Priority Considerations When Investing in Artificial Intelligence


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After several decades of severe volatility in climatology across the fields involved with artificial intelligence (AI), we’ve finally breached the tipping point towards sustainability, which may also represent the true beginnings for a sustainable planet and humanity.

Recent investment in AI is primarily due to the formation of viable components in applied R&D that came together through a combination of purposeful engineering and serendipity, resulting in a wide variety of revolutionary functionality.  However, since investment spikes also typically reflect reactionary herding, asset allocation mandates, monetary policy, and opaque strategic interests among other factors, caution is warranted.

The following considerations are offered as observations from my perch as an architect and founder who has been dealing with many dozens of management teams over the last few years.  The order of priority will not be the same for each organization, though in practice are usually similar within industries.

Risk Management and Crisis Prevention

The nature of AI when combined with computer networking and interconnected emerging technology such as cryptography, 3D printing, biotech and nanotech represents perhaps the most significant risk and opportunity in history.

While the global warnings on AI are premature, often inaccurate, and appear to be a battle for control, catastrophic risk for individual companies is considerable.  For most organizations the risk should be manageable, though not with traditional strategies and tactics.  That is to say that AI within the overall environment requires aggressive behavioral change outside comfort zones.

Recent examples of multi-billion dollar investments in AI include Google, IBM, and Toyota, though multi-million USD investments now number in the thousands if we include internal investments and venturing.  To be sure much of this investment is reactionary and wasteful, but the nature of the technology only requires a small fraction of the functionality to prove successful, which can be decisive in some markets.

For appreciation of the sea change, common functions employed today were deemed futuristic and decades in the future just three or four years ago.  So it’s not surprising that a majority of senior management teams we’ve engaged in the last two years confirm that AI is among their highest priorities, though I must say some are still moving too slow. We’ve observed a wide range of actions from window dressing for Wall Street to confusing to brilliant.

The highest return on investment possible is prevention of catastrophic events, whether an industrial accident, lone wolf bad actors, systemic fraud, or disruption leading to displacement or irrelevance.  Preventable losses in the tens of billions in single organizations have become common.  Smaller events that require a similar core design to prevent or mitigate are the norm rather than the exception, but are often nonetheless career ending in hindsight, and can be fatal to all but the most capitalized companies.  We’ve experienced several multi-billion dollar events in former management teams that likely could have been prevented if they had moved more quickly, including unfortunately loss of lives, which is what gets me up at 3am.

Talent War

An exponential surge in training is underway in machine learning (ML) along with substantial funding in tools, so we can expect the cost of more common technical skills will begin to subside, while other challenges will escalate.

“In their struggle against the powers of the world around them their first weapon was magic, the earliest fore-runner of the technology of to-day.  Their reliance on magic was, as we suppose, derived from their overvaluation of their own intellectual operations, from their belief in the ‘omnipotence of thoughts’, which, incidentally, we come upon again in our obsessional neurotic patients.’’ — Sigmund Freud, 1932.

The challenge is that the magic of the previous century has evolved and matured by necessity from the efforts of many well meaning scientists, but some of the magicians on stage still suffer from neurosis.  Technology is evolving much faster than humans or organizations.

Examples of talent issues commonly found in our communications:

  • Due to long AI winters followed by the recent tipping point in viability, the number of individuals with extensive experience is very small, and most are at a few tech companies attempting to displace other industries.

  • Industry-specific expertise beyond search and robotics is rare and very specialized with little understanding of enterprise-wide potential.
    An exceptional level of caution is warranted on conflicts in AI counsel due to competency and pre-existing alliances.

  • Despite efforts to exploit emerging opportunity, ability to think strategically in AI systems appears to be almost non-existent.

  • CTOs may win some key battles in tactical applications, but CEOs must win the wars with organizational AI systems.

The talent war for the top tier in AI is so severe with such serious implications that hundreds of millions USD have been invested for key individuals.  Of course very few organizations can compete in talent auctions, which is one reason why the Kyield OS is so important.  We automate many AI functions that will be common in organizations and their networks for the foreseeable future while also making deep dive custom algorithmics simpler and more relevant.

