Tag Archives: Google

How to Pursue a Grand Innovation Challenge

GUEST POST from Greg Satell

All too often, innovation is confused with agility. We’re told to “adapt or die” and encouraged to “move fast and break things.” But the most important innovations take time. Einstein spent ten years on special relativity and then another ten on general relativity. To solve tough, fundamental problems, we have to be able to commit for the long haul.

As John F. Kennedy put it in his moonshot speech, “We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills.” Every organization should pursue grand challenges for the same reason.

Make no mistake. Innovation needs exploration. If you don’t explore, you won’t discover. If you don’t discover you won’t invent and if you don’t invent you will be disrupted. It’s just a matter of time. Unfortunately, exploration can’t be optimized or iterated. That’s why grand challenges don’t favor the quick and agile, but the patient and the determined.

1. Don’t Bet The Company

Most grand challenges aren’t like the original moonshot, which was, in large part, the result of the space race with the Soviets that began with the Sputnik launch in 1957. That was a no-holds-barred effort that consumed the efforts of the nation, because it was widely seen as a fundamental national security issue that represented a clear and present danger.

For most organizations, those type of “bet-the-company” efforts are to be avoided. You don’t want to bet your company if you can avoid it, for the simple reason that if you lose you are unlikely to survive. Most successful grand challenges don’t involve a material investment. They are designed to be sustainable.

“Grand challenges are not about the amount of money you throw at the problem, Bernard Meyerson, IBM’s Chief Innovation Officer, told me. “To run a successful grand challenge program, failure should not be a material risk to the company, but success will have a monumental impact. That’s what makes grand challenges an asymmetric opportunity.”

Take, for example Google’s X division. While the company doesn’t release its budget, it appeared to cost the company about $3.5 billion in 2018, which is a small fraction of its $23 billion in annual profits at the time. At the same time, just one project, Waymo, may be worth $70 billion (2018). In a similar vein, the $3.8 billion invested in the Human Genome Project generated nearly $800 billion of economic activity as of 2011.

So the first rule of grand challenges is not to bet the company. They are, in fact, what you do to avoid having to bet the company later on.

2. Identify A Fundamental Problem

Every innovation starts out with a specific problem to be solved. The iPod, for example, was Steve Jobs’s way of solving the problem of having “a thousand songs in my pocket.” More generally, technology companies strive to deliver better performance and user experience, drug companies aim to cure disease and retail companies look for better ways to drive transactions. Typically, firms evaluate investment based on metrics rooted in past assumptions

Grand challenges are different because they are focused on solving fundamental problems that will change assumptions about what’s possible. For example, IBM’s Jeopardy Grand Challenge had no clear business application, but transformed artificial intelligence from an obscure field to a major business. Later, Google’s AlphaGo made a similar accomplishment with self-learning. Both have led to business opportunities that were not clear at the time.

Grand challenges are not just for technology companies either. MD Anderson Cancer Center has set up a series of Moonshots, each of which is designed to have far reaching effects. 100Kin10, an education nonprofit, has identified a set of grand challenges it has tasked its network with solving.

Talia Milgrom-Elcott, Executive Director of 100Kin10, told me she uses the 5 Whys as a technique to identify grand challenges. Start with a common problem, keep asking why it keeps occurring and you will eventually get to the root problem. By focusing your efforts on solving that, you can make a fundamental impact of wide-ranging consequence.

3. Commit To A Long Term Effort

Grand challenges aren’t like normal problems. They don’t conform to timelines and can’t effectively be quantified. You can’t justify a grand challenge on the basis of return on investment, because fundamental problems are too pervasive and ingrained to surrender themselves to any conventional form of analysis.

Consider The Cancer Genome Atlas, which eventually sequenced and published over 10,000 tumor genomes When Jean Claude Zenklusen first came up with the idea in 2005, it was highly controversial, because although it wasn’t particularly expensive, it would still take resources away from more conventional research.

Today, however, the project is considered to be a runaway success, which has transformed the field, greatly expanding knowledge and substantially lowering costs to perform genetic research. It has also influenced efforts in other fields, such as the Materials Genome Initiative. None of this would have been possible without commitment to a long-term effort.

And that’s what makes grand challenges so different. They are not business as usual and not immediately relevant to present concerns. They are explorations that expand conventional boundaries, so cannot be understood within them.

An Insurance Policy Against A Future You Can’t Yet See

Typically, we analyze a business by extrapolating current trends and making adjustments for things that we think will be different. So, for example, if we expect the market to pick up, we may invest in more capacity to profit from greater demand. On the other hand, if we expect a softer market, we’d probably start trimming costs to preserve margins.

The problem with this type of analysis is that the future tends to surprise us. Technology changes, customer preferences shift and competitors make unexpected moves. Nobody, no matter how diligent or smart, gets every call right. That’s why every business model fails sooner or later, it’s just a matter of time.

It’s also what makes pursuing grand challenges is so important. They are basically an insurance policy against a future we can’t yet see. By investing sustainably in solving fundamental problems, we can create new businesses to replace the ones that will inevitably falter. Google doesn’t invest in self-driving cars to improve its search business, it invests because it knows that the profits from search won’t last forever.

The problem is that there is a fundamental tradeoff between innovation and optimization, so few organizations have the discipline to invest in exploration today for a uncertain payoff tomorrow. That’s why so few businesses last.

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

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Is Disruption About to Claim a New Victim?

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Kodak. Blockbuster. Google?

GUEST POST from Robyn Bolton

You know the stories. Kodak developed a digital camera in the 1970s, but its images weren’t as good as film images, so it ended the project. Decades later, that decision ended Kodak. Blockbuster was given the chance to buy Netflix but declined due to its paltry library of titles (and the absence of late fees). A few years later, that decision led to Blockbuster’s decline and demise. Now, in the age of AI, disruption may be about to claim another victim – Google.

A very brief history of Google’s AI efforts

In 2017, Google Research invented Transformer, a neural network architecture that could be trained to read sentences and paragraphs, pay attention to how the words relate to each other, and predict the words that would come next.

In 2020, Google developed LaMDA, or Language Model for Dialogue Applications, using Transformer-based models trained on dialogue and able to chat.

Three years later, Google began developing its own conversational AI using its LaMDA system. The only wrinkle is that OpenAI launched ChatGPT in November 2022.

Now to The Financial Times for the current state of things

“In early 2023, months after the launch of OpenAI’s groundbreaking ChatGPT, Google was gearing up to launch its competitor to the model that underpinned the chatbot.

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The search company had been testing generative AI software internally for several months by then. But as the company rallied its resources, multiple competing models emerged from different divisions within Google, vying for internal attention.”

