Why Every Company Needs a New Type of Operating System Enhanced with Artificial Intelligence


IMG_20150909_130429

Kyield founder Mark Montgomery on top of NM Oct., 2016, taken by Betsy Montgomery with their dog Austin

 

The Amazon acquisition of Whole Foods represents yet another confirmation of our rapidly changing business environment driven by opportunities at the confluence of technology and network dynamics. Although only the latest in a powerful trend initially impacting in this case the grocery industry, the business and technology issues driving the strategy are relevant to most and serves as a reminder that digital convergence is not confined to traditional thinking or industry lines.

A sharp devaluation of public grocery companies followed, so apparently many investors share concerns highlighted in the current HBR article “Managing Our Hub Economy”, which warns that “most companies will not become hubs, and they will need to respond astutely to the growing concentration of hub power”. The devil in the details for management is how to respond astutely. The answer is largely an AI OS.

A recent article at MIT SMR describes the complex operating environment: “The Five Steps All Leaders Must Take in the Age of Uncertainty”:

These ecosystems are nested complex adaptive systems: multilevel, interconnected, dynamic systems hosting local interactions that can give rise to unpredictable global effects and vice versa. Acknowledging the unpredictability, nonlinearity, and circularity of cause-and-effect relationships within these systems is a notable departure from the simpler, linear models that underpin traditional mechanistic management thinking.

One of the reasons for the attention in this latest combination is the integration of virtual networks with physical locations, which has been a priority for many companies, including Kroger, which shares many of the same zip codes as Whole Foods. A few days following the announcement Kroger Chairman and CEO Rodney McMullen revealed that he wasn’t surprised: “you could tell that Amazon wanted to do something from a physical asset standpoint and I think Whole Foods is a great fit for them.” Kroger is a well-managed company known for wise use of analytics, which is reflected in McMullen’s advice to investors: “you should assume that we look at any potential opportunities”.

We needed a new operating system” – Doug McMillion, CEO of Wal-Mart Stores, Inc.

The question is will traditional methods be sufficient moving forward? The answer may be found in part by looking at the world’s largest retailer. Wal-Mart was viewed as one of the most stable companies before Amazon entered their core lines of business, eventually leading to the recent conclusion by Wal-Mart CEO Doug McMillon: “We needed a new operating system”. The company recently paid $3 billion for an e-commerce component of a new OS in Jet.com, which may seem excessive to those of us unaccustomed to managing a half-trillion USD in annual revenue, though represents a relatively minor investment if it works well.

Unfortunately for 99.99% of businesses, investing $3 billion in a native e-commerce platform is not an option, particularly one experiencing significant losses. Very few of the remaining .01% could consider doing so for a partial OS. Even Walmart’s bold actions appear insufficient when we consider that the acquisition of Whole Foods was powered less by the core business of Amazon or Whole Foods than the bold manifestation of what was previously learned, resulting in an entirely new and much better business model in AWS (see article on spinning off AWS). Amazon’s strength is its ability to learn rapidly, recognize potential, and then convert and realize interconnected opportunity to new offerings in a fiercely competitive manner.

Competing in such a hypercompetitive and rapidly changing environment can be especially difficult for companies thinking and behaving in a linear manner. The retraining for me personally that began in our lab in the 1990s was a profound voyage initiating from a relatively high level. The technical training and transition involved a sharp learning curve that has only become steeper and more intense with time.

Native platforms are quite different than corporate networks that have evolved incrementally over decades. Understanding related opportunities and risk many years in advance is a critical challenge. One must peer through an asymmetrical prism constructed from tens of thousands of hours of total immersion and make bold bets that are well timed, particularly with AI systems.

Among many lessons learned is that the network economy is not only interconnected, it is also multidimensional and pre-programmed. When managed optimally and competitively the entire experience of the customer is an obsession with little deference for traditional lines.

Important considerations for an AI OS

1 – A competitive AI OS will be necessary for most to survive

Essentially all the evidence we see with mid-size companies to market leaders across most industries is that a strong AI OS is rapidly becoming the new competitive bar. If a company doesn’t have a competitive AI OS platform and the competition does, it will likely negatively impact the entire organization and each individual within it, as well as customers and partners. Google and Amazon are examples of companies that appear to be employing some functions similar to those found in our Kyield OS. While leadership and corporate strengths are critical, employing advanced AI systems is among the most important improvements any organization can make. The question really is how and when.

2  – An organization OS is not a computer OS

Many different types of operating systems exist. A few minutes of reading my book (condensed version) Ascension to a Higher Level of Performance will highlight the difference between the Kyield OS and a computer OS. The standard system is focused on universal issues common to all organizations, individuals, and networks. We have good reason to believe Kyield is among the world’s competency leaders in knowledge systems, which is a sub-specialty of artificial intelligence.

Our focus is a thin yet broad and very deep specialty with little overlap to most others, including AWS, Azure, and Google (Kyield OS integrates well with most others). Although executed with software, the Kyield OS is a ‘low code’ system compared to a computer OS and more dependent upon data and algorithmics. The system operates in the background with a simple natural language interface for corporate, group, and individual administration. The Kyield OS is transparent, non-intrusive, and interoperable so that any function can be added as needed in a highly efficient manner.

