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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: Business Not as Usual: Companies Stepping up in Crisis
In February 2023, two devastating earthquakes claimed more than 55,000 lives and affected over 10 million people in Turkey and Syria. Responding to this massive humanitarian crisis is everyone’s business – including private companies. Indeed, after the Turkey-Syria earthquakes, semiconductor technology company ASML matched employee donations made to Turkish NGO Ahbap, and IKEA donated funds through its IKEA Foundation, among other acts of generosity by corporate donors.

Such post-disaster responses by the private sector have been observed all over the world: After Hurricane Harvey, the Coca-Cola Company donated water, milk and energy drinks, while Johnson & Johnson sent hygiene kits to the region. But while offering monetary and in-kind donations makes a difference, the private sector can make an even greater impact on response and recovery when companies leverage their core capabilities and ready themselves well before a disaster. This requires commitment and preparedness.

A disrupted community is a disruption to business

In the wake of a disaster, companies may be directly or indirectly affected – be it their employees, physical stores, products or the economic situation in general. This is why disaster preparedness is not merely a humanitarian imperative, but also essential to business continuity.

Walmart’s response to Hurricane Katrina is a good example of alignment between business continuity and disaster preparedness for humanitarian reasons. The company was praised for its efficient response. In fact, Walmart reached the affected region even before the United States Federal Emergency Management Agency (FEMA) did and distributed supplies such as food and water to the community. It even transformed its stores and parking lots into community hubs where people could get supplies and do their laundry.

Such response was possible because Walmart was ready when Hurricane Katrina hit; the company constantly monitors supply chain risks, including natural disasters. It had put in place contingency plans to minimise disruption to the supply chain and reopen stores as soon as possible. Once it managed to restore its supply chain, it could then contribute to humanitarian relief by facilitating aid delivery.

Clearly, a well-prepared company can recover faster (if affected) and even extend timely help to those in need. From the perspective of business operations, preparing for and supporting humanitarian relief operations allows the company to build adaptiveness and agility and, in turn, improve supply chain resilience, which is aligned with the goal of business continuity.

Beyond donations: leveraging core capabilities and strategic partnerships

Walmart’s response shows what a company can do by leveraging its core business capabilities and resources in retail logistics. Similarly, after the Turkey and Syria earthquakes, online shopping websites such as Trendyol, Hepsiburada and Yemeksepeti set up systems to enable donors to purchase in-kind aid to be delivered from the companies’ warehouses to affected population by the main coordinating body known as the Disaster and Emergency Management Presidency (AFAD). Logistics companies such as MNG Kargo allowed individuals to deposit solicited in-kind donations such as blankets and clothes at their stores all over the country, which were sent to the affected regions at no cost.

But beyond logistics, companies may have other core competencies, depending on their sector, size, infrastructure, network, risk management tools and procedures put in place. For instance, health, nutrition and bioscience company Royal DSM is creating fortified, nutritious food solutions and improving food affordability and availability through its partnership with the United Nations World Food Programme (WFP).

Partnering with a humanitarian organisation is another way to get involved, which should ideally start from the preparedness stage through the response and recovery phases. For example, Amazon has established Disaster Relief Hubs with prepositioned inventory from which partners such as Save the Children can tap on during a disaster response.

Alternatively, a company can collaborate with competing firms to support a relief operation. An example is the Logistics Emergency Team (LET). It consists of four major logistics companies including Agility, UPS, A.P. Moller - Maersk and DP World to support the United Nations Logistics Cluster, when called upon by WFP, the cluster lead.

The coalition effectively increases transportation capacity and makes services such as customs clearance, warehousing and information management more accessible. In response to the Turkey and Syria earthquakes, LET evaluated the local logistics and storage capacity, supported operations through airlifts and published flight routing data to help identify available air cargo space for humanitarian operations. Beyond emergency response, LET contributes to preparedness by conducting logistics capacity assessments in high-risk areas that inform WFP.

Understandably, when companies compete in the for-profit space but collaborate in support of a humanitarian cause, it could give rise to unexpected dynamics. But while companies may be wary of collaborating with their competitors, such “coopetition” structure can result in synergies and more efficient processes. Moreover, it can offer co-learning opportunities for companies to build adaptability and agility, while increasing employee and customer satisfaction.

Preparing for the challenges of humanitarian relief

First and foremost, companies that decide to be actively involved in humanitarian relief need to be committed and prepared to manage potential risks. In the case of Walmart, it set up a Global Emergency Management department that constantly collects and analyses data to identify, assess and respond to events such as natural disasters, disease outbreaks and other crises. The team uses data from external sources such as governmental agencies, weather reports and disease outbreak data to perform independent risk and demand assessments. The department also trains associates on preparedness and business continuity after disasters.

An important feature of its organisational structure is its flexibility in times of emergency. The department, which is normally staffed by six to ten employees, can expand to 50 people, with involvement from senior managers in different functions where necessary. More importantly, Walmart gives a great degree of autonomy to district and store managers, allowing them to make swift decisions without the hindrances of bureaucracy. This is in stark contrast to AFAD, which was harshly criticised for its top-heavy structure that led to its slow response in the recent earthquakes.

In the case of partnerships, companies that are already operating in intense contexts due to the massive devastation and human suffering are further challenged by diverse stakeholder priorities. To overcome these challenges, the companies involved should set a clear purpose and commitments towards a common goal and establish partnership rules and operating guidelines from the start.

For example, LET, the pro-bono coalition, is activated upon the request of WFP (or the Logistics Cluster led by WFP) to support humanitarian response to disasters that impact more than 500,000 people. LET is typically active for three to six weeks post-disaster, with the option of reactivation if necessary. This unique partnership has been studied for years by the INSEAD Humanitarian Research Group.

Once all stakeholders are on board, it is essential to work towards a structured and detailed approach of what will be done, as well as when and how. These plans for operations, coordination of human resources and assets, timeframe, locations and exit strategy contribute towards standard operating procedures that can be readily deployed. To develop and improve these plans, a steering committee can provide the global-level partnership governance structure.

A route towards win-win: recovering together

When communication is disrupted, infrastructure is destroyed and the lives of customers, employees and their families are put on hold, nothing is usual – and neither is business. Business continuity after a disaster cannot be viewed separately from humanitarian response and recovery.

