nuances and differences among similar forces acting in similar ways. Altogether, we are creating ideas—concepts—which we can link up in increasingly complex structures through the fourth and final word type, conjunctions.

Now, from the time you were an infant, your brain has been working out all the permutations you could imagine that arise from combining two or more forces. It might have begun with you discovering what happens when you put your finger in your eye, or when you burp, or when your mother smiles at you. Anyway, over the years you have developed a remarkable inventory of what is usually called common sense, as in be careful not to touch a hot stove, or chew with your mouth closed, or don’t accept rides from strangers.

The point is you have the ability to take any two nouns at random and imagine how they might interact with one another, and from that effort, you can draw practical conclusions about experiences you have never actually undergone. You can imagine exception conditions—you can touch a hot stove if you are wearing an oven mitt, you can chew bubble gum at a baseball game with your mouth open, and you can use Uber.

You may not think this is amazing, but I assure you that every AI scientist does. That’s because none of them have come close (as yet) to duplicating what you do automatically. GenAI doesn’t even try. Indeed, its crowning success is due directly to the fact that it doesn’t even try. By contrast, all the work that has gone into GOFAI (Good Old-Fashioned AI) has been devoted precisely to the task of conceptualizing, typically as a prelude to planning and then acting, and to date, it has come up painfully short.

So, yes GenAI is amazing. But so are you.

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

Image Credit: Pixabay

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Derision Means You’re Doing It Right

Derision Means You're Doing It Right

GUEST POST from Mike Shipulski

When you see good work, say so.

When you see exceptional work, say so in public.

When you’ve had good teachers, be thankful.

When you’ve had exceptional teachers, send them a text because texts are personal.

When you do great work and no one acknowledges it, take some time to feel the pain and get back to work.

When you do great work and no one acknowledges it, take more time to feel the pain and get back to work.

When you’ve done great work, tell your family.

When you’ve done exceptional work, tell them twice.

When you do the work no one is asking for, remember your time horizon is longer than theirs.

When you do the work that threatens the successful business model, despite the anguish it creates, keep going.

When they’re not telling you to stop, try harder.

When they’re telling you to stop it’s because your work threatens. Stomp on the accelerator.

When you can’t do a project because the ROI is insufficient, that’s fine.

When no one can calculate an ROI because no one can imagine a return, that’s better.

When you give a little ground on what worked, you can improve other dimensions of goodness.

When you outlaw what worked, you can create new market segments.

When everyone understands why you’re doing it, your work may lead to something good.

When no one understands why you’re doing it, your work may reinvent the industry.

When you do new work, don’t listen to the critics. Do it despite them.

When you do work that threatens, you will be misunderstood. That’s a sign you’re on to something.

When you want credit for the work, you can’t do amazing work.

When you don’t need credit for the work, it opens up design space where the amazing work lives.

When your work makes waves, that’s nice.

When your work creates a tsunami, that’s better.

When you’re willing to forget what got you here, you can create what could be.

When you’re willing to disrespect what got you here, you can create what couldn’t be.

When your work is ignored, at least you’re doing something different.

When you and your work are derided, you’re doing it right.

Image credit: Pexels

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Ten Customer Experience and Service Tips for 2024

Ten Customer Experience and Service Tips for 2024

GUEST POST from Shep Hyken

I want this year to be your best year ever for creating amazing customer service and experiences. And for everything else, too! But as it applies to the customer experience, I thought it would be fun to share some ideas we need to do more of. With that, here are ten (10) ideas. Many, if not all, will apply to you and your business. Do more in 2024!

  1. Be more responsive – We start with one of my favorites. How fast do you respond to customers? Trust me, the faster you respond, the better. Customers appreciate a quick response. I often joke about a company that took four days to get back to me with an answer. If I wanted the answer in four days, I would have waited four days to ask the question! A speedy response creates confidence.
  2. Be more accountable – Don’t make excuses or blame others. Don’t deflect blame if a customer complains about something, even if it’s not your fault. It may not be your fault, but it’s now your opportunity to solve a problem.
  3. Be more flexible – Don’t be so rigid with rules unless they are legal rules. In most instances, the word guidelines are better than rules. You know where you want to go. Be flexible in your thinking when it comes to taking care of customers.
  4. Be more engaged – Your customers want to feel that you’re focusing on them. Actively listen and respond with questions that show you’re paying attention and want more information. Get customers to feel connected to you because they know you care.
  5. Be more consistent – I’m surprised when employees of the same company have different answers to the same question. Or when a company or brand delivers a great experience, but then the next time, it’s just okay. Consistency creates confidence, and confidence can lead to customer loyalty.

