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This is the second and last post with a religious theme to it. Both cover the period during and after University where it was a large part of my life. In the second year at University, I was a delegate to a meeting of the Student Christian Movement. MORE
“General purpose technologies (GPTs) are engines for growth ,…” wrote Erik Brynjolfsson , Daniel Rock , and Chad Syverson in The Productivity J-Curve , a working paper recently published by the National Bureau of Economic Research (NBER). “These are the defining technologies of their times and can radically change the economic environment. They have great potential from the outset, but realizing that potential requires larger intangible and often unmeasured investments and a fundamental rethinking of the organization of production itself.”. As we’ve learned over the past two centuries, there’s generally been a significant time lag between the broad acceptance of a major new transformative technology and its ensuing impact on companies, governments and other institutions. Even after reaching a tipping point of market acceptance, it takes considerable time, - often decades, - for these new technologies and business models to be widely embraced across economies and societies, - and for their full benefits to be realized. In her 2002 influential book, Technological Revolutions and Financial Capital , economic historian Carlota Perez wrote that since the advent of the Industrial Revolution, we’ve had a major technological revolution every 60 years or so. First was the age of machines and factories in the latter part of the 18th century. This was followed by the age of steam, coal and iron in the early to mid 19th century; electricity and steel around the 1870s-1880s; and automobiles, oil and mass production in the early decades of the 20th century. Then came the computer and communications revolution i n the latter part of the 20th century, ushering the transition from the industrial economy of the past two centuries to our present digital economy. According to Perez, the economic transformations accompanying these technologies are composed of two distinct periods, each lasting roughly 20 to 30 years. First comes the installation period when the new technologies emerge into the marketplace, entrepreneurs launch many new startups, and venture capitalists encourage experimentation with new business models. This is then followed by the deployment period, when the now well accepted technologies and business models become the norm, leading to long-term economic and productivity growth. The NBER paper also identifies two phases, investment and harvesting , and explains their evolution i n the life cycle of a historically transformative technology. S ince these technologies are general purpose in nature, they require massive complementary investments , such as business process redesign, co-invention of new products and business models, and the re-skilling of the workforce. Moreover, the more transformative the technologies, the longer it takes for them to reach the harvesting phase when they are widely embraced by companies and industries across the economy. The decades-long time lags between the investment and harvesting periods has led to a kind of productivity paradox that’s puzzled economists seeking to reconcile exciting technological breakthroughs with slow near- and mid-term productivity growth. For example , US labor productivity grew at only 1.5% between 1973 and 1995. This period of slow productivity coincided with the rapid growth in the use of IT in business, giving rise to the Solow productivity paradox , a reference to Nobel Prize MIT economist Robert Solow's 1987 quip: “You can see the computer age everywhere but in the productivity statistics.” But, starting in the mid 1990s, US labor productivity surged to over 2.5%, as fast growing Internet technologies and business process re-engineering helped to spread productivity-enhancing innovations across the economy. Similarly , productivity growth did not increase until 40 years after the introduction of electric power in the early 1880s, because It took until the 1920s for companies to figure out how to restructure their factories to take advantage of electric power with new manufacturing innovations like the assembly line. And, while James Watt’s steam engine ushered the Industrial Revolution in the 1780s, its impact on the British economy was imperceptible until the 1830s because productivity growth was restricted to a few industries. The authors called this phenomenon the Productivity J-Curve , because like the letter ‘J’, GPT productivity dips initially in its investment phase while later rising in the harvesting phase. The paper includes a model that explains these J-curve dynamics, and applies the model to help understand the Solow paradox of recent decades, as well as to analyze whether recent advances in AI, machine learning and related technologies indicate the emergence of AI as a 21st century GPT. Their model takes into account both tangible and intangible inputs in evaluating total productivity growth, that is, the difference between the growth rates of all the inputs and all the outputs in a production process. Tangible capital inputs like physical equipment, infrastructure, and labor expenses are relatively easy to measure. However, along with such tangible inputs, a company spends capital on intangible inputs like innovative offerings, competitive business strategies, streamlined processes, talent development, and the creation of entirely new asset classes. The extensive intangible investments required to embrace a GPT and transform an organization are often forgotten, because they’re hard to quantify and their benefits accrue over a number of years. “Suppose a company wants to become more ‘data-driven’ and reorganize its production processes to take advantage of new machine learning prediction technologies. This firm might want, for example, to change its labor mix to build more software and to teach its customers to order products online instead of in person. While the company develops online product ordering applications and business processes for that purpose, it will not be able to use those investment resources to produce more final goods inventory. At the same time, though, the capital assets the firm is building - institutional software knowledge in the company, hiring practices, organization building, and customer retraining to use digital systems - are left unmeasured on the balance sheet.”