Donations are invaluable, but private companies can do more with their core capabilities. Supporting humanitarian relief at every phase of the disaster – preparation, response, recovery – individually or through partnerships not only helps the affected populations, but also builds a company’s supply chain resilience, as well as its standing as a socially responsible organisation. Swift business recovery can only happen with expeditious recovery of the community it serves. This is best achieved by working and preparing together.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: The Road Ahead for XR Technology in Business Education
Apple recently shook the tech world with the unveiling of its mixed reality headset, reigniting discussion and excitement around immersive technologies such as virtual reality (VR) and augmented reality (AR).

Amid the fervour, INSEAD’s Dean of Innovation Peter Zemsky shared his thoughts on the value of extended realty (XR) technology – an umbrella term that encapsulates VR, AR and mixed reality – in the realm of business education. In a Tech Talk X held at the first Annual Meeting of the Global XR Management Community, Zemsky emphasised the imperative for business schools to carefully assess the potential of VR and equip themselves with the necessary knowledge before diving in.

Moderated by Victoria Woo, Director of INSEAD’s San Francisco Hub for Business Innovation, the Tech Talk X brought together Zemsky, Niron Hashai, Dean of Arison School of Business at Reichman University, and Steven King, Associate Professor of Emerging Technologies and Innovation at the University of North Carolina.

Zemsky, Hashai and King stressed the pressing need for business schools to collaborate to disrupt the traditional written case method and shift to immersive learning experiences. They highlighted that faculty need to champion the development of VR cases and acknowledged the myriad technological and operational challenges. They also underscored the benefits of using VR to enrich the education process, drive behavioural changes and ultimately cultivate better leaders.

The panel also delved into the value of active learning using VR compared to passive learning with video. They spoke of the importance of exploring the unique possibilities of VR, such as tracking where students look during a particularly tense board meeting or simulating decision-making scenarios on a mission to Mars. To date, the INSEAD VR Immersive Learning Initiative, led by INSEAD Professor of Strategy Ithai Stern and Director of Digital Innovation Florian Schloderer, boasts a library of 20 such experiences.

All three experts agreed that VR experiences help solidify learning and push students beyond their comfort zones. According to Zemsky, VR has the potential to unveil new perspectives and uncover insights that students may have never considered before.

Watch the full video to gain a deeper understanding of how XR can empower the next generation of leaders.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: The Beliefs and Emotions That Shape Our Sustainability Journey
As the need to tackle pressing global challenges becomes increasingly apparent, a growing number of business leaders are placing sustainability at the forefront of their organisational priorities. However, they also recognise that sustainability is one of their most formidable challenges, as many companies struggle to successfully realise those aspirations.

To navigate this complex landscape, it helps to understand where we are on our sustainability journey. This involves reflecting on our convictions and experiences and examining our beliefs and emotions. We may learn we are going in circles and discover the key to advancing further along the path.

With such introspection, we enhance our understanding and experience and propel ourselves towards a more sustainable future. This does not only help us reach our full potential, but can also assist us in guiding stakeholders we encounter on our path who may not be at the same stage of their journey.

From unaware to informed

Sustainability journeys begin with the belief that there is no problem. Take climate change for example: Individuals and companies were once blissfully unaware, believing that our Earth was in good health. We then began to move from a place of comfort to one of doubt as we became informed about the alarming environmental changes and realised there is indeed a problem that needs attention.

Believing we have the solution

In our quest for a culprit, we may believe that the solution is to identify and eliminate the “ultimate cause” such as focusing on carbon dioxide and setting net-zero targets or moving away from combustion cars to electric vehicles. However, in doing so, we are staying in our comfort zone, assuming that humans are in control of nature and that we can solve the problem. Many are still stuck in this loop of searching for the elusive ultimate cause and a definitive solution. Reducing our CO2 emissions is only a tiny part of the solution to the polycrisis we are facing. Our entire relationship with nature is at stake. We need to review our understanding of human nature to effectively address these challenges.

Acknowledging despair and rebounding for the battle ahead

The critical step occurs, or does not occur, precisely because we need to acknowledge that the solution is not to change nature, but to change ourselves. We need to adapt to the very problems that we generated by believing that we were in control of nature. In this sense, “the solution” does not exist and recognising this is a challenge to our ego. It is also an emotional challenge because believing that there is no solution to the sustainability problem invokes despair. With the potential collapse of our ego, we may become depressed and even disenchanted. We will only start resolving these problems with a humbler encounter with nature.

The move out of despair is undoubtedly the most difficult phase, yet it is also the closest to us, as it happens within ourselves. To take this step, we must first grasp the multitude of challenges at hand. Ecosystem collapse is just one example of our dysfunctional relationship with nature. Instead of focusing on one problem, we recognise that we are facing a systemic crisis where everything can be seen as a problem. Of course, this is frustrating and induces anxiety.

However, with the understanding that we cannot evade all these challenges, we become a fighter willing to engage, driven by a higher purpose and an overarching dream. While utopias may not always materialise, the journey can infuse our lives with meaning.

Embracing opportunities creates excitement

Once we are past the point of despair, we are able to recognise that the immense challenges we face present many opportunities. In a surprising and magical twist, our problems become ground for solutions, our vulnerabilities become competencies. We are excited by the prospects ahead and feel enlightened.

On the path towards sustainability, we come to terms with our beliefs, emotions and identities in relation to our journey. This helps us to assess the dynamics of our experiences and gain a deeper understanding of ourselves. Through this process, we become more aware of where we were yesterday, where we are now and where we want to be in the future.

It also helps us become more respectful of others when we have a better understanding of where they are on their paths to sustainability. Instead of being defensive with stakeholders, we can become allies and collaborate to create new solutions, letting them emerge from something bigger than ourselves. This empowers us as wiser leaders.

We can listen to and learn from younger generations – many were born into a world where sustainability challenges were already abundantly clear. They are likely further ahead on their journey and can potentially show us the way forward.

It is also possible that we may be at multiple stages of our journey at the same time. We can embrace this multiplicity of selves – recognising there is space to be in a state of comfort, doubt, confidence, despair, anxiety or excitement – and have internal conversations between these multiple identities. By doing so, we can bring harmony to the most important conversation of all, the one with our conscience.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: US-China Ties: Damage Controlled But Obstacles Ahead
Antony Blinken, the United States Secretary of State, has finally visited China, four months after his trip was abruptly postponed over the discovery of a Chinese spy balloon in US airspace. Initial reactions have been positive, with Chinese President Xi Jinping himself declaring that the US and China had made progress in improving their acrimonious relationship.

