Innovation is Overrated: How Execution Can Make Up For an Average Idea

The tech media is obsessed with innovation. Front pages of sites like The Verge, Wired, or Fast Company tell us very clearly that innovation is all about cool, new ideas. Pragmatic iteration is overlooked as the boring rehashing of old things, while exciting ‘moonshots’ and 10X leaps are fetishised. However, the opposite is often true: the most successful companies in the world focus on nailing iterative execution, not constant reinvention. Continue reading “Innovation is Overrated: How Execution Can Make Up For an Average Idea”

What is a great Product Strategy, really?

If software is eating the world, fueled by data, then the product manager defines its course with a product strategy. But what is a great product strategy, really?

After having spent years building products, and recently led the buildup of one of the largest product & tech organizations in Norway, people often ask me what product management is and what defines a great product strategy.

Here is my beta-attempt to give clarity to the ambiguous case of product strategy. I know it has flaws and would love your input on how to improve it! Continue reading “What is a great Product Strategy, really?”

“More than blind trial and error”: Leveraging experimentation to shape the future

What happens when a traditional model no longer works? As Global Head of Strategy & Agency Business Development for Reuters, I’m in charge of identifying new avenues for our media business to pursue. That means I look for new ways to generate revenue and drive profitability beyond our short-term horizon, which entails exploring new areas and trying to bring untested ideas to fruition. Experimentation is a big part of my team’s mission. To rethink the way you can bring value, you must keep a sense of open-mindedness and be accepting of fresh ideas. Over time, I have learned that experimentation has to be more than blind trial and error. It has to be thoughtfully devised and strategically carried out. Here are several characteristics of an experimentation-focused outlook I’ve found have served me well: Continue reading ““More than blind trial and error”: Leveraging experimentation to shape the future”

You Can’t Change Fundamental Behaviors Without Changing Fundamental Beliefs

In 2006, Blockbuster Video launched Total Access, a service that allowed its customers to rent videos online and return them to stores. The strategy was an immediate hit with customers and before long its online unit was making big gains against Netflix. It seemed that the video giant had finally cracked to code to renting videos on the Internet.

Alas, it was not to be. Investors balked at the cost of the new plan, while franchisees feared that online rentals would make them obsolete. In 2007, the company’s CEO, John Antioco, was fired and the online strategy was scrapped. Just three years later, in 2010, Blockbuster filed for bankruptcy.

Traditionally, we have looked at strategy solely as a set of plans designed to achieve specific goals. However, as we increasingly operate in a world of networks rather than hierarchies, leaders need to learn the lessons of social movements and focus on shared values. As the story of Blockbuster shows, you can’t change behaviors without first changing core beliefs. Continue reading “You Can’t Change Fundamental Behaviors Without Changing Fundamental Beliefs”

Innovation Isn’t About What You Control, But What You Can Access

Completed in 1928, Henry Ford’s River Rouge plant was a marvel of its age. It was almost 100% vertically integrated, even producing its own steel and by the 1930s over 100,000 employees worked there, producing nearly every component for the cars that Ford built. It was, at the time, considered to be a key advantage.

Nobody makes factories like that anymore though. It wouldn’t make any sense. In today’s economy, it would be impossible for any one firm to be competitive in more than a handful of the thousands of components that go into a modern automobile. That’s why today we have global supply chains.

All to often, we think of innovation as an problem of developing internal capabilities but in today’s world, far more value can be unlocked by widening and deepening connections. So we need to learn to use the entire ecosystem, including partners, suppliers, customers and open resources and think in terms of value networks rather than value chains. Continue reading “Innovation Isn’t About What You Control, But What You Can Access”

Thud: Why it’s not failure you should be afraid of – Jeff Patton

“Thud” is the sound a bowling ball makes when dropped onto damp earth. But it’s also the sound that most of our software makes when it hits the market. We’re great at celebrating our wild successes, and finding people to blame for catastrophic failures. This talk is about how we spend most of our work trying to figure out which we have on our hands: a success or a failure. Jeff will share stories of how we use discovery work to identify when we’ve got a “thud” on our hands. And, how the hardest thing to do is recognize and let go of our thuds. For more info about this talk with Jeff Patton see the meetup page.