. “On the margin, the (present-discounted and risk-adjusted) value of these unmeasured assets equals the costs incurred to produce them. But during the period in which that output is foregone, the firm’s (traditionally measured) productivity will suffer because it will seem as though the company produces proportionately less output relative to its inputs. Later, when those hidden intangible investments start to generate a yield, it will seem as though the measured capital stock and employed workers have become much more productive. Therefore, in early investment periods productivity is understated, whereas the opposite is true later when investment levels taper off.” Eventually, input and output growth rates reach a steady state and the productivity measurement problems disappear. Is there a way of measuring the value and productivity impact of a company’s intangible investments? The paper proposes a method based on the idea that hidden intangibles are still captured by the markets, and their investment value can be estimated using forward-looking measures derived from stock market valuations. It uses these methods to estimate the impact of intangible capital investments in R&D, software, and computer hardware by comparing a firm’s observable investments to its market valuation. R&D investments are large, but since it’s a mature asset type that has persisted over the long term, its J-curve dynamics are at steady-state levels. In contrast, heavy capital investments in software and computer hardware are a more recent phenomenon, so J-curve dynamics are still present. This is particularly the case with software. “Software investment has been and continues to be growing faster than overall capital investment, and its level is sufficiently large to suggest that part of the productivity slowdown might be explained by a compositional shift of investment toward digital assets.”. “The Productivity J-curve explains why a productivity paradox can be both a recurrent and expected phenomenon when important new technologies are diffusing throughout the economy,” write Brynjolfsson, Rock and Syverson in conclusion. “Adjusting productive processes to take advantage of new types of capital requires the kind of investments the statistics miss. In future, after making appropriate adjustments accounting for the Productivity J-curve, we can see new technologies everywhere including the productivity statistics.”. Artificial Intelligence Complex Systems Economic Issues Future of Work Innovation Management and Leadership Political Issues Society and Culture Technology and Strategy MORE
As we enter the third decade of the 21st century, it is time to question our institutions of governance and commerce that mostly originated during the 18th century Enlightenment. Linearity and Cartesian logic are not suitable for a connected and complex world. MORE
Long the most expensive, time-consuming, and fraught activity in IT, the integration of systems and data has seen recent breakthroughs that make it far easier, faster, and less costly to bring systems together in a high quality way. Here's how SAP is changing the art of the possible MORE
The Gallup Global Financial Health Study was published in May of 2018. The study is based on a survey of over 15,000 people in 10 different countries. MORE
What is innovation? — it is not so much about having ideas as it is about connecting and nurturing ideas. History tells us that innovation is an outcome of a massive collective effort — not just from a narrow group of young white men in California.” — Mariana Mazzucato. MORE
Looking back upon my long career , it’s frankly sobering how many once powerful IT companies are no longer around or are shadows of their former selves, e,g, Digital , Wang , Sun Microsystems , BlackBerry. MORE
By coincidence today is Gynefin day (we thin Beth Smith coined that name) an annual ritual in which UK based staff and any friends get together at my house at Lockeridge with the intention of reducing my stocks of gin. MORE
Every fortnight I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds. Here are some of the best for 2019. Word of the Year. MORE
I have frequently said that leadership today is helping make our networks smarter. Much of what we are is a direct effect of who we know and interact with. Our social networks have significant influence on who we are. MORE
Opera has been a part of my life from an early age and I was torn between the Empire Theatre in Liverpool and the Coliseum home of the English National Opera as the place. The Empire was where I first fell in love with opera on family trips to see the WNO and the SCO on tour. MORE
Today in Ottawa I delivered my 33rd and final keynote of 2019. What a thrill it has been to share my thoughts on leadership, purpose, open thinking and employee engagement to roughly … Continue reading "Five Learning Moments After a Year Working For Myself". MORE
The Los Angeles in your head is a Neutra house. You’ve seen many of them in movies , and some of them in many movies. Some of those are now gone , alas, as is the architect and preservationist who also designed, or helped design, many of the buildings that bear his surname. MORE
I finally decided on the theme for my traditional (well I’ve done it more than once) Twelve Days of Christmas post. I got a myriad of good suggestions in so I am sorted out for a few years to come! MORE
During 2019 I shared hundreds of links to useful articles, posts and resources but here are 30 (listed in chronological order) that I believe highlight 3 key themes for 2020. Modern Workplace Learning MORE
We’re heading into a new decade today. It’s not just a new year, but a new decade.* It’s a turning point, a historic moment, and provides us an opportunity to reflect on where we’ve been and where we’re headed. It’s all in the numbers. I believe in the power of numbers. MORE