A recent note from a Fortune 50 CXO exemplifies the need from a slightly different perspective in response to our recently released HumCat offering—a new model for prevention for human-caused catastrophes, including cyber prevention.

I like your idea of an Operating System. I’m so convinced that the world is too complex and getting more complex every second that human beings cannot manage it in the right way anyway… Now, it is time (for the Kyield OS), otherwise we are on the hook of dark side of cybernetics—cybercrime or cyberwar and nobody can defend us.

3  –  Reinventing AI system wheels is not wise

As I shared with a Fortune 20 team recently, while it may be extraordinarily easy to underestimate the amount of tradecraft and secrets for such an endeavor, it is nonetheless foolhardy to do so (hence the AI talent wars). Fortunately for our customers, we’ve done the bulk of the heavy lifting. It was two decades ago this year that Kyield was originally conceived in the lab as an authentic invention (Optimizing knowledge yield in the digital workplace).

Many research and consulting reports on AI systems are available, and they have improved significantly over the past two years (See reports by MIT SMR & BCG and Nordea as recent examples), but caution is warranted. Some consulting firms are still advising to start small and experiment in areas that no longer need experimentation. Although generally appropriate five years ago, it is increasingly dangerous today as the competitive gap due to AI systems is expanding rapidly.

A good example of an ongoing experiment was highlighted in the WSJ CIO Journal: “Swiss Re Bets AI Can Help Workers Cope with Complexity of Reinsurance”. The goal is admirable, achievable and sounds impressive until reading the subtitle: “The company’s 100 data scientists and AI experts are building software that can read documents on their own.” This is not a new technology. If the article is accurate it appears that Swiss Re is spending between 10-100 times more for a small fraction of the functionality found in our Kyield OS. Other options also exist for the specific function described that would likely be much more wise than a custom effort.

Our friends at Swiss Re are far from alone. Munich Re publishes an IT radar report (with a nice diagram) based on research that “systematically analyzes opportunities, trends and technologies, and provides an ongoing insight into which technologies could be relevant for Munich Re and our customers from a very early phase”. In the current 2017 report Munich Re places predictive analytics in adoption phase while advanced machine learning and robotics process automation in the trial phase. These and other recommendations may raise some eyebrows. Predictive analytics has been deployed for many years as has advanced machine learning for specific purposes. If one is competing with a technical leader—and increasingly most are, waiting too long can be a fatal error. The first mover position, however, is not always an advantage, so such decisions need to be situation-specific.

4  – Method and sequence of adoption

To date the super majority of investment in AI systems have been strategic resulting in a few notable successes. We have also witnessed large and costly errors, including in M&A, VC, internal development, and in system designs and business models by vendors.

Horizontal systems like our Kyield OS serve as an efficient platform to unify the organization and ecosystem. Ideally a native AI OS should be adopted first. Quite apart from significant IP liability risk, since our standard system is focused on universal issues for every type of organization, with improved productivity, security, and prevention, it is difficult at best to justify internal custom efforts that replicate any of this functionality. Strategic functions are best built on top and/or integrated with our networked platform OS so that the organization and ecosystem operate in an optimal manner.

All is not lost, however, for those who have experimented in overlapping areas. The modules within the Kyield OS creates the data structure needed for compliance and then populates across the network in a manner designed to execute the functionality within the system as efficiently as possible, including for accuracy, integration and financial efficiency.

Conclusion

As important as external competition can be in this environment, the degree to which displacement will occur is dependent on a number of factors. All things being equal otherwise, the outcome primarily depends on the incumbent’s actions, its people, systems, and processes. Even though some companies may seem well positioned, the fundamental economic and business environment is rapidly changing. To the best of my awareness, survival from this point forward will essentially require a strong AI OS for the super majority of organizations.

Mark Montgomery is the founder and CEO of Kyield, originator of the theorem ‘yield management of knowledge’, and inventor of the patented AI system that serves as the foundation for Kyield: ‘Modular System for Optimizing Knowledge Yield in the Digital Workplace’. He can be reached at markm@kyield.com.

Advertisements

Why the U.S. Must Lead the World with Intelligent Infrastructure


Americans have been through a great deal in the last two decades. In addition to the network effect that consolidated wealth in a few zip codes, we endured rapid globalization that benefited other nations at the expense of our own, the 9/11 terrorist attacks, multiple wars, and the global financial crisis, all within the first few years of this millennium.

To put this series of interconnected events in perspective, the collective shock is roughly comparable to the impact of a small super volcano, a minor asteroid, or a limited nuclear war. Catastrophes of this scale are thought to be of sufficient size to change the course of modern civilization, depending of course upon our response.

The most recent CBO report forecasts the U.S. federal debt at 150 percent of gross domestic product in 2047, which would place the U.S. as the third most indebted nation in 2017 between Greece and Lebanon. This is obviously not where the U.S. wants to be in 30 years. Fortunately, such a decline is unnecessary and well within our power to avoid, though the path is narrow and hazards are many.

Catching up on deferred maintenance is a necessary but insufficient plan for the challenges facing Americans. A modern strategic infrastructure plan should be focused on unleashing the current national economy similar to previous eras with the intercontinental railroad, interstate highway system, or electric grid.