That last sentence is worrying. Competition in the early days of innovation can be great because it pushes people to think differently, ask tough questions, and take risks. But, eventually, one solution should emerge as superior to the others so you can focus your scarce resources on refining, launching, and scaling it. Multiple models “vying for internal attention” so close to launch indicate that something isn’t right and about to go very wrong.

“None was considered good enough to launch as the singular competitor to OpenAI’s model, known as ChatGPT-4. The company was forced to postpone its plans while it tried to sort through the scramble of research projects. Meanwhile, it pushed out a chatbot, Bard, that was widely viewed to be far less sophisticated than ChatGPT.”

Nothing signals the threat of disruption more than “good enough.” If Google, like most incumbent companies, defined “good enough” as “better than the best thing out there,” then it’s no surprise that they wouldn’t want to launch anything.

What’s weird is that instead of launching one of the “not good enough” models, they launched Bard, an obviously inferior product. Either the other models were terrible (or non-functional), or different people were making different decisions to achieve different definitions of success. Neither is a good sign.

“When Google’s finished product, Gemini, was finally ready nearly a year later, it came with flaws in image generation that CEO Sundar Pichai called ‘completely unacceptable’ – a let-down for what was meant to be a demonstration of Google’s lead in a key new technology.”

“A let-down” is an understatement. You don’t have to be first. You don’t have to be the best. But you also shouldn’t embarrass yourself. And you definitely shouldn’t launch things that are “completely unacceptable.”

What happens next?

Disruption takes a long time and doesn’t always mean death. Blackberry still exists, and integrated steel mills, one of Clayton Christensen’s original examples of disruption, still operate.

AI, LLMs, and LaMDAs are still in their infancy, so it’s too early to declare a winner. Market creation and consumer behavior change take time, and Google certainly has the knowledge and resources to stage a comeback.

Except that that knowledge may be their undoing. Companies aren’t disrupted because their executives are idiots. They’re disrupted because their executives focus on extending existing technologies and business models to better serve their best customers with higher-profit offerings. In fact, Professor Christensen often warned that one of the first signs of disruption was a year of record profits.

In 2021, Google posted a profit of $76.033 billion. An 88.81% increase from the previous year.

2022 and 2023 profits have both been lower.

Image credit: Unsplash

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Happy Employees Make Happy Customers

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Happy Employees Make Happy Customers

GUEST POST from Shep Hyken

Often, the best companies to do business with are the best companies to work for. When you look at the Google ratings for Round Room Holdings’ TCC and Wireless Zone, two Verizon Wireless retailers with approximately 1,200 retail stores throughout the U.S., you’ll find they are “hitting it out of the park” in both customer reviews and employee satisfaction. I had a chance to interview Chad Jensen, president of TCC and Wireless Zone since 2019, and he shed light on their incredible success, how they do it, and how any company can have similar results.

We can break down the company’s success into three areas: employees, customers, and community.

1. Employees: It all starts with the employees. Jensen’s company has a 90% employee satisfaction rating and 70% employee retention in a retail industry with annual employee turnover rates that are well over 100%. Why? Because Jensen made it abundantly clear that the company puts employees first. The best example of this came not even a year after he took over as president when he and the rest of the world faced the pandemic. His leadership style was immediately put to the test. He was adamant about taking care of the employees. First and foremost was safety, as well as a concern for mental health. And he was determined to keep people employed, saying, “Even if it meant we took a hit on our financials, we were okay with that.” He understood early on that the decisions they made would define how they came out of the pandemic. Employees knew the company had their backs. In exchange, they were confident, fulfilled, and engaged with their customers, ensuring they had an experience that would bring them back. Employee satisfaction is at 90%. As I’ve mentioned many times in my past articles, what’s happening inside an organization is felt by customers on the outside. Jensen’s strategy shows this concept can be tremendously successful.

2. Customers: A focus on the employee experience turns into a positive customer experience. The goal is to provide “the best customer service.” Being the best is a lofty goal. While it’s not a contest, the comment speaks to the commitment the retailer has to its customers. The numbers tell the story. The company’s Google score ranges from 4.7 to 4.9 out of five. Jensen beams with pride over the customer satisfaction numbers, as companies he admires, such as Disney and Chick-fil-A, don’t have numbers quite as high. Jensen said, “We checked, and Disneyland’s Google rating was a 4.5. We’re literally (making customers) happier than the ‘Happiest Place on Earth.’” While a high Google rating is validating, Jensen emphasizes it’s really about the experience that gets customers to come back.

3. Community: Jensen’s efforts to give back to the community create positive results on several levels. He explained, “The more we give back to our communities, the more presence we get, and the better employees we get.” Many companies have a purpose beyond profit. It’s typically a recognizable cause, such as sustainability, poverty, medical research, or other popular causes. Companies like Ace Hardware have raised more than $140 million for the Children’s Miracle Network Hospitals. Patagonia gives 1% of its sales to the preservation and restoration of the environment. TCC and Wireless Zone take a more grassroots approach and give back to the communities their stores serve. They sponsor community events, local pet shelters, food banks, school events, and more. They have given more than 1.3 million backpacks filled with school supplies to kids in their communities. While the corporate HQ is behind this “give back” program, it’s the employees who get the most joy out of being a part of it, once again creating a great employee experience.

By prioritizing the TCC and Wireless Zone employee experience, combined with efforts to create an amazing customer experience as well as support for the communities they serve, the result is a company with some of the lowest turnover in the retail industry, higher Google ratings than “The Happiest Place on Earth” and loyal customers who keep coming back. That’s what happens when you create a company that has what Jensen refers to as “a culture of good.”

Image Credits: Pixabay
This article originally appeared on Forbes.com

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

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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

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Big Companies Should Not Try to Act Like Startups

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Big Companies Should Not Try to Act Like Startups

GUEST POST from Greg Satell

In 2009, Jeffrey Immelt set out on a journey to transform his company, General Electric, into a 124 year old startup. Although it was one of the largest private organizations in the world, with 300,000 employees, he sought to become agile and nimble enough to compete with high-flying Silicon Valley firms.

It didn’t end well. In 2017, problems in the firm’s power division led to massive layoffs. Immelt was forced to step down as CEO and GE was kicked off the Dow after 110 years. The company, which was once famous for its sound management, saw its stock tank. Much like most startups, the effort had failed.

Somewhere along the line we got it into our heads that large firms can’t innovate and should strive to act like startups. The truth is that they are very different types of organizations and need to innovate differently. While large firms can’t move as fast as startups, they have other advantages. Rather than try to act like startups, they need to leverage what they have.