  1. Be more accessible – Make it easy for your customers to reach you in multiple ways: phone, email, text, app, and more. Today’s customers will reach out to you in the most convenient way. Today, they may call you. Tomorrow, they may email you. Regardless of the channel, you need to be there and meet their communication expectations.
  2. Be more convenient – Convenience is about being easy to do business with. It used to be a significant competitive differentiator. Today, it’s table stakes. It’s expected that your customer’s experience will be easy with little or no friction. Find ways to be easier to do business with, and customers will spend more money, won’t be as concerned about price, and most importantly, will come back!
  3. Be more proactive – When there’s a problem that you know about, reach out to your customers before they reach out to you. They might not even know there is a problem at all, and the fact that you were proactive builds confidence and trust.
  4. Be more transparent – Don’t hide important information in “fine print.” Be open about policies and anything you know the customer might question or simply not like. Have you ever been hit with a surprise fee? Of course, you have, and I’ll bet you didn’t like that surprise. You don’t want your customers to say, “I’m disappointed. I wish that you told me about that in the beginning.”
  5. Be more memorable – Let’s close with a powerful one. When I’m hired to do a customer service keynote speech, my walk-on music is Bonnie Raitt’s hit song, Let’s Give Them Something to Talk About. That’s what I want you to do with your customers. Give them something (good) to talk about. Why? Because when you give them a memorable experience, it will make them say, “I’ll be back!”

Image Credits: Shep Hyken, Pexels

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Cover versions, Sequels, Taylor Swift and Innovation

Taylor Swift and Innovation

GUEST POST from Pete Foley

An inherent contradiction in almost any new innovation is that it needs to be both new, but also somewhat familiar.  If it doesn’t offer anything new, there is little motivation for consumers to risk abandoning existing habits or preferences to try it.  But if it is not at least anchored in familiarity, then we ask consumers to put a lot of effort into understanding it, in addition to any opportunity cost from what they give up for trying something new.  Innovation is difficult, and a lot of innovations fail, at least in part because of this fundamental contradiction. 

Transformative Performance:  Of course, innovations can be successful, which means we do navigate this challenge.  But how? One way is to deliver something with such transformative benefits that people are willing to push themselves over the hump of learning something new. Huge benefits also create their own ‘gravity’, often spreading via world of mouth via media, social media, and even old-fashioned human-to-human conversations. This avoids the need for brute force mass marketing spend that can create the illusion of familiarity, but with a hefty price tag that is typically beyond smaller companies

Familiarity: The second option is to leverage what people already know in such a way that the ‘adoption hump’ becomes relatively insignificant, because new users intuitively know what the innovation is and how to use it.

Wow!  The best innovations do both.  CHATgpt Generative AI is a contemporary example, where transformative performance has created an enormous amount of word of mouth, but the interface is so intuitive there is little barrier to adoption, at least superficially. 

Of course, using it skillfully is another thing altogether, but I think there is an insight there too.  It’s OK to have an ongoing learning curve after initial adoption, but initial engagement needs to be relatively simple.  The gaming industry are masters of this.    

Little Wows!  CHATgpt is brilliant innovation.  But realistically, few of us are gong to create something quite that extraordinary.  So how do we manage to create more modest wows that still drive trial, engagement and ultimately repeat business?

Science, Art and Analogy:  As a believer that a lot of interesting things happen at the interface between science and art, and that analogy is a great tool, I think we cam learn a little about solving this by taking insight from the arts.  In this case, music and movies. For example, popular music routinely plunders the familiar, and repackages it as new via cover versions.  I often do the same myself!   Movies do something similar, either with the cycle of remakes of classic movies, or with sequels that often closely follow the narrative structure of the original.  

But this highlights some of the challenges in solving this dichotomy.  It’s rare for a remake, cover version, or sequel to do better than the original.  But a few do, so what is their secret?  What works, and what doesn’t? 

  1. Distance from the original.  Some of the best movie remakes completely reframe the original in ways that maintain a largely implicit familiarity, but do so without inviting direct comparisons of alignable differences to the original. For example, West Side Story is a brilliant retelling of Romeo and Juliet, Bridget Jones Diary reframes Pride and Prejudice, She’s All That is a retelling of George Bernard Shaw’s Pygmalion, while The Lion King retools Hamlet, etc.  I’m not suggesting that nobody sees these connections, but many don’t, and even if they do, the context is sufficiently different to avoid constant comparisons throughout the experience.  And of course, in most of these cases, the originals are not contemporary, so there is temporal as well as conceptual distance between original and remake.   Similarly with cover versions, Hendrix and the Byrds both completely and very successfully reframed Dylan (All Along the Watchtower and Mr. Tambourine Man).  Sinead O’Connor achieved similar success with Prince’s “Nothing Compares 2 U”.  For those of you with less grey in their hairl, last summers cover of Tracy Chapman’s ‘Fast Car’ by Luke Combs shows that covers can still do this. 

