Category Archives: Technology

Value Doesn’t Disappear

Leave a reply

It Shifts From One Place to Another

Value Doesn't Disappear

GUEST POST from Greg Satell

A few years ago, I published an article about no-code software platforms, which was very well received. Before long, however, I began to get angry — and sometimes downright nasty — comments from software engineers who were horrified by the notion that you can produce software without actually understanding the code behind it.

Of course, no-code platforms don’t obviate the need for software engineers, but rather automate basic tasks so that amateurs can design applications by themselves. These platforms are, necessarily, limited but can increase productivity dramatically and help line managers customize technology to fit the task at hand.

Similarly, when FORTRAN, the first real computer language, was invented, many who wrote machine code objected, much like the software engineers did to my article. Yet Fortran didn’t destroy computer programming, but democratized and expanded it. The truth is that value never disappears. It just shifts to another place and that’s what we need to learn to focus on.

Why Robots Aren’t Taking Our Jobs

Ever since the financial crisis we’ve been hearing about robots taking our jobs. Yet just the opposite seems to be happening. In fact, we increasingly find ourselves in a labor shortage. Most tellingly, the shortage is especially acute in manufacturing, where automation is most pervasive. So what’s going on?

The fact is that automation doesn’t actually replace jobs, it replaces tasks. To understand how this works, think about the last time you walked into a highly automated Apple store, which actually employs more people than a typical retail location of the same size. They aren’t there to ring up your purchase any faster, but to do all the things that a machine can’t do, like answer your questions and solve your problems.

A few years ago I came across an even more stark example when I asked Vijay Mehta, Chief Innovation Officer for Consumer Information Services at Experian about the effect that shifting to the cloud had on his firm’s business. The first order effect was simple, they needed a lot less technicians to manage its infrastructure and those people could easily be laid off.

Yet they weren’t. Instead Experian shifted a lot of that talent and expertise to focus on creating new services for its customers. One of these, a cloud enabled “data on demand” platform called Ascend has since become one of the $4 billion company’s most profitable products.

Now think of what would have happened if Experian had merely seen cloud technology as an opportunity to cut costs. Sure, it would have fattened its profit margins temporarily, but as its competitors moved to the cloud that advantage would have soon been eroded and, without new products its business would soon decline.

The Outsourcing Dilemma

Another source of disruption in the job market has been outsourcing. While no one seemed to notice when large multinational corporations were outsourcing blue-collar jobs to low cost countries, now so-called “gig economy” sites like Upwork and Fiverr are doing the same thing for white collar professionals like graphic designers and web developers.

So you would expect to see a high degree of unemployment for those job categories, right? Actually no. The Bureau of Labor Statistics expects demand for graphic designers to increase 4% by 2026 and web developers to increase 15%. The site Mashable recently named web development as one of 8 skills you need to get hired in today’s economy.

It’s not hard to see why. While it is true that a skilled professional in a low-cost country can do small projects of the same caliber as those in high cost countries, those tasks do not constitute a whole job. For large, important projects, professionals must collaborate closely to solve complex problems. It’s hard to do that through text messages on a website.

So while it’s true that many tasks are being outsourced, the number of jobs has actually increased. Just like with automation, outsourcing doesn’t make value disappear, but shifts it somewhere else.

The Social Impact

None of this is to say that the effects of technology and globalization hasn’t been real. While it’s fine to speak analytically about value shifting here and there, if a task that you spent years to learn to do well becomes devalued, you take it hard. Economists have also found evidence that disruptions in the job market have contributed to political polarization.

The most obvious thing to do is retrain workers that have been displaced, but it turns out that’s not so simple. In Janesville, a book which chronicles a small town’s struggle to recover from the closing of a GM plant, author Amy Goldstein found that the workers that sought retraining actually did worse than those that didn’t.

When someone loses their job, they don’t need training. They need another job and removing yourself from the job market to take training courses can have serious costs. Work relationships begin to decay and there is no guarantee that the new skills you learn will be in any more demand than the old ones you already had.

In fact, Peter Capelli at the Wharton School argues that the entire notion of a skills gap in America is largely a myth. One reason that there is such a mismatch between the rhetoric about skills and the data is that the most effective training often comes on the job from an employer. It is augmenting skills, not replacing them that creates value.

At the same time, increased complexity in the economy is making collaboration more important, so often the most important skills workers need to learn are soft skills, like writing, listening and being a better team player.

You Can’t Compete With A Robot By Acting Like One

The future is always hard to predict. While it was easy to see that Amazon posed a real problem for large chain bookstores like Barnes & Noble and Borders, it was much less obvious that small independent bookstores would thrive. In much the same way, few saw that ten years after the launch of the Kindle that paper books would surge amid a decline in e-books.

The one overriding trend over the past 50 years or so is that the future is always more human. In Dan Schawbel’s recent book, Back to Human, the author finds that the antidote for our overly automated age is deeper personal relationships. Things like trust, empathy and caring can’t be automated or outsourced.

There are some things a machine will never do. It will never strike out in a little league game, have its heart broken or see its child born. That makes it hard — impossible really — for a machine ever to work effectively with humans as a real person would. The work of humans is increasingly to work with other humans to design work for machines.

That why perhaps the biggest shift in value is from cognitive to social skills. The high paying jobs today have less to do with the ability to retain facts or manipulate numbers (we now use a computer for those things), but require more deep collaboration, teamwork and emotional intelligence.

So while even the most technically inept line manager can now easily produce an application that it would have once required a highly skilled software engineer, to design the next generation of technology, we need engineers and line managers to work more closely together.

