Author Archives: Greg Satell

About Greg Satell

Greg Satell is a popular speaker and consultant. His latest book, Cascades: How to Create a Movement That Drives Transformational Change, is available now. Follow his blog at Digital Tonto or on Twitter @Digital Tonto.

Five Things Most Managers Don’t Know About Innovation

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Five Things Most Managers Don't Know About Innovation

GUEST POST from Greg Satell

Every business knows it needs to innovate. What isn’t so clear is how to go about it. There is no shortage of pundits, blogs and conferences that preach the gospel of agility, disruptive innovation, open innovation, lean startups or whatever else is currently in vogue. It can all be overwhelming.

The reality is that there is no one ‘true’ path to innovation. In researching my book, Mapping Innovation, I found that organizations of all shapes and sizes can be great innovators. Some are lean and nimble, while others are large and bureaucratic. Some have visionary leaders, others don’t. No one model prevails.

However, there are common principles that we can apply. While there is no “right way” to innovate, there are plenty of wrong ways. So perhaps the best way forward is to avoid the pitfalls that can undermine innovative efforts in your organization and kill promising new solutions. Here are five things every business should know about innovation.

1. Every Square-Peg Business Eventually Meets Its Round-Hole World

IBM is many peoples’ definition of a dinosaur. Not too long ago, it announced its 22nd consecutive quarter of declining revenues. Nevertheless, it seems to be turning a corner. What’s going on? How can a century-old technology company survive against the onslaught of the 21st century phenoms like Google, Amazon, Apple and Facebook?

The truth is that this is nothing new for IBM. Today, its business of providing installed solutions for large enterprises is collapsing due to the rise of the cloud. In the 90s it was near bankruptcy. In the 50s, its tabulating machine business was surpassed by digital technology. Each time eulogies are paraded around for Big Blue it seems to come back even stronger.

What IBM seems to understand better than just about anybody else is that every square-peg business eventually meets its round-hole world. Changes in technology, customer preferences and competitive environment eventually render every business model irrelevant. That’s just reality and there really is no changing it.

IBM’s secret weapon is its research division, which explores pathbreaking technologies long before they have a clear path to profitability. So when one business dies they have something to replace it with. Despite those 22 quarters of declining revenues it has a bright future with things like Watson, quantum computing and neuromorphic chips.

It’s better to prepare than adapt.

2. Innovation Isn’t About Ideas, It’s About Solving Problems

Probably the biggest misconception about innovation is that it’s about ideas. So there is tons of useless advice about brainstorming methods, standing meetings and word games, such as replacing “can’t” with “can if.” If these things help you work more productively, great, but they will not make you an innovator.

In my work, I speak to top executives, amazingly successful entrepreneurs and world class scientists. Some of these have discovered or created things that truly changed the world. Yet not once did anyone tell me that a brainstorming session or “productivity hack” set them on the road to success. They were simply trying to solve a problem that was meaningful to them.

What I do hear a lot from mid-level and junior executives is that they are not given “permission” to innovate and that nobody wants to hear about their ideas. That’s right. Nobody wants to hear about your ideas. People are busy with their own ideas.

So stop trying to come up with some earth shattering idea. Go out and find a good problem and start figuring out how to solve it. Nobody needs an idea, but everybody has a problem they need solved.

3. You Don’t Hire Or Buy Innovation, You Empower It

One of the questions I always get asked when I advise organizations is how to recruit and retain more innovative people. I know the type they have in mind. Someone fashionably dressed, probably with some tasteful piercings and some well placed ink, that spouts off a never-ending stream of ideas.

Yet that’s exactly what you don’t want. That’s exactly the type of unproductive hotshot that can stop innovation in its tracks. They talk over other people, which discourages new ideas from being voiced and their constant interruptions kill collaboration.

The way you create innovation is by empowering an innovative culture. That means creating a safe space for ideas, fostering networks inside and outside the organization, promoting collaboration and instilling a passion for solving problems. That’s how you promote creativity.

So if you feel that your people are not innovating, ask yourself what you’re doing to get in their way.

4. If Something Is Truly New And Different, You Need a “Hair On Fire” Use Case

As a general operational rule, you should seek out the largest addressable market you can find. Larger markets not only have more money, they are more stable and usually more diverse. Identifying even a small niche in a big market can make for a very profitable business.

Unfortunately, what thrives in operations can often fail for innovation. When you have an idea that’s truly new and different, you don’t want to start with a large addressable market. You want to find a hair-on-fire use case — somebody that needs a problem solved so badly that they either already have a budget for it or have scotched-taped together some half solution.

The reason you want to find a hair-on-fire use case is that when something is truly new and different, it is untested and poorly understood. But someone who needs a problem solved really badly will be willing to work with you to find flaws, fix them and improve your offer. From there you can begin to scale up and hunt larger game.

5. You Need To Seek Out A Grand Challenge

Most of the problems we deal with are relatively small. We cater to changing customer tastes, respond to competitive threats and fix things that are broken. Sometimes we go a bit further afield and enter a new market or develop a new capability. These are the bread and butter of a good business. That’s how you win in the marketplace.

Yet every business is ultimately disrupted. When that happens, normal operating practice will only make you better and better at things people care less and less about. You can’t build the future by looking to the past. You build the future by creating something that’s new and important, that solves problems that are currently unsolvable.

That’s why every organization needs to seek out grand challenges. These are long, sustainable efforts that solve a fundamental problem in your industry or field that change the realm of what’s considered possible. They are not “bet the company” initiatives and shouldn’t present a material risk to the business if they fail, but have a transformational impact if they succeed.

As I noted above, there is no one “true” path to innovation. Everybody needs to find their own way. Still, there are common principles and by applying them, every business can up their innovation game.

