The Words We Choose

In a recent HBR article “Stop Calling It Innovation,” Nadya Zhexembayeva suggests ditching the term “innovation.” Her point? Employees hate innovation. Rightly or wrongly, they associate innovation with undue risk, extra work without reward, and even job loss. As a result, Prof. Zhexembayeva argues, they tend to avoid corporate innovation activities at all.

I agree with Prof. Zhexembayeva that employees hate certain things. I, however, doubt that changing the terminology alone will achieve much. Everyone hates being reprimanded by their manager but did replacing “negative feedback” with “constructive” help? Or, for that matter, will replacing “lay-offs” with “workforce reduction” or “downsizing” help alleviate the pain of being terminated?

(As a side note, there is a lot of talks these days that the future developments in Artificial Intelligence may result in a massive loss of jobs – and many employees are apprehensive about it. Isn’t it time to rename AI into something else? “Non-human cognition,” perhaps?)

Prof. Zhexembayeva’s attempt to solve corporate innovation problems with terminological fixes isn’t without precedent. Back in 2016, Stefan Lindegaard, too, called for trashing the term “innovation” and replacing it with “transformation.” (Lindegaard also suggested to get rid of the term “Chief Innovation Officer” in favor of “Chief Digital Officer.”) And later, Scott Kirsner advocated eliminating the term “corporate entrepreneur” because, in Kirsner’s opinion, this term was obstructing, rather than facilitating, corporate innovation.

I’m not against changing terminology in principle. The business environment rapidly evolves, and our mental and verbal constructs must reflect that. The critical issue, though, is whether the new term works better than the incumbent.

And this is, in my opinion, the weakest point in Prof. Zhexembayeva’s terminology upheaval. In fact, she doesn’t propose any real replacement to “innovation” except for mentioning that a couple of her clients used verbal constructs including the words “idea” and “reinvention.”

Does Prof. Zhexembayeva really imply that “idea” and “ideation” are the same thing? True, innovation starts with collecting ideas, which are then assessed, validated, tested, and finally implemented into new products, services, and operational improvements. Ignoring this all-important implementation part of the innovation process, the one that follows the ideation part, simply means a misrepresentation of what innovation really is.

Yes, there is no innovation without ideas. But the reverse isn’t true as evidenced by numerous examples of organizations collecting zillions of poorly defined “ideas” (usually during hackathons and other “idea-generating” exercises) and not knowing what to do with them.

I’d argue, however provocatively that might sound, that the periodic calls to ditch the term “innovation” reflect a sort of intellectual cowardice on the part of the corporate innovation leadership. Our corporate innovation leaders often do a very poor job in defining what innovation means specifically for their organizations. Innovation charters, a formal document outlining the major aspects of the organization’s innovation strategy, are almost unheard of. Attempts to introduce portfolio management of innovation projects are often met with a deadly fire because “structure” supposedly kills innovation. A simple idea that for each innovation objective there must be a specific innovation tool most suited for this objective, sounds almost foreign.

And on top of that, corporate innovation leaders fail to explain to their employees that today, innovation isn’t a luxury, not even a dispensable corporate function. It’s the only way to survive – for their organizations and employees alike.

Why bother? Let’s stop calling it innovation instead.

As I wrote just a few weeks ago, it falls on all of us – academics, business writers, and innovation practitioners – to educate corporate leaders and their employees on the very basics of innovation: definitions, typology, infrastructure, processes, metrics, and incentives. We need to create a set of short narratives (“Innovation101,” so to speak) giving organizations a place to start, in a practical and intuitive way.

No one will do that if we don’t. So, let’s do that and leave the fun of playing with words to creative writers.

Check out my eBook, “We the People of the Crowd…,” a collection of stories about crowdsourcing reflecting my personal experience in working with corporate and nonprofit clients.

 Image provided by Tatiana Ivanov

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United We Innovate

As I wrote on numerous occasions, in recent years crowdsourcing has become a popular topic in academic circles, business publications, and social media. Yet, its acceptance as a practical problem-solving tool has been relatively slow.

