Academy19

Leveraging AI to retain customers

You are a leading retailer and you are out of stock of a particular popular item in store or online. A delay in suggesting the best substitute to your customer can cost you the customer. It is that simple. No business can afford this in the prevailing competitive business environment.

Loyal relationships with the customers are really important. Your customer retention can boost your revenue as well as increase the customer’s lifetime value.

Amidst the pandemic online shopping for everyday needs increased exponentially which also led to a significant jump in sales almost everywhere in the world. According to the United Nations Conference on Trade and Development (UNCTAD), fueled by Covid-19 overall global e-commerce sales jumped to $26.7 trillion in 2019. Nonetheless this boom wasn’t without challenges.

Walmart, like many other retailers, faced a unique challenge in the pandemic. As the pressure grew following quick online sell out, the challenge was how to help its customers and personal shoppers choose the best substitute for an out-of-stock item–without wasting time.

The business solution Walmart came with was smart and efficient. And it helped both, online and in-store sales. The solution helped the retailer increase its customer acceptance of substitutions to a whopping over 95%. Amazing, indeed.

So how did Walmart do that? The retail industry giant went for a technology solution–leveraging Artificial Intelligence (AI). The AI-powered system today helps Walmart to identify the next best item for its customers if an item of interest is out of stock.

In order to determine and suggest the best next available item, Walmart’s deep learning AI considers hundreds of variables from size, type and price of its products to aggregate shopper data, individual customer preference and current inventory etc – in real time.

And this system isn’t just limited to performing this task only. The AI-fueled system uses customers’ feedback for its learning algorithms which helps Walmart improve the accuracy of its future recommendations for its customers.

Academy21

AI affordability: From finances to ethics

One of the top takeaways from the latest AI Index Report is affordability of Artificial Intelligence (AI) technologies as the report says, over the years, AI has become affordable and also higher performing.

This affordability is also resulting in more widespread commercial adoption of AI technologies, says the AI Index, an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI).

Owing to its proven transformative role and countless benefits it offers, AI adoption is skyrocketing everywhere and this is not a secret. And for this, the affordability aspect simply cannot be ignored.

Today AI-led transformation is taking place in different sectors and industries while government departments and agencies are also accelerating AI adoption in order to stay ahead of their adversaries and to create and gain a position of strength.

Why is AI becoming cheaper and fast?

While the true potential of AI still remains largely unexplored, it is undoubtedly becoming a mainstream technology for government and the private sector. This speaks volumes about the enormous potential AI holds for all of us.

The momentum AI has picked up over the years is caused by several factors and one of these is said to be the ever growing open source community. From advances in healthcare to space science to new possibilities in modern security apparatus, AI development and deployment is faster than ever before. One possible reason is–race for digital dominance in this era of competition.

And this isn’t just about the countries. The private sector is also leveraging new advances in AI to stay ahead of their competitors within and outside. Another takeaway of the report is unprecedented investment by the private sector. “The private investment in AI in 2021 totaled around $93.5 billion—more than double the total private investment in 2020,” reads the AI Index Report. These investments are also paying off as today companies are achieving returns on their data and AI investments.

Ethical and responsible use of AI

With the fast development and deployment of AI, debate about ethical and responsible use of AI also continues apace. Another recent report talks in detail about trustworthy and responsible AI while highlighting biases in artificial intelligence.

The National Institute of Standards and Technology (NIST) report suggests that rooting out AI biases, introduced purposefully or inadvertently, will require a holistic approach–addressing systemic/institutional and human biases as well as fairness of data and algorithms.

To sum up, this is not just about affordability of AI in terms of finances but development and deployment of AI technologies that can afford to win our trust.

Academy1

From Smartphones to Supercomputers

Who would have imagined that there will be a time when millions of people will carry smartphones, similar to mini computers, in their pockets? But thanks to the ever evolving technology, today this is an undeniable reality.

Most of us are in awe of these handy yet really smart and powerful small machines keeping us connected globally and making our work easier. Today we also have supercomputers that are millions of times more powerful than these mini computers. Just imagine the problem-solving abilities of these supercomputers while not ignoring the fact that presently these supercomputers could not fit in our pockets.

“Summit”—at the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) is one of the world’s most powerful and smartest scientific supercomputers. Its peak performance is 200,000 trillion or 200 petaflops calculations per second! And remember, developing the next generation of artificial intelligence (AI) will require supercomputers, like Exascale which is said to be the next in the series of supercomputers, capable of quintillions (1018) calculations per second.