— Article courtesy of the Digital Tonto blog and previously appeared on Harvard Business Review
— Image credits: Pexels

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How to Avoid AI Project Failures

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How To Avoid AI Project Failures

GUEST POST from Greg Satell

A survey a few years ago by Deloitte of “aggressive adopters” of cognitive technologies found that 76% believe that they will “substantially transform” their companies within the next three years. There probably hasn’t been this much excitement about a new technology since the dotcom boom years in the late 1990s.

The possibilities would seem to justify the hype. AI isn’t just one technology, but a wide array of tools, including a number of different algorithmic approaches, an abundance of new data sources and advancement in hardware. In the future, we will see new computing architectures, like quantum computing and neuromorphic chips, propel capabilities even further.

Still, there remains a large gap between aspiration and reality. Gartner estimated that 85% of big data projects fail. There have also been embarrassing snafus, such as when Dow Jones reported that Google was buying Apple for $9 billion and the bots fell for it or Microsoft’s Tay chatbot went berserk on Twitter. Here’s how to transform the potential of AI into real results.

Make Your Purpose Clear

AI does not exist in a vacuum, but in the context of your business model, processes and culture. Just as you wouldn’t hire a human employee without an understanding of how he or she would fit into your organization, you need to think clearly about how an artificial intelligence application will drive actual business results.

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

While change always has to be driven from the top, implementation is always driven lower down. So it’s important to communicate a sense of purpose clearly. If front-line managers and employees believe that artificial intelligence will help them do their jobs better, they will be much more enthusiastic and effective in making the project successful.

“Those who are able to focus on business outcomes are finding that AI is driving bottom-line results at a rate few had anticipated,” Josh Sutton, CEO of Agorai.ai, told me. He pointed to a McKinsey study from a few years ago that pegs the potential economic value of cognitive tools at between $3.5 trillion and $5.8 trillion as just one indication of the possible impact.

Choose The Tasks You Automate Wisely

While many worry that cognitive technologies will take human jobs, David Autor, an economist at MIT, sees the the primary shift as one of between routine and nonroutine work. In other words, artificial intelligence is quickly automating routine cognitive processes much like industrial era machines automated physical labor.

To understand how this can work, just go to an Apple store. Clearly, Apple is a company that clearly understands how to automate processes, but the first thing you see when you walk into an Apple store you see is a number employees waiting to help you. That’s because it has chosen to automate background tasks, not customer interactions.

However, AI can greatly expand the effectiveness of human employees. For example, one study cited by a White House report during the Obama Administration found that while machines had a 7.5 percent error rate in reading radiology images and humans had a 3.5% error rate, when humans combined their work with machines the error rate dropped to 0.5%.

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

Data Is Not Just An Asset, It Can Also Be A Liability

For a long time more data was considered better. Firms would scoop up as much of it as they could and then feed it into sophisticated algorithms to create predictive models with a high degree of accuracy. Yet it’s become clear that’s not a great approach.

As Cathy O’Neil explains in Weapons of Math Destruction, we often don’t understand the data we feed into our systems and data bias is becoming a massive problem. A related problem is that of over-fitting. It may sound impressive to have a model that is 99% accurate, but if it is not robust to changing conditions, you might be better off with one that is 70% accurate and simpler.

Finally, with the implementation of GDPR in Europe and the likelihood that similar legislation will be adopted elsewhere, data is becoming a liability as well as an asset. So you should think through which data sources you are using and create models that humans can understand and verify. “Black boxes” serve no one.

Shift Humans To Higher Value Tasks

One often overlooked fact about automation is that once you automate a task, it becomes largely commoditized and value shifts somewhere else. So if you are merely looking to use cognitive technologies to replace human labor and cut costs, you are most probably on the wrong track.

One surprising example of this principle comes from the highly technical field of materials science. A year ago, I was speaking to Jim Warren of the Materials Genome Initiative about the exciting possibility of applying machine learning algorithms to materials research. More recently, he told me that this approach has increasingly become a focus of materials research.

That’s an extraordinary shift in one year. So should we be expecting to see a lot of materials scientists at the unemployment office? Hardly. In fact, because much of the grunt work of research is being outsourced to algorithms, the scientists themselves are able to collaborate more effectively. As George Crabtree, Director of the Joint Center for Energy Storage Research, which has been a pioneer in automating materials research put it to me, “We used to advance at the speed of publication. Now we advance at the speed of the next coffee break.”

And that is the key to understanding how to implement cognitive technologies effectively. Robots are not taking our jobs, but rather taking over tasks. That means that we will increasingly see a shift in value from cognitive skills to social skills. The future of artificial intelligence, it seems, is all too human.

— Article courtesy of the Digital Tonto blog and previously appeared on Harvard Business Review
— Image credits: Pexels

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Only One Type of Innovation Will Win the Future

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Only One Type of Innovation Will Win the Future

GUEST POST from Greg Satell

Very few businesses last. While we like to think we live in a particularly disruptive era, this has always been true. Entrepreneurs start businesses because they see opportunity and build skills, practices and processes to leverage it. Yet as the world changes, these strengths often become vulnerabilities.

The problem is that the past is not always a good guide to the future. Business models, even the successful ones, are designed for inertia. They are great for leveraging past insights, but are often resistant to change. Success does not, in fact, always breed more success, sometimes it breeds failure.

That’s why every business needs to innovate. Yet innovation is not, as some would have us believe, just about moving fast and breaking things. It’s about solving the problems you need to create a better future. What most fail to grasp is that a key factor of success is how you source problems, build a pipeline and, ultimately, choose which ones you will work on.

1. Getting Better At What You Already Do

Every year, Apple comes up with a new iPhone. That’s not as exciting as it used to be, but it’s still key to the company maintaining its competitive edge. Every model is a bit faster, more secure and has new features that make it more capable. It’s still an iPhone, but better.