Historic Opportunity

Not only is AI a classic case of ‘offense is the best defense’, when designed and executed well to enhance knowledge workers and customers, the embedded intelligence with prescriptive analytics can accelerate discovery, uncover previously unknown opportunities, providing historically rare potential for new businesses, spin outs, joint ventures and other types of partnering.  Managed well, this is precisely what many companies and national economies need.

Architectural Design

Impacting every part of distributed organizations, the importance of architecture cannot be overstated as it will influence and in many instances determine outcomes in the distributed network environment.  AI is a continuous process, not a one-off project, so it requires pivotal thinking from two decades of fast fail lean innovation that our lab helped pioneer.  Key considerations in architecture we incorporated in the Kyield OS include but are not limited to the following:

  • Optimizing the Internet of Entities

  • Governance, compliance, and data quality

  • Accelerated discovery and innovation

  • Continuous improvement

  • Real-time adaptability

  • Interoperability

  • Business modeling

  • Relationship management

  • Smart contracts

  • Security and privacy

  • Transactions

  • Digital currency

  • Ownership and control of data

  • Audits and reporting

  • Productivity Improvement

A priority outcome for most organizations in competitive environments, productivity improvement is increasingly derived from optimizing embedded intelligence, which is also desperately needed to improve the macro global economic situation.  A large gap remains in most AI strategies with respect to enterprise-wide productivity, which represents the foundation of recurring value to organizations and society, regardless of the specific task of each knowledge worker and organization.

While cultural challenges and defensive efforts are common obstacles to any productivity improvement, strong leadership has proven the ability to triumph.  Internal and external consultants and advisors can help, particularly given the steep learning curve in AI;  just be cautious on unhealthy relationships that may have interests directly opposed to the client organization, as conflicts are pervasive and tactics are sophisticated.

Trust

Just when we thought trust couldn’t become more important, it seems to dominate life on earth.  We’ve come across quite a few trust related issues in our AI voyage. A few examples that come to mind:

Intellectual property:  Trust is a two-way street, particularly when it comes to intellectual assets, so upfront mutual protection is a necessary evil and serves as the first formal step in establishing a trustworthy relationship, without which the other party must presume the worst of intentions.  Once the Kyield OS is installed with partners this problem is effectively eliminated with smart contracts and digital currency based on internal dynamics and verified intelligence (aka evidence).

Fear of displacement:  Since AI is new for most, suffice to say that fear is omnipresent and must be dealt with in a transparent and intelligent manner. At the knowledge worker level we overcome the problem with transparency, which makes it obvious that the Kyield OS is likely their strongest ally.

Modeling:  While motivation to change is often needed from external sources such as regulatory or competition, it’s probably not a good idea to trust a company that has the capability, desire, culture and incentive to displace customers.  Another problem to avoid at the confluence of networked computing and AI is lock-in from technology or talent, including service models.  Beware the overfunded offering that attempts to buy adoption and/or over-reliance on marketing hype.

Authenticity:  Apart from the serious structural economic problems caused by copying or theft of intellectual work, consider the trustworthiness of those who would do so and how much know-how is withheld because of this problem.  Authenticity is especially important in this field due to the length of time required to understand the breadth and depth of implications across the organization and network economy.

Conclusion

Given the strategic implications to organizations, AI should be a top priority led by senior management.  However, since supply chains face similar challenges with AI, traditional methods and channels to technology adoption may not necessarily serve organizations well, and in some cases may be high risk.  Whether for strategic intent, financial return, operational necessity or any combination thereof, investing well in AI is not a trivial undertaking. Integrity, experience, knowledge and freedom from conflicts are therefore critical in choosing partners and investments.

About the author

Mark Montgomery is the founder and CEO of Kyield, which is based on two decades of self-funded R&D.  The Kyield OS is designed around his patented AI system, which tailors data to each entity in the digital network environment.

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.