— 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.

Don’t Blame Technology When Innovation Goes Wrong

Leave a reply

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

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.

Humans Are Not as Different from AI as We Think

1 Reply

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

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.

Technical, Market and Emotional Risks

2 Replies

Technical, Market and Emotional Risks

GUEST POST from Mike Shipulski

Technical risk – Will it work?
Market risk – Will they buy it?
Emotional risk – Will people laugh at your crazy idea?

Technical risk – Test it in the lab.
Market risk – Test it with the customer.
Emotional risk – Try it with a friend.

Technical risk – Define the right test.
Market risk – Define the right customer.
Emotional risk – Define the right friend.

Technical risk – Define the minimum acceptable performance criteria.
Market risk – Define the minimum acceptable response from the customer.
Emotional risk – Define the minimum acceptable criticism from your friend.

Technical risk – Can you manufacture it?
Market risk – Can you sell it?
Emotional risk – Can you act on your crazy idea?

Technical risk – How sure are you that you can manufacture it?
Market risk – How sure are you that you can sell it?
Emotional risk – How sure are you that you can act on your crazy idea?

Technical risk – When the VP says it can’t be manufactured, what do you do?
Market risk – When the VP says it can’t be sold, what do you do?
Emotional risk – When the VP says your idea is too crazy, what do you do?

Technical risk – When you knew the technical risk was too high, what did you do?
Market risk – When you knew the market risk was too high, what did you do?
Emotional risk – When you knew someone’s emotional risk was going to be too high, what did you do?

Technical risk – Can you teach others to reduce technical risk? How about increasing it?
Market risk – Can you teach others to reduce market risk? How about increasing it?
Emotional risk – Can you teach others to reduce emotional risk? How about increasing it?

Technical risk – What does it look like when technical risk is too low? And the consequences?
Market risk – What does it look like when market risk is too low? And the consequences?
Emotional risk – What does it look like when emotional risk is too low? And the consequences?

We are most aware of technical risk and spend most of our time trying to reduce it. We have the mindset and toolset to reduce it. We know how to do it. But we were not taught to recognize when technical risk is too low. And if we do recognize it’s too low, we don’t know how to articulate the negative consequences. With all this said, market risk is far more dangerous.

We’re unfamiliar with the toolset and mindset to reduce market risk. Where we can change the design, run the test, and reduce technical risk, market risk is not like that. It’s difficult to understand what drives the customers’ buying decision and it’s difficult to directly (and quickly) change their buying decision. In short, it’s difficult to know what to change so they make a different buying decision. And if they don’t buy, you don’t sell. And that’s a big problem. With that said, emotional risk is far more debilitating.

When a culture creates high emotional risk, people keep their best ideas to themselves. They don’t want to be laughed at or ridiculed, so their best ideas don’t see the light of day. The result is a collection of wonderful ideas known only to the underground Trust Network. A culture that creates high emotional risk has insufficient technical and market risk because everyone is afraid of the consequences of doing something new and different. The result – the company with high emotional risk follows the same old script and does what it did last time. And this works well, right up until it doesn’t.

Here’s a three-pronged approach that may help.

  1. Continue to reduce technical risk.
  2. Learn to reduce market risk early in a project.
  3. And behave in a way that reduces emotional risk so you’ll have the opportunity to reduce technical and market risk.

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 Materials Science is the Most Important Technology of This Decade

Leave a reply

Why Materials Science is the Most Important Technology of This Decade

GUEST POST from Greg Satell

Think of just about any major challenge we will face over the next decade and materials are at the center of it. To build a new clean energy future, we need more efficient solar panels, wind turbines and batteries. Manufacturers need new materials to create more advanced products. We also need to replace materials subject to supply disruptions, like rare earth elements.

Traditionally, developing new materials has been a slow, painstaking process. To find the properties they’re looking for, researchers would often have to test hundreds — or even thousands — of materials one by one. That made materials research prohibitively expensive for most industries.

Yet today, we’re in the midst of a materials revolution. Scientists are using powerful simulation techniques, as well as machine learning algorithms, to propel innovation forward at blazing speed and even point them toward possibilities they had never considered. Over the next decade, the rapid advancement in materials science will have a massive impact.

The Seeds Of The Materials Revolution

In 2005, Gerd Ceder was a Professor of Materials Science at MIT working on computational methods to predict new materials. Traditionally, materials scientists worked mostly through trial and error, working to identify materials that had properties which would be commercially valuable. Gerd was working to automate that process using sophisticated computer models that simulate the physics of materials.

Things took a turn when an executive at Duracell, then a division of Procter & Gamble, asked if Ceder could use the methods he was developing to explore possibilities on a large scale to discover and design new materials for alkaline batteries. So he put together a team of a half dozen “young guns” and formed a company to execute the vision.

The first project went well and the team was able to patent a number of new materials that hadn’t existed before. Then another company came calling, which led to another project and more after that. Yet despite the initial success, Ceder began to realize that there was a problem. Although the team’s projects were successful, the overall impact was limited.

“We began to realize we’re generating all this valuable data and it’s being locked away in corporate vaults. We wanted to do something in a more public way,” Ceder told me. As luck would have it, it was just then that one of the team members was leaving MIT for family reasons and that chance event would propel the project to new heights.

The Birth Of The Materials Project

In 2008, Kristin Persson’s husband took a job in California, so she left Ceder’s group at MIT and joined Lawrence Berkeley National Laboratory (LBL) as a research scientist. Yet rather than mourn the loss of a key colleague, the team saw the move as an opportunity to shift their work into high gear.