2.  Something New.   A different way to fail is to tap familiarity, but without adding anything sufficiently new or interesting.  All too often covers, sequels and remakes are simply weaker copies of the original.  I’m sure that anyone reading this can come up with their own examples of a disappointing remake or sequel.   Footloose, Annie, Psycho, Tom Cruise’s the Mummy or Karate Kid are all candidates for me.  As for sequels, again, I’m sure you can all name a respectable list of your own wasted 2 hours, with Highlander 2 and Jaws the Revenge being my personal cures for insomnia.   And even if we include novelty, it cannot be too predictable either.  It needs to at least be a little surprising.   For example, the gender reversal of the remake of Overboard has a point of difference in comparison to the Goldie Hawn original, but its not exactly staggeringly novel or surprising.  It’s a lot like a joke, if you can see it coming, it’s not going too create a wow.    

3.  Don’t Get De-Selected.  Learning from the two previous approaches can help us to create sufficient separation from past experience to engage and hopefully delight potential consumers.  But it’s important to not get carried away, and become un-tethered from familiarity.  For example, I personally enjoy a lot of jazz, but despite their often extraordinary skill, jazz musicians don’t fill many arenas.  That’s in part because jazz asks the listener to invest a lot of cognitive bandwidth and time to develop an ‘ear’, or musical expertise in order to appreciate it. It often moves a long way from the familiar original, and adds lot of new into the equation.  As a result, it is a somewhat niche musical form.  Pop music generally doesn’t require the same skill or engagement, and successful artists like Taylor Swift understand that.   And when it comes to innovation, most of us want to be mainstream, not niche. This is compounded because consumers today face a bewildering array of options, and a huge amount of information.  One way our brains have evolved to deal with complexity is to quickly ignore or ‘de-select’ things that don’t appear relevant to our goals. A lot of the time, we do this unconsciously.  Faced with more information than we can process, we quickly narrow our choices down to a consideration set that is ‘right-sized’ for us to make a decision.   From an innovation perspective, if our innovations are too ‘jazzy’, they risk being de-selected by a majority on consumers before they can be fully appreciated, or even consciously noticed.     

There’s no precise right or wrong strategy in this context. It’s possible to deliver successful innovations by tapping and balancing these approaches in many different ways.   But there are certainly good and bad executions, and I personally find it helpful to use these kinds of analogy when evaluating an innovation.   Are we too jazzy? Do we have separation from incumbents that is meaningful for consumers, and not just ourselves? And the latter is a real challenge for experts. When we are deeply engaged in a category, it’s all too easy to get lost in the magic of our own creations.  We see differences more clearly than consumers. It’s easy for us to become overly excited by relatively small changes that excite us, but that lack sufficient newness and separation from existing products for consumers who are nowhere near as engaged in our category as we are.  But it’s also easy to create ‘jazz’ for similar reasons, by forgetting that real world consumers are typically far less interested in our products than we are, and so miss the brilliance of our ‘performance’, or perhaps don’t ‘get it’ at all. 

For me, it is useful to simply ask myself whether I’m a Godfather II or a Highlander II, a Taylor Swift or a Dupree Bolton, or even Larry Coryell.  And there’s the rub.  As a musician, I’d rather be Larry, but as a record company exec, I’d far rather have Taylor Swift on my label. 

Image credits: Wikimedia Commons

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

Top 10 Human-Centered Change & Innovation Articles of March 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 March’s ten most popular innovation posts:

  1. Agile Innovation Management — by Diana Porumboiu
  2. How to Re-engineer the Incubation Zone — by Geoffrey A. Moore
  3. It’s Not Clear What Innovation Success Is — by Robyn Bolton
  4. How Do You Know If Your Idea is Novel? — by Mike Shipulski
  5. How to Tell if You Are Trusted — by Mike Shipulski
  6. Innovation is Rubbish! — by John Bessant
  7. Celebrating the Trailblazing Women Pioneers of Innovation — by Art Inteligencia
  8. Thinking Differently About Leadership and Innovation — by Janet Sernack
  9. The Remarkable Power of Negative Feedback — by Dennis Stauffer
  10. 10 CX and Customer Service Predictions for 2024 (Part 1) — by Shep Hyken

BONUS – Here are five more strong articles published in February 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!