But while any attempt at dialogue and cooperation is welcome, we should be realistic: Blinken’s trip was more about damage control and drawing up a roadmap for stabilising an increasingly shaky relationship. Setting up communication channels to reduce the risks of miscalculation and crises is a minimal but essential achievement. Both sides agreed to establish working groups, initiate more commercial flights, and foster more exchanges among students, academics and journalists. The Chinese, however, rejected military-to-military communication.

That was one sign that the fundamentals of the relationship have not changed. Deep differences persist on Taiwan’s future, tensions remain heightened in the South China Sea, tariffs remain in place, and severe restrictions imposed by the US on technology transfers and access to semiconductor chips and related technology continue.

Cold War 2.0?

So is this a new Cold War? The media, as well as many politicians, think tanks and pundits seem to think so. In the US, Republicans and Democrats remain polarised on all issues save the perceived threat China poses to US dominance. Worryingly, many Western business leaders – the strongest proponent of trade and investment ties – are contemplating or have even gone ahead with disinvestment (e.g. Apple, Samsung, Hasbro, Sequoia Capital and AstraZeneca) from China.

Undoubtedly, the current US-China relationship bears certain similarities to the one between the US and the Soviet Union. Two superpowers with starkly different ideologies and world views – on the importance of political and civil rights, and the degree of intervention in the economy, to name just two – are jockeying for dominance, and the rivalry is likely to play out over a long time. Even the balloon incident had a whiff of the Sputnik launch in 1957 that set off a race to space in the early days of the Cold War.

But a deeper examination reveals major differences between then and now. The Soviet Union was on a par militarily with the US but lagged in infrastructure, innovation and economic growth. China is the opposite. It trails the US in terms of global projection of force (11 aircraft carriers for the US vs. three for China), defence spending (US$877 billion vs. US$292 billion in 2022), and nuclear warheads (3,600 vs. 350) but boasts a bigger economy in terms of GDP at purchasing power parity. Until recently, before President Xi’s crackdown under the slogan of common prosperity, China also had a thriving tech sector with highly innovative firms.

While the country still has far to go in terms of matching the US in GDP per capita (see below), it has pulled ahead of the US in many next-generation industries such as solar panels, solar and wind power generation, electric vehicles and batteries. China leads in AI adoption and has caught up in the number of AI researchers and top-cited AI papers. In infrastructure, it has built a high-speed rail network nearly twice as long as all other high-speed rail networks in the world combined.

At the heart of the Cold War was an ideological contest between communism and capitalism, and a push by the US and the Soviet Union to export their ideology worldwide. In contrast, ideological competition on the economic front between US and China is weak, especially as both deploy different versions of capitalism. Even the modest ideological gap seems to be closing. The US recently embraced state-directed capitalism via the CHIPS and Science Act, which encourages semiconductor companies to manufacture in the US and not in China, and the Inflation Reduction Act, which provides billions in federal investment in clean energy.

Further, there are no proxy wars yet between the two sides. In stark contrast, during the Cold War, both the US and the Soviet Union participated in conflicts around the globe, ranging from Korea to Angola and from Afghanistan to Nicaragua. The very concept of decoupling reflects how coupled the US and China are in terms of the flow of goods, people, ideas and capital. In contrast, there were few trade or investment ties between the US and the Soviet Union and serious travel impediments between the two countries.

Finally, while China is taking a bigger role in the world economy, the US seems to be retreating. We could be one election away from an inward-looking, America-first agenda.

Developments to watch

Therefore, the Cold War analogy may not be helpful. In fact, calling the relationship a war would predispose parties towards confrontation instead of cooperation. We should not overlook the collaboration between the US and the Soviet Union even at the height of the first Cold War that achieved, among other things, the eradication of smallpox, the Helsinki Accords, the Nuclear Non-Proliferation Treaty and START treaties. Likewise, China and the US will have to cooperate on today’s burning issues (literally), including climate change, global pandemics and AI regulation.

Framing discord between the two superpowers in zero-sum terms could also manifest in demonising of the “other” and persecution of business executives, scientists and students. It could blur the boundaries between economic and political decisions, engendering geopolitical repercussions far beyond the two countries. Recall McCarthyism and the communist witch hunt in the US, and American support for the apartheid regime in South Africa as well as autocrats in Iran, Pakistan and Chile.

Both sides should seek to avoid a repeat of such mistakes. We have already seen attacks on Asians during the Covid-19 pandemic encouraged by former US President Donald Trump, tit-for-tat arrests of executives (e.g. Huawei CFO Meng Wanzhou and the two Michaels) and China’s support for the junta in Myanmar, mirrored to some extent by US tolerance of authoritarian populists.

Blinken’s two-day visit ended on a cautiously positive note and can help break the mutually destructive cycle of hostility and mistrust. Nevertheless, there are three things to watch closely: the territorial disputes over Taiwan and the South China Sea, China’s economic recovery, and the 2024 US election.

In the South China Sea, there are increasing incidents of hostile contact between the two sides and their allies. Examples include Chinese and US aircraft flying very close to each other, a near collision between a Chinese naval vessel and a Philippine coast guard, and Chinese warships circling the Japanese home islands in May. This raises the risks of conflict, making it even more critical that mechanisms are put in place to reduce the risks of miscalculation and contain crises.

On the economic front, China is contemplating expansionary monetary and fiscal policies amid flagging growth. Beijing’s worries about the property sector, the rise in youth unemployment and restrictions on technology transfers could lead them down either of two very different paths: focus on growth and prosperity while eschewing conflict, or war as a distraction from worsening economic prospects.

Finally, the start of the 2024 election season in the US will distract President Joe Biden as he pivots towards campaigning for a second term. I foresee both Republican and Democratic candidates becoming increasingly belligerent and competing with each other in talking tough against China. US domestic political pressures can ratchet up hostilities, triggered by cheap talk on the campaign trail.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: ChatGPT and AI Disruption: Is Consulting Next in Line?
Say your company is looking to invest in an African country, specifically in a firm based in Namibia making and exporting shower curtains. Research on the market potential for this relatively obscure product yields little information. Your options are to hire a management consultant to conduct due diligence, which can take months and be very costly, or go without. Should your company proceed with the investment?