Driving Innovation At Scale

The aviation industry is dominated by big companies. With a typical airliner costing tens of millions of dollars, there’s not much room for rapid prototyping. It takes years to develop a new product and the industry, perhaps not surprisingly, moves slowly. Planes today look pretty much the same as ones made decades ago.

Looks, however, can be deceiving. To understand how the aviation industry innovates, consider the case of Boeing’s 787 Dreamliner. Although it may look like any other airplane, Boeing redesigned the materials within it. So a 787 is 20 percent lighter and 20 percent more efficient than similar models. That’s a significant achievement.

Developing advanced materials is not for the faint of heart. You can’t do it in a garage. You need deep scientific expertise, state-of-the-art facilities and the resources to work for years—and sometimes decades— to discover something useful. Only large enterprises can do that,

None of this means that startups don’t have a role to play. In fact one small company, Citrine Informatics, is applying artificial intelligence to materials discovery and revolutionizing the field. Still, to take on big projects that have the potential to make huge global impacts, you usually need a large enterprise.

Powering Startups

All too often, we see large enterprises and startups as opposite sides of the coin, with big companies representing the old guard and entrepreneurs representing the new wave, but that’s largely a myth. The truth is that innovation often works best when large firms and small firms are able to collaborate.

Scott Lenet, President of Touchdown Ventures, sees this first-hand every day. His company is somewhat unique in that, unlike most venture capital firms, it manages internal funds for large corporations. He’s found that large corporations are often seen as value added investors because of everything they bring to the table.

“For example,” he told me, “one of our corporate partners is Kellogg’s and they have enormous resources in technical expertise, distribution relationships and marketing acumen. The company has been in business for over 100 years and it’s learned quite a bit about the food business in that time. So that’s an enormous asset for a startup to draw on.”

He also points out that, while large firms tend to know how to do things well, they can’t match the entrepreneurial energy of someone striving to build their own business. “Startups thrive on new ideas,” Lenet says “and big firms know how to scale and improve those ideas. We’ve seen some of our investments really blossom based on that kind of partnership.”

Creating New Markets

Another role that large firms play is creating and scaling new markets. While small firms are often more agile, large companies have the clout and resources to scale and drive impact. That often also creates opportunities for entrepreneurs as well.

Consider the case of personal computers. By 1980, startups like Apple and Commodore had already been marketing personal computers for years, but it was mostly a cottage industry. When IBM launched the PC in 1981, however, the market exploded. Businesses could now buy a computer from a supplier that they knew and trusted.

It also created fantastic opportunities for companies like Microsoft, Intel and a whole range of entrepreneurs who flocked to create software and auxiliary devices for PCs. Later startups like Compaq and Dell created PC clones that were compatible with IBM products. The world was never the same after that.

Today, large enterprises like IBM, Google and Amazon dominate the market for artificial intelligence, but once again they are also creating fantastic opportunities for entrepreneurs. By accessing the tools that the tech giants have created through APIs, small firms can create amazing applications for their customers.

Innovation Needs Exploration

Clearly, large firms have significant advantages when it comes to innovation. They have resources, customer relationships and deep expertise to not only invent new things, but to scale businesses and bring products to market. Still, many fail to innovate effectively, which is why the average lifespan of companies on the S&P 500 continues to decline.

There’s no reason why that has to be true. The problem is that most large organizations spend so much time and effort fine-tuning their operations to meet earnings targets that they fail to look beyond their present business model. That’s not due to any inherent lack of capability, it’s due to a lack of imagination.

Make no mistake, if you don’t explore, you won’t discover. If you don’t discover you won’t invent and if you don’t invent you will be disrupted. So while you need to focus on the business at hand, you also need to leave some resources un-optimized so that you can identify and develop the next great opportunity.

A good rule of thumb to follow is 70-20-10. Focus 70% of your resources on developing your present business, 20% of your resources on opportunities adjacent to your current business, such as new markets and technologies and 10% on developing things that are completely new. That’s how you innovate for the long term.

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

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We Need To Stop Glorifying Failure

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Here’s What To Do Instead

We Need To Stop Glorifying Failure

GUEST POST from Greg Satell

Over 50% of startups fail (and that number goes up to 75% for venture backed startups). The same is true of about three quarters of corporate transformations, which is probably why the average lifespan on the S&P 500 continues to shrink. These statistics tell a humbling story: few significant endeavors ever actually succeed.

So it’s probably not surprising that we’ve come to glorify failure. We are urged to “fail fast” and are cheered on when we do. Failure, after all, is hard evidence that you’ve tried something difficult and paid the price. Yet failure, as anyone who actually experienced it knows well, is a horrible, painful thing.

As I explain in Cascades, great transformations are achieved not by glorifying failure, but when we learn from mistakes and begin to do things differently. That’s how great enterprises are transformed, industries are disrupted and then remade a new and seemingly all powerful tyrants are overthrown. Failure is something we should never accept, but rather overcome.

Ask The Hard Questions

Go to just about any innovation conference and you will find some pundit on the stage telling the story of some corporate giant, usually Blockbuster, Kodak or Xerox, that stumbled and failed. It is then explained that these firms were run by silly, foolish people who simply didn’t want to see the signs of disruption around them.

These stories are almost never true and, in fact, should be seen as ridiculous on their face. It takes no small amount of intelligence, drive and ambition to run a significant enterprise so to suggest that executives managing highly successful businesses were utter dopes beggars belief. The truth is that smart, hard working people fail all the time.

Once you realize that it forces you to ask some hard questions. Why did these smart, successful people fail? Why weren’t the dangers lurking more obvious? What hidden forces were working against them? Why did they think that they actions they undertook, after no small amount of deliberation, were the best of the available options?

Consider the case of Mahatma Gandhi and his Himalayan miscalculation. In 1919, he organized a series of demonstrations to protest against unjust laws passed by the British Raj. These were successful at first, but soon got out of hand and eventually led to the massacre at Amritsar, in which British soldiers left hundreds dead and more than a thousand wounded.

Most people would have simply concluded that the British were far too cruel and brutal to be dealt with peacefully. Gandhi, however, looked for the error in his own actions and learned from his mistakes. A decade later, rather than embark on a wholesale revolt, he identified a keystone change that would break the logjam. Today, both the salt march that resulted, and Gandhi himself, have become icons.

Test Your Hypotheses (Cheaply)

If you want to get a project going in a typical organization, the first thing you do is try to procure a big budget. So you write up an impressive business plan, examine the political tea leaves and work your contacts. If you’re successful, you can build out a great staff, line up tier-one partners and really hit the ground running.

