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

Alessandro

Terence

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?
<h1>A strategic alliance between fledging frameworks</h1>
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.
<h1>From macro to micro analysis</h1>
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.
<img src="https://illuminem.b-cdn.net/articlebody/c0c317f73800cb43c29205fc90aea0dc899bd841.png" alt="Figure 1. A way to identify growth opportunities">
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.
<h1>Understanding how ’DRIVE’ brings the macro level of analysis to life</h1>
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:
<ul><li>Demographic and Social Changes</li></ul>
<ul><li>Resource Scarcity</li></ul>
<ul><li>Inequalities</li></ul>
<ul><li>Volatility, Scale, and Complexity</li></ul>
<ul><li>Enterprising (Dynamic)</li></ul>
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.
<h1>Discovering the CLEVER way</h1>
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:
<ul><li>Collaborative intelligence</li></ul>
<ul><li>Learning systems</li></ul>
<ul><li>Exponential technologies</li></ul>
<ul><li>Value facilitation</li></ul>
<ul><li>Ethical championship</li></ul>
<ul><li>Responsive decision-making.</li></ul>
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.
<img src="https://illuminem.b-cdn.net/articlebody/53bfe530055d70504e76536199ed11381dfdd49a.png" alt="Table 2. The elements of the CLEVER framework">
<h1>DRIVE + CLEVER</h1>
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.

This article is also published on the <a href="https://blogs.lse.ac.uk/businessreview/2021/10/27/building-the-strategic-architecture-of-the-post-pandemic-corporate-landscape/">London School of Economic&#x27;s Blog</a>. Energy Voices is a democratic space presenting the thoughts and opinions of leading Energy &amp; Sustainability writers, their opinions do not necessarily represent those of illuminem.

Building the strategic architecture of the post pandemic corporate landscape

AI could provide more accurate and timelier ESG-related data, which could in turn help improve the various ESG indices and enable investors to make more informed investment decisions in companies that are truly ESG-compliant.