The focus should be maximize benefits from our inventions, engineered systems and technologies to recreate a sustainable competitive advantage. One benefit of lagging behind other countries in infrastructure is that much progress has been made in recent years. Future projects can be embedded with hardware that enable intelligent networks, which can then be managed with distributed operating systems enhanced with artificial intelligence (AI) to meet the diverse needs of our society.

AI systems can substantially resolve many of the destructive forces and high-risk areas facing the modern economy, including the ability to provide far more effective governance in a highly complex data-driven world, prevention of most types of human-caused catastrophes, improved workplace productivity, more effective security, and reversal of the dangerous trajectory in healthcare costs.

In order to realize the full potential of a national intelligent infrastructure strategy, it must be planned in a highly specific manner. Intelligent infrastructure is driven by physics and engineering, which can be easily damaged by misguided or corrupted politics. The value of AI systems is substantially dependent upon the availability and integrity of data. Important priorities include but are not limited to interoperability, security, privacy, business modeling, cost of ongoing maintenance, and adaptability for future innovations.

The combination of technical viability in AI systems with the current macro economic scenario has created a perfect storm for public-private partnerships. Funds with trillions USD under management are in search of improved yields in mid to long-term bonds that offer lower risk profiles and diversification, which allows risk transfer to investors for specific projects rather than tacked on to an unsustainable national debt trajectory. Moreover, the combination of intelligent infrastructure with AI systems can improve productivity, provide attractive return on investment, and create new high paying jobs that will be competitive far into the future.

The federal government should act as the policy and standards body to avoid hard lessons learned in previous national infrastructure programs. A plug-and-play architecture is needed that encourages economic diversification in all states, fosters new business formation and new wealth creation that has the capacity for reversing the increasingly historic wealth gap.

In today’s fast moving hyper competitive world, the U.S. cannot afford to wait a century to unravel the type of monopolies that were cobbled together during the formation of the electric grid. The network economy increasingly represents the entire economy so must be as diversified and dynamic as society if to remain healthy and sustainable.

If the Trump administration and U.S. Congress seize this historic opportunity for a strategic intelligent infrastructure plan, they should find bipartisan support as it can positively impact every zip code in America, which could serve to reunify the nation around common good. Executed well, a strategic intelligent infrastructure plan can serve as a solid foundational platform to solve many of the current and future challenges facing America and the world.

Mark Montgomery is the founder and CEO of Kyield, originator of the theorem ‘yield management of knowledge’, and inventor of the patented AI system that serves as the foundation for Kyield: ‘Modular System for Optimizing Knowledge Yield in the Digital Workplace’. He can be reached at markm@kyield.com.

We must empower a more diversified economy in 2016


Austin Christmas Hat 1

Those of us growing up in the 1960s and 1970s experienced tumultuous times that had some similarities to the last decade. Among many other contributions from our generation—which include both positive and negative influences—were some great artists, one of whom Bob Dylan is featured in a massive IBM ad campaign. Dylan’s poetry is timeless and quite relevant today:

The post WW2 era we grew up in provided the best economic conditions the world has ever known. The baby boom population explosion, of which I am at the tail end of, combined with vast sequential gains in productivity to create the ‘miracles’ of economies in the U.S., Japan, Germany, and China among others, or so it seemed.

Although a few credible experts have warned all along that the world’s trajectory wasn’t sustainable, and perhaps most of us intuitively realized same, the financial crisis contained a potential silver lining in revealing the stark naked truth: much of that ‘success’ in the post war era came at the direct expense of the future, and the bills are coming due.

Although woefully deficient in ethics with poor visibility of systemic risk—even in cases where desire for prevention existed, master politicians and financial engineers in both the public and private sector have masked structural problems in the economy for decades—from the public and each other, by employing ever-more complex short-term remedies in a misguided game of musical chairs.

Unfortunately, the resolution of the financial crisis has consisted primarily of the very same type of financial engineering—it’s the only hammers central banks have in their toolbox. While central bankers are justified in pointing fingers at political and fiscal malfeasance, it’s up to humble citizens like me to hold up a mirror and suggest that they take a look to see that such malfeasance would not be possible if not empowered by monetary policy.

One certainty is that the super majority of consolidated malfeasance in much of the world has been transferred to the balance sheets of central banks and national debt at direct cost to billions of people, many of whom followed the rules, not least those who saved all their lives just as their public institutions recommended.  Those savings have been taxed for nearly a decade now by monetary policy rather than a democratic process; by devaluation of currency, record low (or negative) interest rates, inflation from asset bubbles such as commodities and housing, and the need for hundreds of millions to tap their principal for survival. Also certain is that regardless of whether or which stimulus measures were necessary, one outcome has been a dangerous expansion of the wealth gap now at record level in the modern era.

It’s very important to better understand that the previous wealth gap peak in the 1920s was partially causal to the Great Depression and WW2, among other earlier great revolutions and loss of life. Today’s billionaires seem to understand the moral hazard and potential for backlash, which is presumably one of the reasons for the philanthropic pledges. A nice gesture that will hopefully do much good, philanthropy is not an alternative for economic diversification, though can help if targeted for that purpose.