There are a few reasons for the slow adoption of this potentially powerful open innovation tool (see, for example, here and here). One of them is a huge number of different crowdsourcing platforms available in the marketplace, with some experts putting this number at 1,000 worldwide.

Obviously, navigating such an ocean of different options is challenging, to say the very least, especially for the organizations new to crowdsourcing. Mistakes are quite often in matching problems organizations want to crowdsource to platforms best suited to deal with each specific problem.

Do we really need a thousand different crowdsourcing platforms? Hardly. In fact, many of them appear and rapidly disappear without leaving any tangible record of performance. Besides, even the platforms that are still in business often do an awful job of differentiating themselves from others.

What is also puzzling is that there is almost no M&A activity in this space. As far as I remember, the only known M&A case involving a crowdsourcing platform was the 2018 Planview’s purchase of Spigit, a developer of innovation management software. And back in 2015, IdeaConnection, an open innovation service provider, and Brightidea, another developer of innovation management software, announced that they had formed a technology and services “partnership.”

The partnership between IdeaConnection and Brightidea, while no formal M&A, was a step in the right direction. Creating a one-stop-shop of innovation services could potentially help organizations launch their open innovation initiatives without the agony of going through an oversized toolbox of almost identical tools. Such partnerships could also emphasize the urgent need for consolidating internal corporate innovation programs with external (open) innovation activities.

It appears that the idea of forming “partnerships” between innovation service providers is gaining traction. Last December, a popular crowdsourcing platform HeroX signed a strategic partnership agreement with Ideanco to launch two challenges focused on climate change and food security. The partnership will also help develop startups incubated within the Ideanco Innovation Lab.

And just a few days ago, a veteran of the open innovation movement InnoCentive has signed a major partnership with the UK idea management company Wazoku with a purpose to create the world’s most comprehensive open innovation platform. The partnership is expected to integrate Wazoku’s Idea Spotlight innovation platform with InnoCentive’s global network of more than 400,000 expert problem “solvers.” Announcing the partnership, Alpheus Bingham, CEO and co-founder of InnoCentive, has specifically mentioned customer demand for “integrated offerings.”

It remains to be seen whether any of the established “partnerships” evolve in something more formal. However, the very trend of consolidating available open innovation services looks very attractive.

Check out my eBook, “We the People of the Crowd…,” a collection of stories about crowdsourcing reflecting my personal experience in working with corporate and nonprofit clients.

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The “French perfume” innovation

(This post originally appeared on Medium)

I grew up in the Soviet Union and know a thing or two about the shortage of goods. The perennial chasing of hard-to-get stuff had instilled in me and my compatriots one simple habit: buy something first, while it’s still available, and then decide whether you need this something or not. This approach was especially useful for imported items, which were rarer and even more difficult to size up.

One such item was “French perfume.” I’m using quotation marks here because when buying a precious bottle – on the black market or through a friendly connection – one didn’t have the luxury to know the brand of the future acquisition. It was just it, a bottle of “French perfume.” Pondering if the intended receiver of the item, your wife or girlfriend, would have preferred Chanel, Guerlain, or Magie Noire, was completely pointless: you could buy only what you were given. On the positive side, your loved one wouldn’t care; she would just be delighted with the gift.  She would also appreciate your effort to please her – and proud of your ability to get things done.

Today’s corporate innovation reminds me of this “French perfume.”

Volumes have been written by Ralph-Christian Ohr and others about the 3-Horizon Model of Innovation that places innovation projects into incremental, “adjacent,” and transformational buckets, each bucket implying a different time horizon and funding level. A complementary, equally useful, classification of corporate innovation projects into market-creating, sustaining, and efficiency innovations – each corresponding to a specific stage of business model development – was proposed by the late Clay Christensen and his colleagues.

And yet, time and again, our corporate innovation leaders can’t provide a working definition of what innovation means for their organizations. It’s just it, “innovation.” Innovation charters, a formal document outlining the major aspects of the organization’s innovation strategy, are almost unheard of. Attempts to introduce portfolio management of innovation projects are often met with a deadly fire because “structure” supposedly kills innovation. Worse, many corporate innovators sincerely believe that every innovation must be “disruptive” while all other types of it are for losers.