Awestruck by the sheer speed and power of these supercomputers, one immediate question that strikes our mind is: Why do we need these supercomputers? The answer is simple. We are witnessing exponential growth of data and these fastest high-performance computing systems phenomenally improve the traditional data processing approach. All this is cost-effective, time saving and above all, what matters the most, it helps us prepare better for the future.

Backed by data and powered by AI and machine learning (ML) algorithms, these machines are helping us to explore more and to demystify complexities of our world such as climate change, biological systems, renewable energy and nuclear weapons to name a few. This isn’t just about demystification but finding workable and sustainable solutions to our modern world problems and building a strong foundation to address future challenges.

Technology is evolving unbelievably fast. Future humans carrying ‘superphones’ like mini supercomputers in their pockets might seem an exaggeration today. But no wonder if tomorrow technology helps it become even a possibility, if not a reality.

Ukraine

Analyzing Storytelling About the Ukrainian Conflict: Countries and Firms

Note: This article series in no way minimizes the Ukrainian suffering. It is simply an attempt to analyze the storytelling applied by various entities in the Ukrainian Conflict.

Humans have been telling war stories for millenniums. Many of those stories were about glorifying the victories and the victorious. Some detailed the brutality of warfare. Others described the inherent passions of war. Since the advent of the Internet, storytelling has become more democratized, more personal, and more instant. With multiple types of unstructured data being uploaded by hundreds of thousands of eyewitnesses of wars to millions of commentators, a new type of storytelling has emerged. The frames and themes of war provide elements whose complex interactions generate narratives that show and document the atrocities of war, human suffering, punitive actions, moral standing, changes in social and global order, and other dynamics of war.

In the theatre of today’s great power competition, the Ukraine conflict offers a first look into the storytelling related to new geopolitical dynamics: a hot conflict where major powers found themselves on the opposing ends. When combined with the deployment of artificial intelligence based narrative analysis, the Ukraine war offers significant insights into the evolving global narratives and how war stories will shape the geopolitics in the future.

This is a series of two parts of articles. The first focuses on major story themes coming out from countries. The second part of the article analyzes the narratives and positions taken by companies.

Theme 1: It’s about the history (Russia’s emotionless story that backfired)

The Russian storyline was based upon showing NATO and the Ukrainian government as the aggressors who were threatening the national security and interests of Russia and attacking ethnic Russians in Ukraine, and therefore Russia had no option but to attack Ukraine. A story that depicts lack of options is often a tragedy that invites sympathy, but its appeal must be emotional. The Russian story came across as too rational and lacked any emotional appeal for the global audiences. President Putin’s attempt to position NATO as a goliath failed terribly. If NATO weren’t turned into a goliath, Russia could not be viewed as David.

Despite the claims that Russian bots were superactive and that Twitter removed over 75,000 accounts during the war, the overall Russian story and communications were highly ineffective.

The text coming out of Kremlin, the images, and the visual storyline, were not designed to determinedly project Russia either as a decisive aggressor (goliath) or as a victim (David). It was somewhat a mixture of the two. Here was President Putin explaining why Russia went to war, but his explanation seemed like a lesson from a boring history class. Dwelling over a century old Ukrainian and Russian history, he easily lost global audiences. His speech was not designed to inspire his nation or to even make a strong case for the invasion, but instead seemed like an attempt to provide a justification for the invasion that was based upon some type of a historical account. In a world where emotions and drama dominate the narrative setting discourse, Putin was walking into a landmine. His grievance centric appeal did not have the emotional depth to resonate with the global audiences. Maria Zakharova, Russia’s spokesperson, became emotional about the fact that her side of the story was not being acknowledged and the plight of ethnic Russian in Ukraine was being ignored. But by that time, it was already too late for global audiences to develop any sympathy for the Russia’s position.

On the visual storytelling side, President Putin’s posture of leaning back, even slouching, while speaking with hands holding the edge of the table depicted someone drowning who needed to hold on to something. It brought to mind the iconic scene from the movie Titanic, where Leonardo DiCaprio was holding on to the edge of the wooden panel, right before he let it go.  But by the time that scene takes place in Titanic, audience has already developed deep sympathy for DiCaprio’s role. With the established image of a strongman, President Putin carried no such sympathy.

Further, on the visual storytelling side, the shots for the speech switched between showing President Putin slouching back and looking tiny behind the desk, overpowered by a TV screen, computer screen, keyboard, mouse, and four phones. The noise in the imagery was intense. If the idea was to show President Putin in action, that was not the right time or avenue. If the goal was to show him deliver a strong message, the visual noise was intense.

