The COVID-19 crisis, with its unpredictability and turbulence, has disrupted the way companies build their business strategies. Mark Esposito, Alessandro Lanteri, and Terence Tse write that two strategy frameworks combined can become part of a new strategic architecture to empower firms to identify and pursue growth opportunities in the post-pandemic world economy.

Mark Esposito

Alessandro Lanteri

Terence Tse

The COVID-19 pandemic has been a major disruptor for tried and tested approaches to strategy. More specifically, it has promoted two business practices: digital transformation and sustainability. Even as the pandemic still impacts our lives, companies are stepping up their efforts and new imperatives and business models keep emerging as a validation of new organisational practices and norms. The pandemic has not only changed our perception of the world but equally accelerated our adoption of new work and production models which become visible and shape a ‘new normal.’ How do business leaders seize the opportunities in the new normal?

A strategic alliance between fledging frameworks

To successfully navigate the upheaval caused by the pandemic, business leaders must reconsider their strategy. Two of the key strategy questions they must answer are: where their company will play and how it will win. This article provides two research-based frameworks to answer these two critical questions.

To answer the first question, DRIVE (Tse and Esposito, 2017) assists decision-makers in tracking the megatrends that shape the competitive landscape and in identifying major emerging opportunities, which traditional tools for strategic planning might only capture when it is too late. For the second question, CLEVER (Lanteri, 2019) points out the drivers of strategic advantage decision-makers should leverage to maintain a competitive edge. Jointly, the two frameworks constitute the foundation of a new strategic architecture that empower firms to identify growth opportunities and successfully organise to pursue them in the turbulent, post-pandemic world economy.

The two frameworks were originally designed with a focus on businesses’ future fitness, integrating adaptable and flexible thought processes in their functioning. They were designed as non-exhaustive instruments to grasp the flow of the times in which we are living, in an increasingly complex world, where linear models of distribution are less and less functional to the purpose of prediction and forecast.

As shown in figure 1, the macro level of analysis assesses the environment and the nature of the forces that are shaping the conditions for business and public policy to operate. From the scanning of the externalities to the detection of grassroot movements, to the discovery of growth models that traditional economic indicators can easily miss, this level of analysis is necessary to determine the magnitude and direction of the trends.

The meso level of analysis refers to the institutional and intermediated level of the economy in which a chain of entities operates as connectors between forces at the macro, and execution at the micro level. The meso level provides opportunities for formulation of strategy and ideation of business models that probe markets and reconfigure systems.

The micro level involves the most active agents in the economy, including consumers and individuals, who make decisions based on limited information, sentiments and access to opportunities. It is where markets get validated and where decision-making blends theoretical assumptions with projectable actionability and where the socio-economic system enables itself.

Our frameworks combine all three analysis levels in rigorous research with practical design aimed at facilitating the transformation of systems. A deeper understanding emerges from the nature of the trends as well as from the ideation of how the trends can shape strategy.

Understanding how ’DRIVE’ brings the macro level of analysis to life

During the research, we investigated the junctions where current macro trends set the trajectory for future events. In the vast ocean of knowledge, information, and data, we found five undercurrents that can give us some directions as to how the future would unfold. These five paths aren’t exclusive or exhaustive. Most are likely a minimal representation of all undercurrents that interplay with the shaping forces of our societies, but with this in mind, we have narrowed the research down to five megatrends, which we call the DRIVE framework, consisting of the following:

Each of these megatrends is unique, but, in combination, they can present a comprehensive picture of what the future holds and help us picture a future in the making.

Looking through the meso-level lens, CLEVER lays out the drivers and forces that offer strategic advantages and – therefore – trigger strategic change. The drivers are:

The first three represent the ‘hard’ features—the technological components, with the remaining three capturing decision-making and managerial styles. Table 2 describes the definition and strategic advantages of each driver as well as illustrative examples and counterexamples.

The challenge in this new normal is identifying existing frameworks that can help synthesize some of the issues we are facing. The two frameworks we discuss here capture emerging approaches to strategic decision-making and jointly help decision-makers identify growth opportunities and successfully organise to pursue them in the turbulent, post-pandemic world economy.

These frameworks do not aspire to be perfect. In turbulent times, with multi-causal patterns and non-linear trajectories of change, the future cannot be predicted. So, even equipped with the DRIVE and the CLEVER frameworks, decision-makers might still face sudden black swans and wildcards that lead to unexpected circumstances and rapidly shifting competitive scenarios.

The frameworks should instead be judged by their usefulness. In the years spent researching and refining our frameworks, we have witnessed first-hand how they have been applied in dozens of companies across industries and geographies. Business leaders can use the two frameworks to understand where strategic opportunities emerge, to find inspiration for strategic action, and so ensure their organisations become future-ready in the post-pandemic era.