You also can’t make any mistakes. Unless your plan was truly bulletproof from conception (and it never is) or you just get really lucky, you’re going to make some big, well-funded, well-staffed blunder that you’ll have to scramble to recover from. Unless you catch it early or have the political clout within your organization to get more money, you are likely to fail.

Now consider how Nick Swinmurn started his business. As Eric Ries explained in The Lean Startup, instead of spending money on some expensive marketing study to see if people would buy shoes online, he simply built a cheap site. When he got an order, he would go to the store, buy the pair at retail, and ship it out. He lost money on every sale.

That’s a terrible way to run a business, but a great way to test a business hypothesis. Once he knew that people were willing to buy shoes online, he started Zappos, which quickly grew to dominate the market for selling shoes online. It was sold to Amazon in 2009, ten years after Swinmurn started, for $940 million.

Build A Network

We tend to think that success is the result of hard work and talent. Yet look at any category and one brand tends to dominate. There are many search engines, but only one Google, just like there are many smartphone manufacturers, but only one Apple. Both are great products, but they end up taking the vast majority of profits in their industry. Are they really that much better than their competitors?

The truth is, as Albert-László Barabási explains in The Formula, is that performance is bounded, but success isn’t. You can be better than your competitors, but not that much better. On the other hand, there are no limits to success because networks tend to be dominated by a central node.

To understand why, consider the case of Albert Einstein. Until April 3rd, 1921, he was a prominent scientist, but by no means an icon. In fact, much of his press coverage was negative. But on that date, he arrived in America with the Zionist leader Chaim Weizmann. Reporters covering the event mistook the enormous crowds there to meet Weizmann as fans of Einstein and the story made the first page of all major newspapers.

That, along with his brilliance and endearing personality, is what catapulted Einstein to iconic status. In a similar vein, Google launched its product on the techie-dense Stanford computer network and Apple introduced the iPhone to its already expansive fan base. It’s networks, not nodes, that drive success.

Stop Disrupting And Start Solving Problems

Walk down any grocery store aisle and it becomes clear that there is no shortage of ideas. At any given time there are countless opportunities for line extensions, expansions into new categories, partnerships and other things. Executives spend countless hours discussing the merits and demerits of ideas like these.

Yet innovation isn’t about ideas, it’s about solving problems. That’s why most ideas fail, because they don’t address a meaningful problem that people really need solved. Nobody really needs a different flavor of cereal, but Zappos, Google and Apple all met needs that people cared about and that made all the difference.

That’s why companies that last not only look to solve problems for today’s customers, but also take on grand challenges. These are not “bet the company” type of propositions, but long, sustained efforts that seek to fundamentally change the realm of the possible, like Google’s more than decade long quest to create a self-driving car or IBM’s generational pursuit of quantum computing.

The truth is that you never really have to fail because, if you make your efforts sustainable, you can always learn from mistakes and try again. Failure rarely stems from a lack of effort, but is guaranteed by a myopic vision.

— Article courtesy of the Digital Tonto blog and an earlier version appeared on Inc.com
— Image credit: Unsplash

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Why Most Corporate Innovation Programs Fail

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(And How To Make Them Succeed)

Why Most Corporate Innovation Programs Fail

GUEST POST from Greg Satell

Today, everybody needs to innovate. So it shouldn’t be surprising that corporate innovation programs have become wildly popular. There is an inherent tradeoff between innovation and the type of optimization that operational executives excel at. Creating a separate unit to address innovation just makes intuitive sense.

Yet corporate innovation programs often fail and it’s not hard to see why. Unlike other business functions, like marketing or finance, in a healthy organization everybody takes pride in their ability to innovate. Setting up a separate innovation unit can often seem like an affront to those who work hard to innovate in operational units.

Make no mistake, a corporate innovation program is no panacea. It doesn’t replace the need to innovate every day. Yet a well designed program can augment those efforts, take the business in new directions and create real value. The key to a successful innovation program is to develop a clear purpose built on a shared purpose that can solve important problems.

A Good Innovation Program Extends, It Doesn’t Replace

It’s no secret that Alphabet is one of the most powerful companies in the world. Nevertheless, it has a vulnerability that is often overlooked. Much like Xerox and Kodak decades ago, it’s highly dependent on a single revenue stream. In 2018, 86% of its revenues came from advertising, mostly from its Google search business.

It is with this in mind that the company created its X division. Because the unit was set up to pursue opportunities outside of its core search business, it didn’t encounter significant resistance. In fact, the X division is widely seen as an extension of what made Alphabet so successful in the first place.

Another important aspect is that the X division provides a platform to incubate internal projects. For example, Google Brain started out as a “20% time project.” As it progressed and needed more resources, it was moved to the X division, where it was scaled up further. Eventually, it returned to the mothership and today is an integral part of the core business.

Notice how the vision of the X division was never to replace innovation efforts in the core business, but to extend them. That’s been a big part of its success and has led to exciting new business like Waymo autonomous vehicles and the Verily healthcare division.

Focus On Commonality, Not Difference

All too often, innovation programs thrive on difference. They are designed to put together a band of mavericks and disruptors who think differently than the rest of the organization. That may be great for instilling a strong esprit de corps among those involved with the innovation program, but it’s likely to alienate others.

As I explain in Cascades, any change effort must be built on shared purpose and shared values. That’s how you build trust and form the basis for effective collaboration between the innovation program and the rest of the organization. Without those bonds of trust, any innovation effort is bound to fail.

You can see how that works in Alphabet’s X division. It is not seen as fundamentally different from the core Google business, but rather as channeling the company’s strengths in new directions. The business opportunities it pursues may be different, but the core values are the same.

The key question to ask is why you need a corporate innovation program in the first place. If the answer is that you don’t feel your organization is innovative enough, then you need to address that problem first. A well designed innovation program can’t be a band-aid for larger issues within the core business.

Executive Sponsorship Isn’t Enough

Clearly, no corporate innovation program can be successful without strong executive sponsorship. Commitment has to come from the top. Yet just as clearly, executive sponsorship isn’t enough. Unless you can build support among key stakeholders inside and outside the organization, support from the top is bound to erode.

For example, when Eric Haller started Datalabs at Experian, he designed it to be focused on customers, rather than ideas developed internally. “We regularly sit down with our clients and try and figure out what’s causing them agita,” he told me, “because we know that solving problems is what opens up enormous business opportunities for us.”

Because the Datalabs units works directly with customers to solve problems that are important to them, it has strong support from a key stakeholder group. Another important aspect at Datalabs is that once a project gets beyond the prototype stage it goes to one of the operational units within the company to be scaled up into a real business. Over the past five years businesses originated at Datalabs have added over $100 million in new revenues.