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Good Intentions Pave the Way to Innovation Hell

The road to hell is paved with good intentions, and nowhere is that more true than in innovation.

Good Intentions Pave the Way to Innovation Hell

GUEST POST from Robyn Bolton

That’s one of the insights I took away from InnoLead’s Q1 report on corporate innovation priorities.  The report is an eye-opening look at the impact of AI on corporate innovation as experienced by corporate entrepreneurs themselves.  But before deep diving into that topic, the report’s authors shared intriguing data about member companies’ innovation structure, leadership engagement, organizational connections, and results. Nestled amongst the charts were several that, when taken together, got my Spidey senses tingling.

61.0% of innovation teams are “directly under a high-visibility leader with a broad company focus.”

This is great because innovation needs senior leaders’ support and active engagement to survive, let alone survive for long enough to produce meaningful results. Add this to the fact that 45% of senior leadership teams frequently discuss the “progress and value of the innovation program,” and all signs point to innovation as a strategic priority.

But (you knew there was a but, didn’t you)…

If “broad company focus” means “no P&L responsibility,” we have a problem.  In every for-profit company I’ve worked for and with, people with P&L responsibility have greater power, influence, and access to resources than people without a P&L.  This division may not feel fair, but it makes sense – the people who bring in profit and revenue will always be more influential than people who represent “cost centers.”

You can see the impact of P&L owners who are, understandably, focused entirely on delivering short-term results throughout the report – 75% of companies have shifted their focus more towards near-term priorities, and 61% shifted their innovation portfolio away from Horizon 3 (also known as radical, breakthrough, or disruptive innovation).

As for all those discussions, it’d be great if they focused on walking the talk of innovation. But suppose it’s only innovation platitudes or, worse, questioning innovation’s ROI. That doesn’t bode well for the “high-visibility leader with broad company focus,” the innovation team, or the company’s culture.

71.2% of innovation teams’ customers or business partners are unaware of the team’s existence, don’t engage, or engage only occasionally.

Welcome to Innovation Island!  Where the cool people work on cool things in cool offices while all you drones slave away doing the same thing you’ve always done and making the money that pays for the cool people to do cool things in their cool offices.

I’m sure this isn’t the message the innovation team intends to send, but it’s the one received by most organizations.

When arguing for Innovation Island, managers often point to the organizational antibodies likely to swarm and kill H3/radical/breakthrough innovation and even some H2/adjacent innovations.  They’re right, and those innovations must be “protected.” But not every innovation needs protection.  H2 and certainly H1 innovations, where most portfolios are now, should be shared with the core business because the core business will eventually run them.

The bigger problem, in my opinion, is that innovation teams don’t seem to be reaching out to others in the organization.  Like the P&L owners they report to, people in the core business are busy running the business and generating revenue.  Very few have the time or energy to seek out the innovation team to discuss and explore innovation.  Companies that want to build a culture of innovation need to turn their innovators into evangelists, not residents of an island connected to the mainland by a single drawbridge.

23.4% of innovation teams are considered outsiders or actively undermined by other functions and business units.

This may not sound bad, but add to it the 55.0% that are “somewhat integrated with occasional collaboration” with other departments and business units, and you may be tempted to believe that Innovation Island would be wise to invest in a surface-to-air missile defense system.

Sadly, this perception of the innovation team as “The Others” isn’t surprising when considering that the most important tactic for building a relationship between innovation and the functions or business units is already having strong relationships and interpersonal trust (75.3% of respondents).  The least effective (4.7% of respondents) is “writing down shared objectives and expectations.”  So, no, the email you sent is not enough to win friends and influence people.

Bottom line

Well-intended companies appoint a senior executive to lead the innovation team because they’ve been told that doing so is powerful proof that innovation is a strategic priority.  They hire outsiders to inject new thinking into the organization because they know that “what got you here won’t get you there.”  They cordon the team and their work off from the rest of the organization because they read that separation is essential to preserving innovation’s disruptive nature. 

But if the senior executive doesn’t have the organizational power and influence that comes with P&L ownership, the team doesn’t have strong personal relationships with others in the business, and other functions and business units don’t know the team exists or how to interact with it, innovation will go nowhere.

But that’s better than where it could go.