Some self-appointed ‘innovation gurus” often scoff at this type of innovation as “incremental” and favor new technologies that are more “radical” or “disruptive,” but the truth is that this is where you derive the most value from innovation — getting better at what you already do and selling to customers what you already know.

So the first line of defense against irrelevance is to identify ways to improve performance in current practices and processes. The challenge, of course, with this type of innovation is that your competitors will be working on the same problems you are and it takes no small amount of agility and iteration to stay ahead. Even then, any victory is short-lived.

Still, most technologies can be improved for a long time. Moore’s Law, for example, has been around for almost 50 years and is just ending now.

2. Applying What You’re Already Good At To A Different Context

Amazon started out selling books online. It then applied its approach to other categories, such as electronics and toys. That took enormous investments in technology, which it then used to create new businesses, such as Amazon Web Services (AWS), Kindle tablets and its Echo line of smart speakers.

In each case, the company took what it already did well and expanded to an adjacent set of markets or capabilities, often with great success. The Kindle helped the company dominate e-books and strengthened its core business. AWS is far more profitable than online retail and accounts for virtually all of Amazon’s operating income.

Still, adjacent opportunities are can be risky. Amazon, despite its huge successes, has had its share of flops too. Whenever you go into a new business you are, to a greater or lesser extent, charting a course into the unknown. So you need to proceed with some caution. When you launch a new business into an adjacency, you are basically launching a startup and most of those fail.

3. Finding A Completely New Problem To Solve

Besides getting better at what you already do and applying things you already know to a different market or capability, you can also look for a new problem to solve. Clearly, this the most uncertain type of opportunity, because no one knows what a good solution will look like.

To return to the Moore’s law example, everybody knows what a 20% performance improvement in computer chips looks like. Metrics for speed and power consumption have long been established, so there is little ambiguity around what would constitute success. Customers will instantly recognize the improvement as having a specific market value.

On the other hand, no one knows what the value of a quantum computer will be. It’s a fundamentally new kind of technology that will solve new types of problems. So customers will have to explore the technology and figure out how to use it to create better products and services.

Despite the uncertainty though, I found in the research that led to my book, Mapping Innovation, that this type of exploration is probably the closest thing to a sure bet that you’re going to find. Every single organization I studied that invested in exploration found that it paid off big, with extremely high returns even accounting for the inevitable wrong turns and blind alleys.

The 70-20-10 Rule

Go to any innovation conference and you will find no shortage of debates about what type of approach creates the most value, usually ending with no satisfying conclusion. The truth is that every organization needs to improve what they already do, search for opportunities in adjacencies and explore new problems. The key is how you manage resources.

One popular approach is the 70-20-10 rule, which prescribes investing 70% of your innovation resources in improving existing technologies, 20% in adjacent markets and capabilities and 10% in markets and capabilities that don’t exist yet. That’s more of a rule of thumb than a physical law and should be taken with a grain of salt, but it’s a good guide.

Practically speaking, however, I have found that the exploration piece is the most neglected. All too often, in our over-optimized business environment, any business opportunity that can’t be immediately quantified in considered a non-starter. So we fail to begin to explore new problems until their market value has been unlocked by someone else. By that point, we are already behind the curve.

Make no mistake. The next big thing always starts out looking like nothing at all. Things that change the world always arrive out of context for the simple reason that the world hasn’t changed yet. But if you do not explore, you will not discover. If you do not discover, you will not invent. And if you do not invent, you will be disrupted. It’s just a matter of time.

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

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Your Strategy Must Reach Beyond Markets to Ecosystems

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Your Strategy Must Reach Beyond Markets to Ecosystems

GUEST POST from Greg Satell

In the 1960s and 70s, Route 128 outside of Boston was the center of technology, but by the 1990s Silicon Valley had taken over and never looked back. As AnnaLee Saxenian explained in Regional Advantage, the key difference was that while Route 128 was a collection of value chains, Silicon Valley built an ecosystem.

Clearly, ecosystems are even more important today than they were back then. In fact, a study by Accenture Strategy a few years ago found that ecosystems are a “cornerstone” of future growth and that 60% of executives surveyed viewed ecosystems as a way to disrupt their industry. A similar number saw them as key to increasing revenue.

The problem is that competing in an ecosystem environment is vastly different than a traditional value chain strategy. While a value chain is driven by efficiencies, an ecosystem is driven by connections in a network. So we need to do more than adapt our strategy and tactics, we need to learn how to play a whole new game. The first step is to learn the rules.

First, Start Early

One of the key aspects of ecosystems is that they don’t seem all that important at first. By the time it becomes clear that a change is underway, it is often too late to adapt. The demise of Boston’s technology companies is a great example of how that can happen. Dominant firms such as DEC, Data General and Wang Laboratories found themselves irrelevant so quickly that they never recovered.

Network scientists call this an ‘instantaneous phase transition’ and it happens because connections tend to form slowly. They start as isolated clusters that, even taken in sum, don’t seem to amount to much. However, when those clusters connect, a cascade ensues and what once seemed inconsequential suddenly becomes predominant.

That’s why it’s so important to become active in an ecosystem before those clusters connect, when things are moving relatively slowly, everybody wants to talk to you and the price of admission is still fairly cheap. Once an ecosystem begins to thrive, things move much faster and costs for entry raise exponentially.

Consider the automobile industry, which is now spending billions to set up research centers in Silicon Valley. Just think of how much cheaper — and more effective — it would have been for those companies to have started 20 or 30 years ago.

Not Just Spinning Out, But Spinning In

A typical strategy for an enterprise looking to leverage an ecosystem is to spin out a division to focus on activities that are relevant to it. These spinoffs tend to have a lot more in common with the ecosystem firms than the parent company and therefore are much more able to connect. However, because links to the parent company become more tenuous over time, benefits are limited.

A potentially more successful strategy is to spin ecosystem firms in. For example, the National Labs have set up programs like Cyclotron Road, Chain Reaction and Innovation Crossroads that invite entrepreneurial firms to come work at the labs, make use of the scientific facilities and be mentored by top scientists.