The financial crisis represents precisely how corrosive moral hazard is realized at dangerous levels that can reach critical mass, which could be triggered by unforeseen events.  Moral hazard is a psychological phenomenon, which occurs from regulatory, governance and policy failures that then combine with the ensuing economic weakness to cause the next crisis.  In this case the trigger was regulatory failure followed by heavy-handed resolution that caused massive collateral damage, further harming innocent citizens worldwide. In such cases where the non-virtuous (aka vicious) cycle is not interrupted by a moral realignment, typically through accountability by the justice system, strong credible governance, and adoption of new systems that punishes crimes and rewards beneficial behavior, then civilizations can and do rapidly decline.

In hindsight from a high level view, from a hopefully wiser former business consultant who has studied related phenomenon for decades now, it appears that we enjoyed a long period of one-off exploitations of planet and people combined with ever-increasing public debt and corruption supporting promises by politicians and institutions that were far beyond their means to deliver.

The bad news is that the combination of public debt and future liabilities tragically promised by politicians—and now expected—some portion of which is necessary to survive in the high cost modern economy caused by these policies, can’t possibly be paid by the current economy.

The good news is that not all of that massive spend on R&D over decades has gone to waste, and we now have much more accountable systems that can indeed prevent the super majority of future crises, if only we can muster the courage to adopt them. We are also seeing dramatic improvements in systems that have the capacity for exponential productivity growth over time, which is the only method in our current economic system to cover national debt, unfunded liabilities, and the needs of a quickly aging global population, given the immense future needs in healthcare, environment and economics.

So my plan for 2016 is to tap the exponentially decreased cost and performance improvement in computing hardware and algorithmics to extend our networked artificial intelligence system to the mid-market, NGOs, and governments to provide them with a world class system unavailable to anyone at any cost until very recently. My hope is that our Kyield OS will help even the playing field and lead to a more dynamic and robust economy of the type that is only possible with healthy balance of diversification. Soon thereafter we plan to do the same directly for small business and individuals.

“If your time to you is worth savin’
you better start swimmin’, or you’ll sink like a stone
For the times they are a-changin’”
– Bob Dylan

Revolution in IT-Enabled Competitiveness


Four Stages of Enterprise Network Competence

Most current industry leaders owe their existence beyond basic competencies and resources to a strong competitive advantage from early adoption of systems engineering and statistical methods for industrial production that powered much of the post WW2 economy. These manual systems and methods accelerated global trade, extraction, logistics, manufacturing and scaling efficiencies, becoming computerized over the last half-century.

The computer systems were initially highly complex and very expensive, though resulted in historic business success such as American Airlines’ SABRE in 1959 [1] and Walmart’s logistics system staring in 1975 [2], which helped Walmart reach a billion USD in sales in a shorter period than any other company in 1980.

As those functions previously available to only a few became productized and widely adopted globally, the competitive advantage began to decline. The adoption argument then changed from a competitive advantage to an essential high cost of entry.[3]   When functionality in databases, logistics and desktops became ubiquitous globally the competitive advantage was substantially lost, yet costs continued to rise in software while falling dramatically in hardware, causing problems for customers as well as national and macro global economics. In order to achieve a competitive advantage in IT, it became necessary for companies to invest heavily in commoditized computing as a high cost of initial entry, and then invest significantly more in customization on top of the digital replicas most competitors enjoyed.

The network era began in the 1990s with the commercialization of the Internet and Web, which are based on universal standards, introduced a very different dynamic to the IT industry that has now impacted most sectors and the global economy. Initially under-engineered and overhyped for short-term gains during the inflation of the dotcom bubble, long-term impacts were underestimated as evidenced by ongoing disruption today causing displacement in many industries. We are now entering a new phase Michael Porter refers to as ‘the third wave of IT-driven competition’, which he claims “has the potential to be the biggest yet, triggering even more innovation, productivity gains, and economic growth than the previous two.” [4]

While I see the potential of smart devices similar to Porter, the potential for AI-enhanced human work for increased productivity, accelerated discovery, automation, prevention and economic growth is enormous and, similar to the 1990s, while machine intelligence is overhyped in the short-term, the longer term impact could indeed be “the biggest yet” of the three waves. This phase of IT-enabled competitiveness is the logical extension of the network economy benefiting from thousands of interoperable components long under development from vast numbers of sources to execute the ‘plug and play’ architecture many of us envisioned in the 1990s. This still emerging Internet of Entities when combined with advanced algorithmics brings massive opportunity and risk for all organizations in all sectors, requiring operational systems and governance specifically designed for this rapidly changing environment.

This is a clip from an E-book nearing completion titled: The Kyield OS: A Unified AI System; Rapid Ascension to a Higher Level of Performance. Existing or prospective customers are invited to send me an email for a copy upon completion within the next month – markm at kyield dot com.

[1] https://www.aa.com/i18n/amrcorp/corporateInformation/facts/history.jsp

[2] http://www.scdigest.com/ASSETS/FIRSTTHOUGHTS/12-07-26.php?cid=6047

[3] Lunch discussion on topic with Les Vadasz in 2009 in Silicon Valley.