“At MIT, we pretty much hacked everything together,” Ceder explains. “It all worked, but it was a bit buggy and would have never scaled beyond our small team. At a National Lab, however, they had the resources to build it out properly and create a platform that could really drive things forward.” So Persson hit the ground running, got a small grant and stitched together a team to combine the materials work with the high performance supercomputing done at the lab.

“At LBL there were world class computing people,” Persson told me. “So we began an active collaboration with people that were on the cutting edge of computer science, but didn’t know anything about materials and our little band of ‘materials hackers’. It was that interdisciplinary collaboration that was really the secret sauce and helped us gain ground quickly.”

Traditional, materials science could take a class of alloys for use in, say, the auto industry and calculate things like weight vs. tensile strength. There might be a few hundred of those materials in the literature. But with the system they built at LBL, they could calculate thousands. That meant engineers could identify candidate materials exponentially faster, test them in the real world and create better products.

Yet again, they felt that the impact of their work was limited. After all, not many engineers from private industry spend time at National Laboratories. “Our earlier work convinced us that we were on the cusp of something much bigger,” Persson remembers. That’s what led them to create The Materials Project, a massive online database that anyone in the world can access.

A Massive Materials Initiative

The Materials Project went online early in 2011 and drew a few thousand people. From there it grew like a virus and today has more than 50,000 users, a number that grows by about 50-100 per day. Yet its impact has become even greater than that. The success of the project caught the attention of Tom Kalil, then Deputy Director at the White House Office of Science and Technology Policy, who saw the potential to create a much wider initiative.

In the summer of 2011, the Obama administration announced the Materials Genome Initiative (MGI) to coordinate work across agencies such as the Department of Energy, NASA, the Department of Energy and others to expand and complement the work being done at LBL. These efforts, taken together, are creating a revolution in materials science and the impacts are just beginning to be felt by private industry.

The MGI is based on three basic pillars. The first is computational approaches that can accurately predict materials properties, like the ones Gerd Ceder’s team pioneered. The second is high throughput experimentation to expand materials libraries and the third are programs that mine existing materials in the scientific literature and promote the sharing of materials data.

For example, one project applied machine learning algorithms to experimental materials data to identify forms of a super strong alloy called metallic glass. While scientists have long recognized its value as an alternative to steel and as a protective coating, it is so rare that relatively few forms of it were known. Using the new methods, however, researchers were able to perform the work 200 times faster and identify 20,000 in a single year!

Creating A True Materials Revolution

Thomas Edison famously remarked that if he tried 10,000 experiments that failed, he didn’t actually consider it a failure, but found 10,000 things that didn’t work. That’s true, but it’s also incredibly tedious, time consuming and expensive. The new methods, however, have the potential to automate those 10,000 failures, which is creating a revolution in materials science.

For example, at the Joint Center for Energy Storage Research (JCESR), a US government initiative to create the next generation of advanced batteries, the major challenge now is not so much to identify potential battery chemistries, but that the materials to make those chemistries work don’t exist yet. Historically, that would have been an insurmountable problem, but not anymore.

“Using high performance computing simulations, materials genomes and other techniques that have been developed over the last decade or so, we can often eliminate as much as 99% of the possibilities that won’t work,” George Crabtree, Director at JCESR told me. “That means we can focus our efforts on the remaining 1% that may have serious potential, and we can advance much farther, much faster for far less money.”

The work is also quickly making an impact on Industry. Greg Mulholland, President of Citrine Informatics, a firm that applies machine learning to materials development, told me, “We’ve seen a huge broadening of companies and industries that are contacting us and a new sense of urgency. For companies that historically invested in materials research, they want everything yesterday. For others that haven’t, they are racing to get up to speed.”

Jim Warren, a Director at the Materials Genome Initiative, thinks that is just the start. “When you can discover new materials for hundreds of thousands or millions dollars rather than tens or hundreds of millions you are going to see a vast expansion of use cases and industries that benefit,” he told me.

As we have learned from the digital revolution, any time you get a 10x improvement in efficiency, you end up with a transformative commercial impact. Just about everybody I’ve talked to working in materials thinks that pace of advancement is easily achievable over the next decade. Welcome to the materials revolution.

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

Can You Become the Earth’s Most Customer-Centric Company?

1 Reply

Can You Become the Earth's Most Customer-Centric Company?

GUEST POST from Shep Hyken

If I asked 10 people who they thought could be planet Earth’s most customer-centric company, I bet a majority would have the same answer. I’ll share that company’s name at the end of this article. For now, you can guess.

Cindy, from my office, had a customer service issue. Here are the steps she took to resolve the problem:

  1. She went to the company’s website and clicked on customer support.
  2. She answered a few questions, and once the technology identified her problem, a chatbot popped up.
  3. After interacting with the chatbot briefly, the bot wrote, “Let me transfer you to an agent,” moving from a chatbot to live chat.
  4. At some point, the agent suggested getting on the phone, and rather than have Cindy call, she asked for Cindy’s number. Once Cindy shared it, the phone rang almost instantly.
  5. From there, the agent carried out a conversation that eventually resolved Cindy’s problem.

I asked Cindy how she liked that experience, and she quickly answered, “Amazing!”

Just a few minutes later, Cindy received a short survey asking for her feedback with the message:

Your feedback is helping us build Earth’s Most Customer-Centric Company.