Don’t Bet On Someone Else’s Success Story

Every bold new business idea starts with a success story. Either it is a single organization or an aggregate sample that implemented a particular strategy and achieved outstanding results. That solid track record helps to convince others to adopt it, yet somehow the new management fad fails to deliver as promised.

The problem is often one of survivorship bias. While it’s fairly easy to find an examples of those who were successful with a particular strategy, the ones who tried it and failed are often overlooked. Other times, a post hoc fallacy is at work. Just because someone implemented a particular strategy doesn’t mean that’s what led to success.

The truth is that a strategy can never be validated backward, only forward. The past is a very imperfect indicator for the future because circumstances are constantly in flux. Technology, competition and customer preferences change all the time, so whenever anybody tells us that they have come up with a sure-fire way to succeed, we need to be skeptical. Continue reading “Don’t Bet On Someone Else’s Success Story”

Develop Your Culture Like Software

Recently, I tried out a new talk at La Victoria Lab’s innovation festival in Lima where I covered an experiment we have been engaging in, somewhat by chance, at The New York Times: working on our culture like it was software. I’m not sure how the talk went over, but personally, I think we are onto something good and novel at The Times.

Nick Rockwell/The New York Times

The story I told at the FEST was about how my team and I have gone about trying to impact the tech culture at The New York Times. It should be obvious to my readers why we want to work on the culture: we want to be better — better environment, better capability, better talent, better decisions and better results. Focusing on the team is the leverage point for all of those things, and culture is the leverage point on the team. As I put it in my talk, the benevolent laziness of the software engineer led us straight to culture. Continue reading “Develop Your Culture Like Software”

AI Weekly: There are more pressing problems than god-like AI

A religion based around artificial intelligence is in the news again, this time helmed by Anthony Levandowski, a former member of Google’s self-driving car team. His argument is that humans will eventually create AI that is more intelligent than we are, making it functionally god-like, so we might as well start planning for that eventuality.

His thinking about the rise of super intelligent machines runs parallel to that of Elon Musk, who has been trumpeting the risks of artificial superintelligence on Twitter and in public appearances. (At one point, the Tesla CEO said that threats from AI posed a greater risk than North Korea.)

But while talking about an AI god grabs headlines, we have more pressing problems to consider. The AI experts I get to speak with aren’t concerned about an artificial superintelligence suddenly cropping up in the next few months and taking over the world.

Meanwhile, there’s plenty to be concerned about when it comes to immediate and unintended consequences of the machine learning techniques already available. There’s been no shortage of ink spilled over how the algorithms behind Facebook, Google, and the like are influencing our daily lives, and even our elections. And algorithmic bias continues to plague many other systems we use on a regular basis.

Take the case of speech recognition for virtual assistants like Alexa and Siri. As a white dude who grew up in California, I have little trouble conversing with those systems, but friends and acquaintances with non-standard accents are far less lucky. That may seem like a moderate source of frustration at worst, but imagine those systems becoming portals to key services, discounts, or other functionality that’s otherwise unavailable.

In earlier eras, structural biases that didn’t involve revolutionary technology have had far-reaching effects. Consider the impact of racial bias in the design of expressways and parkways in the New York metropolitan area. And photographers are still contending with the legacy of decisions that made film better suited to capturing people with lighter skin.

It stands to reason that decisions we make about AI systems today, even if their intelligence is far from godlike, could have similarly outsized impacts down the road.

As always, for AI coverage, send news tips to Blair Hanley Frank and Khari Johnson and guest post submissions to Cosette Jarrett — and be sure to bookmark our AI Channel.

Thanks for reading,

Blair Hanley Frank

AI Staff Writer

P.S. Please enjoy this video: Where AI is today and where it’s going

From the AI Channel

Alexa and Google Assistant should tell you when the next bus is coming

Rarely a week goes by without news of a new feature for AI assistants like Alexa, Bixby, or Siri. It’s a fast-moving competition between tech giants like Amazon, Samsung, and Apple, but despite billions of investment in AI and everyone from Softbank to Will.I.Am entering this space, sometimes critical or easily accomplishable tasks for the uberbots aren’t immediately addressed.