Image credit: Unsplash

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Transformation is Human Not Digital

Transformation is Human Not Digital

GUEST POST from Greg Satell

A decade ago, many still questioned the relevance of digital technology. While Internet penetration was already significant, e-commerce made up less than 6% of retail sales. Mobile and cloud computing were just getting started and artificial intelligence was still more science fiction than reality.

Yet today, all of those things are not only viable technologies, but increasingly key to effectively competing in the marketplace. Unfortunately, implementing these new technologies can be a thorny process. In fact, research by McKinsey found that fewer than one third of digital transformation efforts succeed.

For the most part, these failures have less to do with technology and more to do with managing the cultural and organizational challenges that a technological shift creates. It’s relatively easy to find a vendor that can implement a system for you, but much harder to prepare your organization to adapt to new technology. Here’s what you need to keep in mind:

Start With Business Objectives

Probably the most common trap that organizations fall into is focusing on technology rather than on specific business objectives. All too often, firms seek to “move to the cloud” or “develop AI capabilities.” That’s a sure sign you’re headed down the wrong path.

“The first question you have to ask is what business outcome you are trying to drive,” Roman Stanek, CEO at GoodData, told me. “Projects start by trying to implement a particular technical approach and not surprisingly, front-line managers and employees don’t find it useful. There’s no real adoption and no ROI.”

So start by asking yourself business related questions, such as “How could we better serve our customers through faster, more flexible technology?” or “How could artificial intelligence transform our business?” Once you understand your business goals, you can work your way back to the technology decisions.

Automate The Most Tedious Tasks First

Technological change often inspires fear. One of the most basic mistakes many firms make is to try to use new technology to try and replace humans and save costs rather than to augment and empower them to improve performance and deliver added value. This not only kills employee morale and slows adoption, it usually delivers worse results.

A much better approach is to use technology to improve the effectiveness of human employees. For example, one study cited by a White House report during the Obama Administration found that while machines had a 7.5 percent error rate in reading radiology images and humans had a 3.5% error rate, when humans combined their work with machines the error rate dropped to 0.5%.

The best way to do this is to start with the most boring and tedious tasks first. Those are what humans are worst at. Machines don’t get bored or tired. Humans, on the other hand, thrive on interaction and like to solve problems. So instead of looking to replace workers, look instead to make them more productive.

Perhaps most importantly, this approach can actually improve morale. Factory workers actively collaborate with robots they program themselves to do low-level tasks. In some cases, soldiers build such strong ties with robots that do dangerous jobs that they hold funerals for them when they “die.”

Shift Your Organization And Your Business Model

Another common mistake is to think that you can make a major technological shift and keep the rest of your business intact. For example, shifting to the cloud can save on infrastructure costs, but the benefits won’t last long if you don’t figure out how to redeploy those resources in some productive way.

For example, when I talked to Barry Libenson, Global CIO of the data giant, Experian, about his company’s shift to the cloud, he told me that “The organizational changes were pretty enormous. We had to physically reconfigure how people were organized. We also needed different skill sets in different places so that required more changes and so on.”

The shift to the cloud made Experian more agile, but more importantly it opened up new business opportunities. Its shift to the cloud allowed the company to create Ascend, a “data on demand” platform that allows its customers to make credit decisions based on near real time data, which is now its fastest growing business.

“All of the shifts we made were focused on opening up new markets and serving our customers better,” Libenson says, and that’s what helped make the technological shift so successful. Because it was focused on business results, it was that much easier to get everybody behind it, gain momentum and create a true transformation.

Humans Collaborating With Machines

Consider how different work was 20 years ago, when Windows 95 was still relatively new and only a minority of executives regularly used programs like Word, Excel and PowerPoint. We largely communicated by phone and memos typed up by secretaries. Data analysis was something you did with a pencil, paper and a desk calculator.

Clearly, the nature of work has changed. We spend far less time quietly working away at our desks and far more interacting with others. Much of the value has shifted from cognitive skills to social skills as collaboration increasingly becomes a competitive advantage. In the future, we can only expect these trends to strengthen and accelerate.

To understand what we can expect, look at what’s happened in the banking industry. When automatic teller machines first appeared in the early 1970s, most people thought it would lead to less branches and tellers, but actually just the opposite happened. Today, there are more than twice the number of bank tellers employed as in the 1970s, because they do things that machines can’t do, like solve unusual problems, show empathy and up-sell.

That’s why we need to treat any technological transformation as a human transformation. The high value work of the future will involve humans collaborating with other humans to design work for machines. Get the human part right and the technology will take care of itself.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Dall-E via Bing

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