In the private sector, corporate venture capital operations, as well as incubators and accelerators, can be a great way to connect with small entrepreneurial companies early in the ecosystem lifecycle. Beyond the actual investments made, these programs give you the opportunity to connect with hundreds of small firms, some of which can become important partners, suppliers and customers later on.

What’s crucial is that you are not seen as an interloper, but a true source of value, whether that value is in actual monetary investment, access to facilities and expertise or connection to points of market access. What may be insignificant to your company may be incredibly valuable to a small, entrepreneurial firm.

Maintaining Open Nodes

One of Saxenian’s most interesting findings in Regional Advantage was how differently the Boston technology firms treated outsiders compared to the Silicon Valley companies. The Boston firms were vertically integrated and sought to keep everything in-house. The Silicon Valley companies, on the other hand, thrived on connection.

For example, in Silicon Valley if you left your employer to start a company of your own, you were still considered part of the family. Many new entrepreneurs became suppliers or customers to their former employers and still socialized actively with their former colleagues. In Boston, if you left your firm you were treated as a pariah.

When technology began to shift in the 80s and 90s, the Boston firms had little, if any, connection to the new ecosystems that were evolving. In Silicon Valley, however, connections to former employees acted as an antenna network, providing early market intelligence that helped those companies adapt.

So while it is necessary to reach out to evolving ecosystems, it is just as important to ensure that there are also paths for small entrepreneurial firms to engage within your enterprise. Ecosystems thrive on personal connections. Those may not show up on a strategic plan or a balance sheet, but they are just as important as any other asset.

The New Competitive Advantage

Ever since Harvard professor Michael Porter published his seminal book, Competitive Strategy in 1980, strategists have sought advantage through driving efficiencies in order to maximize bargaining power against customers, suppliers, substitute goods and new market entrants. By doing so, they could achieve higher margins and invest in greater efficiencies, creating a virtuous cycle.

Yet today things move much too fast for that kind of chess game. To compete in a networked world, you must constantly widen and deepen connections. Instead of always looking to maximize bargaining power, you need to look for opportunities to co-create with customers and suppliers, to integrate your products and services with potential substitutes and form partnerships with new market entrants.

Power no longer resides at the top of value chains, but rather at the center of networks and collaboration has become the new competitive advantage. Value is no longer merely a target for extraction, but an asset for connection. You need to be seen to be adding value to the ecosystem in order to get value out.

The truth is that we can no longer manage for stability, we must manage for disruption. We can’t predict the future, but we can connect to it, nurture it and profit from it. Yet to do so requires far more than a simple shift in strategy and tactics. It requires a fundamental change in mindset.

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

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How to Pursue a Grand Innovation Challenge

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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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Four Hidden Secrets of Innovation

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Four Hidden Secrets of Innovation

GUEST POST from Greg Satell

Every enterprise needs to innovate. It doesn’t matter whether you are a profit-seeking business, a nonprofit organization or a government entity, the simple truth is that every business model fails eventually, because things change over time. We have to manage not for stability, but for disruption or face irrelevance.

There is no shortage of advice for how to go about it. In fact, there is far too much advice. Design thinkers will tell you to focus on the end user, but Harvard’s Clayton Christensen says that listening too much to customers is how good business fail. Then there’s open innovation, lean startups and on and on it goes.

The truth is that there is no one path to innovation. Everybody has to find their own way. Just because someone had success with one strategy, doesn’t mean that it’s right for the problem you need to solve. So the best advice is to gather as many tools for your toolbox as you can. Here are four things about innovation you rarely hear, but are crucially important.

1. Your Success Often Works Against You

For the most part, managers aren’t responsible for innovation, but as the name implies, to manage operations. That involves hiring and empowering strong employees, optimizing practices and processes and reducing errors and mistakes. You’re generally not trying to build a better mousetrap, you are trying to run things smoothly and efficiently.

It’s easy for someone to stand up on stage at a conference and paint operational managers as dimwits with their heads in the sand, but the truth is that managing a quality operation is a very tough job and requires a lot of talent, dedication and skill. So unless you’ve actually done the job, don’t be too quick to judge.

However, managers do need to realize that there is a fundamental tradeoff between innovation and optimizing operations. Running efficient operations requires standardization and control to yield predictable outcomes. Innovation, on the other hand requires experimentation. You need to try a lot of new things, most of which are going to fail.

That’s why success so often leads to failure. What makes you successful in one competitive environment will likely be a hindrance when things change. So you need to work to find a healthy balance between squeezing everything you can out of the present, while still leaving room to create and build for the future.

2. Don’t Look For A Large Addressable Market, Look For A Hair-On-Fire Use Case

Good operational managers learn to identify large addressable markets. Bigger markets help you scale your business, drive revenues and allow you invest back into operations to create more efficiency. Greater efficiencies lead to fatter profit margins, which allow you to invest even more on improvements, creating a virtuous cycle.

Yet when you are trying something to do something truly new and different, trying to scale too fast can kill your business even before it’s really gotten started. A truly revolutionary product is unpredictable because, by its very nature, it’s not well understood. Charging boldly into the unknown is a sure way to run into unanticipated problems that are expensive to fix at scale.

A better strategy is to identify a hair on fire use case — someone who needs a problem fixed so badly that they are willing to overlook the inevitable glitches. They will help you identify shortcomings early and correct them. Once you get things ironed out, you can begin to scale for more ordinary use cases.

For example, developing a self-driving car is a risky proposition with a dizzying amount of variables you can’t account for. However, a remote mine in Western Australia, where drivers are scarce and traffic nonexistent, is an ideal place to test and improve the technology. In a similar vein, Google Glass failed utterly as a mass product, but is getting a second life as an industrial tool. Sometimes it’s better to build for the few than the many.