[4] https://hbr.org/2014/11/how-smart-connected-products-are-transforming-competition

New Video- Kyield Enterprise: Human Economics in Adaptive NN


Five Essential Steps For Strategic (adaptive) Enterprise Computing


Given the spin surrounding big data, duopoly deflection campaigns by incumbents, and a culture of entitlement across the enterprise software ecosystem, the following 5 briefs are offered to provide clarity for improving strategic computing outcomes.

1)  Close the Data Competency Gap

Much has been written in recent months about the expanding need for data scientists, which is true at this early stage of automation, yet very little is whispered in public on the prerequisite learning curve for senior executives, boards, and policy makers.

Data increasingly represents all of the assets of the organization, including intellectual capital, intellectual property, physical property, financials, supply chain, inventory, distribution network, customers, communications, legal, creative, and all relationships between entities. It is therefore imperative to understand how data is structured, created, consumed, analyzed, interpreted, stored, and secured. Data management will substantially impact the organization’s ability to achieve and manage the strategic mission.

Fortunately, many options exist for rapid advancement in understanding data management ranging from off-the-shelf published reports to tailored consulting and strategic advisory from individuals, regional firms, and global institutions. A word of caution, however—technology in this area is changing rapidly, and very few analysts have proven able to predict what to expect within 24-48 months.

Understanding Data Competency

    • Data scientists are just as human as computer or any other type of scientist
    • A need exists to avoid exchanging software-enabled silos for ontology-enabled silos
    • Data structure requires linguistics, analytics requires mathematics, human performance requires psychology, predictive requires modeling—success requires a mega-disciplinary perspective

2)  Adopt Adaptive Enterprise Computing

A networked computing workplace environment that continually adapts to changing conditions based on the specific needs of each entity – MM 6.7.12

While computing has achieved a great deal for the world during the previous half-century, the short-term gain became a long-term challenge as ubiquitous computing was largely a one-time, must-have competitive advantage that everyone needed to adopt or be left behind.  It turns out that creating and maintaining a competitive advantage through ubiquitous computing within a global network economy is a much greater challenge than initial adoption.

A deep misalignment of interests now exists between customer entities that need differentiation in the marketplace to survive and much of the IT industry, which needs to maintain scale by replicating the precise same hardware and software at massive scale worldwide.

When competitors all over the world are using the same computing tools for communications, operations, transactions, and learning, yet have a dramatically different cost basis for everything else, the region or organization with a higher cost basis will indeed be flattened with economic consequences that can be catastrophic.

This places an especially high burden on companies located in developed countries like the U.S. that are engaged in hyper-competitive industries globally while paying the highest prices for talent, education and healthcare—highlighting the critical need to achieve a sustainable competitive advantage.

Understanding adaptive enterprise computing:

    • Adaptive computing for strategic advantage must encompass the entire enterprise architecture, which requires a holistic perspective
    • Adaptive computing is strategic; commoditized computing isn’t—rather should be viewed as entry-level infrastructure
    • The goal should be to optimize intellectual and creative capital while tailoring product differentiation for a durable and sustainable competitive advantage
    • Agile computing is largely a software development methodology while adaptive computing is largely a business strategy that employs technology for managing the entire digital work environment
    • The transition to adaptive enterprise computing must be step-by-step to avoid operational disruption, yet bold to escape incumbent lock-in

3)  Extend Analytics to Entire Workforce

Humans represent the largest expense and risk to most organizations, so technologists have had a mandate for decades to automate processes and systems that either reduce or replace humans. This is a greatly misunderstood economics theory, however. The idea is to free up resources for re-investment in more important endeavors, which has historically employed the majority of people, but in practice the theory is dependent upon long-term, disciplined, monetary and fiscal policy that favors investment in new technologies, products, companies and industries. When global automation is combined with an environment that doesn’t favor re-investment in new areas, as we’ve seen in recent decades, capital will sit on the sidelines or be employed in speculation that creates destructive bubbles, the combination of which results in uncertainty with high levels of chronic unemployment.

However, while strategic computing must consider all areas of cost competitiveness, it’s also true that most organizations have become more skilled at cost containment than human systems and innovation. As we’ve observed consistently in recent years, the result has been that many organizations have failed to prevent serious or fatal crises, failed to seize missed opportunities, and failed to remain innovative at competitive levels.

While hopefully the macro economic conditions will broadly improve with time, the important message for decision makers is that untapped potential in human performance analytics that can be captured with state-of-the-art systems today is several orders of magnitude higher than through traditional supply chain analytics or marketing analytics alone.

Understanding Human Performance Systems:

    • Improved human performance systems improves everything else
    • The highest potential ROI to organizations today hasn’t changed in a millennium: engaging humans in a more competitive manner than the competition
    • The most valuable humans tend to be fiercely protective of their most valuable intellectual capital, which is precisely what organizations need, requiring deep knowledge and experience for system design
    • Loyalty and morale are low in many organizations due to poor compensation incentives, frequent job change, and misaligned motivation with employer products, cultures and business models
    • Motivation can be fickle and fluid, varying a great deal between individuals, groups, places, and times
    • For those who may have been otherwise engaged—the world went mobile

4)  Employ Predictive Analytics

An organization need not grow much beyond the founders in the current environment for our increasingly data rich world to require effective data management designed to achieve a strategic advantage with enterprise computing. Indeed, often has been the case where success or failure depended upon converting an early agile advantage into a more mature adaptive environment and culture. Within those organizations that survive beyond the average life expectancy, many cultures finally change only after a near-death experience triggered by becoming complacent, rigid, or simply entitled to that which the customer was in disagreement—reasons enough for adoption of analytics for almost any company.