This is where generative AI comes in. Algorithms can generate business intelligence reports in a manner analogous to a consultant analysing markets and offering advice – except for a fraction of the fee and almost at real-time. INSEAD’s TotoGEO AI lab, for example, has created algorithms that have generated 1.2 million reports covering world market outlook and trade forecasts across virtually all known product categories (especially obscure ones) for all national markets.

What does a GPT chatbot do or not do?

Here’s how the process works. When you ask a chatbot – the application for which generative AI is best known – a simple question that you might ask a consultant, it kickstarts a process known as “question decomposition”. The decomposition algorithm typically deconstructs the question (or prompt) using various approaches such as natural language understanding. This involves ignoring some of the text, including “a” and “of”, identifying the subject (e.g. shower curtains, copper oxychloride, six-inch copper nails), finding the user’s intent and requirements. For instance, the user may ask for a poem consisting of six lines, making “six lines” a constraint on the output.

The system passes these chunks of information to a trained model such as ChatGPT which then generates an answer that passes a filter before it is displayed to the user. This is a layer of programming to avoid violating rules established by the model’s owner (OpenAI in the case of ChatGPT). This filter avoids responses that might appear racist, politically controversial or, perhaps, unprofitable to the owner of the algorithm. In these cases, users may receive a boilerplate response: “I’m sorry, but I am not programmed to XYZ” etc. Here’s an example.
While ChatGPT falls short in this area, alternative approaches have come of age, mostly without people noticing. One of the earliest forms to hit the business scene was co-authored by John D.C. Little at MIT. Little developed algorithms that mimicked PhD econometricians in the way that they detect trends, variations and anomalies in vast data sets such as those generated by optical scanners at grocery stores.

To make the intelligence actionable, an “authoring” or “generative” layer of algorithm writes a memo to the marketing manager. An example: “Your competitor is promoting in Cleveland and it seems to be a tactical experiment. You have three useful options: 1. Watch and learn; 2. Mess up their experiment by increasing your prices four percent; or 3. Gain share in Cleveland by moving your product to the end of the aisle.”

The final layer is distribution. The memo is sent to the manager in real time, via email, text message or other means. This article by Little highlights how the technology was deployed at Ocean Spray Cranberries, a fruit processing cooperative.

INSEAD’s journey into AI-powered business intelligence

Full disclosure: I am Little’s academic “grandson”; his student Leonard Lodish was my dissertation chairman at the Wharton School. At INSEAD, the former Dean of Executive Education, Arnold De Meyer, gave our TotoGEO AI lab a small budget to create executive education materials that were tailored to each individual attending a two-week programme. The common subject covered was strategic planning but a participant from the semi-conductor industry, for example, would receive course materials focused on that industry while another participant in the same room might receive materials on the toothpaste industry.

It worked. No matter how obscure the participant’s industry (e.g. copper oxychloride), the course programme had maximal relevance and impact. Feedback from the participants included “can I meet the analyst who prepared the materials?” With this encouragement, we set about putting our AI-powered approach on steroids.

The idea is simple. Prior to my academic career, my work involved estimating the market potential for cellular telephone networks across granular geographies. These estimates proved useful for cell site optimisation modelling. I also worked in the Caribbean, Asia, Africa and the Middle East, estimating the export potential of firms, some of which made rather obscure products like shower curtain rings or toilet seats. Turns out, unsurprisingly, that the more obscure the product, the less anything is published on it – just try Googling the market potential for shower curtain rings in Sri Lanka.

Foreign direct investment to such countries is hampered by the lack of data required to conduct full due diligence. Indeed, information asymmetries between buyers and sellers have long been cited as a reason why companies fail to sign all-important contracts. This problem is especially acute for small, underserved communities, especially those in emerging economies. By focusing on the long tail of products across traditionally remote geographies, AI algorithms can help reduce these asymmetries, thereby increasing investments, employment and value-creation opportunities within these regions.

At INSEAD’s TotoGEO AI lab, we set about creating algorithms leveraging various economic theories (proposed by the likes of John Maynard Keynes, Franco Modigliani, Milton Friedman and Irving Fisher, among others) to extrapolate from sparse data sets. This involves accurately estimating the consumption of a specific product category in one country and applying those consumption patterns in other countries after making the necessary adjustments for local conditions. Once estimates are generated, the algorithm takes care of the entire value chain of content creation, including all meta data, marketing collateral and distribution. MAID plc was an early distributor. Others soon followed.

Reports generated by our algorithms were priced under US$1,000, no matter how obscure the product, covering markets across all countries and cities. Even if data are not readily available online – the algorithm mimics the economist facing a “data desert” (i.e. where only sparse data are available, or too “dirty” to use in their raw format).

The Uber pitch deck and other reactions

Over the years, hundreds of Fortune 500 companies have either purchased one-off studies or subscribed to entire catalogues generated by TotoGEO’s algorithms. Perhaps the most interesting is Uber (then known as UberCab), which cited one of our reports in its now famous 2009 pitch deck:
In fact, most consulting firms now have internal research departments, such as McKinsey’s Research and Information Group, that purchase such reports rather than have human consultants create them at higher costs to their clients.

But other stakeholders’ reactions to AI-generated reports have been decidedly mixed. Some lament the death of human authorship. Others, such as Chris McManus at the University College London, have expressed concerns about the metaphysical and philosophical implications of automated writing.

Many disparage generative AI without adequate understanding of the underlying algorithms. In Wikipedia, for example, the editors write that one title results from “Computer-generated combination of boilerplate text and public-domain data related to… a type of cheese”. In fact, no public domain data exists in that report; all the data are AI-generated since no public source of the data exists.

Finally, some have responded with good-natured humour. American comedian and chat show host Jimmy Fallon poked fun at one of the research reports in his TV show for its droll title, “The 2009-2014 Outlook for Wood Toilet Seats in Greater China”. Another report, a study on fromage frais, won the Diagram Prize for Oddest Book Title of the year. Meanwhile humorous reviews have been written on the obscurity of some products.

Business consultants are still in business

What are the implications of these AI-generated reports? Does it mean that strategic consulting is doomed? Of course not. It just means consultants need to focus on non-formulaic tasks. This begs the question: Which parts of consultants’ jobs are formulaic and which are not?

Long gone are the days where consulting companies could crank out a 500-slide deck, leave it on the CEO’s desk and ride off into the sunset. I have concluded that some 70 percent or more of any given strategic consulting presentation is simply a subset of around 600 PowerPoint slides and therefore ripe for commoditisation.