With that in mind, let’s look at some lessons we can learn from the company that aspires to be the most customer-centric company on the planet:

  1. Digital First – The company made it easy to start the customer support process with a digital self-service solution. While there was a live agent option, it wasn’t presented until later. Cindy had to answer a few questions and click a few boxes before moving on. And this part is important. The process was easy and intuitive. She was digitally “hand-held” through the process, which included the chatbot.
  2. The Human Backup – The chatbot was programmed to understand when it wasn’t getting Cindy’s answer, and it immediately transferred her to a live chat with a customer support agent. Eventually, the live online chat turned into a phone call when the agent wanted more details and knew it would be easier to talk than text. Rather than Cindy calling the company, she simply had to enter her phone number into the chat, and within seconds, the phone rang, and she was talking to the customer support agent.
  3. A Seamless Omni-Channel Experience – The definition of an omni-channel experience is a continuous conversation moving from one form of communication to the next. Cindy went from answering questions on the website to a chatbot, to live chat, and then to the phone. All was seamless, and the “conversation” continued rather than forcing Cindy to tell her story repeatedly. The agent on the phone picked up where the chat ended and quickly solved her problem. This is the way omni-channel is supposed to work.

This is a perfect example of the modern customer support experience. And did you guess what company this article is about? If you said Amazon, you’re absolutely right!

Image Credits: Shep Hyken, 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.

Powering the Google Innovation Machine with the World’s Top Minds

Leave a reply

Powering the Google Innovation Machine with the World's Top Minds

GUEST POST from Greg Satell

It’s no secret that Google is one of the most innovative companies on the planet. Besides pioneering and then dominating the search industry, it has also become a leader in developing futuristic technologies such as artificial intelligence, driverless cars and quantum computing. It has even launched a life science company.

What makes Google so successful is not one particular process, but how it integrates multiple strategies into a seamless whole. For example, Google Brain started out as a 20% time project, then migrated out to its “X” Division to accelerate development and finally came back to the mothership, where it now collaborates closely with engineering teams to build new products.

Yet perhaps its most important strategy, in fact the one that makes much of the rest possible, is how it partners with top scientists in the academic world. This is no “quick hit,” but a well thought out, long-term game plan designed to establish deep relationships based on cutting edge science and embed that knowledge deeply into just about everything Google does.

Building Deep Relationships to the Academic Community

“We design a variety programs that widen and deepen our relationships with academic scientists,” Maggie Johnson, who heads up University Relations at Google, told me. In fact, there are three distinct ways that Google engages directly with scientists beyond the typical research partnerships with universities.

The first is its Faculty Research Awards program, which are small one-year grants, usually to graduate students or postdocs whose work may be of interest to Google. These are unrestricted gifts, although recipients are highly encouraged to publish their work publicly, that allow the company to develop relationships with young talent at the beginning of their careers.

While anybody can apply for a Faculty Research Award, Focused Research Awards are only available by invitation. Typically, these are awarded to more senior researchers that Google has already had some contact with and last two to three years. However, they are also unrestricted grants that researchers can use as they see fit.

The third way that Google engages with scientists to to proactively engage leaders in a particular field of interest. Geoffrey Hinton, for example, is a pioneer in neural networks and widely considered one of the top AI experts in the world. He splits his time between his faculty position at the University of Toronto and working on Google Brain.

“Spinning In” World Class Scientists

The academic research programs provide many benefits to Google as a company. They give access to the most promising students for recruiting, allow it to help shape university curriculums and keep it connected to breakthrough research in important fields. However, the most direct benefits probably come inviting researchers to spend a sabbatical year at Google, which it calls its Visiting Faculty Program.

For example, Andrew Ng, a top AI researcher, decided to spend a year working at Google and quickly formed a close working relationship with two of the company’s brightest minds, Greg Corrado and Jeff Dean, who were interested in what was then a new brand of artificial intelligence called deep learning. Their collaboration became the Google Brain project.

The Visiting Faculty Program touches on everything Google does. Recently they’ve had people visiting the company like John Canny at UC Berkeley, who helped with the development of TPU’s, chips specialized to run Google’s AI algorithms and Michael Rabin, a Turing Award winning mathematician who was working on auction algorithms. For every Google priority, at least one of the world’s top minds is working with the company on it.

What makes the sabbatical program unusual is how deeply it is integrated into everyday work at the company. “In most cases, these scientists have already been working with our teams through one of our other programs, so the groundwork for a productive relationship has already been laid,” Maggie Johnson told me.

Developing “Win-Win” Relationships

One of the things that makes Google’s outreach to researchers work so well is that it is truly a win-win arrangement. Yes, the company gets top experts in important fields to work on its problems, but the researchers themselves get to work with unparalleled tools and data sets. They also get a much better sense of what problems are considered important in a commercial environment.

Katya Scheinberg, a Professor at Lehigh University who focuses on optimization problems, found working at Google to be a logical extension of her earlier collaboration with the company. “I had been working on large-scale machine learning problems and had some connections with Google scientists. So spending part of my sabbatical year at the company seemed fairly natural. I learned a lot about the practical problems that private sector researchers are working on,” she told me.

Since leaving Google, she’s found that her time at the company has shifted the focus of her research. “Working at Google got me interested in some different problems and alerted me to the possibility of applying some approaches I had worked on before to different fields of application.”

Sometimes scholars stay for longer and can have a transformative impact on the company. As noted above, Andrew Ng spent several years at the company. Andrew Moore, a renowned computer scientist and a former Dean of Carnegie Mellon’s computer program, took a leave of absence from his university to set up Google’s Research Center in Pittsburgh. Lasting relationships like these are rare in industry, but incredibly valuable.