3. Start With The Monkey First

When I work with executives, they often have a breakthrough idea they are excited about. They begin to tell me what a great opportunity it is and how they are perfectly positioned to capitalize on it. However, when I begin to dig a little deeper it appears that there is some big barrier to making it happen. When I try to ask about that, they just shut down.

Make no mistake. Innovation isn’t about ideas, it’s about solving problems. The truth is that nobody cares about what ideas you have, they care about the problems you can solve for them. The reason that most people can’t innovate isn’t because they don’t have ideas, but because they lack the perseverance needed to stick with a really tough problem until it’s cracked.

At Google X, the tech giant’s “moonshot factory,” the mantra is #MonkeyFirst. The idea is that if you want to get a monkey to recite Shakespeare on a pedestal, you start by training the monkey, not building the pedestal, because training the monkey is the hard part. Anyone can build a pedestal.

The problem is that most people start with the pedestal, because it’s what they know and by building it, they can show early progress against a timeline. Unfortunately, building a pedestal gets you nowhere. Unless you can actually train the monkey, working on the pedestal is wasted effort.

4. The Next Big Thing Always Starts Out Looking Like Nothing At All

When Alexander Fleming first published his discovery of penicillin, no one really noticed. When Xerox executives first got a look at the Alto — the machine that would become the model for the Macintosh seven years later — they didn’t see what the big deal was. When Jim Allison first showed pharmaceutical executives his idea for cancer immunotherapy, not one would invest in it.

We always think that when we see the next big thing it will be obvious, but the truth is that it always starts out looking like nothing at all. The problem is that when something truly has the power to change the world, the world isn’t ready for it yet. It needs to build advocacy, gain traction among a particular industry or field and combine with other innovations before it can make an impact.

But no one ever tells you that. We are conditioned to think that someone like Steve Jobs or Elon Musk just stands up on stage, announces that the world has changed and everybody just goes along. It never really happens that way because innovation is never a single event. It is a long process of discovery, engineering and transformation that usually takes about 30 years to fully complete.

Don’t worry about people stealing your ideas,” said the computing pioneer Howard Aiken. “If your ideas are any good, you’ll have to ram them down people’s throats” and never were truer words spoken. Great innovators aren’t just people with ideas, they are people who are willing to stick it out, take the shots from people who ridicule them and, eventually, if they are lucky, they really do change the world.

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

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What Top Innovators Do Differently

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What Top Innovators Do Differently

GUEST POST from Greg Satell

I’ve never really liked the phrase “innovate or die.” Why not, “finance or die” or “sell or die” or even “manage or die?” Clearly every business function is essential and no organization can survive without building some competency in all of them. In an ultra-competitive business environment, you have to do more than just show up.

What makes great innovators different is that they succeed where most others fail. They not only come up with new ideas, they find ways to make them work and create value for the rest of us. Even more importantly, they are able to do it consistently, year after year, decade after decade.

Over the years, I’ve gotten to know many of these extraordinary people and they are all impressive in their own way, but what has struck me is not their differences, but what they have in common. It seems that there are some things that all great innovators share and, importantly, they are all things that we can do as well. So there is hope for the rest of us.

1. They Seek Out Problems, Not Ideas

Elance launched as a startup in 1999 to do for freelancers what Monster.com did for full-time positions — create a marketplace to match employers with talent that had the skills they were looking for. It seemed like a great idea, but it turned out to be a total bust and the company soon shifted to developing vendor management software, where it had better success.

The company sold its software business in 2006 and decided to return to the original idea, but focused on a different problem. Instead of merely making matches, it would design algorithms to make engagements more successful. This time it began to gain traction and soon saw its business grow.

The team also began to see more problems it could solve. Freelancers needed to update their skills, so it added training and certification programs. Employers needed to track freelancers internally, so it created private talent clouds. Every new problem it identified led to a new solution and more value created. Elance merged with rival oDesk in 2014 to form Upwork, and continues to thrive to this day.

I found that every great innovator I met had a similar stories. To my surprise, most didn’t have a lot of ideas and the ones they did come up with weren’t necessarily any better than anybody elses. What they did have was a passion for solving problems. Some spent years or even decades to solve a single grand challenge. That passion, it seems, is what makes all the difference.

2. They Don’t Shout Eureka!

Another thing I began to notice with the best innovators, those who came up with ideas that truly changed the world, is that when they described their moment of discovery they didn’t recall any excitement. No high-fives. No shouting to the rooftops. No alerting of the media. Nothing like that at all.

Now don’t get me wrong. I’m sure that they felt excited, but it was other thoughts that were dominant. Did they get it right? What could they do to validate their findings? Were their other explanations that could explain the data they were seeing? How could they apply these new insights to a bigger problem?

One conversation in particular I remember is with Jim Allison, who developed cancer immunotherapy. When he described his discovery to me he said he “slowly started to put the pieces together.” He didn’t seem to feel brilliant. In fact, he seemed to feel a bit foolish for not noticing where the data was so clearly leading him.

Suffice it to say, nobody else saw it either until Jim pointed it out. In fact, for three years he had to pound the pavement to get anyone to invest in his idea (and that never makes you feel particularly good about yourself). But he saw that as just another problem to be solved and, through sheer will and perseverance, he prevailed. Untold thousands are alive today because he did.

3. They Are Active Collaborators

One of the people I enjoy talking to most is Bernie Meyerson, the Chief Innovation Officer Emeritus at IBM. Bernie is not only a brilliant scientist in his own right, his position puts him at the nexus of much of the really advanced work being done in a number of fields. If something important is going on, chances are he knows about it. Besides, Bernie is a tremendous amount of fun!

He was also kind enough to write the Foreword to my book, Mapping Innovation in which he recounts how he developed the Silicon-Germanium chips that make WiFi Internet connections possible. He explained how at each stage of the development process, they needed to widen the circle to bring in new people with the expertise to take the invention to the next level.