In fact, INSEAD's TotoGEO AI lab is working with promising generative AI start-ups to help businesses and consulting companies be more efficient and spend less time on slides. For many consulting firms, a large chunk of their fees relate to recommending, not doing, things. With the proliferation of AI tools, the value of consultants will lie in their people skills as opposed to their analytical skills.

Of course, this focus on people skills and value-added thinking started in tandem with the development of generative AI back in the 1990s. Internal strategy teams are often composed of ex-consultants who nudge consulting firms to focus on implementation or new areas like sustainability, agility, eco-system management, automation or growth in slowing markets.

Finally, since many consultants are shifting to performance-based compensation, they need to spend less time tweaking PowerPoint slides and more on measurable value creation. Rapidly generated strategic reports will facilitate better alignment of time allocation for both the consultant and the client.

If an algorithm can create high-end, customised research reports and PowerPoint decks, why stop there? What about videos? Games? Online newspapers? In my next few articles, I will describe how generative AI will shape the future of a host of sectors, from education and gaming to global search engines.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: How AI Can Improve Human Performance
As artificial intelligence (AI) advances at breakneck speed, there is growing concern about the possibility of it replacing humans in almost every task. However, recent research sheds light on AI’s capacity to train and augment human performance, specifically in complex strategic interactions.

To understand how AI can train employees and improve performance, it is worth looking to the past. In our latest study, we explored how AI transformed the domain of chess. Chess computers have been at the frontier of AI and exemplify two fundamental aspects: complexity and the capability to mimic human thought processes. Our study revealed that chess computers served as artificial training partners and led to a significant improvement in player performance.

To examine how AI helps people improve, we leveraged the staggered availability of chess computers in Western Europe and the Soviet Union from the 1970s. Chess computers became widely available for players in Western countries from 1977 onwards. In contrast, for geopolitical reasons, chess computers remained practically unavailable for players in the Soviet Union until the late 1980s.

We analysed the performance of more than 20,000 chess players across half a million tournament games. Our analysis illustrated that having access to chess computers accelerated learning and gave players a competitive advantage, as chess computers helped players improve by serving as a substitute for scarce human training partners.

How AI can level the playing field

We found that disadvantaged players with inferior skills benefitted more from chess computers. The extent to which people benefitted varied due to two key factors. First, the AI system had to surpass the player's skill level to provide effective training. Otherwise, the player would be able to anticipate the AI's moves, thereby limiting the training value. A useful analogy is how non-native speakers benefit more from a spelling and grammar checker than native speakers.

Second, the difference in benefits can be attributed to the lack of access to human training partners. In such cases, the chess computer served as an essential replacement. This underscores the potential of AI as a substitute for human training partners, particularly in situations where resources and opportunities are scarce.

AI has the ability to democratise access to training and enable individuals from diverse backgrounds to develop their skills and excel in their respective fields. For instance, in industries with a high volume of customer complaints, training customer service representatives to handle various scenarios can be a challenging task, especially when training needs to be conducted at scale. Traditional methods like role-playing exercises can be expensive, time-consuming and difficult to replicate realistically. By generating simulated complaint scenarios, AI systems could help with large-scale individualised training.

During conversations with a leading facilities management company in France, an executive emphasised the financial challenges associated with training a significant number of employees to handle customer interactions. By using AI-powered training platforms, such companies can provide consistent and scalable training experiences to their employees, enabling them to acquire the necessary skills and knowledge. This not only reduces the financial burden but also ensures that a larger workforce can benefit from extensive training.

Where AI falls short

However, AI is not a perfect substitute for human training partners. Our research findings highlighted that players who trained with chess computers were less proficient at recognising and capitalising on human errors, as AI does not exhibit the same vulnerabilities or make the same types of mistakes as human opponents.

This is particularly important when it comes to learning strategic interactions such as negotiation or competition. Chess – with its interactive nature, large number of scenarios and the difficulty of credit assignment – is often considered a prime illustration of strategic interaction.

Much like playing chess with another human, engaging in live negotiations, competitive games or role-playing exercises with human counterparts allows individuals to develop their ability to recognise and exploit human blunders, adapt to changing circumstances and effectively respond to interpersonal dynamics.

Our findings suggest that a balanced approach is recommended. Combining AI-powered training with opportunities for real-world practice and interactions with human partners can offer a more comprehensive learning experience.

While AI-powered training platforms provide accessible and scalable learning opportunities, relying solely on AI for training has its limitations. To excel in strategic interactions, it is crucial to harness the strengths of both AI and human intelligence. Even though the complete replacement of humans by AI is not imminent, we may soon be seeing humans armed with AI outperforming those without AI.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: Navigating Trust and Safety in the World of Generative AI
Keeping up with technological innovations and the debates surrounding their influence on our lives is proving to be extremely challenging for citizens, executives, regulators and even tech experts. Generative AI has ushered in a new era where the creation and dissemination of nearly infinite content has become a tangible reality. Tools like large language models (LLMs), such as GPT-4, and text-to-image models, such as Stable Diffusion, have sparked global discussions from Washington to Brussels and Beijing.

As regulatory bodies race to catch up, critical questions arise concerning the implications for online platforms and, more importantly, trust and safety on the internet. These AI tools may lead to an increase in illegal or harmful content or manipulation-at-scale, potentially impacting our decisions about health, finances, the way we vote in elections or even our own narratives and identity. At the same time, such powerful technologies present significant opportunities to improve our digital world.

It is critical to emphasise that it is not all about an impending AI apocalypse. While that is always a possibility – and entirely up to us to avoid – we should be motivated by how we can leverage AI technologies to positively impact our online and offline lives. These tools can be used as weapons for information warfare, or they can be used to defend against online harms originating from both AI and human sources.

Both Google and Microsoft have started utilising generative AI to “supercharge security” and better equip security professionals to detect and respond to new threats. Larger online platforms are already using  AI tools to detect whether certain content is generated by AI and identify potentially illegal or harmful content. The new generation of AI can provide even more powerful tools to detect harmful behaviours online, including cyber bullying or grooming of children, the promotion of illegal products or malicious actions by users.

The good and the ugly

In addition to reactive protection, generative AI tools can be used for proactive education. One example is tailoring user prompting and policy communications to individuals, so that when they run afoul of a particular platform’s policy or act in a borderline harmful manner, AI tools can step in to promote higher quality behaviour. By regularly guiding and supporting users, AI tools can help everyone better understand and adopt best practices.