At the ceremony of signing DPR and LPR, the visual storytelling seemed cold and impersonal. The size of the room presented the image of a lonely emperor conducting business in a giant palace. There were no cheers or human emotions. From a Russian perspective, two states were being born, but the presentation seemed so solemn as if a death sentence was being awarded. Contrast that with when American presidents sign bills, applauding supporters of the bills are shown standing behind the president. Similarly, the audience at bills signing ceremonies is composed of citizens who cheer and applaud.

Russia’s failure to make the alleged NATO push and the Ukrainian neo-Nazism an emotional sell to the world was obvious. Unlike the US president who talked to the global audiences, President Putin limited his messaging to his domestic audience. There was no emotional depth, no drama, and no framing in President Putin’s claim. His speeches looked like long and boring history lessons rather than the great oratory to inspire nations or to make a strong case.

If this was all done intentionally, it is hard to imagine what it accomplished. If it happened because of incompetence, it would probably be seen as a bigger failure for Russia than the poorly executed war itself.

Theme 2: “We Understand” (Minimizing the Ordeal, China)

The Russian story was communicated in China very differently than in many other parts of the world. The word “invasion” was not used in China, and the images shown on national TV were of Russian soldiers distributing food and water to the Ukrainians. On social media any criticism of Russian aggression was removed. The official line of the Chinese government continued to be: We don’t want war in Europe but also don’t want any sanctions on Russia.

This narrative is designed to position NATO as the aggressor and Russia as the optionless victim. The official line of the Chinese government attempted to position NATO as hegemonic and intrusive, and that NATO was the threatening and destabilizing force against Russia. This was architected to create a preemptive story about Pacific QUAD (not a formal alliance, officially the Quadrilateral Security Dialogue, is a group of four countries: the United States, Australia, India, and Japan) as a hegemonic and destabilizing force. This narrative was being framed by not only what was being said or communicated but also by what was not being said and shown. Thus, not communicating a story is itself a story.

The dominant narrative in China was shaped by silence and gentle nudge to request both parties to reach peace.

Theme 3: We have come to honor that allegiance (Indian business-ism)

In the second Lord of the Rings movie, Haldir says: I bring word from Lord Elrond of Rivendell. An Alliance once existed between Elves and Men. Long ago we fought and died together. We come to honor that allegiance. The messaging coming out of India was similar. As the war progressed, India increased its trade with Russia. It was more than business as usual. The White House’s reaction to India’s position was that they found it to be “unsatisfactory” but “unsurprising”. While it was hard for India to justify its position with its Western partners, the Indian story needed to be of pragmatism and opportunism with an undertone of amorality of political affiliations. Terminating its longstanding relationship with Russia was not an option. Neither was becoming a hard critic of President Putin. If you can’t take the Western side, might as well benefit from the situation – turned out to be India’s story. India was willing to trade its “Gandhi” image for the image of India is open for business. The Indian story was of pragmatism and astuteness. It was saying: you don’t have to trust us that we will always stand for the oppressed, but you can trust us that we will make good business decisions.

Theme 4: Please let us help you (Caught in the middle, Israel and Turkey)

Israel was caught in the middle. On one hand Israel needed to stand with America and its Western allies and on the other hand Israel had to maintain good relations with Russia. The Israeli story could not have been about taking sides. Unlike India which took the Russian side, the Israeli story developed as of a mediator and of a refugee host. In the mediator role, Israel offered to mediate peace between the two warring parties. On the refugee host side, the country’s situation gets even more complicated as about two-third of the Ukrainian refugees arriving in Israel are non-Jewish. Israel was able to keep a balanced position. Turkey’s position, like Israel’s, also turned out to be of a mediator.

Theme 5: We are against the war but stand with Russia (the anti-imperialist story, Iran)

Iran took a position of blaming the NATO for pushing Russia into a corner but also claimed that it is against the war and human suffering. This position allowed Iran to develop a narrative of standing for the oppressed without disparaging its close ally Russia. This was in line with the Islamic Republic’s overall narrative of anti-imperialism.

Theme 6: We are all victims of hegemonic powers (Pakistan’s story)

Pakistan’s prime minister Imran Khan used a unique angle and turned his story into a narrative of victimhood of weak nations suffering the consequences of a war between the two giants (America and Russia). It brought to mind the images from the first Hobbit movie where two mountain giants are fighting as Bilbo’s party tries to save itself from the falling rocks. Pakistan’s story of strategic neutrality with victimhood was designed to deflect the decision to choose. That positioning of a victim was also meant for the domestic audiences who are greatly impacted by the rising inflation in Pakistan. PM Khan is fighting for his prime minister position against a no-confidence motion in the parliament, and the broader global conflict allows him to blame the rising inflation on geopolitical realities.