Read the full story here.

AISense wants to deliver total recall by transcribing all your conversations

There’s a new machine learning company on the block, with big ambitions to help people remember every conversation they’ve ever had. Called AISense, the company operates a voice transcription system that’s designed to work through long conversations using machine learning and provide users with a full text record of what was said.

Read the full story here.

Google gives developers more tools to make better voice apps

Google Assistant received some major upgrades in recent days, and today Google Assistant product manager Brad Abrams announced a series of changes to help developers make voice apps that interact with Google’s AI assistant, including ways to give them more expressive voices and send push notifications, as well as new subcategories for the Assistant’s App Directory.

Read the full story here.

PullString debuts Converse, a simple Alexa skills maker for marketers

PullString today announced plans to launch a simplified version of its platform, this one aimed at professionals who want to quickly design and launch voice apps. A marked departure from the company’s more complicated Author platform, Pullstring’s Converse will be available November 27 to coincide with AWS Re:Invent.

Read the full story here.

Microsoft’s Visual Studio gets new tools to help developers embrace AI

Microsoft announced today that its Visual Studio integrated development environment is getting a new set of tools aimed at easing the process of building AI systems.

Visual Studio Tools for AI is a package that’s designed to provide developers with built-in support for creating applications with a wide variety of machine learning frameworks, like Caffe2, TensorFlow, CNTK, and MXNet.

Read the full story here.

Google launches TensorFlow Lite developer preview for mobile machine learning

Google today launched TensorFlow Lite to give app developers the ability to deploy AI on mobile devices. The mobile version of Google’s popular open source AI program was first announced at the I/O developer conference.

Read the full story here.

Beyond VB

Inside the first church of artificial intelligence

Anthony Levandowski makes an unlikely prophet. Dressed Silicon Valley-casual in jeans and flanked by a PR rep rather than cloaked acolytes, the engineer known for self-driving cars—and triggering a notorious lawsuit—could be unveiling his latest startup instead of laying the foundations for a new religion. But he is doing just that. (via Wired)

Read the full story.

Where self-driving cars go to learn

Three weeks into his new job as Arizona’s governor, Doug Ducey made a move that won over Silicon Valley and paved the way for his state to become a driverless car utopia. (via The New York Times)

Read the full story.

AI could help reporters dig into grassroots issues once more

Last year’s divisive American presidential race highlighted the extent to which mainstream media outlets were out of touch with the political pulse of the country. (via MIT Technology Review)

Read the full story.

AI’s latest application: wasting scammers’ time

Schadenfreude is one of life’s simplest pleasures — especially when the victim in question is an email scammer. That’s the service Netsafe’s Re:scam provides. Simply forward your Nigerian prince emails to the service and it’ll use machine learning to generate conversations to waste the nefarious Nancy’s time. (via Engadget)

Read the full story.

4 Innovation Mistakes That You Really Need To Avoid

One of the things that startup guru Steve Blank likes to say is that no business plan survives first contact with a customer. What he means that every idea is wrong. Sometimes it’s off by a little and sometimes it’s off by a lot, but it’s always wrong and the sooner we find its flaws the sooner we can start making it work.

This holds true even when an idea is revolutionary, like electricity or the personal computer. There is always a gap between an idea and it’s impact. In fact, on average takes about 30 years to go from an initial discovery to a significant effect on our lives. That means that the “next big thing” is usually about 29 years old!

So there is no lack of fertile ideas, but still most organizations fail to innovate. The problem is not a lack of intelligence or ambition — any enterprise that is able to stay in business for any length of time obviously has both — but that innovating successfully is profoundly different than running operations, which leads managers to make four fatal innovation mistakes.