For online content moderators responsible for reviewing user-generated content, precision and recall are key. Generative AI can help moderators quickly scan and summarise content such as relevant news events. It can also provide links to related policy or training documents to upskill moderators and make them more efficient. Used responsibly, tools such as ChatGPT or Google’s Bard can also help creators ensure content is aligned with a particular platform’s policies or written in a helpful, inclusive and informative manner.

However, there are various factors that Trust & Safety policy professionals need to consider before relying on generative AI tools for their daily tasks. Take development of online platform policies for example. Crafting an effective, robust and accessible set of policies typically takes years, involving many consultations with experts, regulators and lawyers. As of now, tasking a generative AI tool with this nuanced work is dangerous or, at best, imprecise. While these tools can improve the productivity of policy professionals, the extent to which generative AI can be considered safe and reliable for creating and updating policies and other legal documentation remains to be seen.

It is wise to remain cautious and consider the massive volume of content that generative AI can flood the internet with – making content moderation more challenging and costly – as well as the potential harm such content can cause at scale. For example, one of the earliest observed behaviours of large language models is their tendency to “hallucinate” by creating content that neither exists in the data used for their training nor factually true. As hallucinated content spreads, it may be used to train more LLMs. This would lead to the end of the internet as we know it.

To avoid this disaster, there is a relatively simple solution: Humans must be looped into the development of policy, moderation decisions and other crucial Trust & Safety workflows.

Another problem with LLM-generated content is obfuscation of the original information sources. This differs from traditional online searches where users can evaluate reliability by assessing the content provider or user reviews. Substantial political and social risks arise when users are unable to differentiate between genuine and manipulated content. China, for one, is already regulating the generation and dissemination of AI-generated fake videos, or deep fakes.

Managing the risks instead of imposing bans

The rise of generative AI prompted a wave of discussions about whether technological progress should be put on hold, with thousands signing a letter to this purpose. But while a pause may provide short-term “relief” that we are not hurtling towards some unpredictable AI apocalypse, it is not a satisfactory or even practical long-term solution, especially given the competition between companies and countries. Instead, we need to concentrate efforts on ensuring online trust and safety is not negatively impacted by these technologies.

First, while technologies may be new, the risk management practices and principles employed do not necessarily have to be. Trust & Safety teams have been creating and enforcing policy around misleading and deceptive online content for decades and are uniquely prepared to tackle these new challenges. Common practices for managing other risks, such as cybersecurity, can be leveraged to ensure trust and safety in the world of generative AI.

For instance, OpenAI hired Trust & Safety experts for “red teaming” exercises prior to the release of ChatGPT. In red teaming, experts challenge a new product in the same way malicious actors would. By exposing the risks and vulnerabilities early on, red teamers contribute to the development of effective strategies and measures to minimise those risks. OpenAI’s now-famous “As a large language model trained by OpenAI, I cannot…” response to potentially dangerous prompts is a direct result of red team efforts.

The skill and creativity needed to be a successful red team member is a burgeoning industry in itself. AI security firm Lakera created “Gandalf”, an AI game to model the problem of prompt injection attacks, where malicious actors inject harmful content into prompts provided to an LLM. To win the game you need to get the Gandalf chatbot to reveal a password seven times. By crowdsourcing “red teaming”, LLMs can be improved to resist prompt injections and other harmful vectors of attack.  

Second, guidelines and best practices for how to use these new technologies need to be developed and shared widely. Alongside regulatory efforts, the Trust & Safety industry is collaborating to develop solutions that can be used by all platforms, ensuring users’ safety no matter where they roam online. The Trust & Safety Hackathon was created so industry professionals can share knowledge and identify such solutions. For example, the industry practice of hash-sharing – sharing cryptographic hashes so companies can quickly identify and remove illegal digital content – has led to a dramatic decrease in child sexual abuse material on platforms.

Third, there will be an increased need to assess the quality of new AI tools, especially as many more versions are being built using “fine tuning” or reinforcement learning from human feedback. A lot can be gleaned from decades of research on evaluating “traditional” AI systems. One common approach is to use statistical metrics such as false positive or negative rates of AI classifiers to measure how accurate these systems are in their predictions. However, assessing generative AI systems may prove more challenging as the quality of their output should not only be measured in terms of accuracy, but also in terms of how harmful it can be.

Measuring harm is difficult as it depends on culture, interpretation and context, among other factors. Similarly, challenges arise when it comes to evaluating the quality of AI tools that determine if content is harmful or not, such as tools that detect illegal products in images or videos. Ironically, LLMs and generative AI can be valuable in evaluating the effectiveness of other AI detection tools and even in managing risks associated with LLMs. It may be that we need more powerful AI in order to manage the risks AI poses.

Finally, after more than a quarter of a century since the dawn of the commercial internet, we need to double down our efforts to increase awareness around online trust and safety. Investments in education around disinformation and scams will help protect individuals from being deceived by AI-generated content that is presented as genuine. The intelligence and analysis provided by Trust & Safety teams are essential for developing systems that effectively utilise AI to facilitate more authentic connections among individuals, rather than diminishing them.

As our lives have gradually moved largely online, and AI is adopted across industries and an ever-widening range of products, ensuring our digital world is safe and beneficial is becoming increasingly challenging and urgent. Online platforms have already spent many years on their online trust and safety practices, processes and tools. Typically, this work has been invisible, but now is the time for these learnings and experts to take centre stage. We all must work together to chart humanity's path forward as we live alongside AI, rather than being overshadowed by it.

Jeff Dunn and Alice Hunsberger are Trust & Safety executives for large online platforms.

The lead image was created by generative artificial intelligence program Midjourney using the following prompts: hundreds of computer screens and people in a dark room, minimalistic shapes and cinematic.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: The Importance of Strategic Minds for Effective Governance
The relationship between governance and strategy in organisations has long been tenuous. CEOs often view governance merely as a regulatory requirement, delegating it to the CFO, auditors and legal experts, with only scripted meetings with the board. Conversely, our experience with directors at INSEAD seminars reveals that boards tend to overlook strategic matters.