The lack of understanding of the various types of innovation inevitably leads to confusion about the available innovation tools. A simple idea that for each innovation objective there must be a specific innovation tool most suited for this objective, sounds almost foreign. Instead, one-size-fits-all fads follow each other like ocean waves hitting the innovation process shoreline – hackathons, skunkworks, innovation labs, corporate accelerators, corporate venture funds – with inevitable complaints of low innovation returns later. “Idea generation” campaigns are omnipresent, confusing minds, draining resources, frustrating participants, and resulting in pretty much nothing.

Steve Blank has a perfect definition for our corporate innovation process: innovation theater – and I humbly hope that my own term, “the French perfume innovation,” will become as popular as Blank’s.

What is to be done? My solution is simple, if not quite revolutionary: education. We need to get back to the drawing board and help organizations understand the very basics of innovation: definitions, typology, infrastructure, processes, metrics, and incentives. We need to create a set of short narratives (“Innovation101,” so to speak) giving organizations a place to start, in a practical and intuitive way.

No, I’m not calling on academics to stop deepening our scientific understanding of the innovation process. I’m urging them not to forget that by leaving behind knowledge that the innovation practitioners can’t use they make their further work less meaningful. Nor am I saying that the “new models of innovation” are completely useless. What I’m saying is let’s learn what we already have first.

Check out my eBook, “We the People of the Crowd…,” a collection of stories about crowdsourcing reflecting my personal experience in working with corporate and nonprofit clients.

Image provided by Tatiana Ivanov

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Detecting cancer in a-intelligent way

Good news from the front lines of the War on Cancer. The American Cancer Society reported the sharpest drop in cancer death rates in the United States between 2016 and 2017. The 2.2% drop, the biggest single-year drop on record, seems to be driven by accelerating declines in mortality from lung cancer, the leading cause of cancer death in the U.S.

The decline is attributed to two major causes: reduction in smoking, the biggest risk factor for lung cancer, and the development of new cancer treatments.

The ACS report also touches upon one of the bumpiest corners of the cancer field: cancer screening. Many diagnostic tests do identify cancers early when treatment is usually more effective. But they also identify growths that would never turn deadly – a phenomenon called “overdiagnosis.”

Many experts hope that further improvements in the efficiency of cancer screening can be achieved using advances in Artificial Intelligence. Two recent studies lend credit to this hope.

In the first study, a joint team of British and U.S. researchers trained an AI system to identify breast cancers using a set of ~29,000 mammograms. The authors of the study then showed that AI was able to identify cancers at least with a similar level of accuracy – and even higher in a separate experiment – as expert radiologists. Moreover, using the AI system allowed to reduce, by a few percent, the number of false-positive and false-negative results.

In the second study, conducted by a diversified team of U.S. scientists and clinicians, an AI system was fed with 2.5 million labeled samples of brain cancer biopsies and taught to identify 13 different types of brain cancer. In a side-by-side comparison, the AI system needed less than 150 seconds to make a diagnosis (as compared to about 30 minutes for a neuropathologist), with the accuracy being at least equal to that of a human tester (94.6% and 93.9%, respectively). Interestingly, the human testers were able to correctly identify samples AI could not and, vice versa, AI was able to identify samples the human testers diagnosed incorrectly.

Both studies imply that combining human testing with AI-assisted one can dramatically speed up the process of cancer screening while at the very minimum preserving the accuracy of testing.

Do we have bad news? Yes, we do. It will take years until the AI diagnostic systems become a mainstream of medical practice as large-scale clinical trials are required to get the green light from regulatory authorities.


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Computational propaganda: another dark corner of the net

Sir Tim Berners-Lee has all the reason to be proud of his life’s crown achievement: the World Wide Web. But he is not. In a series of interviews last fall, Berners-Lee complained that the internet today isn’t what he imagined almost 30 years ago when he invented it. In a long list of specific concerns, Berners-Lee mentioned the pervasiveness of ads, privacy breaches, hate speech, and fake news.