1. Seeking Only Large Addressable Markets

Every business plan starts with assumptions. You need to estimate costs, sales and growth for years in advance. Obviously, the more favorable your assumptions are, the better your business plan looks and the more likely it is to attract investment, so you want to build a case for high sales, rapid growth and low costs.

Of course, to be earn support, a business plan also needs to look realistic. Costs need to reflect market rates. Revenues need to be based on actual spending trends. There needs to be an analysis rooted in tangible benchmarks, you can’t just pull stuff out of thin air and expect to convince anybody that your plan is viable.

One solution to the problem is to target the largest addressable market you can find. In a multi-billion dollar market, capturing just a few points of market share can seem like a very reasonable assumption and, all of a sudden, your numbers start to work. From a small foothold, you can show years of steady growth.

Unfortunately, the world is not an Excel spreadsheet. No matter how big the market, somebody has to want to buy what you’re selling. So if your product is truly new and different, you need to build for the few, not the many and that means identifying a “hair on fire” use case — a customer who needs your product so badly that they will overlook early glitches.

2. Getting Trapped In Your P&L

The reason that executives feel pressure to identify large addressable markets is that it’s good operational practice. When you want to grow your business, it makes sense to look for more customers. However, there is a fundamental difference between innovation and optimizationthat leaders too often ignore.

When you are working to improve an existing business, you have a lot of information to work with. You already have customers and should have a good understanding of how they use your product or service. So identifying a large swath of new customers that have similar needs is an entirely sensible way to grow.

However, when you seek to create something that’s truly new and different, you have no way of knowing what the demand will be. The truth is that the next big thing always starts looking like nothing at all. So if you limit yourself to only the opportunities that you can quantify, you’re never going to achieve anything more than an incremental improvement.

One thing that I noticed in researching my book Mapping Innovation is that the best innovators always invest in uncertainty. Although they remain disciplined about resources and manage risk effectively, they make sure they are devoting resources to explore new areas with no idea about what the return will be. Ironically, this was the closest thing I found to a sure bet.

3. Failing To Look Beyond Internal Capabilities

A basic tenet of good management is that you want to leverage your capabilities over as large a footprint as possible. For example, once Amazon learned how to sell books online better than anyone else, it leveraged those same capabilities into other product categories and achieved similar success in the expanded markets.

So, not surprisingly, the first thing most companies look for is a proprietary asset or capability they can apply to a new market. That’s generally very sensible, but it’s also very limiting, because it ignores an enormous range of capabilities and assets among customers, partners, vendors and open platforms.

Sun Microsystems Cofounder Bill Joy once famously said, “no matter who you are, most of the smartest people work for someone else.” That’s very true, but it doesn’t go nearly far enough. The best of almost everything resides somewhere else, so by limiting yourself to what you have internally, you’re putting yourself at a real disadvantage.

Competitive advantage is not something you start with, but something you build over time. So focus less on the assets and capabilities you control and more on those you can access. By using the whole innovation ecosystem, you can greatly extend your ability to innovate.

4. Looking For A Great Idea Instead Of A Good Problem

At any given time, there are far more ideas buzzing around an organization than it can pursue. Rapid changes in technology, market trends and consumer behavior only fuel the fire. With everything that’s going on it seems that the potential for innovation is endless and, in a sense, it is. Every year countless new startups are funded and new products are launched.

Most fail. Just because you have an idea doesn’t mean it’s viable or that you can make it work. The truth is that innovation isn’t about idea, it’s about solving problems. The truth is that nobody cares about your ideas, they care about meaningful problems you can solve for them and that’s where you need to focus you energy.

While writing Mapping Innovation, I came across dozens of stories from every conceivable industry and field and it always started with someone who came across a problem they wanted to solve. Sometimes, it happened by chance, but in most cases I found that great innovators were actively looking for problems that interested them.

That’s why the firms that are able to innovate consistently — not one-hit wonders but those who make it happen year after year and decade after decade — have a systematic and disciplined process for identifying new problems. How they do that varies widely, but the core concept remains the same.

So forget about the hype and the myths. It’s the ability to identify and solve important problems that transforms disruption into opportunity.

– Greg

An earlier version of this article first appeared in

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