<a href="https://academ.escpeurope.eu/pub/IP%202021-06-EN.pdf">It is understandable</a> that some people should be sceptical about artificial intelligence (AI) but there is no shortage of ways companies can use technologies not just to do good but also to do well, like using AI to help <a href="https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Sustainability/Our%20Insights/Artificial%20intelligence%20and%20the%20circular%20economy%20AI%20as%20a%20tool%20to%20accelerate%20the%20transition/Artificial-intelligence-and-the-circular-economy.pdf">reach circular economy goals</a>. Yet the progress for technology-driven pursuit of sustainability <a href="https://www2.deloitte.com/content/dam/Deloitte/fr/Documents/sustainability-services/deloitte_escp-look-around-economie-circulaire.pdf">remains slow</a>.
One reason for such sluggishness is that there remains a lack of economic incentive for companies – and their investors – to make huge investments concerning social and environmental gains and benefits. This also provides an explanation as to why, despite years of discussions on the importance of the so-called “triple bottom line” – the need to care for not just profit but also people and the planet –, it has hardly become a mainstream practice among today’s businesses. At the same time, there is increasing interest in investing in socially and environmentally responsible companies.
Looking at it from this vantage point, two lessons are clear:
<ul><li>The first is that unless investors are getting the satisfactory return, it will be difficult to get businesses to orientate themselves towards goals related to people and the planet.</li></ul>
<ul><li>The second is that investors need to have timely and accurate information to make informed decisions.</li></ul>
In this respect, AI presents a welcome and potentially extremely beneficial tool to help the latest idea in sustainability: environmental, social and corporate governance (ESG).
<h2>Sustainable investments reached $31 trillion in 2018</h2>
Investments in ESG have fast become a socially important asset class. <a href="http://www.gsi-alliance.org/wp-content/uploads/2019/03/GSIR_Review2018.3.28.pdf">One study</a> points out that sustainable investments reached $31 trillion in 2018, up by 34 percent from 2016. The result: we need to know if and how different companies are acting in accordance to ESG. Simultaneously, as revealed in <a href="https://www.omfif.org/esg2020/">a recent survey</a>, it is now becoming evident to managers that ESG is an essential ingredient to the long-term survival of their businesses. Not surprisingly, with the raised interest in the subject, a number of companies such as MSCI, Bloomberg and Sustainalytics have proliferated in recent years to offer ESG-based rating and index services to guide investors.
<h1>Boohooing ESG ratings</h1>
Yet these ratings can be heavily flawed. Take for example, the ultra-fast-fashion retail phenomenon Boohoo. The retailer received well-above ESG ratings before <a href="https://www.thetimes.co.uk/article/boohoo-fashion-giant-faces-slavery-investigation-57s3hxcth">being accused of modern slavery</a> for underpaying their workers in Leicester (England!) and not providing them with proper protective equipment against Covid-19. Another example is Wirecard, a german payment processor and financial services provider. The company went bankrupt in June 2020, but news of its dodgy and questionable business practices had been uncovered as early as 2015. Yet throughout all this time, Wirecard received <a href="https://www.ft.com/content/3aeb9395-1f4d-4774-8f4b-5331acc28e04">median-grade ratings</a> from a number of ESG ratings agencies.
How could the rating companies have gotten it so wrong? The answer: information asymmetry. It appears that all parties involved face different challenges with obtaining and processing information. To begin with, rating producers and indices deploy their own proprietary methodologies and data to analyse companies. The result of them using different ESG definitions, measurements and weightings for different indicators often lead to conclusions and verdicts that can be distinctly different from one index to another. <a href="https://qgroup.wildapricot.org/resources/Documents/Rigobon_Aggregate%20Confusion%20Paper.pdf">A recent study</a> has found that in a dataset of five ESG rating agencies, correlations between scores on 823 companies were on average only 0.61. Furthermore, they rely heavily on information provided by the companies being rated. In this case, the rating companies are essentially giving the latter a free pass to pick and choose the data to be used.
This, in turn, poses problems for the investors. First, without standardisation across ratings, it is difficult for investors to compare across the indices created by different providers. Another key problem the investors face is that they have got little choice but to rely on the rating and index producers to capture the latest information and news and to incorporate them into their ratings. The lack of timely and accurate information makes it difficult to make the right decisions. Take the example of Tesla. Is this electric car company really an ESG company considering their batteries depend on nickel, the extraction of which comes at an environmental and health cost? What about Tesla’s recent purchase of some $1.5 billion worth of bitcoins, which require a great deal of energy to process?
For the rated companies, even though they can select the information to be submitted, they often suffer from other problems. For example, how can they improve their own ratings if different raters use different methodologies? Also, how can they ensure the investors will get hold of the right information and interpret it correctly?
In short, the problems emerge from a lack of clarity, consistency, and transparency of ESG ratings as well as information asymmetry and shortage.
<h1>AI as a tech for good</h1>
The key to solving these problems is to regularly and rapidly collect qualitative information to supplement the publicly available data. Up-to-date qualitative data has the ability to not only help investors and rating producers to be much better informed, but such data can also be used to set up key inputs that could be used as the basis to form common minimum standards.
Until now, gathering and gleaning insights from social media, daily local news, and freshly available reports has been carried out manually, which is error-prone, slow and very costly.
Algorithm-driven systems are a potential game-changer in pushing the ESG agenda as they can easily and effectively crawl the worldwide web and scrape unstructured data on companies from a range of sources. They can then swiftly parse and convert the excavated data into usable structured ones. This, in turn, allows for curated output that is valuable for all parties involved, thereby drastically mitigating the information asymmetry problem. In addition, using natural language processing technologies to perform analyses that capture sentimental, contextual and semantic factors embedded in the collected data, it will be possible to discern the tone of the information provided. Such analytical algorithms could be trained to go through a certain type of conversation and identify the tone by comparing the words used to a reference set of existing information.
<h1>Something for everyone</h1>
This should produce a “win-win-win” situation:
<ul><li>For the rating and index providers, real-time signals often represent early warnings and timely indicators, giving them much clearer pictures of the companies in their indices. They can also broaden the scope of analysis using AI-derived information to complement their current quantitative data and methods.</li></ul>
<ul><li>For the rated companies, they can be assured that their ESG-compliant activities will probably be captured and integrated in indices and ratings.</li></ul>
<ul><li>Finally, for investors, they will have better quality ESG data quicker, which in turn can help them with their investment strategies.</li></ul>
It must be noted that we are only at the very beginning of using AI technologies towards ESG improvements. And the results of what this would succeed is yet to be seen. However, history is filled with examples of how technologies have helped attain social goals and create a better society. It is very much hoped that through the empowerment of investors, particularly those who are ESG-orientated, it will become possible for ESG to be a well-respected and common business practice instead of just another fad or slogan advocating sustainability. Using AI in the right way can no doubt help with our endeavour to raise ethical standards.
This article is also published by <a href="https://thechoice.escp.eu/tomorrow-choices/ai-for-good-the-case-of-using-the-technology-for-esg/">The Choice</a>. Energy Voices is a democratic space presenting the thoughts and opinions of leading Energy &amp; Sustainability writers, their opinions do not necessarily represent those of illuminem.

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Terence Tse

Terence Tse

Building the strategic architecture of the post pandemic corporate landscape

AI for good: The case of using the technology for ESG

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