While the need for more accurate predictive abilities is obvious for marketers, it is no less important for risk management, investment, science, medicine, government, and most other areas of society.

Key elements that impact predictive outcomes:

    • Quality of data, including integrity, scale, timeliness, access, and interoperability
    • Quality of algorithms, including design, efficiency, and execution
    • Ease of use and interpretation, including visuals, delivery, and devices
    • How predictions are managed, including verification, feed-back loops, accountability, and the decision chain

5)  Embrace Independent Standards

Among the most important decisions impacting the future ability of organizations to adapt their enterprise computing to fast changing external environmental forces, which increasingly influences the ability of the organization to succeed or fail, is whether to embrace independent standards for software development, communications, and data structure.

Key issues to understand about independent standards:

    • Organizational sovereignty—it has proven extremely difficult and often impossible to maintain control of one’s destiny in an economically sustainable manner over the long-term with proprietary computing standards dominating enterprise architecture
    • Trade secrets, IP, IC, and differentiation are very difficult to secure when relying on consultants who represent competitors in large proprietary ecosystems
    • Lock-in and high maintenance fees are enabled primarily by proprietary standards and lack of interoperability
    • Open source is not at all the same as independent standards, nor necessarily improve adaptive computing or TCO
    • Independent standards bodies are voluntary in most of the world, slow to mature, and influenced by ideology and interests within governments, academia, industry, and IT incumbents
    • The commoditization challenge and need for adaptive computing is similar with ubiquitous computing regardless of standards type

Best of Kyield Blog Index


I created an index page containing links to the best articles in our blog with personal ratings:

Best of Kyield Blog Index.

Clarifying Disruption: Operations vs. Innovation


Part 1 of Series

The word disruption has multiple meanings in global business with the most commonly used definition some variation of “the act of interrupting continuity”.  Within the context of logistics, supply chain, manufacturing, IT, and other business operations, disruption is obviously an experience managers work diligently to avoid.  A good example of a recent operational disruption was caused by the Sendai quake and tsunami; a natural disaster which was unpreventable, but predictable and therefore can be mitigated with careful risk management planning.

In the context of innovation, however, and long-term economic survival, disruption can be paradoxical when “the act of interrupting continuity” of tightly controlled markets, stale products, and outdated business models is not an evil, but rather can be a savior to businesses, ecosystems, and economies, preventing eventual operational disruption, or as we’ve seen in many cases—complete failure.

Animal Instincts

Central to the theme of disruption in innovation is the nature of our species.  We humans tend to be creatures of habit even when presented with evidence that the behavior is self-destructive in the long-term.  In similar fashion, individuals and organized groups such as governments and corporations often refuse to change behavior even when continually presented with evidence that the cost of the short-term comfort zone may well be long-term survival, and of course fear and greed are ever present.

While resistance to change is often strongest in absolute monopolies, similar cultures are commonly found anywhere deep disequilibrium exists in the tension between security and progress, speaking to the need for competition.  Entire industries or regions can become static relative to the world quickly today, displaying little evidence of awareness in decision making.  Mix in a heavy dose of risk averse corporate cultures, conflicting (real and perceived) interests internally and externally, a bit of PR spin, and regional translation leakage between multiple native languages, confusion surrounding the issue of disruption becomes the norm rather than the exception.

History is overflowing with examples of the high costs of failing to intentionally disrupt the status quo with innovation.  A few recent cases that come to mind include:

Government

  • Failure to disrupt poor U.S. fiscal management and lack of accountability (in part with innovation) over a long period now threatens operational disruption

  • Failure to disrupt the U.S. healthcare and public educational system has greatly exacerbated the U.S. fiscal challenge, reflecting why prevention of negative spirals with continual improvement is so important

Mobile Technology

  • Nokia’s failure to maintain leadership in smart phones is now significantly impacting not just Nokia, but Finland’s national economy

  • Rim’s response to the iPad, which seemed unable to take the risk to cannibalize, failed to physically disrupt by tethering the Playbook with the Blackberry phone

  • Border’s failure to embrace disruptive digital publishing ended with liquidation

Offensive and Defensive Strategies

The need to disrupt static cultures, reform or replace decaying business models, and introduce competitive products is well known in management circles of course, so many kinds of offensive strategies, tactics, and systems have been crafted to overcome this age-old challenge, including motivational techniques, educational tools, recruitment practices, incentives, internal R&D, outsourcing, partnering, spin-offs, join ventures, acquisitions, IP licensing, and strategic venture capital. Quite a few companies have prospered through multiple business cycles employing a variation of all of the above in a persistent quest to achieve and maintain an optimal balance between growth and risk over the short-term and long.  The number of companies achieving mediocrity upon maturity is far greater, however.