Strategy For and With AI , a recent article in the MIT Sloan Management Review by David Kiron and Michael Schrage , argues that while formulating a comprehensive strategy for the use of AI technologies is absolutely necessary, it’s not sufficient. “Creating strategy with AI matters as much - or even more - in terms of exploring and exploiting strategic opportunity.” The article is based on a survey of over 3,000 executives, managers and analysts from companies in over 100 countries and 20 industries, as well as on interviews with executives and academics. AI is expected to be the biggest commercial opportunity for companies and industries over the next couple of decades. Two 2018 reports, one by PwC and one by McKinsey , estimated that AI has the potential to incrementally add around $13-$15 trillion to global economic output by 2030. The growth will come from productivity gains, - e,g, the continuing automation of routine tasks, and AI-based tools to augment human capabilities, - and from increasingly sophisticated, AI-enhanced products, services, and system-wide applications. Machine learning advances, like deep learning , have played a central role in AI’s recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed. Machine learning methods for solving problems, - subtle adjustments to the numerical weights that interconnect huge number of artificial neurons, - are radically different and complement the way humans solve problems. But, while being a very powerful tool, machine learning, and AI in general, lack all-important human qualities like common sense and empathy , so their decisions and suggestions should be carefully reviewed by humans. People and technology must closely collaborate , with each playing the particular role they are best at. Every day we can read about the latest AI advances from research labs, startups and large companies. AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, playing championship-level Go , and the early detection and diagnosis of various forms of cancer. But, can AI help address broad, open-ended and ambiguous problems like developing and executing a competitive business strategy? Strategy typically involves a series of interrelated steps, including analyzing the overall business and market environment; setting one or more end goals; formulating a high level plan to achieve the end goals; and mobilizing resources to execute the plan. Experimentation and market data help to continuously redefine and reframe the problems being addressed as well as their solutions. In a well-functioning organization, the key responsibility of operational and mid-level managers is to execute business plans and deliver against their commitments. Strategies are generally based on specific beliefs about a changing and unpredictable future. Those at the higher levels of the organization, - senior managers, executives and board members, - are responsible for managing strategic uncertainties by understanding the risks inherent in their commitments, carefully monitoring business results and market conditions, and adjusting the firm’s strategy as appropriate. These should be their top priorities , because firms can only prosper over the long term if they’re able to learn, adapt, and regularly transform themselves. Given the fast pace of technologies, markets and economies, successful firms may well need to reinvent their strategies every five to ten years. According to Kiron and Schrage, AI can play a major role in translating technological advances into strategic advantage. AI can assist management teams in the creation of novel strategies, and help them determine which outcomes to measure, how to measure them, and how to prioritize them. . In today’s data-rich markets, top business leaders rely heavily on analytics and quantitive measures to define, communicate and drive strategy. Such a reliance plays strongly to AI’s strengths. In their research, t he authors found that in an era of increasing AI investments and capabilities, enterprise strategy is defined by the key performance indicators (KPIs) that business leaders choose to optimize, which can be customer centric, cost driven, process specific or investor oriented. “These are the measures organizations use to create value, accountability, and competitive advantage. Bluntly: Leadership teams that can’t clearly identify and justify their strategic KPI portfolios have no strategy.”. The article cites the Internet as a technology that’s played a major role in transforming a company’s overall strategy. Internet-based omnichannel strategies, for example, have been used in a number of industries, - e.g., retail, financial services, healthcare, government, - to provide an integrated, seamless user experience to their customers. Internet-based platform strategies are another example which have transformed major industries, like retail, transportation and lodging. Strategies express what company leaders seek to emphasize and prioritize over a given time frame. They articulate how and why an organization expects to succeed in its chosen market, be it a superior customer experience, increased profitability or greater market share. Organizations then create measures, like KPIs, to characterize and communicate the strategic outcomes they’re after, and to hold their managers accountable for the results. “Data-driven systems, enhanced by machine learning, convert these aspirations into computation. World-class organizations can no longer meaningfully discuss optimizing strategic KPIs without embracing machine learning (ML) capabilities.”. “In an always-on big data world, your system of measurement is your strategy. Determining the optimal ‘metrics mix’ for key enterprise stakeholders becomes an executive imperative… Our research shows that AI transforms the strategist’s choices about which KPIs to optimize and how to optimize them… For any KPI portfolio, identifying and calculating how best to weight and balance individual KPIs becomes the strategic optimization challenge… AI makes that feasible, affordable, and desirable… The true strategic opportunity and impact of these technologies is the chance to rethink and redefine how the enterprise optimizes value for itself and its customers.”. “These principles have sweeping and disruptive implications. As ‘accountable optimization’ becomes an AI-enabled business norm, there is no escaping analytically enhanced oversight. Boards of directors and members of the C-suite will have a greater fiduciary responsibility to articulate which KPIs matter most - and why - to shareholders and stakeholders alike. Transformative capabilities transform responsibilities. You are what your KPIs say you are.”. Artificial Intelligence Complex Systems Data Science and Big Data Economic Issues Innovation Management and Leadership Services Innovation Smart Systems Technology and Strategy MORE