Connecting to Discovery Is Something Anyone Can Do, But You Have to Make the Effort

Clearly, Google is an unusual company. There’s not many places that can attract the type of talent that it can. However, just about any business can, for example, support the work of a young graduate student or postdoc at a local university. In much the same way, inviting even a senior researcher to come for a short time is not prohibitively expensive.

Innovation is never a single event, but a process of discovery, engineering and transformation. It is by connecting to discovery that businesses can truly see into the future and develop the next generation of breakthrough products. Unfortunately, few businesses realize the importance of connecting with the academic world.

Make no mistake, if you don’t discover, you won’t invent and if you don’t invent you will be disrupted eventually. It’s just a matter of time. However, you can’t just show up one day and decide you want to work with the world’s greatest minds. Even Google, with all its resources and acumen, has had to work really hard at it.

It’s made these investments in time, focus and resources because it understands that the search business, as great as it is, won’t deliver outsized profits forever. Today, we no longer have the luxury to manage for stability, but must prepare for disruption.

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

Implementing Successful Transformation Initiatives for 2024

1 Reply

Implementing Successful Transformation Initiatives for 2024

GUEST POST from Janet Sernack

Transformation and change initiatives are usually designed as strategic interventions, intending to advance an organization’s growth, deliver increased shareholder value, build competitive advantage, or improve speed and agility to respond to fast-changing industries.  These initiatives typically focus on improving efficiency, and productivity, resolving IT legacy and technological issues, encouraging innovation, or developing high-performance organizational cultures. Yet, according to research conducted over fifteen years by McKinsey & Co., shared in a recent article “Losing from day one: Why even successful transformations fall short” – Organizations have realized only 67 percent of the maximum financial benefits that their transformations could have achieved. By contrast, respondents at all other companies say they captured an average of only 37 percent of the potential benefit, and it’s all due to a lack of human skills, and their inability to adapt, innovate, and thrive in a decade of disruption.

Differences between success and failure

The survey results confirm that “there are no short­cuts to successful transformation and change initiatives. The main differentiator between success and failure was not whether an organization followed a specific subset of actions but rather how many actions it took throughout an organizational transformation’s life cycle” and actions taken by the people involved.

Capacity, confidence, and competence – human skills

What stands out is that thirty-five percent of the value lost occurs in the implementation phase, which involves the unproductive actions taken by the people involved.

The Boston Consulting Group (BCG) supports this in a recent article “How to Create a Transformation That Lasts” – “Transformations are inherently difficult, filled with compressed deadlines and limited resources. Executing them typically requires big changes in processes, product offerings, governance, structure, the operating model itself, and human behavior.”

Reinforcing the need for organizations to invest in developing the deep human skills that embed transformation disciplines into business-as-usual structures, processes, and systems, and help shift the culture. Which depends on enhancing people’s capacity, confidence, and competence to implement the “annual business-planning processes and review cycles, from executive-level weekly briefings and monthly or quarterly reviews to individual performance dialogue” that delivers and embeds the desired changes, especially the cultural enablers.

Complex and difficult to navigate – key challenges

As a result of the impact of our VUCA/BANI world, coupled with the global pandemic, current global instability, and geopolitics, many people have had their focus stolen, and are still experiencing dissonance cognitively, emotionally, and viscerally.

This impacts their ability to take intelligent actions and the range of symptoms includes emotional overwhelm, cognitive overload, and change fatigue.

It seems that many people lack the capacity, confidence, and competence, to underpin their balance, well-being, and resilience, which resources their ability and GRIT to engage fully in transformation and change initiatives.

The new normal – restoring our humanity

At ImagineNation™ for the past four years, in our coaching and mentoring practice, we have spent more than 1000 hours partnering with leaders and managers around the world to support them in recovering and re-emerging from a range of uncomfortable, disabling, and disempowering feelings.

Some of these unresourceful states include loneliness, disconnection, a lack of belonging, and varying degrees of burnout, and have caused them to withdraw and, in some cases, even resist returning to the office, or to work generally.

It appears that this is the new normal we all have to deal with, knowing there is no playbook, to take us there because it involves restoring the essence of our humanity and deepening our human skills.

Taking a whole-person approach – develop human skills

By embracing a whole-person approach, in all transformation and change initiatives, that focuses on building people’s capacity, confidence, and competence, and that cultivates their well-being and resilience to:

  • Engage, empower, and enable them to collaborate in setting the targets, business plans, implementation, and follow-up necessary to ensure a successful transformation and change initiative.
  • Safely partner with them through their discomfort, anxiety, fear, and reactive responses.
  • Learn resourceful emotional states, traits, mindsets, behaviors, and human skills to embody, enact and execute the desired changes strategically and systemically.

By then slowing down, to pause, retreat and reflect, and choose to operate systemically and holistically, and cultivate the “deliberate calm” required to operate at the three different human levels outlined in the illustration below:

The Neurological Level – which most transformation and change initiatives fail to comprehend, connect to, and work with. Because people lack the focus, intention, and skills to help people collapse any unconscious RIGIDITY existing in their emotional, cognitive, and visceral states, which means they may be frozen, distracted, withdrawn, or aggressive as a result of their fears and anxiety.