Perhaps most importantly, Haller is acutely aware how innovation programs can cause resentment, so he works hard to reduce tensions through building collaborations around the organization. Datalabs is not where “innovation happens” at Experian. Rather it serves to augment and expand capabilities that were already there.

Don’t Look For Ideas, Identify Meaningful Problems

Perhaps most importantly, an innovation program should not be seen as a place to generate ideas. The truth is that ideas can come from anywhere. So designating one particular program in which ideas are supposed to happen will not only alienate the rest of the organization, it is also likely to overlook important ideas generated elsewhere.

The truth is that innovation isn’t about ideas. It’s about solving problems. In researching my book, Mapping Innovation, I came across dozens of stories from every conceivable industry and field and it always started with someone who came across a problem they wanted to solve. Sometimes, it happened by chance, but in most cases I found that great innovators were actively looking for problems that interested them.

If you look at successful innovation programs like Alphabet’s X division and Experian’s Datalabs, the fundamental activity is exploration. X division explores domains outside of search, while Datalabs explores problems that its customers need solved. Once you identify a meaningful problem, the ideas will come.

That’s the real potential of innovation programs. They provide a space to explore areas that don’t fit with the current business, but may play an important role in its future. A good innovation program doesn’t replace capabilities in the core organization, but leverages them to create new opportunities.

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

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Underground Innovation

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How giving people space can make a big difference to your innovation profile

Underground Innovation

GUEST POST from John Bessant

If you’d snuck up behind me last weekend you’d have caught me in the act of painting walls. Not the most exciting of pursuits but it needed to be done so that now I can sit here and write in a freshly-painted room. And importantly one where even my clumsy brushwork doesn’t show in unsightly streaks and overruns. I am amongst millions of painters, professional and otherwise who regularly mutter small votes of thanks to Richard Drew and his invaluable contribution to the world of painting and decorating — masking tape.

This humble but essential innovation is getting on in years but still turns a profit for the company which originated it way back in 1925–3M. But it would never have seen the light of day if company strategy and official policy had prevailed. It exists because of Drew’s late night and unofficial efforts in direct defiance of his boss’s orders.

Drew was working as a technical salesman, dealing with some of the copmpany’s biggest customers for their core product — sandpaper. He spent a lot of time visiting car factories in that newly-growing industry, and in particular the paint shops where sandpaper was used to prepare metal surfaces for painting.

The paint crews were well aware of the good old days when Henry Ford had simplified their job — in 1909 he’d outlined a strategy for his company, which concentrated on a single model (the Model T) which could be built in high volume at low price. Doing this involved a number of trade-offs, not least in terms of massively editing down the choices available to customers. It was at this strategy meeting that he reputedly said ‘Any customer can have a car painted any colour that he wants so long as it is black.’

That decision helped establish the Model T as ‘a car for Everyman at a price every man can afford’, bringing the price down by 75% and putting it within the reach of many people. But it didn’t satisfy the market for long. People wanted more choice in models, styles — and colour schemes. All of which made life more difficult for the skilled craftsmen in the paint shops, trying to deliver ever more exotic paint jobs without slowing down production.

The problem is that when you want to paint with more than one colour then you need to cover up the area you don’t want painted. Which is a clumsy fussy business; early attempts involved using rags, newspapers and scraps of cardboard but then they had to be held in place, making a one-man job into a two-man job. Attempts to solve this by using sticky tape to hold the mask in place also failed; the solvents in the paint dissolved the adhesive on the tape making the whole mask slip and slide all over the surface.

An Innovation Dust-up

Which is where Richard Drew came in, trying to sell a new kind of sandpaper which 3M had launched which offered to cut down the dust created when preparing a metal surface for painting. Hearing some choice language coming from one corner of the shop he walked over to ask what the problem was — to be given an expletive filled tutorial in how not to mask up a paint job. What was needed — he was told in no uncertain terms — was a better adhesive tape which would actually stick and stay stuck!

He went back to his office and began to tinker around with various formulations to try and make something suitable. His boss wasn’t too pleased, ordering him to get back to his main job of selling sandpaper — but he kept on with the quest.

It took him two years and involved a variety of vegetable oils, chicle, linseed, various resins, glue, glycerine and treated crepe paper. What he eventually came up with was a tape strong enough to stick to the surfaces but easy enough to peel off without leaving any scars on the paintwork. Despite its promise his boss wouldn’t allow him to buy the machinery he needed to produce it in quantity — so Drew turned his innovative skills to the problem of financing capital equipment. He bought his machinery in small pieces, each of which cost less than the $99 he was permitted to spend on an item of equipment., and then assembled the machine himself.

This last act finally convinced his boss to let him go ahead — and also provided a lesson which became a company mantra. The boss in question was William McKnight and he made a key policy out of the experience. “If you have the right person on the right project, and they are absolutely dedicated to finding a solution — leave them alone. Tolerate their initiative and trust them.”

And so 3M’s ‘bootlegging’ approach was born, and it persists today embodied now in formal company policy. Give people permission to play around, don’t control them too tightly and let their natural creativity and entrepreneurship do the rest. Their 15% policy (allowing employees to spend up to 15% of their time in pursuit of their own ideas and hunches) has been responsible for thousands of product and process innovations, a few of which (like PostIt Notes) have gone on to be breakthrough radical innovations.

Operating Below the Radar

The masking tape story is a classic example of innovation happening below the radar screen (except the radar wasn’t invented in 1925!). We know today that smart companies who care about innovation invest in the capacity for innovation — R&D and market research, future scoping, etc. Organized innovation, buying themselves options on the future. All good — but maybe only focusing on the formal means potentially missing out on what might be happening underground. Because by their nature people are innovators, prone to experiment and tinker around, frustrated with aspects of their work which they think a little hacking around the edges might help them with. Why not tap into this as another source of innovation?

(Especially since it’s actually not that expensive in terms of lost productive time. The origin of the 15% figure at 3M was McKnight’s the observation that this was the time people spent on coffee breaks and on lunch breaks and so on, times when they could do some of this unofficial innovation).