Innovation is never a single event, but a process of discovery, engineering and transformation and those three things almost never happen in the same place. So creating anything that’s truly new and important involves a series of hand-offs. Your ability to create and manage those hand-offs will, to a large extent, determine your ability to innovate.

The truth is that, when it comes to innovation, collaboration is a key competitive advantage. The lone genius is a myth. No one ever truly creates the future by themselves.
Everyone Can Innovate (Which Means That You Can Too)

G.H. Hardy was undoubtedly one of the great mathematicians of the 20th century, but he considered his greatest discovery not a theory, but a person — Srinivasa Ramanujan, the self-taught Indian prodigy. Ramanujan had sent his theories to three great English mathematicians, but it was Hardy — and only Hardy — who was able to see the breathtaking genius beneath the almost indecipherable scrawl.

That’s not to say that Hardy was the only one capable of recognizing Ramanujan’s genius, but he was the only one who took the time to look closely at the humble correspondence of a destitute Indian amateur mathematician. It was his passion, rather than any innate ability, that led him to greatness. In concluding his memoir, Hardy wrote:

The case for my life, then, or for that of any one else who has been a mathematician in the same sense which I have been one, is this: that I have added something to knowledge, and helped others to add more; and that these somethings have a value which differs in degree only, and not in kind, from that of the creations of the great mathematicians, or of any of the other artists, great or small, who have left some kind of memorial behind them.

The truth is that seeking out problems to solve, rigorously checking your facts and actively collaborating with others who can drive an idea forward are all things that anyone can do, but most don’t. It is those things that set great innovators apart.

What makes the difference is not brilliance or even hard work. Lots of brilliant people work hard and achieve little. It is the passion to contribute something, to add not only knowledge but to the collective well being, that sets great innovators apart.

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

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Humans Wanted for the Decade’s Biggest Innovation Challenges

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Humans Wanted for the Decade's Biggest Innovation Challenges

GUEST POST from Greg Satell

Every era is defined by the problems it tackles. At the beginning of the 20th century, harnessing the power of internal combustion and electricity shaped society. In the 1960s there was the space race. Since the turn of this century, we’ve learned how to decode the human genome and make machines intelligent.

None of these were achieved by one person or even one organization. In the case of electricity, Faraday and Maxwell established key principles in the early and mid 1800s. Edison, Westinghouse and Tesla came up with the first applications later in that century. Scores of people made contributions for decades after that.

The challenges we face today will be fundamentally different because they won’t be solved by humans alone, but through complex human-machine interactions. That will require a new division of labor in which the highest level skills won’t be things like the ability to retain information or manipulate numbers, but to connect and collaborate with other humans.

Making New Computing Architectures Useful

Technology over the past century has been driven by a long succession of digital devices. First vacuum tubes, then transistors and finally microchips transformed electrical power into something approaching an intelligent control system for machines. That has been the key to the electronic and digital eras.

Yet today that smooth procession is coming to an end. Microchips are hitting their theoretical limits and will need to be replaced by new computing paradigms such as quantum computing and neuromorphic chips. The new technologies will not be digital, but will work fundamentally different than what we’re used to.

They will also have fundamentally different capabilities and will be applied in very different ways. Quantum computing, for example, will be able to simulate physical systems, which may revolutionize sciences like chemistry, materials research and biology. Neuromorphic chips may be thousands of times more energy efficient than conventional chips, opening up new possibilities for edge computing and intelligent materials.

There is still a lot of work to be done to make these technologies useful. To be commercially viable, not only do important applications need to be identified, but much like with classical computers, an entire generation of professionals will need to learn how to use them. That, in truth, may be the most significant hurdle.

Ethics For AI And Genomics

Artificial intelligence, once the stuff of science fiction, has become an everyday technology. We speak into our devices as a matter of course and expect to get back coherent answers. In the near future, we will see autonomous cars and other vehicles regularly deliver products and eventually become an integral part of our transportation system.

This opens up a significant number of ethical dilemmas. If given a choice to protect a passenger or a pedestrian, which should be encoded into the software of a autonomous car? Who gets to decide which factors are encoded into systems that make decisions about our education, whether we get hired or if we go to jail? How will these systems be trained? We all worry about who’s educating our kids, but who’s teaching our algorithms?

Powerful genomics techniques like CRISPR open up further ethical dilemmas. What are the guidelines for editing human genes? What are the risks of a mutation inserted in one species jumping to another? Should we revive extinct species, Jurassic Park style? What are the potential consequences?

What’s striking about the moral and ethical issues of both artificial intelligence and genomics is that they have no precedent, save for science fiction. We are in totally uncharted territory. Nevertheless, it is imperative that we develop a consensus about what principles should be applied, in what contexts and for what purpose.

Closing A Perpetual Skills Gap

Education used to be something that you underwent in preparation for your “real life.” Afterwards, you put away the schoolbooks and got down to work, raised a family and never really looked back. Even today, Pew Research reports that nearly one in four adults in the US did not read a single book last year.

Today technology is making many things we learned obsolete. In fact, a study at Oxford estimated that nearly half of the jobs that exist today will be automated in the next 20 years. That doesn’t mean that there won’t be jobs for humans to do, in fact we are in the midst of an acute labor shortage, especially in manufacturing, where automation is most pervasive.

Yet just as advanced technologies are eliminating the need for skills, they are also increasingly able to help us learn new ones. A number of companies are using virtual reality to train workers and finding that it can boost learning efficiency by as much as 40%. IBM, with the Rensselaer Polytechnic Institute, has recently unveiled a system that help you learn a new language like Mandarin. This video shows how it works.

Perhaps the most important challenge is a shift in mindset. We need to treat education as a lifelong need that extends long past childhood. If we only retrain workers once their industry has become obsolete and they’ve lost their jobs, then we are needlessly squandering human potential, not to mention courting an abundance of misery.