You can build your capacity, confidence, and competence to operate at this level by accepting “what is”:

  • Paying attention and being present with whatever people are experiencing neurologically by attending, allowing, accepting, naming, and acknowledging whatever is going on for them, and by supporting and enabling them to rest, revitalize and recover in their unique way.
  • Operating from an open mind and an open heart and by being empathic and compassionate, in line with their fragility and vulnerability, being kind, appreciative, and considerate of their individual needs.
  • Being intentional in enabling them to become grounded, mindful conscious, and truly connected to what is really going on for them, and rebuild their positivity, optimism, and hope for the future.
  • Creating a collective holding space or container that gives them permission, safety, and trust to pull them towards the benefits and rewards of not knowing, unlearning, and being open to relearning new mental models.
  • Evoking new and multiple perspectives that will help them navigate uncertainty and complexity.

The Emotional Cognition Levels – which most transformation and change initiatives fail to take into account because people need to develop their PLASTICITY and flexibility in regulating and focusing their thoughts, feelings, and actions to adapt and be agile in a world of unknowns, and deliver the outcomes and results they want to have.

You can build your capacity, confidence, and competence to operate at this level by supporting them to open their hearts and minds:

  • Igniting their curiosity, imagination, and playfulness, introducing novel ideas, and allowing play and improvisation into their thinking processes, to allow time out to mind wander and wonder into new and unexplored territories.
  • Exposing, disrupting, and re-framing negative beliefs, ruminations, overthinking and catastrophizing patterns, imposter syndromes, fears of failure, and feelings of hopelessness and helplessness.
  • Evoking mindset shifts, embracing positivity and an optimistic focus on what might be a future possibility and opportunity.
  • Being empathic, compassionate, and appreciative, and engaging in self-care activities and well-being practices.

The Generative Level – which most transformation and change initiatives ignore, because they fail to develop the critical and creative thinking, and problem sensing and solving skills that are required to GENERATE the crucial elastic thinking and human skills that result in change, and innovation.

You can build your capacity, confidence, and competence to operate at this level by:

  • Creating a safe space to help people reason and make sense of the things occurring within, around, and outside of them.
  • Cultivating their emotional and cognitive agility, creative, critical, and associative thinking skills to challenge the status quo and think differently.
  • Developing behavioral flexibility to collaborate, being inclusive to maximize differences and diversity, and safe experimentation to close their knowing-doing gaps.
  • Taking small bets, giving people permission and safety to fail fast to learn quickly, be courageous, be both strategic and systemic in taking smart risks and intelligent actions.

Reigniting our humanity – unlocking human potential  

At the end of the day, we all know that we can’t solve the problem with the same thinking that created it. Yet, so many of us keep on trying to do that, by unconsciously defaulting into a business-as-usual linear thinking process when involved in setting up and implementing a transformation or change initiative.

Ai can only take us so far, because the defining trait of our species, is our human creativity, which is at the heart of all creative problem-solving endeavors, where innovation can be the engine of change, transformation, and growth, no matter what the context. According to Fei-Fei Li, Sequoia Professor of Computer Science at Stanford, and co-director of AI4All, a non-profit organization promoting diversity and inclusion in the field of AI.

“There’s nothing artificial about AI. It’s inspired by people, created by people, and most importantly it has an impact on people”.

  • Develop the human skills

When we have the capacity, confidence, and competence to reignite our humanity, we will unlock human potential, and stop producing results no one wants. By developing human skills that enable people to adapt, be resilient, agile, creative, and innovate, they will grow through disruption in ways that add value to the quality of people’s lives, that are appreciated and cherished, we can truly serve people, deliver profits and perhaps save the planet.

Find out more about our work at ImagineNation™

Find out about our collective, learning products and tools, including The Coach for Innovators, Leaders, and Teams Certified Program, presented by Janet Sernack, is a collaborative, intimate, and deeply personalized innovation coaching and learning program, supported by a global group of peers over 9-weeks, and can be customized as a bespoke corporate learning and coaching program for leadership and team development and change and culture transformation initiatives.

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.

Innovation the Star of the 2024 NBA All-Star Game

1 Reply

Innovation the Star of the 2024 NBA All-Star Game

by Braden Kelley

Eight years ago, back in 2016, I wrote an article titled What April Fool’s Day Teaches Us About Innovation about an April Fool’s prank played my alma mater, the University of Oregon involving an announcement that football games at Autzen Stadium would no longer played on artificial turf, but would be played on a giant digital screen instead. Here is the video:

It seemed preposterous at the time (2016) during the era of the technologically ancient Apple iPhone 7 and Samsung Galaxy S7 when the average LCD TV size according to Statista was only 43 inches.

Fast forward to February 16, 2024 and NBA All-Star Weekend in Indianapolis, Indiana and we saw the first ever basketball game of note held on a glass basketball court. But isn’t glass slippery when wet? Yes, but so is a heavily lacquered hardwood court – believe me I know from repeated spills during pickup basketball games. To help give it the traction of a hardwood court they’ve engineered thousands (or maybe millions) of tiny raised dots onto the glass surface.

Sports are always experimenting with various technologies, some of which don’t work out (like the tail following the puck on hockey broadcasts), and others which are executed so well that they enhance the viewing experience (first down yardage line in American football) or that most people don’t even know that they exist (advertisements projected onto the court in basketball television broadcasts that aren’t actually on the court but look as if they are).