I pretty much left Twitter - and my blog - in 2013. Whereas I usually quit cold turkey, this time I lingered a bit, publishing a handful of blog posts in 2013, even one in 2014, and I'm sure that my Twitter activity followed much the same pattern; I've always said that the combination of the two is what upholds my 'Circle of Inspiration'. Why did I leave? MORE
A Holiday Greeting from Xyleme. Previous. Localize and personalize your content with Xyleme. Xyleme’s industry-leading Learning Content Management System (LCMS) helps some of the world’s largest organizations personalize and scale their content for global audiences. Contact us to find out how Xyleme can help you achieve your content goals in the new year. The post A Holiday Greeting from Xyleme appeared first on Xyleme. Blog Uncategorized MORE
If you’re a creative person, a pack rat, a shopper or a collector, minimalist living and the idea that fewer things equals greater happiness may feel impossible to you. It doesn’t have to be! MORE
In 1972 I went up to University to read Philosophy and Physics. it was a bit an indulgence at the time but has proved more than useful in later life. They are of course the foundation disciplines in the Humanities and Sciences, whatever the mathematicians say. MORE
Thank you all so much for your support of NineShift over the past 20 years! The NineShift story, which predicted and then documented the transformation of society from the Industrial Age of 2000 into the Knowledge Society of 2020, has been complete. . MORE
Deepfakes are a big thing, and a bad one. On the big side, a Google search for deepfake brings up more than 23 billion results. On the bad side, today’s top result in a search on Twitter for the hashtag #deepfake says, “Technology is slowly killing reality. MORE
High tolerance for ambiguity is a critical skill as we live and work in increasingly complex, networked environments. Navigating through turbulent times requires the ability to deal with ambiguity by seeking and making sense through a diverse network of connections of people and knowledge. MORE
I love paradox. Today I’m going explore not just one, but two, paradoxes. Both are related to the role of leadership in our changing world. MORE
Some 400 people attended the final NineShift presentation November 21, 2019, at Paradise Point Resort in San Diego. To mark the celebration, we made 'Game Over' buttons to mark the first full year of the Knowledge Society, which began Jan 1, 2020. If you want 2 'Game Over' buttons, just mail a self addressed stamped envelope to: LERN, PO Box 9, River Falls, WI 54022. Thank you for NineShift MORE
The only person to ever have guest blogged here is Graham Watt, a friend for almost 20 years. I met Graham as I was beginning my freelance career in 2003. With no commute or regular hours I could cycle during the day and drop by the local café for a chat. MORE
While my the first hill was Moel Fammau, my first mountain was Yr Wyddfa, the name being a reference to a legend of it being a cairn thrown over the giant Rhitta Gawr after his defeat by Arthur. It’s English name Snowdon means snow hill in Old English. MORE
In my book Engaging Learning , I had a suite of elements for both effective education practice and engaging experiences. Of course, the point was that they perfectly aligned. MORE
Long the most expensive, time-consuming, and fraught activity in IT, the integration of systems and data has seen recent breakthroughs that make it far easier, faster, and less costly to bring systems together in a high quality way. Here's how SAP is changing the art of the possible MORE
A few days ago a Twitter exchange contained an “OK Boomer” response to one of my tweets. At the time I laughed it off, tweeting back a pointer to Report: Burying, Cremating Baby Boomers To Generate $200 Trillion In GDP , which ran five years ago in The Onion. But it got me thinking that “OK Boomer” might be more—and worse—than a mere meme. Still, I wasn’t moved to say anything, because I had better stuff to do. MORE
Recently, Social Misfits Media’s Community Manager, Angharad Francis, interviewed the brilliant Pushpa Aman Singh, Founder of GuideStar India. MORE
In a conversation last week (ok, an engagement), the topic of content systems came up. Now this is something I’ve argued for before , in several ways. For one, separate content from how it’s delivered. And, pull content together by rules, not hardwired. MORE
This is the analysis of the UNESCO OER resolution I provided to the Canadian Commission for UNESCO prior to the discussion and vote on the resolution concerning Open Educational Resources It is now a matter of historical record that the resolution did pass. Here is the final resolution. MORE
Training is incredibly important to your employees. A survey mentioned by Your Training Edge indicated that 70 percent of workers claim training directly influences their decision to stick with their current employer or look for a new job. MORE
How do I drive myself? I was asked that in a coaching session. The question is asking how I keep learning. There are multiple answers, which I’ve probably talked about before, but I’ll reflect here. I think it’s important to regularly ask: “how do you drive yourself?” MORE
Jane Hart
DECEMBER 30, 2019
During 2019 I shared hundreds of links to useful articles, posts and resources but here are 30 (listed in chronological order) that I believe highlight 3 key themes for 2020. Modern Workplace Learning
Clark Quinn
DECEMBER 12, 2019
How do I drive myself? I was asked that in a coaching session. The question is asking how I keep learning. There are multiple answers, which I’ve probably talked about before, but I’ll reflect here. I think it’s important to regularly ask: “how do you drive yourself?”
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Dan Pontefract
DECEMBER 12, 2019
Today in Ottawa I delivered my 33rd and final keynote of 2019. What a thrill it has been to share my thoughts on leadership, purpose, open thinking and employee engagement to roughly … Continue reading "Five Learning Moments After a Year Working For Myself".