Theme 7: I am on nobody’s side, because nobody is on my side, little orc (Hungary)

Just as Treebeard in the movie The Two Towers claims that he is on nobody’s side, Viktor Orban, Prime Minister of Hungary said “Russia looks at Russian interests, while Ukraine looks at Ukrainian interests. Neither the United States, nor Brussels would think with Hungarians’ mind and feel with Hungarians’ hearts. We must stand up for our own interests.” The story of Hungary is about focusing on its own interests. This is different from India’s story as it is based upon a clear and overt claim of self-interest whereas the Indian story is based upon preserving old friendships.

Theme 8: The David vs. Goliath (Perfect execution by Ukraine)

On the other side, Ukraine offered the story of Russian aggression and backed it up with strong emotional data. President Zelenskyy’s used the right messaging and imagery – both in text and the visual elements. President Zelenskyy ditched suit and put on a military t-shirt and allowed his beard to grow. The visual imagery of a leader fighting aggression was delivered perfectly. The Russian aggression was captured in video and images, in news and social media, and the story was backed by proofs and strong emotional content. It quickly became the most touching story in the world. Even those countries that did not vote against Russia at the United Nations condemned the Russian war against the Ukrainian civilians. For example Iran, a strong ally of Russia, offered help to Ukrainians. The power of the Ukrainian storytelling was amazing. It touched hearts and it appealed to reason. The Russian story was crushed by the power of the Ukrainian story. The Ukrainian story gained enough momentum for the US and EU to enact sanctions and terminate business with Russia.

But David vs. Goliath story requires David to have a stone and a sling, and for him to use that to hit Goliath with some force. For Ukraine to pull that story, a significant victory over Russia, even if turns out to be of resistance and defiance, will be critical. So far, the storyline of the Kiev defense is working out in Ukraine’s favor. Russia understands the risk and has decided to refocus the campaign in the eastern Ukraine and has announced that the Phase One of the war is over. The end of Phase One was not turned into a victory lap by Kiev. This could be because the timing of the story is slipping. The story has climaxed, and short attention spans of modern audiences quickly lead to cognitive saturation. The long-drawn fight and the words like “stalemate” and “stymied”, if not linked to a victory lap, may work against the Ukrainian story. For global audiences these words signal “move on to something else”. Suddenly, Will Smith punching Chris Rock in Oscars will become a far bigger story.

Important Considerations

The question here is that whether Ukrainian story can sustain its power? Emotional stories can lose momentum quickly as human emotions are designed to be reactive, but they can’t maintain a state of hyperactivity for too long – especially when things are not too personal. Slowly, people will move on to other stories. Alternative explanations will emerge. Counternarratives will rise. NATO will be blamed for fueling and extending the war. The stories about the economic toll of the war for Americans will start taking center stage. Would Americans be okay with paying nearly twice at the gas pumps in the first summer after two years of struggling with Covid related restrictions? President Biden’s approval rating has already fallen to the lowest level in his presidency.

Russia will undoubtedly try to take advantage of these conditions and rearticulate its story to a narrative that shows that the world’s largest democracy (India), the world’s largest economy (China), and a country that understands human rights better than anyone (South Africa) stand on its side. But that depends upon Russia recognizing the importance of storytelling. Since President Putin likes to talk about history, if the recent history is any indicator, Russian storytelling was a dismal failure.

American Institute of Artificial Intelligence (AIAI) is an institute focused on using Machine Learning to analyze stories and narratives of companies, countries, and government agencies.

Academy13

How to design ESG programs that don’t get your CEOs fired? Here is the clue: Use AI.

Introduction

At least two major activist investors (Bluebell Capital Partners, Artisan Partners) joined the calls for the removal of Emmanuel Faber, CEO of Danone (the French firm known for its yogurt products and bottled water). The board heard the message loud and clear, and Mr. Faber was removed from his position (see Figure 1). During Mr. Faber’s tenure, Danone’s stock value increased 11% – meanwhile during the same period its competitors Nestle and Unilever increased by 43% and 55% respectively. Since underperformance often leads to the removal of CEOs, that is not what is special about this firing. What makes this unique is that Mr. Faber was the icon of stakeholder activism, sustainability leadership, and ESG management.

He is not alone. We have observed a recent trend of sustainability champion CEOs being toppled. For example, Isabelle Kocher of Engie and Sacha Romanovitch of Grant Thornton, both great supporters of the sustainability and ESG movement, were also ousted.

Does this signal that Milton Freidman is back and the ESG movement is dead?

I believe that is not the case.