So, how has this 2016 April Fool’s prank visualization evolved into a 2024 reality? What does it look like? Here is a video that will give you a sense of its capabilities:

First let me say this a pretty incredible technology that has definitely added to the excitement of this year’s NBA All-Star Weekend, but second I must also say that I would NEVER want to watch a regular NBA, college or international game played on a court like this because for me, sporting events are a time to unplug from technology, not be over-stimulated by it. But, for a special event like NBA All-Star Game Weekend or maybe the Harlem Globetrotters I think it makes sense.

How does this court make the leap from invention to innovation you might ask?

How does this court not find itself in the digital trash can next to the tail on the hockey puck?

The short answer is that scores well on my Innovation is All About Value framework. It creates value by adding value to the contest (skills challenge, celebrity all-star game), translates that value very quickly because it’s all visual, and the barriers to value access are non-existent for all but the visually impaired.

The court allowed the NBA to hold different games with different rules and lines on the same court without changing courts or making physical modifications. For example, the celebrity all-star game had a four point line (sponsored by Frito Lay) and at times the three point and four point lines even were actively moving. There was also a micro competition in game where three people ran to stars that appeared on the floor and shot and when they made a shot there a new star appeared and you could see over time which side of the court was winning because you could see which side had more stars. There was another moment where for a limited time the coaches faces appeared on the court and six points were awarded for each shot made from that spot. The dynamic nature of the game meant that you almost didn’t know what might come next – which was kind of exciting.

The integration of the court into the competition occurring upon it is what helps this technology make the leap from invention to innovation. But again, for me, only in special use cases like an All-Star game, an entertainment-based event or skills competition, but NOT for a pure competition use case where in my mind it distracts from the sport.

Here is a video of the skills challenge relay race – notice that the floor shows the player which way to go, but despite that Tyrese Maxey still goes the wrong way and has to double back. 😉

Then in the skills challenge passing team event the floor showed players where to stand and how many points had been scored at each of three targets. Again, it felt like part of the event and it allows the court to be instantly and uniquely re-configured.

And here are video highlights of the celebrity all-star game where you can see some of what I mentioned above:

So, what do you think? Innovation or not?

Image credit: NBA.com

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






Will Innovation Management Leverage AI in the Future?

1 Reply

Will Innovation Management Leverage AI in the Future?

GUEST POST from Jesse Nieminen

What role can AI play in innovation management, and how can we unlock its true potential?

Unless you’ve been living under a rock, you’ve probably heard a thing or two about AI in the last year. The launch of ChatGPT has supercharged the hype around AI, and now we’re seeing dramatic progress at a pace unlike anything that’s come before.

For those of us into innovation, it’s an exciting time.

Much has been said about the topic at large so I won’t go over the details here. At HYPE, what we’re most excited about is what AI can do for innovation management specifically. We’ve had AI capabilities for years, and have been looking into the topic at large for quite some time.

Here, I share HYPE’s current thinking and answer some key questions:

  • What can AI do for innovation management?
  • What are some common use cases?
  • How can you operationalize AI’s use in innovation management?

The Current State of Innovation Management

Before we answer those questions, let’s review how most organizations carry out innovation management.

We’re all familiar with the innovation funnel.

Hype Innovation Image 1

To oversimplify, you gather ideas, review them, and then select the best ones to move forward to the pilot stage and eventual implementation. After each phase, poor ideas get weeded out.

It’s systematic, it’s conceptually simple, and investment is tiered so that you don’t spend too much time or money before an idea has shown its potential. What’s not to love?

Well, there are a few key challenges: the process is slow, linear, and is usually biased due to the evaluation criteria selected for the gates or decision points (if you use a Phase-Gate model).

Each of these challenges can be mitigated with smart adaptations of the process, but the funnel has another fundamental limitation: It’s generally built for a world where innovation requires significant capital expenditures and vast amounts of proprietary information.

But, regardless of your industry, that just isn’t the case anymore. Now most information is freely available, and technology has come a long way, in many cases because of AI. For example, pharmaceutical companies use AI to accelerate drug discovery while infrastructure and manufacturing companies use advanced simulation techniques, digital twins (virtual replicas of physical objects or systems), and rapid prototyping.

It’s now possible to innovate, test, and validate ideas faster than ever with minimal investment. With the right guidance, these tasks don’t have to be limited to innovation experts like you anymore. That can be an intimidating thought, but it’s also an empowering one. Soon, thanks to AI, you’ll be able to scale your expertise and make an impact significantly bigger than before.

For more than 20 years, we’ve been helping our customers succeed in this era of systematic innovation management. Today, countless organizations manage trends at scale, collect insights and ideas from a wide and diverse audience, and then manage that funnel highly effectively.

Yet, despite, or maybe because of this, more and more seemingly well-run organizations are struggling to keep up and adapt to the future.

What gives?

Some say that innovation is decelerating. Research reveals that as technology gets more complex, coming up with the next big scientific breakthrough is likely to require more and more investment, which makes intuitive sense. This type of research is actually about invention, not innovation per se.

Innovation is using those inventions to drive measurable value. The economic impact of these inventions has always come and gone in waves, as highlighted in ARK Investment’s research, illustrated below.

Throughout history, significant inventions have created platforms that enable dramatic progress through their practical application or, in other words, through innovation. ARK firmly believes that we’re on the precipice of another such wave and one that is likely to be bigger than any that has come before. AI is probably the most important of these platforms, but it’s not the only one.

Mckinsey Hype Innovation Image 2

Whether that will be the case remains to be seen, but regardless, the economic impact of innovation typically derives from the creative combination of existing “building blocks,” be they technologies, processes, or experiences.