John Hagel
DECEMBER 31, 2019
We’re heading into a new decade today. It’s not just a new year, but a new decade.* It’s a turning point, a historic moment, and provides us an opportunity to reflect on where we’ve been and where we’re headed. It’s all in the numbers. I believe in the power of numbers.
Dave Snowden
DECEMBER 26, 2019
While my the first hill was Moel Fammau, my first mountain was Yr Wyddfa, the name being a reference to a legend of it being a cairn thrown over the giant Rhitta Gawr after his defeat by Arthur. It’s English name Snowdon means snow hill in Old English.
Irving Wladawsky-Berger
DECEMBER 2, 2019
Looking back upon my long career , it’s frankly sobering how many once powerful IT companies are no longer around or are shadows of their former selves, e,g, Digital , Wang , Sun Microsystems , BlackBerry.
Harold Jarche
DECEMBER 2, 2019
I have frequently said that leadership today is helping make our networks smarter. Much of what we are is a direct effect of who we know and interact with. Our social networks have significant influence on who we are.
Working Smarter Daily brings together the best content for professionals interested in working smarter from the widest variety of industry thought leaders.
Nine Shift
DECEMBER 13, 2019
Thank you all so much for your support of NineShift over the past 20 years! The NineShift story, which predicted and then documented the transformation of society from the Industrial Age of 2000 into the Knowledge Society of 2020, has been complete. .
John Hagel
DECEMBER 19, 2019
I love paradox. Today I’m going explore not just one, but two, paradoxes. Both are related to the role of leadership in our changing world.
Dave Snowden
DECEMBER 30, 2019
By coincidence today is Gynefin day (we thin Beth Smith coined that name) an annual ritual in which UK based staff and any friends get together at my house at Lockeridge with the intention of reducing my stocks of gin.
Irving Wladawsky-Berger
DECEMBER 23, 2019
“General purpose technologies (GPTs) are engines for growth ,…” wrote Erik Brynjolfsson , Daniel Rock , and Chad Syverson in The Productivity J-Curve , a working paper recently published by the National Bureau of Economic Research (NBER). “These are the defining technologies of their times and can radically change the economic environment. They have great potential from the outset, but realizing that potential requires larger intangible and often unmeasured investments and a fundamental rethinking of the organization of production itself.”. As we’ve learned over the past two centuries, there’s generally been a significant time lag between the broad acceptance of a major new transformative technology and its ensuing impact on companies, governments and other institutions. Even after reaching a tipping point of market acceptance, it takes considerable time, - often decades, - for these new technologies and business models to be widely embraced across economies and societies, - and for their full benefits to be realized. In her 2002 influential book, Technological Revolutions and Financial Capital , economic historian Carlota Perez wrote that since the advent of the Industrial Revolution, we’ve had a major technological revolution every 60 years or so. First was the age of machines and factories in the latter part of the 18th century. This was followed by the age of steam, coal and iron in the early to mid 19th century; electricity and steel around the 1870s-1880s; and automobiles, oil and mass production in the early decades of the 20th century. Then came the computer and communications revolution i n the latter part of the 20th century, ushering the transition from the industrial economy of the past two centuries to our present digital economy. According to Perez, the economic transformations accompanying these technologies are composed of two distinct periods, each lasting roughly 20 to 30 years. First comes the installation period when the new technologies emerge into the marketplace, entrepreneurs launch many new startups, and venture capitalists encourage experimentation with new business models. This is then followed by the deployment period, when the now well accepted technologies and business models become the norm, leading to long-term economic and productivity growth. The NBER paper also identifies two phases, investment and harvesting , and explains their evolution i n the life cycle of a historically transformative technology. S ince these technologies are general purpose in nature, they require massive complementary investments , such as business process redesign, co-invention of new products and business models, and the re-skilling of the workforce. Moreover, the more transformative the technologies, the longer it takes for them to reach the harvesting phase when they are widely embraced by companies and industries across the economy. The decades-long time lags between the investment and harvesting periods has led to a kind of productivity paradox that’s puzzled economists seeking to reconcile exciting technological breakthroughs with slow near- and mid-term productivity growth. For example , US labor productivity grew at only 1.5% between 1973 and 1995. This period of slow productivity coincided with the rapid growth in the use of IT in business, giving rise to the Solow productivity paradox , a reference to Nobel Prize MIT economist Robert Solow's 1987 quip: “You can see the computer age everywhere but in the productivity statistics.” But, starting in the mid 1990s, US labor productivity surged to over 2.5%, as fast growing Internet technologies and business process re-engineering helped to spread productivity-enhancing innovations across the economy. Similarly , productivity growth did not increase until 40 years after the introduction of electric power in the early 1880s, because It took until the 1920s for companies to figure out how to restructure their factories to