One common method of defense is the formation of cartels, particularly with commodities or commoditized products that are susceptible to innovative new comers or companies moving into their markets.  Cartels and oligopolies can generate high margins for long periods of time and form very strong barriers to innovation, but eventually market and trade imbalances combined with innovation and conflicting interests of the members begin to fragment the cartel and erode market power, opening a window for competition that has proven to be healthy for incumbents, markets, and economies.  When economies stagnate, it’s generally a sign that incumbents have too much market power, usually achieved in part by manipulating the political processes, which is just one reason of many why corruption should be avoided.

The word cannibalism is sometimes used to describe what is often a difficult internal corporate process of intentionally replacing aging products that are still providing a significant portion of cash flow, with more competitive products. Another term used to describe disruptive innovation in the broader economy is creative destruction, popularized by Joseph Schumpeter in the 1940s, which describes the theory of replacing the old with the new in the entrepreneurial process. In the modern global economy, situations and cultures that allow progress without disrupting entrenched interests are quite rare.

In part 2 of the series, we’ll explore how innovation is beginning to revolutionize the innovation process in the digital enterprise.

On Her 235th Birthday, America Desperately Needs Lean, Open, and Secure Governance


Baby boomers like myself clearly recall the tumultuous years leading up to the Bicentennial of the United States.  The world we grew up in was near the peak of the industrial revolution, dominated by the aftermath of the Great Depression, WW2, and the Cold War.  We were raised in a culture that had witnessed first-hand the power of a unified government, which led to the victory of fascism in our parent’s generation, followed by a round trip to the moon in our own. In the childhood of my generation, nothing was impossible with sufficient government power.

By 1976, however,  America had endured the 1960s cultural revolution, the Vietnam War,  a serious energy crisis, stagflation, and Watergate.  We were experiencing the shocking end to the post war boom, with new revelations that success had a price, military power had limits, government was not always trustworthy, and our industrial economy had a soft underbelly leaking oil.

By the late 1970s, interest rates were skyrocketing, inflation seemed out of control, the Cold War was threatening to become white hot, and U.S. public debt had risen to the shocking level of $900 billion, representing one third of U.S. GDP.  During the next decade of economic expansion led largely by financial engineering and services, the U.S. debt more than tripled in dollar terms, rising to nearly 60% of GDP.

During the 1990s, with the commercialization of the Internet and exponential adoption of computer networking worldwide, the global economy began to shift, but the information revolution did not result in taming the industrial revolution—at least in the short-term, but rather acted as a catalyst in shifting heavy industry from West to East in our never ending quest for growth and scale. The dot-com bubble provided a very brief respite from accumulating debt in the form of capital gains, but it was a one-time gain.

By the late 1990s it became apparent that the unfettered Internet, in ironic contrast to the core message in The Wealth of Nations, offered such disruptive efficiency that many industries would be radically transformed, including the service economy that had become dominant in the U.S.

Meanwhile, global companies became too big to fail, increasingly divorcing themselves from U.S.interests in what became the primary global strategy for risk reduction and growth, which only compounded the challenges facing the U.S. economy.  By extension, regional and national economies dependent on the industrial revolution or services would also need to adopt the efficiencies offered by the new medium in order to avoid eventual bankruptcy.  In modern parlance, the trajectory of our national budget was increasingly in misalignment with the needs of our economy, the super majority of our citizens, and our collective future.

Rather than downsize to meet the new reality and future obligations, the post 9/11 economy witnessed increased liquidity that  “saved the economy” (Alan Greenspan), combined with post war guarantees in banking, systemic corruption, and ideological activism to enable the mega housing bubble, followed by the inevitable correction and almost certain economic depression if not for historic levels of Keynesian intervention. Rather than invest massive stimulus in converting to a sustainable trajectory, however, most of the spending was targeted at populist programs that continued to expand government overhead, thus increasing long-term liabilities, primarily in very temporary form that now leaves regional economies facing an even more challenging future, and citizens faced with much greater national debt; short, mid, and long-term.  The promises made by government during and after the Great Depression were obviously not only unfunded, but increasingly unfundable.

The most recent example of kicking the can down the road has been unprecedented life support from the FRB in financing 70% of the U.S. debt in QE2, while once again warning Congress and the White House to get its long-term fiscal house in order.  The result, once again, was to witness excess liquidity flow to the most speculative markets, not the fundamental investments required to transition to a sustainable economy, confirming that we have yet to address the underlying structural problems.  The cost of avoiding another Great Depression by stimulus and liquidity has been to advance U.S. insolvency by more than a decade; and quite probably more than two.

Port of Call in the Voyage of Fiscal Denial

Regardless of how one interprets the voyage, the destination that our culture is finally beginning to awaken to is tragic. Under what most believe to be an optimistic forecast, the Congressional Budget Office (CBO) warns us that public debt will rise from around 70% of GDP currently to 84% by 2035, with interest payments rising to 4% of GDP from 1% at current levels. This “extended-baseline” scenario is dependent upon a great many things that have not occurred in the past, however, nor are expected by most, including low inflation and a relatively disciplined Congress. The more consensus forecast, or “alternative fiscal scenario”, projects public debt to rise to 100% of GDP by 2021 and 190% by 2035. However, anyone observing financial crises can attest that these events do not occur on an even gradual basis, but rather reach a tipping point.