A recent report documents the rise and rapid maturation of yet another troublesome net tool: organized social media manipulation or computational propaganda, in the words of the report’s authors, Samantha Bradshaw and Philip N. Howard of the University of Oxford.

Bradshaw and Howard argue that computational propaganda, which they define as “the use of algorithms, automation, and big data to shape public life,” is becoming a pervasive and ubiquitous part of everyday life. Its presence can be spotted in 70 countries, up from 48 countries in 2018 and 28 countries in 2017.

The most troubling techniques of computational propaganda include the use of “political bots” to amplify hate speech or other forms of manipulated content, the illegal collection of data and micro-targeting, and deploying of trolls to bully or harass political opponents or journalists. Seven countries – China, India, Iran, Pakistan, Russia, Saudi Arabia, and Venezuela – have also used computational propaganda to influence political events in foreign countries.

Even nations not known for the aggressive weaponizing of the net take advantage of some “mild” forms of computational propaganda. For example, in Germany and Sweden, political parties and/or non-government entities use social media manipulation to advance political and social causes.

Facebook remains the most popular platform for social media manipulation, with some evidence of computational propaganda campaigns on Facebook found in 56 countries. At the same time, the increased use of YouTube, Instagram, and WhatsApp has also been reported.

The usage of human-operated social media accounts is still the most popular way of conducting computational propaganda: 60 out of the 70 countries use them. Bot accounts come next, with 50 out of 70 countries employing bot accounts. Of special concerns is the use of stolen or hacked accounts to conduct social media manipulation campaigns. Five countries – Guatemala, Iran, North Korea, Russia, and Uzbekistan – have been marked for this type of behavior.

In the United States, social media manipulation is actively used by government agencies, private contractors, and to a lesser extent, political parties. Human, bot, and cyborg (a blend of automation with human curation) accounts are being employed. There is no evidence of the United States’ use of computational propaganda in foreign countries; however, it was reported that a fake social network in Cuba had been created by the USAID.

The current spread of computational propaganda, as troubled as it already appears, is obviously just a beginning. Its further growth and maturation will be augmented by new technologies, such as AI, VR, and IoT. Eventually, countries and societies will have to deal with this phenomenon. Unfortunately, there are no signs that it’ll happen any time soon.


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Don’t blame crowdsourcing for “bad ideas”

As I mentioned a couple of years ago, I try to follow what academic researchers write about crowdsourcing. As a crowdsourcing practitioner, I welcome the clarity, holistic approach, and intellectual vigor academic research brings to the table. On occasion, however, I come across a paper that instead of clarifying the crowdsourcing water, muddies it.

Unfortunately, a recent HBR article (“Why Crowdsourcing Often Leads to Bad Ideas”) by Oguz A. Acar of the University of London’s Cass Business School falls in the latter category. Prof. Acar complains – and not unreasonably, I must add – that “most crowdsourcing initiatives end up with an overwhelming amount of useless ideas.” Prof. Acar believes he has identified a reason for this tsunami of “bad ideas”: our inattention to what motivates the crowd producing these ideas.

To make his point, Prof. Acar studied the motivation of the crowd members of InnoCentive, a popular crowdsourcing platform. He found that top-quality solutions usually come from crowd members driven by intrinsic and extrinsic motivation, whereas learning and social motivation have no positive effects on the quality of solutions. Prof. Acar then recommends that in order to improve the quality of submitted ideas, crowdsourcing campaigns should be designed in a way that would encourage the participation of crowd members with intrinsic and extrinsic motivations.

This is where I agree with Prof. Acar: crowd motivation does matter. I wrote about that back in 2014, and the study that Doug Williams and I conducted the same year added some field data to this conclusion.