take advantage of electric power with new manufacturing innovations like the assembly line. And, while James Watt’s steam engine ushered the Industrial Revolution in the 1780s, its impact on the British economy was imperceptible until the 1830s because productivity growth was restricted to a few industries. The authors called this phenomenon the Productivity J-Curve , because like the letter ‘J’, GPT productivity dips initially in its investment phase while later rising in the harvesting phase. The paper includes a model that explains these J-curve dynamics, and applies the model to help understand the Solow paradox of recent decades, as well as to analyze whether recent advances in AI, machine learning and related technologies indicate the emergence of AI as a 21st century GPT. Their model takes into account both tangible and intangible inputs in evaluating total productivity growth, that is, the difference between the growth rates of all the inputs and all the outputs in a production process. Tangible capital inputs like physical equipment, infrastructure, and labor expenses are relatively easy to measure. However, along with such tangible inputs, a company spends capital on intangible inputs like innovative offerings, competitive business strategies, streamlined processes, talent development, and the creation of entirely new asset classes. The extensive intangible investments required to embrace a GPT and transform an organization are often forgotten, because they’re hard to quantify and their benefits accrue over a number of years. “Suppose a company wants to become more ‘data-driven’ and reorganize its production processes to take advantage of new machine learning prediction technologies. This firm might want, for example, to change its labor mix to build more software and to teach its customers to order products online instead of in person. While the company develops online product ordering applications and business processes for that purpose, it will not be able to use those investment resources to produce more final goods inventory. At the same time, though, the capital assets the firm is building - institutional software knowledge in the company, hiring practices, organization building, and customer retraining to use digital systems - are left unmeasured on the balance sheet.”. “On the margin, the (present-discounted and risk-adjusted) value of these unmeasured assets equals the costs incurred to produce them. But during the period in which that output is foregone, the firm’s (traditionally measured) productivity will suffer because it will seem as though the company produces proportionately less output relative to its inputs. Later, when those hidden intangible investments start to generate a yield, it will seem as though the measured capital stock and employed workers have become much more productive. Therefore, in early investment periods productivity is understated, whereas the opposite is true later when investment levels taper off.” Eventually, input and output growth rates reach a steady state and the productivity measurement problems disappear. Is there a way of measuring the value and productivity impact of a company’s intangible investments? The paper proposes a method based on the idea that hidden intangibles are still captured by the markets, and their investment value can be estimated using forward-looking measures derived from stock market valuations. It uses these methods to estimate the impact of intangible capital investments in R&D, software, and computer hardware by comparing a firm’s observable investments to its market valuation. R&D investments are large, but since it’s a mature asset type that has persisted over the long term, its J-curve dynamics are at steady-state levels. In contrast, heavy capital investments in software and computer hardware are a more recent phenomenon, so J-curve dynamics are still present. This is particularly the case with software. “Software investment has been and continues to be growing faster than overall capital investment, and its level is sufficiently large to suggest that part of the productivity slowdown might be explained by a compositional shift of investment toward digital assets.”. “The Productivity J-curve explains why a productivity paradox can be both a recurrent and expected phenomenon when important new technologies are diffusing throughout the economy,” write Brynjolfsson, Rock and Syverson in conclusion. “Adjusting productive processes to take advantage of new types of capital requires the kind of investments the statistics miss. In future, after making appropriate adjustments accounting for the Productivity J-curve, we can see new technologies everywhere including the productivity statistics.”. Artificial Intelligence Complex Systems Economic Issues Future of Work Innovation Management and Leadership Political Issues Society and Culture Technology and Strategy
Harold Jarche
DECEMBER 24, 2019
The only person to ever have guest blogged here is Graham Watt, a friend for almost 20 years. I met Graham as I was beginning my freelance career in 2003. With no commute or regular hours I could cycle during the day and drop by the local café for a chat.
Clark Quinn
DECEMBER 3, 2019
In my book Engaging Learning , I had a suite of elements for both effective education practice and engaging experiences. Of course, the point was that they perfectly aligned.
Doc Searls
DECEMBER 16, 2019
Deepfakes are a big thing, and a bad one. On the big side, a Google search for deepfake brings up more than 23 billion results. On the bad side, today’s top result in a search on Twitter for the hashtag #deepfake says, “Technology is slowly killing reality.
Dave Snowden
DECEMBER 25, 2019
I finally decided on the theme for my traditional (well I’ve done it more than once) Twelve Days of Christmas post. I got a myriad of good suggestions in so I am sorted out for a few years to come!