However, strategy-making and governance should be strongly linked. The growing complexity of the business environment necessitates a strategic approach to governance. But directors, especially those representing large passive investment funds, are hesitant to delve into strategy due to their fiduciary responsibility. CEOs often fail to establish a collaborative relationship with the board, maintaining a distance that gives them autonomy but impedes strategic thought. They can overcome this divide by developing a shared strategic perspective on the future.

The key to successfully overseeing a company's future and shaping its strategic direction lies in cultivating strategic minds among directors and top management teams. Irrespective of the skills, knowledge and experience directors and senior executives bring to the table, it is essential for them to be skilled strategists. By developing strategic minds, they can serve as effective guardians of a company's future, framing purpose, setting strategic direction and stimulating ambitious strategic intent.

Pekka Ala-Pietilä, one of the key strategic minds behind Nokia’s success in mobile phones in the 1990s, believed that “no one can be a board member without having a real understanding of strategy.” This understanding goes beyond expertise in strategy functions or business leadership roles. What makes a strategic mind is a combination of largely innate traits which enable individuals to see the world differently from most, question things that others don’t recognise or take for granted and make connections between seemingly disparate events and information in a way that allows new opportunities or threats come into focus.

Although there has been limited research on the traits of a strategic mind, our decades of observation and study of strategists, ranging from exceptional to mediocre, have enabled us to identify the following key traits that underpin a strategic mind:

Cognitive flexibility

We interpret the world through the lens of our experience, our surroundings, the prevailing norms and their resulting mental frameworks. Cognitive flexibility is the skill to move beyond this context to recognise, interpret, synthesise and see the significance of things which are cognitively distant, difficult to perceive or unfamiliar – and then discover and explore relatedness and linkages between these distant facts or observations.

Non-linear thinking

Linear or sequential thinking is vital for effective operations – it allows you to get from A to Z via a defined set of steps – but studies have shown that non-linear approaches, such as systems thinking or associative thinking produce more creative outcomes. It is this type of thinking we need in strategic minds. To some extent everyone uses associative thinking, as we intuitively make sense of the world by making an association between the new unfamiliar and our past experience. But people with strategic minds are much more open than most and are able to consciously “free associate”, linking multiple inputs with existing knowledge. They don’t see the world in a simple linear way but as a series of complex interlinking systems in which small changes can result in dramatic effects.

Handling ambiguity

Most people are uncomfortable with ambiguity, preferring clarity and a vision of the future in which little change is required as this enables them to retain a sense of control. Not so people with a strategic mind – they have a high tolerance for ambiguity which is found in competing points of view, contradictory inputs and complexity. They understand that ambiguity is a fact and control is illusory and so remain open to multiple hypotheses about the changes they perceive.

Tackling hard problems

Perhaps resulting from their cognitive flexibility, non-linear thinking and openness to ambiguity, people with strategic minds do not shy away from difficult problems or attempt to simplify them. In contrast, most managers avoid hard problems and when they can’t be avoided, resort to “cognitive simplification”, by using a set of heuristics they have learnt over their careers to break down the problems so they can apply existing hypotheses.

Big picture
Strategic minds are intuitively more drawn to and influenced by the big picture than isolated events. As such they cultivate a wide set of relationships both inside and outside the company. In a sense they are the modern equivalent of the polymaths of old, seeking out knowledge and understanding across multiple different disciplines and fields.

Consummate questioners
As we progress through education and the rungs of corporate hierarchies, we are trained to provide answers, to the extent that questions can be seen as a weakness, singling one out as failing to understand something. While Hal Gregersen’s work on the importance of asking questions has brought the idea of question brainstorming into vogue in the last few years, people with strategic minds have always stood out from the throng of executives armed with ready answers. Led by their enquiring minds they ask questions to broaden their understanding, challenge potential biases and help others see new threats and opportunities.

Mindfulness and sense-making

To the strategic mind, the journey is never complete. There is a strong awareness that ideas and assumptions need to be continually challenged and refined as new knowledge or experience comes into play. But merely being highly perceptive isn’t enough – abstract ideas and perceptions have to be translated into actionable frameworks which make sense to the rest of the organisation. In other words, meaning has to be attached.

Self-awareness

It would be unusual to find a person with a truly strategic mind who was also narcissistic or hungry for power. The very essence of having a strategic mind means actively seeking to challenge one’s worldview and assumptions and thus one’s own identity and power – something which is anathema to most managers. It takes a high level of self-awareness to be able to continually and openly question, and potentially undermine, the context within which one operates and not fall victim to the dominant logic of a company. Self-awareness is also critical in getting others on board with new directions or ideas, particularly when dealing with defensive CEOs in successful, mature companies.

By cultivating a strategic mind, directors can become effective custodians of a company's future and drivers of its strategic direction. Embracing these traits enables directors, CEOs and their teams to guide their organisations more creatively and effectively towards success in today’s rapidly evolving business landscape.

This article is adapted from Escaping the Growth Curse: Paths Toward Stronger Strategies, which will be published by Berrett-Koehler Publishers in 2024.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
How to get into IESE, INSEAD or HEC with a low GMAT?

Here's a link that could be beneficial for some people. :-)
https://gmatclub.com/forum/how-to-get-i ... 15391.html
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: Better Geographic Investing Begins With an Inclusive Index
The rapid pace of globalisation in the past 20 years has blurred geographic boundaries in many aspects, including investing. When an asset manager picks a company, they are really investing in a basket of countries, since the firm is more likely than not operating across multiple countries. Known as geographic investing, this type of investment necessarily requires asset managers to study the composition of the basket and the risks associated with each country before making investment decisions.

One of the tools at their disposal is country-level stock market indices. However, traditional indices such as Japan’s Nikkei and Germany’s DAX, or those compiled by global providers including MSCI, FTSE and S&P, comprise only stocks issued by firms domiciled in the country. Foreign firms are excluded even if they derive a substantial proportion of their revenue in said country. These indices are therefore poor gauges of countries’ economic risks in the age of globalisation. Asset managers may very well invest in a firm that is domiciled in one country but makes most of its money in other countries – while overlooking foreign firms headquartered elsewhere that conduct substantial business in the country.

To help portfolio managers track geographic risks much more accurately and make more informed investment decisions, my co-authors* and I developed indices using a statistical technique called expectation-maximisation (EM). As we show in this paper published in the Financial Analysts Journal, compared to traditional indices, our EM indices are more representative of the distinct business risks of each country as well as the relative importance of multinationals. They thus do a much better job of capturing a firm’s operational risks as a function of its exposure to various countries.

