Tag Archives: Artificial Intelligence

Don’t Blame Technology When Innovation Goes Wrong

Don't Blame Technology When Innovation Goes Wrong

GUEST POST from Greg Satell

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

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

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

The Frankenstein Archetype

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

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

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

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

The New Frankensteins

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

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

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

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

Revealing And Building

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

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

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

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

Learning To Ask The Hard Questions

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

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

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

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

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

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Humans Are Not as Different from AI as We Think

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Humans Are Not as Different from AI as We Think

GUEST POST from Geoffrey A. Moore

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

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

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

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

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

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

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

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

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

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

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

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

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

Image Credit: Pixabay

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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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Will Innovation Management Leverage AI in the Future?

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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.

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

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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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Is AI Saving Corporate Innovation or Killing It?

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Is AI Saving Corporate Innovation or Killing It?

GUEST POST from Robyn Bolton

AI is killing Corporate Innovation.

Last Friday, the brilliant minds of Scott Kirsner, Rita McGrath, and Alex Osterwalder (plus a few guest stars like me, no big deal) gathered to debate the truth of this statement.

Honestly, it was one of the smartest and most thoughtful debates on AI that I’ve heard (biased but right, as my husband would say), and you should definitely listen to the whole thing.

But if you don’t have time for the deep dive over your morning coffee, then here are the highlights (in my humble opinion)

Why this debate is important

Every quarter, InnoLead fields a survey to understand the issues and challenges facing corporate innovators. The results from their Q2 survey and anecdotal follow-on conversations were eye-opening:

Resources are shifting from Innovation to AI: 61.5% of companies are increasing the resources allocated to AI, while 63.9% of companies are maintaining or decreasing their innovation investments
IT is more likely to own AI than innovation: 61.5% of companies put IT in charge of exploring potential AI use cases, compared to 53.9% of Innovation departments (percentages sum to greater than 0 because multiple departments may have responsibility)
Innovation departments are becoming AI departments. In fact, some former VPs and Directors of Innovation have been retitled to VPs or Directors of AI

So when Scott asked if AI was killing Corporate Innovation, the data said YES.

The people said NO.

What’s killing corporate innovation isn’t technology. It’s leadership.

Alex Osterwalder didn’t pull his punches and delivered a truth bomb right at the start. Like all the innovation tools and technologies that came before, the impact of AI on innovation isn’t about the technology itself—it’s about the leaders driving it.

If executives take the time to understand AI as a tool that enables successful outcomes and accelerates the accomplishment of key strategies, then there is no reason for it to threaten, let alone supplant, innovation.

But if they treat it like a shiny new toy or a silver bullet to solve all their growth needs, then it’s just “innovation theater” all over again.

AI is an Inflection Point that leaders need to approach strategically

As Rita wrote in her book Seeing Around Corners, an inflection point has a 10x impact on business, for example, 10x cheaper, 10x faster, or 10x easier. The emergence and large-scale adoption of AI is, without doubt, an inflection point for business.

Just like the internet and Netscape shook things up and changed the game, AI has the power to do the same—maybe even more. But, to Osterwalder’s point, leaders need to recognize AI as a strategic inflection point and proceed accordingly.

Leaders don’t need to have it all figured out yet, but they need a plan, and that’s where we come in.

This inflection point is our time to shine

From what I’ve seen, AI isn’t killing corporate innovation. It’s creating the biggest corporate innovation opportunity in decades. But it’s up to us, as corporate innovators, to seize the moment.

Unlike our colleagues in the core business, we are comfortable navigating ambiguity and uncertainty. We have experience creating order from what seems like chaos and using innovation to grow today’s business and create tomorrow’s.

We can do this because we’ve done it before. It’s exactly what we do,

AI is not a problem. It’s an opportunity. But only if we make it one.

AI is not the end of corporate innovation —it’s a tool, a powerful one at that.

As corporate innovators, we have the skills and knowledge required to steer businesses through uncertainty and drive meaningful change. So, let’s embrace AI strategically and unlock its full potential.

The path forward may not always be crystal clear, but that’s what makes it exciting. So, let’s seize the moment, navigate the chaos, and embrace AI as the innovation accelerant that it is.

Image Credit: Pixabay

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Framing Your 2024 Strategy

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Framing Your 2024 Strategy

GUEST POST from Geoffrey A. Moore

Fall is in the air, which brings to mind the season’s favorite sport—no, not football, strategic planning! Let’s face it, 2023 has been a tough year for most of us, with few annual plans surviving first contact with an economy that was not so much sluggish as simply hesitant. With the exception of generative AI’s burst onto the scene, most technology sectors have been more or less trudging along, and that begs the question, what do we think we can do in 2024? Time to bring out the strategy frameworks, polish up those crystal balls that have been a bit murky of late, and chart our course forward.