Figure 1: Difference between March 1st and March 15th press releases (source https://www.danone.com/media/corporate-press-releases.html)

What are ESG programs missing?

After years of research on this problem, I have concluded that the real culprit is how ESG programs are approached. The standard designs of ESG programs fluctuate between “denial and avoidance” and “check-the-box compliance”. Neither brings out the creativity and power that sustainability movement gives to a firm.

The problem happens because ESG program designs have become unauthentic and removed from business realities. They are designed to be responsive to large and powerful rating agencies and standard setting bodies and not to the needs of the business. They are not integrated with the business strategy. They lack authenticity and creativity. They have turned into large checklists designed to cater to the never-ending demands built upon the limited visions of rating agencies. More vocal CEOs try to lead with strategic visions of sustainability, but their programs turn into either lofty dreams of grandeur, or boring checklists. In the former case organizations do not buy into the programs and resist them with full force. In the latter case no one is inspired to take ESG seriously.

The question is: how can you design ESG programs that work? There are 5 steps.

Step 1: Start by analyzing your value chain

Many firms start their program designs by scoping out materiality and understanding standards. This can lead to problem programs that get CEOs fired. Start with first analyzing your business’s value chain. Look for the opportunities where you can add sustainability value by fine-tuning your value chain.

Do not start with stakeholder materiality analysis. While knowing what is important for stakeholders is important, a program designed from one-time analysis of stakeholder priorities and preferences will have a structure but it will lack flexibility. Since both stakeholders and their values are constantly shifting – such a design will lack the much-needed adaptive capability. Conversely, a program designed to be fully responsive to the changes in stakeholder priorities and preferences will lack much needed structure.

Similarly, avoid starting your programs with the sole purpose of complying with the standards. The ESG field is experiencing a standards proliferation – as anywhere you turn there seems to be new ESG standards thrown your way – and hence the starting point could not be standards. Standards are meant to be guidelines until they are not. If they become too rigid, they will lack “generally applicable” characteristic. If they are too loose, they will lack any enforceability. Unlike the financial accounting standards that are based upon theoretical foundations of how to classify transactions and report on financial performance, ESG standards lack that rigor. They tend to be practice and process guidelines.

Just as you do not design your business strategy based upon how numbers are reported in financial statements, your starting point should not be standards. Analyzing your value chain gives you the first view of ESG priorities for your firm. This means you can integrate ESG into your overall business strategy.

Step 2: Create a strategy

Once your initial analysis is complete, create a broad strategic plan that ties in an integrated ESG-Value Chain vision. This integrated value creation system does not approach sustainability as separate from the core business strategy. Instead, it views the transformation as central and core to the business strategy. The plan must have clearly defined metrics that tie into financial value creation for the firm.

Step 3: Create a broad narrative

Understand and manage broad themes and create narratives around them. Narratives show strategic intent and give meaning to initiatives and numbers. Without narratives, reporting is meaningless. Narratives are also linked to the business results.

Step 4: Link with stakeholder and standards

Now you can link the program with stakeholder preferences and any applicable standards. Again, these links are not the drivers of your plan and strategy but only a result. Doing the right thing should not be dependent upon how various stakeholders would feel about it. It should be done anyway.

Step 5: Transform your firm to the modern economy

ESG is a key ingredient in building a modern firm. As you set your strategic transformation program – make sure that ESG is well integrated into the DNA of your firm.

Using AI for ESG Management

The above 5-steps cannot be achieved without relying upon data and using the right tools. Fortunately, the advances in machine learning have given us the ability to design and manage successful and integrated ESG programs. AI/ML helps in implementing the five steps and ensures rapid and deep stay power of the ESG program.

After all, sustaining sustainability program should not get CEOs fired. The key is to identify the integrated value.

Academy10

Nonergodic, history retaining trajectories of Disruptive Innovation

By: Al (Ali) Naqvi

My goal in this article is to expand the traditional disruptive innovation investment analytical framework with the additional constructs of nonergodic nature of disruptive innovations. Funds have a tendency to formulate investment thesis based upon a technology’s innovation potential and not necessarily its innovation path. Potential based analyses absorb all the nuances of behavioral elements, hype, marketing, and promoting a technology. Its reasoning mechanism is often comparative in terms of using a baseline historical event to establish performance potential of the disruptive innovation. When viewed from a path perspective, the analysis becomes far more complex – but the benefit of path-based analysis can result in far greater investment value.

The potential based analysis

Artificial intelligence has often been compared to electricity. Just as what the electric current flowing through the wires did, AI can revolutionize everything else. Some scholars and practitioners term such innovations as General Purpose Technologies, transformative technologies, or innovation platforms. For example, Brett Winton of ARK Investments argues that in the course of the last two centuries such innovations have triggered major market capitalizations (Winton, 2019). And today, he points out, waves of several transformative technologies are cresting, leading to a massive potential of market capitalization.