Energy Voices is a democratic space presenting the thoughts and opinions of leading Energy & Sustainability writers, their opinions do not necessarily represent those of illuminem.

Building the strategic architecture of the post pandemic corporate landscape

The platformization of the economy has been a reckless phenomenon of the last few months, with the rise of numerous digital marketplaces, which have contributed to widen the gap between the real and the digital economy. The risk we are running is that, while we witness the rise of digital industrial complexes capable of derailing market efficiency, untenable digital rents are being imposed upon every aspect of the real economy that still requires access to technological solutions of daily use.

The platformization of the economy has been a reckless phenomenon of the last few months, with the rise of numerous digital marketplaces, which have contributed to widen the gap 

. The risk we are running is that, while we witness the rise of digital industrial complexes capable of derailing market efficiency, untenable digital rents are being imposed upon every aspect of the real economy that still requires access to technological solutions of daily use.

The premises of the Fourth Industrial Revolution

 are based largely on the symbiotic ability of the tangible and intangible poles of the economy to co-exist and lean on each other. The tangible side of the economy provides the infrastructure required by automation, manufacturing, and the complex network of trade and shared production. This physical infrastructure is the foundation upon which efficiency enablers such as logistics, communication, energy and labor, can be built.

The tangible economy is often a pre-requisite for the intangible economy

: It is then that a hybrid infrastructure arises, where physical and digital interfaces can exist, and digitalization provides the acceleration required for those ‘tangibles’ to become ‘intangibles’ and to bypass limitations by shifting large chunks of economic value creation to the digital datasphere. So far, not much drama can be found in this process, which, while heavily transactional and capital-intensive, is still a positive mechanism for growth and provides some equity of opportunities for small and large countries alike.

What this story doesn’t include is that in recent years, and especially in 2020, the digital sector of the economy has often decoupled itself from the real economy, and digitally native companies, which arose out of a dis-intensified use of traditional factors of production, are today growing at a pace that since September 2020 

, and has kept increasing since then. By the beginning of September, the share prices of Facebook, Amazon, and Apple had more than doubled since the worst of the pandemic. Apple hit the surreal $2 trillion valuation mark – a first in the history of our global economy. Shares of Netflix and Alphabet (Google), the other so-called FAANG firms, hadn’t quite doubled but were also trading at or near all-time highs. Meanwhile, 

the oldest and most iconic member of the S&P 500 since 1928 – ExxonMobil

 – left the index on Aug. 31, driven out by Apple’s decision to split its stock. Those who own and run the tech giants make ever more money, while the rest of the world suffers from economic devastation.

This estranged and partially de-normalizing trend positions real economy assets far below digital financial assets and a clear K shape seems to emerge: thrive and grow if you are digital, stifle and decline if you are part of a real economy, where growth is only proportional to how finite the system is. This trend is not only challenging some neoliberal economic assumptions about the creation of value, but it is also pushing us towards a complex scenario where government policies or the redistribution of value will no longer be plausible options, given the width of a gap that already appears unsurmountable.

Governments and private sectors have already thoughts of plausible remedies, but, while some governments contemplate 

 in order to redistribute tsome value back to the real economy, opponents continue to believe that any form of market intervention creates even greater market distortion, with deregulation and laissez-faire as the only acceptable ways forward. We fear that neither approach has garnered satisfactory evidence of success. Fragmentation and inefficiency continue to reign.

The remedial options must be consolidated in a more systemic way, and we argue for 3 possible actionable ways forward:

 to promote technology diffusion, so that the 

 between platforms and SMEs that require technology can be closed. rather than expecting the market to provide access to technologies through the mechanism of economies of scale, government funding can accelerate access by allowing millions of smaller firms to gain access 

. While this would increase public debt, a more balanced distribution of power goes a long way in strengthening a country’s ability to build productivity.

and multi-stakeholder collaboration model

, conciliating the fast pace of technological innovation with the imperative of inclusion and representation of all parties for effective outcomes which benefit the broader number without discouraging innovations. This will also help reconciling tensions between winners and losers among value chains’ stakeholders. 

 have demonstrated that the fair representation of interests favors a governance that works for most.

: just as trade tariffs are used in developing economies to incentivize the rise of nascent local production, the introduction of digital tariffs may help the development of local innovation ecosystems in countries where the technological threshold is strong enough to create a hub and where grassroots, spontaneous organization of the management of technology can fuel the ecosystem itself and the inflow of capitals. This would empower those who can stand on their own two feet and would selectively reduce dependency on the large multinational companies, which currently operate at scale.

The post-pandemic world will not only present us with a limping economy, a generalized state of fear for the future, and the realization that the character of the global economy has been profoundly changed. We will also find ourselves facing one of the most acute crises of opportunity and technology access of all time, driven by a digitally invasive economic model that is incapable of building equity for all or at least for most. The times are critical, and the stakes are high, but this is also the moment to let go of the failed promise of market efficiency theory and simply fix this. It must be now, as it can’t be tomorrow.