This post will kick off a series of blogs about framing strategy, all organized around a meta-model we call the Hierarchy of Powers:

Geoffrey Moore Strategy Framework

The inspiration for this model came from looking at how investors prioritize their portfolios. The first thing they do is allocate by sector, based primarily on category power, referring both to the growth rate of the category as well as its potential size. Rising tides float all boats, and one of the toughest challenges in business is how to manage a premier franchise when category growth is negative. In conjunction with assessing our current portfolio’s category power, this is also a time to look at adjacent categories, whether as threats or as opportunities, to see if there are any transformative acquisitions that deserve our immediate attention.

Returning to our current set of assets, within each category the next question to answer is, what is our company power within that category? This is largely a factor of market share. The more share a company has of a given category, the more likely the ecosystem of partners that supports the category will focus first on that company’s installed base, adding more value to its offers, as well as to recommend that company’s products first, again because of the added leverage from partner engagement. Marketplaces, in other words, self-organize around category leaders, accelerating the sales and offloading the support costs of the market share leaders.

But what do you do when you don’t have company power? That’s when you turn your attention to market power. Marketplaces destabilize around problematic use cases that the incumbent vendors do not handle well. This creates openings for new entrants, provided they can authentically address the customer’s problems. The key is to focus product management on the whole product (not just what your enterprise supplies, but rather, everything the customer needs to be successful) and to focus your go-to-market engine on the target market segment. This is the playbook that has kept Crossing the Chasm on entrepreneur’s book lists some thirty years in, but it is a different matter to execute it in a large enterprise where sales and marketing are organized for global coverage, not rifle-shot initiatives. Nonetheless, when properly executed, it is the most reliable play in all of high-tech market development.

If market power is key to taking market share, offer power is key to maintaining it, both in high-growth categories as well as mature ones. Offer power is a function of three disciplines—differentiation to create customer preference, neutralization to catch up to and reduce a competitor’s differentiation, and optimization to eliminate non-value-adding costs. Anything that does not contribute materially to one of these three outcomes is waste.

Finally, execution power is the ability to take advantage of one’s inertial momentum rather than having it take advantage of you. Here the discipline of zone management has proved particularly valuable to enterprises who are seeking to balance investment in their existing lines of business, typically in mature categories, with forays into new categories that promise higher growth.

In upcoming blog posts I am going to dive deeper into each of the five powers outlined above to share specific frameworks that clarify what decisions need to be made during the strategic planning process and what principles can best guide them. In the meantime, there is still one more quarter in 2023 to make, and we all must do our best to make the most of it.

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

Image Credit: Pixabay, Geoffrey A. Moore

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Innovation Evolution in the Era of AI

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Innovation Evolution in the Era of AI

GUEST POST from Stefan Lindegaard

Half a decade ago, I laid out a perspective on the evolution of innovation. Now, I return to these reflections with a sentiment of both awe and unease as I observe the profound impacts of AI on innovation and business at large. The transformation unfolding before us presents a remarkable panorama of opportunities, yet it also carries with it the potential for disruption, hence the mixed feelings.

1. The Reign of R&D (1970-2015): There was a time when the Chief Technology Officer (CTO) held the reins. The focus was almost exclusively on Research and Development (R&D), with the power of the CTO often towering over the innovative impulses of the organization. Technology drove progress, but a tech-exclusive vision could sometimes be a hidden pitfall.

2. Era of Innovation Management (1990-2001): A shift towards understanding innovation as a strategic force began to emerge in the ’90s. The concept of managing innovation, previously only a flicker in the business landscape, began its journey towards being a guiding light. Pioneers like Christensen brought innovation into the educational mainstream, marking a paradigm shift in the mindsets of future business leaders.

3. Business Models & Customer Experience (2001-2008): The millennium ushered in an era where simply possessing superior technology wasn’t a winning card anymore. Process refinement, service quality, and most critically, innovative business models became the new mantra. Firms like Microsoft demonstrated this shift, evolving their strategies to stay competitive in this new game.

4. Ecosystems & Platforms (2008-2018): This phase saw the rise of ecosystems and platforms, representing a shift from isolated competition to interconnected collaboration. The lines that once defined industries began to blur. Companies from emerging markets, particularly China, became global players, and we saw industries morphing and intermingling. Case in point: was it still the automotive industry, or had the mobility industry arrived?

5. Corporate Transformation (2019-2025): With the onslaught of digital technologies, corporations faced the need to transform from within. Technological adoption wasn’t a mere surface-level change anymore; it demanded a thorough, comprehensive rethinking of strategies, structures, and processes. Anything less was simply insufficient to weather the storm of this digital revolution.