It’s not just the benefits in terms of the possible product and process innovations which it might lead to. It’s also a powerful motivator, something which can help retain and inspire employees. Allowing people time and space to explore communicates a core company value — — it’s an invitation to tinker to hack things, to play around. And it has certainly paid off for 3M and other companies; consider these examples:

The Sony PlayStation started as a bootleg project by Ken Kutaragi, an engineer who secretly worked on a video game console with Nintendo without Sony’s approval.
The HP DeskJet printer was originally developed by a group of HP engineers who wanted to create a low-cost inkjet printer for personal use. They used bootleg parts and software to build their first prototype, which they hid under a tablecloth when not in use.
The first spreadsheet software was created by two programmers Dan Bricklin and Bob Frankston, who worked on their project without any formal support or funding from their employers. They went on to found their own company, Visicalc, which for a while was the market leader in the field.
Google’s 20% allowing employees time to spend on personal projects led to several innovations including Google Maps, Google News and Gmail.
Toshiba’s pioneering notebook computer was developed by a team of engineers who worked on it covertly for four years. They used their own laptops and software tools to create a prototype that featured innovative elements such as a lightweight design, a long battery life and a high-performance processor. The project was initially rejected by the management, but later accepted after some modifications. Introduced in 1985 it became a global leader in the portable computer market.
BMW has a long history of bootleg innovations which have gone on to become success stories. For example the Z1 roadster was developed by a small team of engineers who worked on it secretly for four years. They used their own time and resources to create a prototype that featured innovative elements such as a plastic body, retractable doors and a modular design. The project was eventually discovered by the top management and approved for production in 1986. And the iDrive was developed by a team of engineers who worked on it without any formal mandate or budget, using their own laptops and software tools. They also conducted user tests with their own cars and friends. The project was initially rejected by the management, but later became a standard feature in many BMW models. These projects helped legitimise what the company now calls ‘U-boat’ projects , recognising the value of the bootlegging approach.

Forbidden Fruit

Peter Augsdorfer made a classic study of the phenomenon, reported it in his wonderful book ‘Forbidden fruit’ in which he highlights many examples of such ‘bootlegging’ approaches. (The term originated during the 1920s when the US government banned the manufacture and sale of hard liquor; the measure didn’t have the desired effect of wiping out the industry and sobering up the country. Instead it triggered a wave of illegal but at times highly innovative ways around the problem, essentially driving innovation underground and out of sight . This included hiding illicit liquor down the inside of boots).

Augsdorfer argues that bootlegging can be seen as a form of learning under uncertainty, where employees experiment with new ideas and technologies without formal approval or support. In other words it’s an unofficial extension of the R&D/exploration work which companies need to do anyway.

Importantly it’s an approach which can have other positive benefits for organizations beyond the innovations which its employees create, such as enhancing motivation and employee retention and fostering a culture of internal entrepreneurship. But it has its ‘dark side’; there are negative outcomes including wasting time and resources, violating ethical norms and — a big challenge for those trying to ‘manage’ it — undermining organizational control and co-ordination frameworks.

Innovation Missionaries

Augsdorfer orginally wrote about this 25 years ago but a recent article in the Sloan Management Review reminds us that such underground innovation is alive and well. It’s not a case of ‘one size fits all’ and their article highlights a number of different approaches. It also usefully identifies three key archetypes of characters who may be innovators of this kind. They call them ‘missionaries’, ‘users’ and ‘explorers’.

Missionaries have a particular interest in the development of the company; their self-adopted ‘mission’ is to improve things. Characters like Richard Drew would fall into this category, seeing their own progress as being tied up with the fortunes of the company they work for and tapping into its resources to help them achieve their goals.

User innovators are essentially frustrated in what they are doing — they develop hacks and work arounds to solve problems particularly in the area of process innovation and their ideas can often be surfaced through suggestion schemes and other mechanisms.

And explorers are concerned with pushing the frontiers of what they do, sometimes going in directions which the company does not believe is possible. The risk here is that they pursue their ideas too far, detracting from their mainstream work and official company strategy.

Making Space for Innovation

So what makes underground innovation work? It’s not simply waving a magic wand, Harry Potter style, and casting the ‘Innovate!’ spell. Instead a number of things need to come together:

Allowing space — time, access to resources, etc. The exact amount — 15, 20 or even higher percentages of time — is irrelevant. It’s the signal that matters, communicating that it is OK to experiment around the edges and that there won’t be negative consequences for such action. What often happens is that this small amount of investment encourages employees to spend much more of their own time and initiative, often working long unpaid hours in pursuit of their ideas. At the limit (as Paula Criscuouolo and her colleagues point out) there are good examples of bootlegging arising from contexts in which there is no formal space or time allocation but an underlying perception that it is still OK to ‘dig around a little’.
Giving boundaries — defining the space within which innovation is possible and permission to explore there. For example we don’t necessarily want bootleg innovation in the formulation of pharmaceutical products but that leaves plenty of scope for other ideas, particularly in process innovation.
Establishing a development pathway to pick up on bootleg ideas. There’s no point stimulating lots of bootlegging behaviour if employees have nowhere to channel their ideas once they start to develop. In the case of 3M there’s a clear pathway which allows employees to take bright ideas and pitch for varying amounts of internal funding and other resources to grow and scale their innovations. Such functionality is increasingly built into innovation collaboration platforms and many companies — such as Liberty Global with their Spark programme — have established employee entrepreneurship pathways in parallel to their suggestion schemes.
Communicate trust as a core value — allowing bootleggers to feel a sense of psychological safety about what they are doing and that they will not be penalised for their activities.
Reward and recognise — it’s no coincidence that one of the things about 3M is that the people who have been involved in developing bootleg projects to fruition are then rewarded not just with resources and money but also with the opportunity to carry their venture forward. One of the two people involved in the development of Post it notes was Art Fry who moved on to run the division for 3M. The originator of the laptop computer within Toshiba similarly went on to run that division of their business.
Encourage intelligent failure — the down-side of allowing people to take initiative is that they will make mistakes. Importantly one of McKnight’s famous comments was that ‘Management that is destructively critical when mistakes are made kills initiative. And it’s essential that we have many people with initiative if we are to continue to grow.’

Underground innovation has a lot to offer -but as the above suggests it isn’t a simple matter of mimicking Google or 3M, allocating a percentage of time and then waiting for the magic to happen. Successful organizations make employee involvement a key plank in building their innovation culture; something William Mcknight learned from his experience as Richard Drew’s manager. By 1929 he was running the entire 3M company and he pulled together some of the core principles through which their culture developed — including what he called his ‘Basic rule of management’. It’s deceptively simple and it serves well as a motto for anyone interested in tapping into underground innovation:

“delegate responsibility and encourage men and women to exercise their initiative.”