Shifting Value To Humans

The industrial revolution replaced the physical labor of humans with that of machines. The result was often mind-numbing labor in factories. Yet further automation opened up new opportunities for knowledge workers who could design ways to boost the productivity of both humans and machines.

Today, we’re seeing a similar shift from cognitive to social skills. Go into a highly automated Apple Store, to take just one example, and you don’t see a futuristic robot dystopia, but a small army of smiling attendants on hand to help you. The future of technology always seems to be more human.

In much the same way, when I talk to companies implementing advanced technologies like artificial intelligence or cloud computing, the one thing I constantly hear is that the human element is often the most important. Unless you can shift your employees to higher level tasks, you miss out on many of the most important benefits

What’s important to consider is that when a task is automated, it is also democratized and value shifts to another place. So, for example, e-commerce devalues the processing of transactions, but increases the value of things like customer service, expertise and resolving problems with orders, which is why we see all those smiling faces when we walk into an Apple Store.

That’s what we often forget about innovation. It’s essentially a very human endeavor and, to measure as true progress, humans always need to be at the center.

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

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We Must Reinvent Our Organizations for A New Era of Innovation

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We Must Reinvent Our Organizations for A New Era of Innovation

GUEST POST from Greg Satell

In the first half of the 20th century, Alfred Sloan created the modern corporation at General Motors. In many ways, it was based on the military. Senior leadership at headquarters would make plans, while managers at individual units would be allocated resources and made responsible for achieving mission objectives.

The rise of digital technology made this kind of structure untenable. By the time strategic information was gathered centrally, it was often too old to be effective. In much the same way, by the time information flowed up from operating units, it was too late to alter the plan. It had already failed.

So in recent years, agility and iteration has become the mantra. Due to pressures from the market and from shareholders, long-term planning is often eschewed for the needs of the moment. Yet today the digital era is ending and organizations will need to shift once again. We’re going to need to learn to combine long-range planning with empowered execution.

Shifting From Iteration To Exploration

When Steve Jobs came up with the idea for a device that would hold “a thousand songs in my pocket,” it wasn’t technically feasible. There was simply no hard drive available that could fit that much storage into that little space. Nevertheless, within a few years a supplier developed the necessary technology and the iPod was born.

Notice how the bulk of the profits went to Apple, which designed the application and very little to the supplier that developed the technology that made it possible. That’s because the technology for developing hard drives was very well understood. If it hadn’t been that supplier, another would have developed what Jobs needed in six months or so.

Yet today, we’re on the brink of a new era of innovation. New technologies, such as revolutionary computing architectures, genomics and artificial intelligence are coming to the fore that aren’t nearly as well understood as digital technology. So we will have to spend years learning about them before we can develop applications safely and effectively.

For example, companies ranging from Daimler and Samsung to JP Morgan Chase and Barclays have joined IBM’s Q Network to explore quantum computing, even though that it will be years before that technology has a commercial impact. Leading tech companies have formed the Partnership on AI to better understand the consequences for artificial intelligence. Hundreds of companies have joined manufacturing hubs to learn about next generation technology.

It’s becoming more important to prepare than adapt. By the time you realize the need to adapt, it may already be too late.

Building A Pipeline Of Problems To Be Solved

While the need to explore technologies long before they become commercially viable is increasing, competitive pressures show no signs of abating. Just because digital technology is not advancing the way it once did doesn’t mean that it will disappear. Many aspects of the digital world, such as the speed at which we communicate, will continue.

So it is crucial to build a continuous pipeline of problems to solve. Most will be fairly incremental, either improving on an existing product or developing new ones based on standard technology. Others will be a bit more aspirational, such as applying existing capabilities to a completely new market or adopting exciting new technology to improve service to existing customers.

However, as the value generated from digital technology continues to level off, much like it did for earlier technologies like internal combustion and electricity, there will be an increasing need to pursue grand challenges to solve fundamental problems. That’s how truly new markets are created.

Clearly, this presents some issues with resource allocation. Senior managers will have to combine the need to move fast and keep up with immediate competitive pressures with the long-term thinking it takes to invest in years of exploration with an uncertain payoff. There’s no magic bullet, but it is generally accepted that the 70/20/10 principle for incremental, adjacent and fundamental innovation is a good rule of thumb.

Empowering Connectivity

When Sloan designed the modern corporation, capacity was a key constraint. The core challenge was to design and build products for the mass market. So long-term planning to effectively organize plant, equipment, distribution and other resources was an important, if not decisive, competitive attribute.

Digitization and globalization, however, flipped this model and vertical integration gave way to radical specialization. Because resources were no longer concentrated in large enterprises, but distributed across global networks, integration within global supply chains became increasingly important.

With the rise of cloud technology, this trend became even more decisive in the digital world. Creating proprietary technology that is closed off to the rest of the world has become unacceptable to customers, who expect you to maintain API’s that integrate with open technologies and those of your competitors.

Over the next decade, it will become increasingly important to build similar connection points for innovation. For example, the US military set up the Rapid Equipping Force that was specifically designed to connect new technologies with soldiers in the field who needed them. Many companies are setting up incubators, accelerators and corporate venture funds for the same reason. Others have set up programs to connect to academic research.

What’s clear is that going it alone is no longer an option and we need to set up specific structures that not only connect to new technology, but ensure that it is understood and adopted throughout the enterprise.

The Leadership Challenge

The shift from one era to another doesn’t mean that old challenges are eliminated. Even today, we need to scale businesses to service mass markets and rapidly iterate new applications. The problems we need to take on in this new era of innovation won’t replace the old ones, they will simply add to them.

Still, we can expect value to shift from agility to exploration as fundamental technologies rise to the fore. Organizations that are able to deliver new computing architectures, revolutionary new materials and miracle cures will have a distinct competitive advantage over those who can merely engineer and design new applications.