This article was co-authored by Landry Signe, Nicholas Davis and Mark Esposito.

This article is also published on Project Syndicate and on 

. Energy Voices is a democratic space presenting the thoughts and opinions of leading Energy & Sustainability writers, their opinions do not necessarily represent those of illuminem.

Are platforms silently killing the Fourth Industrial Revolution?

Investors face at least two challenges when making environmental, social, and governance (ESG) decisions. One is the discrepancy in definitions, scoring methodologies, and assessments. The second one is the dearth of timely and accurate data. Terence Tse, Marissa Lum, Danny Goh and Mark Esposito write that AI carries the promise to provide an immediate solution to mitigate the data problem.

, we explored how artificial intelligence (AI) can help reach environmental, social and governance (ESG) goals. Here we intend to look deeper into the AI technologies and how they extract and handle data to make investors better informed. Such technologies are increasingly, if not urgently, needed as ESG issues are fundamentally changing the investment landscape.

Yet, there are at least two informational challenges that investors face. The first, as we mentioned in our previous article, is that there are discrepancies among rating and index producers – even when scoring the exact same companies. A recent 

 has found that in a dataset of five ESG rating agencies, correlations between scores on 823 companies were on average only 0.61. The reasons for the inconsistency lie in the differences in definitions, scoring methodologies and assessments used. Investors often don’t have the time to go through, compare and reconcile the differences of views and ratings from different suppliers.

The second is the dearth of timely and accurate information to make informed decisions. Here is an 

: does Tesla qualify as an ESG company given that electric cars are purportedly good for the environment? If the answer is yes, what about the fact that the batteries used in Tesla cars depend on nickel, the extraction of which comes at an environmental and health cost? How about the fact that Tesla’s recent purchase of $1.5 billion worth of bitcoins, the processing of which is extremely energy-demanding if not downright wasting?

AI carries the promise to provide an immediate solution to mitigate the latter problem. As machines are much more capable than humans to gather and handle qualitative information at scale, cheaply and rapidly, the supply of such information will in turn improve the completeness and timeliness of data, and hence the overall quality of ESG data available to investors.

Typically, AI technologies follow three steps to produce the data: search, screen and structure.

 The process starts by using AI to search for and extract company data from a range of sources including news coverages, messages and mentioning in social media as well as company disclosures and external reports. To do so manually would be a time-consuming, labour-intensive and costly effort. Even if this is bearable, manual approach to search and import data in real-time fashion on an ongoing basis would be impossible. Indeed, as the universe of ESG data continues to expand, AI represents the only feasible means to collect data.

 After data collection comes screening and entering extracted data on the database. Traditionally, this entails humans to first “eye-ball” the information and then manually key in the data, a process that is demanding in both time and efforts. Today’s technologies make it possible for us to skip this process to a great extent. Based on some pre-defined logic and using HTML analysis, machines can parse and convert a huge volume of such unstructured data into structured data that is readily usable – error-free and swiftly.

 The next phase is about discovering and gleaning valuable insights from the structured data set. This involves developing various natural language processing techniques such as those related to classifications and taxonomies. It also makes use of analyses that capture sentimental, contextual and semantic factors embedded in the collected data.

One giant step forward to making ESG work

The market is keen to invest in ESG-compliant companies for several reasons. Some investors are genuinely interested in changing the world. Others are keen to minimise harm with their investments. Finally, there are those who are far more concerned with protecting their portfolios. But without the right information, it will be difficult for them to invest in ESG properly.

Take the example of Wirecard, the disgraced German payment processor and financial services provider. While the company only filed for bankruptcy in June 2020, news of its questionable business practices broke out as early as 2015. Indeed, until its collapse, there had been a series of (journalistic) investigations in the company, unearthing ever more evidence of wrongdoings. Yet throughout all this time, Wirecard received 

 from a number of ESG ratings agencies. We believe that AI-driven technologies can shed some light on the current ESG ratings black box. With real-time data, it will be much easier for investors to identify and track events, which will enable them to act and respond without delay. As such, we will be able to get ever closer to achieving ESG goals.

This article was co-authored by Terence Tse, Marissa Lum, Danny Goh and Mark Esposito.

Using AI to screen, search, and structure environmental, social, and governance data

Dr. Mark Esposito is a bestselling author, and the co-founder & Chief Learning Officer at 

 and advisor to national governments. He is currently advisor for the Prime Minister Office in the UAE. Mark has also held several academic appointments for some of the world’s leading institutions such as 

 (where he is Director of the Future Readiness Impact Lab), 

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Mark Esposito

Mark Esposito

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