Image Credits: Pixabay

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

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

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How AI is Reshaping Brainstorming

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The Future of Ideation

How AI is Reshaping Brainstorming

GUEST POST from Chateau G Pato

For decades, the classic brainstorming session has been the centerpiece of innovation. A whiteboard, a room full of energetic people, and a flow of ideas, from the brilliant to the absurd. The goal was simple: quantity over quality, and to build on each other’s thoughts. However, as a human-centered change and innovation thought leader, I’ve come to believe that this traditional model, while valuable, is fundamentally limited. It’s often hindered by groupthink, a fear of judgment, and the cognitive biases of the participants. Enter Artificial Intelligence. AI is not here to replace human ideation, but to act as the ultimate co-pilot, fundamentally reshaping brainstorming by making it more data-driven, more diverse, and more powerful than ever before. The future of ideation is not human or AI; it’s human-plus-AI.

Generative AI, in particular, has a unique ability to break us out of our mental ruts. It can process vast amounts of data—market trends, scientific research, customer feedback, and design patterns—and instantly synthesize them into novel combinations that a human team might never consider. It can challenge our assumptions, expose our blind spots, and provide a constant, unbiased source of inspiration. By offloading the “heavy lifting” of data synthesis and initial idea generation to an AI, human teams are freed up to focus on what they do best: empathy, intuition, ethical consideration, and the strategic refinement of an idea. This isn’t just a new tool; it’s a new paradigm for creative collaboration.

The AI-Powered Ideation Blueprint

Here’s how AI can revolutionize the traditional brainstorming session, transforming it into a dynamic, data-rich experience:

Pre-Brainstorming Research & Synthesis: Before the team even enters the room, an AI can be tasked with a prompt: “Analyze the top customer complaints for Product X, cross-reference them with emerging technologies in the field, and generate 50 potential solutions.” This provides a rich, data-backed foundation for the session, eliminating the “blank page” syndrome.
Bias-Free Idea Generation: AI doesn’t have a boss to impress or a fear of sounding foolish. It can generate a wide range of ideas, including those that are counterintuitive or seem to come from left field. This helps to overcome groupthink and encourages more divergent thinking from the human participants.
Real-Time Augmentation: During a live session, an AI can act as an instant research assistant. A team member might suggest an idea, and a quick query to the AI can provide immediate data on its feasibility, market precedents, or potential risks. This allows for a more informed and efficient discussion.
Automated Idea Clustering & Analysis: After the session, an AI can quickly analyze all the generated ideas, clustering them by theme, identifying unique concepts, and even flagging potential synergies that humans might have missed. This saves countless hours of manual post-it note organization and analysis.
Prototyping & Visualization: With the right tools, a team can go from a text prompt idea to a basic visual prototype in minutes. An AI can generate mockups, logos, or even simple user interfaces, making abstract ideas tangible and easy to evaluate.

“AI isn’t the brain in the room; it’s the nervous system, connecting every thought to a universe of data and possibility.”

Case Study 1: Adobe’s Sensei & The Future of Creative Ideation

The Challenge:

Creative professionals—designers, marketers, photographers—often face creative blocks or repetitive tasks that slow down their ideation process. Sifting through stock photos, creating design variations, or ensuring brand consistency for thousands of assets can be a time-consuming and manual process, leaving less time for truly creative, breakthrough thinking.

The AI-Powered Solution:

Adobe, a leader in creative software, developed Adobe Sensei, an AI and machine learning framework integrated into its Creative Cloud applications. Sensei is not a tool for generating an entire masterpiece; rather, it’s a co-pilot for ideation and creative execution. For example, a designer can provide a few images and a text prompt to Sensei, and it can generate dozens of logo variations, color palettes, or photo compositions in seconds. In another example, its content-aware fill can instantly remove an object from a photo and seamlessly fill in the background, a task that used to take hours of manual work.

Accelerated Exploration: Sensei’s generative capabilities allow designers to explore a vast “idea space” much faster than they could on their own, finding new and unexpected starting points.
Automation of Repetitive Tasks: By handling the tedious, low-creativity tasks, Sensei frees up the human designer to focus on the higher-level strategic and aesthetic decisions.
Enhanced Personalization: The AI can analyze a user’s style and past work to provide more personalized and relevant suggestions, making the collaboration feel seamless and intuitive.

The Result:

Adobe’s integration of AI hasn’t replaced creative jobs; it has transformed them. By accelerating the ideation and creation process, it has empowered creative professionals to be more prolific, experiment with more ideas, and focus their energy on the truly unique and human-centric aspects of their work. The AI becomes a silent, tireless brainstorming partner, pushing creative teams beyond their comfort zones and into new territories of possibility.

Case Study 2: Generative AI in Drug Discovery (Google’s DeepMind & Isomorphic Labs)

The Challenge:

The ideation process in drug discovery is one of the most complex and time-consuming in the world. Identifying potential drug candidates—novel molecular structures that can bind to a specific protein—is a task that traditionally requires years of laboratory experimentation and millions of dollars. The number of possible molecular combinations is astronomically large, making it impossible for human scientists to explore more than a tiny fraction.

The AI-Powered Solution:

Google’s DeepMind, through its groundbreaking AlphaFold AI model, has fundamentally changed the ideation phase of drug discovery. AlphaFold can accurately predict the 3D structure of proteins, a problem that had stumped scientists for decades. Building on this, Google launched Isomorphic Labs, a company that uses AI to accelerate drug discovery. Their models can now perform “in-silico” (computer-based) ideation, generating and testing millions of potential molecular structures to find those most likely to bind with a target protein.

Exponential Ideation: The AI can explore a chemical idea space that is orders of magnitude larger than what a human team or even a traditional lab could ever hope to.
Rapid Validation: The AI can predict the viability of a molecule almost instantly, saving years of physical lab work on dead-end ideas.
New Hypotheses: The AI can propose novel molecular structures and design principles that are outside the conventional thinking of human chemists, leading to breakthrough hypotheses.

The Result:

By using AI for the ideation phase of drug discovery, companies are drastically reducing the time and cost it takes to find promising drug candidates. The human scientist is not replaced; they are empowered. They can now focus on the higher-level strategy, the ethical implications, and the final verification of a drug, while the AI handles the tireless and rapid-fire brainstorming of molecular possibilities. This is a perfect example of how AI can move an entire industry from incremental innovation to truly transformative, world-changing breakthroughs.

Conclusion: The Human-AI Innovation Symbiosis

The future of ideation is a collaboration, a symbiosis between human creativity and artificial intelligence. The most innovative organizations will be those that view AI not as a threat to human ingenuity, but as a powerful amplifier of it. By leveraging AI to handle the data crunching, the pattern recognition, and the initial idea generation, we free our teams to focus on what truly matters: asking the right questions, applying empathy to solve human problems, and making the final strategic and ethical decisions.