6. Comborg Transformation (2025-??): As we gaze into the future, the ‘Comborg’ era comes into view. This era sees organizations fusing human elements and digital capabilities into a harmonious whole. In this stage, the equilibrium between human creativity and AI-driven efficiency will be crucial, an exciting but challenging frontier to explore.

I believe that revisiting this timeline of innovation’s evolution highlights the remarkable journey we’ve undertaken. As we now figure out the role of AI in innovation and business, it’s an exciting but also challenging time. Even though it can be a bit scary, I believe we can create a successful future if we use AI in a responsible and thoughtful way.

Stefan Lindegaard Evolution of Innovation

Image Credit: Stefan Lindegaard, Unsplash

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An Innovation Rant: Just Because You Can Doesn’t Mean You Should

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An Innovation Rant: Just Because You Can Doesn’t Mean You Should

GUEST POST from Robyn Bolton

Why are people so concerned about, afraid of, or resistant to new things?

Innovation, by its very nature, is good. It is something new that creates value.

Naturally, the answer has nothing to do with innovation.

It has everything to do with how we experience it.

And innovation without humanity is a very bad experience.

Over the last several weeks, I’ve heard so many stories of inhuman innovation that I have said, “I hate innovation” more than once.

Of course, I don’t mean that (I would be at an extraordinary career crossroads if I did). What I mean is that I hate the choices we make about how to use innovation.

Just because AI can filter resumes doesn’t mean you should remove humans from the process.

Years ago, I oversaw recruiting for a small consulting firm of about 50 people. I was a full-time project manager, but given our size, everyone was expected to pitch in and take on extra responsibilities. Because of our founder, we received more resumes than most firms our size, so I usually spent 2 to 3 hours a week reviewing them and responding to applicants. It was usually boring, sometimes hilarious, and always essential because of our people-based business.

Would I have loved to have an AI system sort through the resumes for me? Absolutely!

Would we have missed out on incredible talent because they weren’t out “type?” Absolutely!

AI judges a resume based on keywords and other factors you program in. This probably means that it filters out people who worked in multiple industries, aren’t following a traditional career path, or don’t have the right degree.

This also means that you are not accessing people who bring a new perspective to your business, who can make the non-obvious connections that drive innovation and growth, and who bring unique skills and experiences to your team and its ideas.

If you permit AI to find all your talent, pretty soon, the only talent you’ll have is AI.

Just because you can ghost people doesn’t mean you should.

Rejection sucks. When you reject someone, and they take it well, you still feel a bit icky and sad. When they don’t take it well, as one of my colleagues said when viewing a response from a candidate who did not take the decision well, “I feel like I was just assaulted by a bag of feathers. I’m not hurt. I’m just shocked.”

So, I understand ghosting feels like the better option. It’s not. At best, it’s lazy, and at worst, it’s selfish. Especially if you’re a big company using AI to screen resumes.

It’s not hard to add a function that triggers a standard rejection email when the AI filters someone out. It’s not that hard to have a pre-programmed email that can quickly be clicked and sent when a human makes a decision.

The Golden Rule – do unto others as you would have done unto you – doesn’t apply to AI. It does apply to you.

Just because you can stack bots on bots doesn’t mean you should.

At this point, we all know that our first interaction with customer service will be with a bot. Whether it’s an online chatbot or an automated phone tree, the journey to a human is often long and frustrating. Fine. We don’t like it, but we don’t have a choice.

But when a bot transfers us to a bot masquerading as a person? Do you hate your customers that much?

Some companies do, as my husband and I discovered. I was on the phone with one company trying to resolve a problem, and he was in a completely different part of the house on the phone with another company trying to fix a separate issue. When I wandered to the room where my husband was to get information that the “person” I was talking to needed, I noticed he was on hold. Then he started staring at me funny (not as unusual as you might think). Then he asked me to put my call on speaker (that was unusual). After listening for a few minutes, he said, “I’m talking to the same woman.”

He was right. As we listened to each other’s calls, we heard the same “woman” with the same tenor of voice, unusual cadence of speech, and indecipherable accent. We were talking to a bot. It was not helpful. It took each of us several days and several more calls to finally reach humans. When that happened, our issues were resolved in minutes.

Just because innovation can doesn’t mean you should allow it to.

You are a human. You know more than the machine knows (for now).

You are interacting with other humans who, like you, have a right to be treated with respect.

If you forget these things – how important you and your choices are and how you want to be treated – you won’t have to worry about AI taking your job. You already gave it away.

Image Credit: Pexels

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