ARK identifies the attributes of disruptive innovations as being:

  • Across multiple sectors,
  • Upend existing or incumbent providers,
  • Create new business potential,
  • Deliver dramatic cost reductions,
  • Serve as a platform for new innovation, and
  • Propel global economic growth.

ARK keenly observes that dramatic decline in prices of such technologies creates rapid adoption, making existing technologies obsolete while creatively destroying established competitors. Similar observations were also made by Carlota Perez when describing the dynamics of technological revolutions  (Perez, 2002).

Fig 1 Adopted from ARK Investments (Winton, 2019)

 

Fig 2 Adopted from ARK Investments (Winton, 2019)

The above investment hypothesis, while impressive, covers only half the story. What it fails to capture are the distinct dynamics of the artificial intelligence revolution. These dynamics are not only widely different than anything we have experienced before, but are also far more complex than the subtleties and developments of technologies with ubiquitous adoption leading to market structures that gave rise to traditional public utilities.

The equilibrium is transitionary

In this regard the term “history matters” is more applicable than “history repeats itself”. Perhaps, history repeats itself in the sense that history does not repeat itself. Yet, history plays an important role, but not in the comparative sense of what transpired in the prior innovations somehow applies to the current innovation under the microscope, but instead in the sense that the evolutionary and dynamical development of the current disruptive innovation greatly depends upon its own history. The history preserved in the trajectory representing the evolutionary dynamics greatly affects the development and potential of the disruptive innovation. The transitions to the next states of development, adoption, and growth depends upon the prior states and the influences and conditions in the current states.

Unlike the stable equilibrium often claimed by the analysts, the development of technology follows more dynamical and evolutionary equilibrium where equilibrium’s stability is defined by the change itself.

Thus, discovering the right investment opportunities is not as much a function of generalizing broad equilibriums, as it is trying to understand the next transitory state and modeling the history and current state accurately to identify the specific trajectory of the innovation. Identifying this nonergodic development path is where the pool of opportunities is discovered.

Disruptive innovation is path dependent

A dynamical process whose evolution is governed by its own history is ‘‘path dependent.’’ (David, 2007). We have been cautioned against mixing economic history and economic theory. Nathan Rosenberg illuminated that innovation is path dependent and that the nonergodic nature of technological change requires analytical models to capture the inherent uniqueness and complexity (Rosenberg, 1994). Thus, analyzing the status of innovation at each state of its transitory movement to the next state can help clarify the trajectory. History matters. Furthermore, at each state of the path the numerous forces and state interval specific factors contribute to setting the subsequent direction of the innovation. Such forces may include previously committed investment, institutions, initial conditions, noise, and many other factors.

The scientific process is changing

The traditional scientific process is driven by seeking data as a function of hypothesis and experiment design. In the new era, scientific discovery can materialize from large datasets such that insights and findings precede hypothesis and experiment design. This reversal of the process introduces epistemological transformation and leads to hyper-acceleration and novelty. It can also create explain-ability challenge where innovators have to grapple with the findings that seem to hit the mark but lack theoretical explanation or justification.

Geopolitical constraints must not be ignored

Related to path dependent trajectories, geopolitical developments often alter the expected growth patterns. The emergence of China US trade tensions and eventual blocking of several Chinese firms by the US serves as a reminder that transitions in trajectories can introduce new patterns of unanticipated dynamics at the transition stages.

Noise impacts outcomes

Social systems are influenced by noise. Noise can result from faulty analysis and methods, misaligned incentives, managerial oversight, and other issues that can affect the transition into the next stage. Some of the path distractors can come from unanticipated forces. The highly admired dynamics of global brain to “help organize our social organism into a more coordinated, more efficient, more democratic, and more collectively potent entity” and in its ability “to foster more numerous and more diverse communications between both humans and technology, and then better link those communications to mechanisms of action”  – do not necessarily lead to stability (Rosenblum, 2015).  As seen in the recent retail investor mob raids to artificially raise the prices of assets – the global mind can become a counter force to stability.

Institutions matter

The nature of institutions plays a major role in disruptive innovations and technological growth (North, 1990). The governing philosophy, operational mode, and strategic outlook of institutions greatly influence how innovation transpires in the economy. Technologies affect institutional performance and institutions impact technological trajectories. In this bidirectional nudge pattern, the role of institutions to both embrace and influence innovation requires constant monitoring.