Famously, the more such building blocks, or types of innovation, you combine to solve a specific pain point or challenge holistically, the more successful you’re likely to be. Thanks to more and more information and technology becoming free or highly affordable worldwide, change has accelerated rapidly in most industries.

That’s why, despite the evident deceleration of scientific progress in many industries, companies have to fight harder to stay relevant and change dramatically more quickly, as evidenced by the average tenure of S&P500 companies dropping like a stone.

Hype Innovation 3

In most industries, sustainable competitive advantages are a thing of the past. Now, it’s all about strategically planning for, as well as adapting to, change. This is what’s known as transient advantage, and it’s already a reality for most organizations.

How Innovation Management Needs to Change

In this landscape, the traditional innovation funnel isn’t cutting it anymore. Organizations can’t just focus on research and then turn that into new products and expect to do well.

To be clear, that doesn’t mean that the funnel no longer works, just that managing it well is no longer enough. It’s now table stakes. With that approach, innovating better than the next company is getting harder and more expensive.

When we look at our most successful customers and the most successful companies in the world in general, they have several things in common:

  • They have significantly faster cycle times than the competition at every step of the innovation process, i.e., they simply move faster.
  • For them, innovation is not a team, department, or process. It’s an activity the entire organization undertakes.
  • As such, they innovate everything, not just their products but also processes, experiences, business models, and more.

When you put these together, the pace of innovation leaves the competition in the dust.

How can you then maximize the pace of innovation at your organization? In a nutshell, it comes down to having:

  • A well-structured and streamlined set of processes for different kinds of innovation;
  • Appropriate tools, techniques, capabilities, and structures to support each of these processes;
  • A strategy and culture that values innovation;
  • A network of partners to accelerate learning and progress.

With these components in place, you’ll empower most people in the organization to deliver innovation, not just come up with ideas, and that makes all the difference in the world.

Hype Innovation 4

What Role Does AI Play in Innovation Management?

In the last couple of years, we’ve seen massive advancements not just in the quality of AI models and tools, but especially in the affordability and ease of their application. What used to be feasible for just a handful of the biggest and wealthiest companies out there is now quickly commoditizing. Generative AI, which has attracted most of the buzz, is merely the tip of the iceberg.

In just a few years, AI is likely to play a transformative role in the products and services most organizations provide.

For innovation managers too, AI will have dramatic and widely applicable benefits by speeding up and improving the way you work and innovate.

Let’s dive a bit deeper.

AI as an Accelerator

At HYPE, because we believe that using AI as a tool is something every organization that wants to innovate needs to do, we’ve been focusing on applying it to innovation management for some time. For example, we’ve identified and built a plethora of use cases where AI can be helpful, and it’s not just about generative AI. Other types of models and approaches still have their place as well.

There are too many use cases to cover here in detail, but we generally view AI’s use as falling into three buckets:

  • Augmenting: AI can augment human creativity, uncover new perspectives, kickstart work, help alleviate some of the inevitable biases, and make top-notch coaching available for everyone.
  • Assisting: AI-powered tools can assist innovators in research and ideation, summarize large amounts of information quickly, provide feedback, and help find, analyze, and make the most of vast quantities of structured or unstructured information.
  • Automating: AI can automate both routine and challenging work, to improve the speed and efficiency at which you can operate and save time so that you can focus on the value-added tasks at the heart of innovation.

In a nutshell, with the right AI tools, you can move faster, make smarter decisions, and operate more efficiently across virtually every part of the innovation management process.

While effective on their own, it’s only by putting the “three As” together and operationalizing them across the organization that you can unlock the full power of AI and take your innovation work to the next level.

In a nutshell, with the right AI tools, you can move faster, make smarter decisions, and operate more efficiently across virtually every part of the innovation management process.

While effective on their own, it’s only by putting the “three As” together and operationalizing them across the organization that you can unlock the full power of AI and take your innovation work to the next level.

Putting AI Into Practice

So, what’s the key to success with AI?

At HYPE, we think the key is understanding that AI is not just one “big thing.” It’s a versatile and powerful enabling technology that has become considerably cheaper and will likely continue on the same trajectory.

There are significant opportunities for using AI to deliver more value for customers, but organizations need the right data and talent to maximize the opportunities and to enable AI to support how their business operates, not least in the field of innovation management. It’s essential to find the right ways to apply AI to specific business needs; just asking everybody to use ChatGPT won’t cut it.

The anecdotal evidence we’re hearing highlights that learning to use a plethora of different AI tools and operationalizing these across an organization can often become challenging, time-consuming, and expensive.

To overcome these issues, there’s a real benefit in finding ways to operationalize AI as a part of the tools and processes you already use. And that’s where we believe The HYPE Suite with its built-in AI capabilities can make a big difference for our customers.

Final Thoughts

At the start of this article, we asked “Is AI the future of innovation management?”

In short, we think the answer is yes. But the question misses the real point.

Almost everyone is already using AI in at least some way, and over time, it will be everywhere. As an enabling technology, it’s a bit like computers or the Internet: Sure, you can innovate without them, but if everyone else uses them and you don’t, you’ll be slower and end up with a worse outcome.

The real question is how well you use and operationalize AI to support your innovation ambitions, whatever they may be. Using AI in combination with the right tools and processes, you can innovate better and faster than the competition.

At HYPE, we have many AI features in our development roadmap that will complement the software solutions we already have in place. Please reach out to us if you’d like to get an early sneak peek into what’s coming up!

Originally published at https://www.hypeinnovation.com.

Image credits: Pixabay, Hype, McKinsey

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