Irving Wladawsky-Berger
DECEMBER 9, 2019
Strategy For and With AI , a recent article in the MIT Sloan Management Review by David Kiron and Michael Schrage , argues that while formulating a comprehensive strategy for the use of AI technologies is absolutely necessary, it’s not sufficient. “Creating strategy with AI matters as much - or even more - in terms of exploring and exploiting strategic opportunity.” The article is based on a survey of over 3,000 executives, managers and analysts from companies in over 100 countries and 20 industries, as well as on interviews with executives and academics. AI is expected to be the biggest commercial opportunity for companies and industries over the next couple of decades. Two 2018 reports, one by PwC and one by McKinsey , estimated that AI has the potential to incrementally add around $13-$15 trillion to global economic output by 2030. The growth will come from productivity gains, - e,g, the continuing automation of routine tasks, and AI-based tools to augment human capabilities, - and from increasingly sophisticated, AI-enhanced products, services, and system-wide applications. Machine learning advances, like deep learning , have played a central role in AI’s recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed. Machine learning methods for solving problems, - subtle adjustments to the numerical weights that interconnect huge number of artificial neurons, - are radically different and complement the way humans solve problems. But, while being a very powerful tool, machine learning, and AI in general, lack all-important human qualities like common sense and empathy , so their decisions and suggestions should be carefully reviewed by humans. People and technology must closely collaborate , with each playing the particular role they are best at. Every day we can read about the latest AI advances from research labs, startups and large companies. AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, playing championship-level Go , and the early detection and diagnosis of various forms of cancer. But, can AI help address broad, open-ended and ambiguous problems like developing and executing a competitive business strategy? Strategy typically involves a series of interrelated steps, including analyzing the overall business and market environment; setting one or more end goals; formulating a high level plan to achieve the end goals; and mobilizing resources to execute the plan. Experimentation and market data help to continuously redefine and reframe the problems being addressed as well as their solutions. In a well-functioning organization, the key responsibility of operational and mid-level managers is to execute business plans and deliver against their commitments. Strategies are generally based on specific beliefs about a changing and unpredictable future. Those at the higher levels of the organization, - senior managers, executives and board members, - are responsible for managing strategic uncertainties by understanding the risks inherent in their commitments, carefully monitoring business results and market conditions, and adjusting the firm’s strategy as appropriate. These should be their top priorities , because firms can only prosper over the long term if they’re able to learn, adapt, and regularly transform themselves. Given the fast pace of technologies, markets and economies, successful firms may well need to reinvent their strategies every five to ten years. According to Kiron and Schrage, AI can play a major role in translating technological advances into strategic advantage. AI can assist management teams in the creation of novel strategies, and help them determine which outcomes to measure, how to measure them, and how to prioritize them. . In today’s data-rich markets, top business leaders rely heavily on analytics and quantitive measures to define, communicate and drive strategy. Such a reliance plays strongly to AI’s strengths. In their research, t he authors found that in an era of increasing AI investments and capabilities, enterprise strategy is defined by the key performance indicators (KPIs) that business leaders choose to optimize, which can be customer centric, cost driven, process specific or investor oriented. “These are the measures organizations use to create value, accountability, and competitive advantage. Bluntly: Leadership teams that can’t clearly identify and justify their strategic KPI portfolios have no strategy.”. The article cites the Internet as a technology that’s played a major role in transforming a company’s overall strategy. Internet-based omnichannel strategies, for example, have been used in a number of industries, - e.g., retail, financial services, healthcare, government, - to provide an integrated, seamless user experience to their customers. Internet-based platform strategies are another example which have transformed major industries, like retail, transportation and lodging. Strategies express what company leaders seek to emphasize and prioritize over a given time frame. They articulate how and why an organization expects to succeed in its chosen market, be it a superior customer experience, increased profitability or greater market share. Organizations then create measures, like KPIs, to characterize and communicate the strategic outcomes they’re after, and to hold their managers accountable for the results. “Data-driven systems, enhanced by machine learning, convert these aspirations into computation. World-class organizations can no longer meaningfully discuss optimizing strategic KPIs without embracing machine learning (ML) capabilities.”. “In an always-on big data world, your system of measurement is your strategy. Determining the optimal ‘metrics mix’ for key enterprise stakeholders becomes an executive imperative… Our research shows that AI transforms the strategist’s choices about which KPIs to optimize and how to optimize them… For any KPI portfolio, identifying and calculating how best to weight and balance individual KPIs becomes the strategic optimization challenge… AI makes that feasible, affordable, and desirable… The true strategic opportunity and impact of these technologies is the chance to rethink and redefine how the enterprise optimizes value for itself and its customers.”. “These principles have sweeping and disruptive implications. As ‘accountable optimization’ becomes an AI-enabled business norm, there is no escaping analytically enhanced oversight. Boards of directors and members of the C-suite will have a greater fiduciary responsibility to articulate which KPIs matter most - and why - to shareholders and stakeholders alike. Transformative capabilities transform responsibilities. You are what your KPIs say you are.”. Artificial Intelligence Complex Systems Data Science and Big Data Economic Issues Innovation Management and Leadership Services Innovation Smart Systems Technology and Strategy
Harold Jarche
DECEMBER 15, 2019
High tolerance for ambiguity is a critical skill as we live and work in increasingly complex, networked environments. Navigating through turbulent times requires the ability to deal with ambiguity by seeking and making sense through a diverse network of connections of people and knowledge.