Enabling technologies and data

The states of development and adoption of support and complementary technologies that form the production platform of the primary disruptive innovation are critical analytical factors (Mowery and Rosenberg, 1989). Since many of those complementary and support technological innovation now depend upon the availability of datasets, the growth potential and adoption trajectories can produce different outcomes.

Lock-in

The initial conditions at the beginning states of the trajectory can influence the subsequent states and that is why it is important to take a note of the initial conditions. There were many advanced economies with highly educated workforce, but the performance of India in the early stages of the computer and internet revolution greatly impacted the subsequent history where India emerged as a powerful player in the software development industry.

Reindustrialization Dynamics are different

This leads us to the main point I am trying to make. Reindustrialization takes into consideration both history and the evolution of history within the time segment or states being analyzed. That is why, while the relationships and projections presented by ARK in Figure 1 and 2, can be accurate for a time segment, the number of exogenous and unknown variables impacting the transition state are enormous. In my next article I show how to measure and track the trajectories that enable phase transition.

But the same element that produces the ailment also gives the cure. Deep learning can help assess the state of a fast-changing reality, assuming a wide enough net is thrown to capture data. ARK’s (or other disruptive innovation investors) assertions, therefore, must constantly be tested, measured, reevaluated, and reported. This means analyzing and measuring innovation trajectories with a path dependence perspective. Path must integrate with potential to create investment value.

 

References

David, P. A. (2007) Path dependence : a foundational concept for historical social science. Cliometrica. [Online] 191–114.

Mowery, D. C. & Rosenberg, N. (1989) Technology and the pursuit of economic growth. Cambridge University Press.

North, D. (1990) Institutions, Institutional change, and Economic performance. Cambridge University Press.

Perez, C. (2002) Technological Revolutions and Financial Capital: The dynamics of bubbles and golden ages. Northampton, MA, USA: Edward Elgar.

Rosenberg, N. (1994) Exploring the black box: Technology, economics, and history. Cambridge University Press.

Rosenblum, F. (2015) Power and politics: A threat to the Global Brain. Technological Forecasting and Social Change. [Online] 11443–47. [online]. Available from: http://dx.doi.org/10.1016/j.techfore.2016.06.035.

Winton, B. (2019) Disruptive Innovation: Why Now? [online]. Available from: www.ark-invest.com. [online]. Available from: www.ark-invest.com.

Academy9

Neuralizing a Private Equity Firm

By Al Naqvi

This article reviews how to reindustrialize a private equity firm?

NEURALIZING A FIRM

Information technology (IT) is no longer a source of competitive advantage for companies. Too easy for competitors to copy. Too clunky. Too limited. Unlike artificial intelligence (AI), the legacy IT is deterministic and incapable of learning, adapting, or accumulating experience.

Neuralizing a company is the science of inducing behavioral modernity in the performance of a firm by using intelligent machines. It happens when a firm evolves to deploy integrated artificial intelligence infrastructure to automate and enhance work. This unleashes a powerful new wave of value creation.

Neuralization impacts six different aspects of human work (Figure 1):

Physical and Cognitive Automation: Automates existing work processes where work is composed of physical and cognitive components.

New Process Enablement: Enables new processes.

New Business Models: Allows firms to develop new business models.

Make New Scientific Discoveries: Accelerates new scientific discoveries.

Expands Human Cognitive Capacity and Awareness: Empowers executives with higher cognitive capacity and situational awareness.

Helps Improve ESG Value: Enables firms to develop stronger ethical and governance frameworks, enhance the effectiveness of corporate social responsibility initiatives, and improve positive environmental impact.

To read the full article please download pdf here

Academy8

Why analysts overlook the greatest opportunities?

Reindustrialization is enabled by disruptive innovation, and historically, great transformation times offer opportunities to create powerful returns for investors. It is often hard for analysts to develop a perspective that allows them to make sense of such powerful developments.

Why Financial Analysts miss the Reindustrialization Opportunity?

Analyzing a new technology is never easy but the current processes, approaches, and methods of analysis make it impossible for traditional analysts to understand the powerful dynamics of reindustrialization. Reindustrialization is not like any other times. It is a great transformation with a massive potential to create change. Here are some of the attributes that make reindustrialization analysis hard for traditional analysts:

Nature of Innovation: The nature of innovation in reindustrialization is not the same as in ordinary times. Analysts are used to analyzing innovations that do not exhibit the unique features of innovations that initiate and propel reindustrialization. At the most fundamental level, in reindustrialization the scientific process itself becomes more efficient or is enhanced by a more efficient process. This change happens at epistemological, ontological, and ethical constructs of science. The scientific efficiency enables new and unexpected technologies to appear on the horizon. The rate at which technologies appear and their novelty increase significantly. Analysts are trained to observe the technologies based on their adoption trends, market share, and features and function – and not on their scientific paradigms.