As leaders, our challenge is to move beyond the fear of automation and embrace the promise of augmentation. It’s time to build a new kind of brainstorming room—one with a whiteboard, a team of passionate innovators, and a smart, tireless AI co-pilot ready to turn our greatest challenges into an infinite number of possibilities. The era of the augmented innovator has arrived, and the future of great ideas is here.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pixabay

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Detecting the Seeds of Future Innovation

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Weak Signals, Strong Insights

GUEST POST from Chateau G Pato

In our hyper-connected world, we are inundated with information. Market data, analyst reports, and competitive intelligence systems all provide a clear picture of the present. But as a human-centered change and innovation thought leader, I argue that the most transformative opportunities don’t emerge from this flood of “strong signals.” They emerge from the subtle, often contradictory, and easily dismissed weak signals on the periphery. These are the whispers of change, the fringe trends, the unarticulated customer frustrations, and the strange technological mashups that hint at a future yet to be built. The ability to detect, interpret, and act on these weak signals is the single most powerful competitive advantage an organization can cultivate. It’s the difference between reacting to disruption and proactively creating it.

Weak signals are, by definition, not obvious. They are often dismissed as anomalies, niche behaviors, or fleeting fads. They can come from anywhere: a casual comment in a user forum, a viral video that defies a category, a surprising scientific breakthrough in an unrelated field, or a quiet startup with a baffling business model. The challenge for leaders is to move beyond the comfort of big data analytics and embrace the messy, qualitative, and deeply human work of foresight. This isn’t about guesswork; it’s about building a systematic, human-centered practice for sensing the future and turning those faint whispers into a clear vision for innovation.

Why Weak Signals are Your Best Innovation GPS

Cultivating a weak-signal detection capability offers profound benefits:

Foresight, Not Just Hindsight: While strong signals confirm what has already happened, weak signals provide clues about what is *about to* happen. This gives you a critical head start in preparing for, or even driving, market shifts.
The Source of True Disruption: Most truly disruptive innovations—from personal computing to smartphones—began as weak signals on the fringe, often dismissed by established players who were focused on optimizing their core business.
Uncovering Unmet Needs: Weak signals are often an early indicator of deep, unarticulated human needs. They are the seeds of a problem that a current market solution isn’t addressing.
Building a Culture of Curiosity: Actively looking for weak signals encourages a culture of curiosity, open-mindedness, and a willingness to challenge assumptions—all essential traits for innovation.

“Strong signals confirm your past. Weak signals whisper your future. The most innovative leaders are the best listeners.”

A Human-Centered Approach to Detecting Weak Signals

Detecting weak signals is not an automated process. It is a deeply human activity that requires a specific mindset and intentional practice:

Go to the Edge: Move beyond your core market and familiar customer base. Talk to fringe users, early adopters, and even those who reject your product. Spend time in adjacent industries and with unconventional thinkers.
Embrace a Beginner’s Mindset: Temporarily suspend your expertise. Look at your industry as if you are seeing it for the first time. Why do customers do what they do? What seems strange or inefficient to an outsider?
Connect the Unconnected Dots: A single weak signal means little. The true insight comes from identifying patterns. Is a new technology in one field combining with a new consumer behavior in another? The unexpected combination of two seemingly unrelated signals is often where the magic happens.
Create “Listening Posts”: Form small, cross-functional teams whose sole purpose is to scan the periphery. Empower them to read obscure journals, follow niche social media communities, and report back on anything that feels “off” or interesting.

Case Study 1: The Rise of Social Media – A Weak Signal Ignored by the Giants

The Challenge:

In the early 2000s, the internet was dominated by large, content-heavy portals like Yahoo! and search engines like Google. Communication was primarily through email and instant messaging. The idea of people building public profiles to share personal updates and connect with friends was seen as a niche, even trivial, activity. It was a weak signal, a seemingly minor behavior on college campuses.

The Weak Signal Ignored:

For established tech giants, the signal was too faint. They were focused on the strong signals of search queries and content monetization. Facebook, MySpace, and Friendster were dismissed as “just for kids” or a “niche social trend.” The idea of a public profile as a primary mode of online identity and communication was too far outside their core business model to be taken seriously. They saw a minor curiosity, not the future of human connection.

The Result:

The companies that paid attention to this weak signal—and understood the human-centered need for connection and self-expression—went on to build a multi-trillion-dollar industry. The giants who ignored it were forced to play a decade-long game of catch-up, and many lost their dominant position. The weak signal of a simple public profile evolved into the foundational architecture of the modern internet and the economy built on it. Their failure to see this wasn’t a failure of technology; it was a failure of imagination and human-centered listening.

Case Study 2: Netflix and the Streaming Revolution – From DVDs to a Weak Signal

The Challenge:

In the early 2000s, Blockbuster was the undisputed king of home entertainment. Their business model was robust, profitable, and built on a physical presence of thousands of stores and a lucrative late-fee system. The internet was a nascent and unreliable platform for video, and streaming was a faint, almost invisible signal on the horizon.

The Weak Signal Detected:

While Blockbuster was focused on optimizing its core business (e.g., store layout, inventory management), Netflix, then a DVD-by-mail service, saw a weak signal. The signal wasn’t just about faster internet; it was about the human frustration with late fees and the inconvenience of physical stores. The company’s leaders started to talk about the concept of “on-demand” content, long before the technology was ready. They were paying attention to the unarticulated desire for convenience and unlimited choice, a desire that was a whisper to Blockbuster but a deafening call to Netflix. They began to invest in streaming technology and content licensing years before it was profitable, effectively cannibalizing their own profitable DVD business.

The Result:

Blockbuster famously dismissed Netflix’s weak signal, seeing it as a minor inconvenience to their existing business model. They believed a physical store experience would always win. Netflix, by acting on the weak signal and a deep understanding of human frustration, was able to pivot from being a DVD service to the global streaming behemoth we know today. Their foresight, driven by a human-centered approach to a technological trend, allowed them to disrupt an entire industry and become a dominant force in the future of entertainment. Blockbuster, unable to see beyond the strong signals of its profitable past, is now a cautionary tale.

Conclusion: The Foresight Imperative

The future is not a surprise that happens to you. It is a collection of weak signals that you either choose to see or ignore. In an era of constant disruption, relying on strong signals alone is a recipe for stagnation. The most resilient and innovative organizations are those that have built a human-centered practice for sensing change on the periphery. They have created a culture where curiosity is a core competency and where questioning the status quo is a daily ritual.

As leaders, our most critical role is to shift our focus from optimizing the past to sensing the future. We must empower our teams to go to the edge, listen to the whispers, and connect the dots in new and creative ways. The future of your industry is already being born, not in the center of the market, but on its fringes. The question is, are you listening?

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