Stephen Downes: Half an Hour
DECEMBER 5, 2019
This is the analysis of the UNESCO OER resolution I provided to the Canadian Commission for UNESCO prior to the discussion and vote on the resolution concerning Open Educational Resources It is now a matter of historical record that the resolution did pass. Here is the final resolution.
Nine Shift
DECEMBER 13, 2019
Some 400 people attended the final NineShift presentation November 21, 2019, at Paradise Point Resort in San Diego. To mark the celebration, we made 'Game Over' buttons to mark the first full year of the Knowledge Society, which began Jan 1, 2020. If you want 2 'Game Over' buttons, just mail a self addressed stamped envelope to: LERN, PO Box 9, River Falls, WI 54022. Thank you for NineShift
Doc Searls
DECEMBER 7, 2019
The Los Angeles in your head is a Neutra house. You’ve seen many of them in movies , and some of them in many movies. Some of those are now gone , alas, as is the architect and preservationist who also designed, or helped design, many of the buildings that bear his surname.
Dave Snowden
DECEMBER 31, 2019
Opera has been a part of my life from an early age and I was torn between the Empire Theatre in Liverpool and the Coliseum home of the English National Opera as the place. The Empire was where I first fell in love with opera on family trips to see the WNO and the SCO on tour.
Irving Wladawsky-Berger
DECEMBER 16, 2019
The Gallup Global Financial Health Study was published in May of 2018. The study is based on a survey of over 15,000 people in 10 different countries.
Harold Jarche
DECEMBER 27, 2019
Every fortnight I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds. Here are some of the best for 2019. Word of the Year.
Xyleme
DECEMBER 12, 2019
A Holiday Greeting from Xyleme. Previous. Localize and personalize your content with Xyleme. Xyleme’s industry-leading Learning Content Management System (LCMS) helps some of the world’s largest organizations personalize and scale their content for global audiences. Contact us to find out how Xyleme can help you achieve your content goals in the new year. The post A Holiday Greeting from Xyleme appeared first on Xyleme. Blog Uncategorized
Doc Searls
DECEMBER 3, 2019
A few days ago a Twitter exchange contained an “OK Boomer” response to one of my tweets. At the time I laughed it off, tweeting back a pointer to Report: Burying, Cremating Baby Boomers To Generate $200 Trillion In GDP , which ran five years ago in The Onion. But it got me thinking that “OK Boomer” might be more—and worse—than a mere meme. Still, I wasn’t moved to say anything, because I had better stuff to do.
Dave Snowden
DECEMBER 29, 2019
This is the second and last post with a religious theme to it. Both cover the period during and after University where it was a large part of my life. In the second year at University, I was a delegate to a meeting of the Student Christian Movement.
Martijn Linssen
DECEMBER 15, 2019
I pretty much left Twitter - and my blog - in 2013. Whereas I usually quit cold turkey, this time I lingered a bit, publishing a handful of blog posts in 2013, even one in 2014, and I'm sure that my Twitter activity followed much the same pattern; I've always said that the combination of the two is what upholds my 'Circle of Inspiration'. Why did I leave?
Harold Jarche
DECEMBER 8, 2019
What is innovation? — it is not so much about having ideas as it is about connecting and nurturing ideas. History tells us that innovation is an outcome of a massive collective effort — not just from a narrow group of young white men in California.” — Mariana Mazzucato.
Dion Hinchcliffe
DECEMBER 13, 2019
Long the most expensive, time-consuming, and fraught activity in IT, the integration of systems and data has seen recent breakthroughs that make it far easier, faster, and less costly to bring systems together in a high quality way. Here's how SAP is changing the art of the possible
KCC Business Psychology
DECEMBER 11, 2019
If you’re a creative person, a pack rat, a shopper or a collector, minimalist living and the idea that fewer things equals greater happiness may feel impossible to you. It doesn’t have to be!
TOPYX LMS
DECEMBER 3, 2019
Training is incredibly important to your employees. A survey mentioned by Your Training Edge indicated that 70 percent of workers claim training directly influences their decision to stick with their current employer or look for a new job.
Dave Snowden
DECEMBER 28, 2019
In 1972 I went up to University to read Philosophy and Physics. it was a bit an indulgence at the time but has proved more than useful in later life. They are of course the foundation disciplines in the Humanities and Sciences, whatever the mathematicians say.
Beth Kanter
DECEMBER 12, 2019
Recently, Social Misfits Media’s Community Manager, Angharad Francis, interviewed the brilliant Pushpa Aman Singh, Founder of GuideStar India.
Harold Jarche
DECEMBER 29, 2019
As we enter the third decade of the 21st century, it is time to question our institutions of governance and commerce that mostly originated during the 18th century Enlightenment. Linearity and Cartesian logic are not suitable for a connected and complex world.
Dion Hinchcliffe
DECEMBER 13, 2019
Long the most expensive, time-consuming, and fraught activity in IT, the integration of systems and data has seen recent breakthroughs that make it far easier, faster, and less costly to bring systems together in a high quality way. Here's how SAP is changing the art of the possible
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