The warning I offer today is that economists have based their forecasting on comparable situations in very small economies relative to the U.S., not the world’s largest that also manages the global currency, not to mention the only global military power.  Every forecast, scenario, and metric I have observed in economics is based on a very different history than the situation we face today, all of which assumes the post war experience of a stable U.S. economy.

To capture the situation, consider that while each have proposed different remedies, the best economic forecasters of our time, to include investors, Nobel Laureates, current and past FRB chairs, and regardless of party or ideology, all essentially agree that this unsustainable trajectory has nearly reached its pinnacle.  All are raising red flags, and none can (or have to my knowledge) deny that when the herd finally changes course in bond markets, as we’ve seen most recently in Greece, the stampede is swift and brutal.

Lean, Open, and Secure Governance = The Semantic Enterprise

The Levin–Coburn Report found that the financial crisis was the “result of high risk, complex financial products; undisclosed conflicts of interest; and the failure of regulators, the credit rating agencies, and the market itself to rein in the excesses of Wall Street.”

The U.S. Financial Crisis Inquiry concluded that the crisis was caused by:

  • “Widespread failures in financial regulation, including the FRB’s failure to stem the tide of toxic mortgages”

  • “Dramatic breakdowns in corporate governance”

  • Key policy makers “ill prepared for the crisis, lacking a full understanding of the financial system they oversaw”

  • “Systemic breaches in accountability and ethics at all levels”

In early January of 2008, former GAO Director David Walker suggested that four types of deficits caused the underlying fiscal problem: budget, trade, savings, and leadership. While these four causal factors are without question, I suggest that all of our deficits depend upon the integrity of governance structure, including our increasing deficits in knowledge, competitiveness, security, and happiness.

The only reliable method to achieve a sustainable governance infrastructure in the network economy is with semantic enterprise architecture, which is based on many years of research and testing. For a brief video description of the semantic enterprise, see my elevator pitch, and for a more in-depth discussion, view this keynote at the recent SemTech conference by Dennis Wisnosky on the transformation of the DoD.

Is the customer’s customer a tipping point for enterprise IT?


In early 1996 we spun out a radical concept from my consulting firm on the newly commercialized web that attempted to level the playing field between small business and large. The vision was grander than the technical capabilities at the time, but despite our many weaknesses it became a niche market leader.

Even though we had recent experience representing clients who were competing with market leaders, I was still surprised by the response in some sectors. In our attempt to partner with multinationals, we found primarily fear and defense, including in finance. It was quite clear that the majority of leaders in the corporate world were not terribly thrilled with our efforts. From my perspective, however, given the advantages of incumbents, the long-term risk was far greater to most of their companies if such efforts did not succeed. Having been on all sides of this issue, I was closer to the challenges than they were (and had deeper intel).

Fast forward to 2008. During the initial wave of the global financial crisis I had a private email exchange with one of the leading economic editors, who is a respected centrist thought leader I had known for over a decade. While we have very different backgrounds and experiences, we were in agreement that the initial reaction to the crisis, even though understandable, were misguided. Due to a myriad of factors, including consolidation, centralization, internal financial conflicts, expediency, scale, political activism favoring large institutions, and technology, the small business engine was already in trouble in the west, buoyed primarily by easy credit and the housing bubble in previous years. Based on the evidence in previous recessions, we had very little confidence that the existing financial infrastructure could serve the needs of small business, particularly in current form. One need only travel in the rural U.S. or observe a few SME P&Ls to conclude in hindsight that we were correct.

Fast forward to the present day. On Meet The Press last Sunday, David Brooks sent a warning that I fear will go unheard in the very ivory towers that need to heed the message: “I was up on Wall Street the other day. I know political risk better than they do; they are vastly underestimating the source of political risk out there. We could have a massive problem in the next couple of years.” The source Brooks is referring to, of course, is the American citizen and consumer.

A headline on Wednesday (6/1) at CNBC echoes the disconnect: Wall Street Baffled by Slowing Economy, Low Yields. These are not isolated cases, but rather symptoms of a greater problem at work in the decision process found in every crisis over the past 15 years. I don’t know what data these analysts are consuming, or what tools they are using, but their systems and methods continue to fail them if these and other reports are true.

A glance at the quarterly reports of even the largest consumer companies would reveal a combination of inflation and weak spending that is beginning to negatively impact earnings. Given the massive scale and tight margins facing most of these companies, it should serve as a long over-due wake-up call that it’s time for the IT industry cluster to execute competitive, cost effective solutions to help the customer’s customers compete. This is not an immediate crisis begging for knee jerk reactions, but rather a trend long in the making, dealing with underlying structural problems in the economy that are essential to overcome.

One does not need to search far and wide to discover a variety of profitable methods and models to extend high-end functionality to the SME market, provided of course one is looking, not consumed with protectionism, and obstacles are removed. Regardless of what sector of the economy each serves, ultimately there is no escaping the impact of macro economic conditions, to include the impact of technology on customers of customers.