It is only senior leaders that can empower these shifts and it won’t be easy. Shareholders will continue to demand quarterly profit performance. Customers will continue to demand product performance and service. Yet it is only those that are able to harness the technologies of this new era — which will not contribute to profits or customer satisfaction for years to come — that will survive the next decade.

The one true constant is that success eventually breeds failure. The skills and strategies of one era do not translate to another. To survive, the key organizational attribute will not be speed, agility or even operational excellence, but leadership that understands that when the game is up, you need to learn how to play a new one.

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

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Department Of Energy Programs Helping to Create an American Manufacturing Future

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Department Of Energy Programs Helping to Create an American Manufacturing Future

GUEST POST from Greg Satell

In the recession that followed the dotcom crash in 2000, the United States lost five million manufacturing jobs and, while there has been an uptick in recent years, all indications are that they may never be coming back. Manufacturing, perhaps more than any other sector, relies on deep networks of skills and assets that tend to be highly regional.

The consequences of this loss are deep and pervasive. Losing a significant portion of our manufacturing base has led not only to economic vulnerability, but to political polarization. Clearly, it is important to rebuild our manufacturing base. But to do that, we need to focus on new, more advanced, technologies

That’s the mission of the Advanced Manufacturing Office (AMO) at the Department of Energy. By providing a crucial link between the cutting edge science done at the National Labs and private industry, it has been able to make considerable progress. As the collaboration between government scientists widen and deepens over time, US manufacturing may well be revived.

Linking Advanced Research To Private Industry

The origins of the Department of Energy date back to the Manhattan Project during World War II. The immense project was, in many respects, the start of “big science.” Hundreds of top researchers, used to working in small labs, traveled to newly established outposts to collaborate at places like Los Alamos, New Mexico and Oak Ridge, Tennessee.

After the war was over, the facilities continued their work and similar research centers were established to expand the effort. These National Labs became the backbone of the US government’s internal research efforts. In 1977, the National Labs, along with a number of other programs, were combined to form the Department of Energy.

One of the core missions of the AMO is to link the research done at the National Labs to private industry and the Lab Embedded Entrepreneurship Programs (LEEP) have been particularly successful in this regard. Currently, there are four such programs, Cyclotron Road, Chain Reaction Innovations, West Gate and Innovation Crossroads.

I was able to visit Innovation Crossroads at Oak Ridge National Laboratory and meet the entrepreneurs in its current cohort. Each is working to transform a breakthrough discovery into a market changing application, yet due to technical risk, would not be able to attract funding in the private sector. The LEEP program offers a small amount of seed money, access to lab facilities and scientific and entrepreneurial mentorship to help them get off the ground.

That’s just one of the ways that the AMO opens up the resources of the National Labs. It also helps business get access to supercomputing resources (5 out of the 10 fastest computers in the world are located in the United States, most of them at the National Labs) and conducts early stage research to benefit private industry.

Leading Public-Private Consortia

Another area in which the AMO supports private industry is through taking a leading role in consortia, such as the Manufacturing Institutes that were set up to to give American companies a leg up in advanced areas such as clean energy, composite materials and chemical process intensification.

The idea behind these consortia is to create hubs that provide a critical link with government labs, top scientists at academic universities and private companies looking to solve real-world problems. It both helps firms advance in key areas and allows researchers to focus their work on where they will have the greatest possible impact.

For example, the Critical Materials Institute (CMI) was set up to develop alternatives to materials that are subject to supply disruptions, such as the rare earth elements that are critical to many high tech products and are largely produced in China. A few years ago it developed, along with several National Labs and Eck Industries, an advanced alloy that can replace more costly materials in components of advanced vehicles and aircraft.

“We went from an idea on a whiteboard to a profitable product in less than two years and turned what was a waste product into a valuable asset,” Robert Ivester, Director of the Advanced Manufacturing Office told me.

Technology Assistance Partnerships

In 2011, the International Organization for Standardization released its ISO 50001 guidelines. Like previous guidelines that focused on quality management and environmental impact, ISO 50001 recommends best practices to reduce energy use. These can benefit businesses through lower costs and result in higher margins.

Still, for harried executives facing cutthroat competition and demanding customers, figuring out how to implement new standards can easily get lost in the mix. So a third key role that the AMO plays is to assist companies who wish to implement new standards by providing tools, guides and access to professional expertise.

The AMO offers similar support for a number of critical areas, such as prototype development and also provides energy assessment centers for firms that want to reduce costs. “Helping American companies adopt new technology and standards helps keep American manufacturers on the cutting edge,” Ivester says.

“Spinning In” Rather Than Spinning Out

Traditionally we think of the role of government in business largely in terms of regulation. Legislatures pass laws and watchdog agencies enforce them so that we can have confidence in the the food we eat, the products we buy and the medicines that are supposed to cure us. While that is clearly important, we often overlook how government can help drive innovation.

Inventions spun out of government labs include the Internet, GPS and laser scanners, just to name a few. Many of our most important drugs were also originally developed with government funding. Still, traditionally the work has mostly been done in isolation and only later offered to private companies through licensing agreements.

What makes the Advanced Manufacturing Office different than most scientific programs is that it is more focused on “spinning in” private industry rather than spinning out technologies. That enables executives and entrepreneurs with innovative ideas to power them with some of the best minds and advanced equipment in the world.

As Ivester put it to me, “Spinning out technologies is something that the Department of Energy has traditionally done. Increasingly, we want to spin ideas from industry into our labs, so that companies and entrepreneurs can benefit from the resources we have here. It also helps keep our scientists in touch with market needs and helps guide their research.”

Make no mistake, innovation needs collaboration. Combining the ideas from the private sector with the cutting edge science from government labs can help American manufacturing compete for the 21st century.

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

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