My problem with Prof. Acar’s reasoning is that he seems to ignore the fact that the term “crowdsourcing” may mean many different things – and one mustn’t confuse them. In a nutshell, crowdsourcing consists of two major types of activities: adding capacity (“microtasking”) and accessing expertise (“crowdsourced innovation”). The second type, accessing expertise crowdsourcing, can be further divided into idea generation and problem-solving, which I propose calling the “bottom-up” and “top-down” crowdsourcing, respectively (and wrote about benefits and drawbacks of both here and here).

It’s the idea generation (“bottom-up”) version of crowdsourcing that routinely produces a large number of useless ideas. But InnoCentive, the platform Prof. Acar invoked, is the problem-solving (“top-down”), not idea generation platform. Learning what motivates people trying to solve precisely defined technical and business problems and applying this knowledge to the motivation of folks asked to generate vaguely defined “ideas” doesn’t make much sense.

But Prof. Acar’s arguments are also misleading for another, much more important reason. As other academic researchers repeatedly pointed out (see, for example, here) – and most crowdsourcing practitioners would confirm – the key factor defining the ultimate success or failure of a crowdsourcing campaign is not a crowd (its size, composition, motivation, etc.), but the question this crowd is presented with.

I call it the “80:20 rule”: 80% of unsuccessful crowdsourcing campaigns I’m aware of have failed because of the inability to properly formulate the question to be presented to a crowd; only 20% have done so because of the poor performance of the crowd.

Crowdsourcing campaigns mentioned by Prof. Acar generate a lot of useless ideas not because the crowd was badly motivated, but because the parameters of “ideas” were poorly defined by the campaign managers (“crowdmasters”). So, my recommendation to them would be this: master first the art and science of formulating a question that you’ll ask your crowd—by both properly defining the problem and describing a “perfect” solution to it.

And if the outcome of the crowdsourcing campaign falls below your expectations, don’t automatically blame the crowd – or crowdsourcing in general. Start with looking closer to yourself.

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Innovation and inequality

High-tech innovation has been a powerful driver of the U.S. economy – and as such can take full credit for the country’s prosperity since World War II. Yet, as a recent report by the Brookings Institution suggests, it has also led to what the report calls a “crisis of regional imbalance.”

The report draws attention to the fact that five top innovation metro areas – Boston, San Francisco, San Jose, Seattle, and San Diego – accounted for more than 90% of the nation’s innovation-sector growth between 2005 and 2017.

Over the same period, these metro areas increased their share of the U.S. total innovation employment from 18% to 23% – all at the expense of the bottom 90% of metro areas. As a result, fully one-third of the nation’s high-paying innovation jobs now reside in just 16 counties (and more than half in 41 counties), mostly on the West Coast and in Northeast.

Such an excessive concentration of tech (and wealth) has serious negative consequences. If for superstar hubs, that would “only” mean skyrocketing home prices and traffic gridlock, for the areas at the bottom, the situation is much worse. Deprived of the top tech talent and investment, whole portions of the country are falling into traps of perennial technological and economic underdevelopment.

Economic inequality inevitably raises social justice issues. Political backlash follows, as became apparent during the 2016 presidential election.

The report argues that markets alone won’t solve the problem and that a new nation-wide program is needed. More specifically, the report proposes creating eight to 10 new regional “growth centers” across the heartland to catalyze innovation-driven economic development in these areas.

The report estimates that the cost of such a program for the federal government would be on the order of $100 billion over 10 years. (For comparison, this is less than the cost of U.S. fossil fuel subsidies).

The report finally picks up 35 metro areas in 19 states (the Great Lakes, Upper South, and Intermountain West areas) as possible candidates for growth center designations.

A recent report by the Council on Foreign Relations, a think tank specializing in U.S. foreign policy and international affairs, highlighted the crucial role innovation plays for the American national security (I wrote about it here). Like the Brookings’ report, the CFR report calls for a national security innovation strategy to ensure the U.S. leadership in foundational and emerging technologies over the next 20 years.

Innovation needs a serious conversation in Washington, DC. Unfortunately, this is not a conversation we’re having now. Worse, given the political realities, this is a conversation we’re not likely to have any time soon.


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