Analyst’s Positioning: Analysts occupy a certain placement in their corporate positions. Their respective position determines their vantage point. The reindustrialization dynamics are different as they requires simultaneously analyzing multiple industries, various sectors, multidisciplinary developments, and competitive structures.

Competitive Dynamics: The competitive dynamics of reindustrialization are different than normal competitive times. Specifically, the traditional competitive boundaries and moats do not exist during reindustrialization times. The shape, structure, and boundaries of industries and sectors are in flux. Nontraditional actors from one industry can enter other industries unexpectedly. Industry analysts are trained to think in terms of industries. As recent experience with Tesla shows, Tesla cannot be analyzed as an auto company.

Technical Characteristics: The technical characteristics of reindustrialization companies and innovations are widely different than that of ordinary innovations. For example, the artificial intelligence technology is about systems that are developed from data vs. regular IT systems that are made for data.

Operating Models: The operating models for reindustrialization are different. In many cases the innovation transforms the operating models itself. The operational and execution plans and strategies need to be analyzed from the reindustrialization viewpoint. Both product and production platforms change.

Go to Market Strategy: The market entry strategy of a firm, its positioning, and go to market plans change. The change happens at the most fundamental level.

Geopolitical Rivalries: Reindustrialization often resets the global friendships and rivalries and changes the rules of competition. Supply chains are realigned and remapped. Analysts often attribute such changes to political developments – even though such changes are often due to realignment of global competitive forces due to reindustrialization. For example, China and US relations changed with the advent of artificial intelligence and quantum computing.

Value Mapping: Understanding and mapping value creation in reindustrialization is not as straightforward as analysts are used to. It requires rethinking the production, distribution, and sales process.

Narratives: Reindustrialization carries its own narratives. Understanding and acting upon those narratives require developing new research methods. They include the use of natural language processing and ethnographic studies.

Behaviors (expectations): Deciphering shareholder, investors, and customer expectations is an important element of sensemaking.

Financial Models: The standard valuation models may not offer the best base to understand asset prices. Reindustrialization requires the introduction of new methods to study asset values.

The above factors greatly contribute to the inability of research analysts to understand the dynamics of reindustrialization.

Academy7

Improving Audit with AI

As scandal after scandal have destroyed audit credibility, the audit profession needs a total restructuring. One type of restructuring happened when PCAOB (regulator, Public Company Accounting Oversight Board) took control away from accounting firms’ self-regulation. The ongoing problems with audit are now forcing the exploration of new ideas. Some believe that the large accounting firms should be fragmented into smaller firms and must require the inclusion of smaller firms as partners. Others are suggesting having government take over the entire audit business (like IRS for taxes).

Audit suffers from both effectiveness and efficiency. In fact, the problem with audit is that effectiveness and efficiency goals tend to work against each other. If you seek efficiency, you may have to compromise on effectiveness and vice versa. Machine Learning can greatly improve audit outcomes. The application of machine learning happens in all stages of audits. Machine learning can also help discover new business models for audit firms.

Audit automation can be viewed as automation of audit planning, audit evaluation, internal controls risk assessment, reporting, fraud detection, valuation, and other such audit process tasks. AIAI offers a report on machine learning in audit.

Academy6

Going Social and Quantamental with AI

By: Al Naqvi

This is quick snapshot of some thought provoking ideas

Marshall Wace uses MW TOPS trading system which collects investment ideas from over two hundred sell side institutions and independent research providers. With millions of trades conducted on the platform, the TOPS architecture allows for global, diversified portfolios with differing risk and trading profiles. With the TOPS architecture, the firm is able to offer both fundamental and systematic styles of investment and integrated them to alpha.

MW claims to focus on ‘Quantamental’ investment tools which find trading signals in complex data patterns. Marshall Wace says that the firm uses innovative dataset knowledge and systematic investment discipline to augment and improve its fundamental portfolio management approach. The firms also claims the development of entirely new strategies from data.

MW has now announced that it is raising a $1 Billion fund. It is expected to be part of the TOPS system. The real questions are:

  • What would it mean to integrate ESG with quantamental strategies? In other words, in addition to fundamentals and systematic or market based strategy development, integrating ESG would add a third factor.
  • The second question is what would an AI solution look like when ESG is integrated with quantamental?

Discovering and identifying investment strategies is a complicated business. Add to that the ESG component and the job becomes extremely hard.