Globalisation’s imprint

Here’s how we built our EM index: From the World’Vest Base database, which transcribes annual report information for some 50,000 active listed firms worldwide, we culled data on the revenue of firms’ whose stock returns were available in the Datastream database. Our final sample covered 1999 through 2014 and factored in the risks involved when investing in 12 countries: Australia, Brazil, Canada, China, France, Germany, Japan, India, Malaysia, Singapore, the United Kingdom and the United States.

Except for Japan, all the countries saw a marked decline in the percentage of locally domiciled firms selling in the domestic market. In the US, for example, it went from 73.3 percent in 2000 to 61.1 percent in 2014; in France, from 52 percent to 36 percent; and in Germany, a dramatic fall from 60.5 percent to 24.6 percent.

Using the data collected, we constructed three indices: a national “ISIN” domicile-based index; a domestic index which we call the “70-percent index”; and the EM index. The 70-percent index is a middle-of-the-road approach that other researchers have advocated. It consists of firms that derive 70 percent or more of their revenues from one country (almost always the country where the firm is domiciled). This index is therefore presumably more representative than traditional national ones even though, like the latter, it disregards foreign companies as well as domestic firms’ foreign revenues.

The EM index, in contrast, consists of the stock returns of three types of companies: “domestic” firms; MNCs that sell to the country but also bring with them the influence of stock returns linked to other countries; and MNCs that may not sell anything to the country but serve to offset the unrelated returns linked to the previous group of MNCs.

Take the 20 German firms with the highest percentage of foreign relative to domestic revenues during our sample period, such as Daimler-Benz, SAP and Adidas. In 2014, these 20 firms accounted for 14.95 percent of the EM index for Germany, 2.53 percent of the index for France and 3.93 percent for the UK -- but are conspicuously absent from the national and domestic indices for France and the UK.

How EM indices ace the test

Comparing the volatility and correlations of the three indices across our sample timeframe and countries, we found the following:

EM is more distinctive than traditional indices

Our novel EM approach is markedly less correlated to either the national ISIN or the domestic 70-percent indices than the latter two are to each other. This suggests that the EM index is more indicative of the distinct risks of each country.

EM is less volatile than traditional indices

We surmised that this is due to our sample being dominated by developed countries. Some of the volatility of the national and domestic indices of these countries could stem from the operations of MNCs – which tend to be headquartered in developed countries – in emerging countries. Our EM method, on the other hand, removes the influence of MNCs’ operations in foreign markets during the statistical analysis.

EM is less linked across countries

EM indices are less correlated across countries compared to national or domestic indices, presumably due in part to the huge overlap of MNCs from different countries selling in the same foreign markets such as China and Mexico. Again, this suggests that the EM index is more representative of each country’s risks.

Further, the more a country is penetrated by foreign firms, the bigger the difference between its EM and domestic indices, which only imperfectly capture the influence of domestic versus foreign business activities. For example, the correlation between the EM and 70-percent indices is lower for Canada (0.899) than India (0.968).

EM is better at capturing business risks

Firms’ stocks are more sensitive to foreign EM indices than those of their country of domicile. This suggests that EM indices are better at capturing the business risks of a firm’s operations than traditional indexes.

A more reliable tool for investors

In the face of prolonged market uncertainty, asset managers need to pull out all stops to reassure existing clients and secure new ones. As shown above, EM indices can help. By culling information from all listed firms, domestic and foreign, EM indices reliably size up firms’ exposure to countries better than traditional indices. In other words, EM indices could be used to confidently deconstruct the returns of a particular firm by country source, and hence, ascertain the firm’s exposure to a specific country.

We recommend that portfolio managers who are bullish on a country tap EM indices to identify companies that provide maximum exposure to a country, regardless of where the companies are headquartered. Managers wary of investor protections in a country but bullish on its economic prospects can use EM indices to identify non-domestic firms with high exposure to that country.

All that remains is for index providers or investment consulting firms to compile the indices on a routine basis – a time-consuming, expensive endeavour that will prove to be well worth the trouble.

*Tymur Gabuniya, University College London, and Richard C. Marston, the Wharton School.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: Stop Going It Alone
History is filled with tales of courageous and decisive heroes. Individuals like Julius Caesar and Winston Churchill, for instance, have led from the front to guide their people through adversity and achieve ultimate success. This myth building is especially prominent in the business sector, with stories of inspiring CEOs parachuting in to rescue and revive troubled organisations.

But is a strong leader who takes a centralised approach to company strategy really the best thing for an organisation experiencing change? Based on research I conducted with Professor Russ Vince from the University of Bath, the answer may often be no. This was our conclusion after studying the emotions of members of the senior management team at KleanCo (name changed), an FTSE 100 company headquartered in Europe.

A decisive leader can have a positive, and sometimes rapid, impact during periods of transformation. But we found that the emotional stresses that such centralised authority places on the organisation, and specifically on those in top management roles, make it difficult to maintain long-term. The tensions such an approach creates typically lead to failure for either the company, CEO, or both.

At the time of our study, KleanCo was going through a period of transformation. A “rag bag of loosely coupled silos”, the multinational was confronting a shift from a position of market dominance to one of fierce competition. Declining performance had led to calls for radical change from stakeholders, and a boardroom coup ushered in the arrival of a new CEO tasked with halting the slide.

Our research, conducted over six years, saw us examine the emotional responses of the individuals involved, as the new leader looked to enact an aggressive turnaround strategy. What we discovered were four distinct stages or emotional reactions that the organisation’s top executives experienced in relation to the authority of the CEO: compliance, ambivalence, fragmentation and engagement.

The first phase was one of total compliance to the new leader’s central authority. Managers were completely dependent upon him. He bought emotional relief, especially as his strategy initially had positive results on the organisation’s performance. However, this quickly shifted towards ambivalence. Executives found themselves beginning to question the CEO’s authority as performance started to decline.

The CEO’s efforts to further streamline decision-making only served to upset existing and deeply embedded lines of authority. This resulted in heightened anxiety and fragmentation among the senior team as different factions competed to regain some of that lost authority. Ironically, the increasing resistance to the CEO’s authority ultimately resulted in greater engagement between board members and the top management team. They ended up clubbing together. Consequently, a shared enthusiasm for a more diverse and decentralised model of strategic leadership led them towards cooperation – which ultimately resulted in the CEO’s departure.

So, what could the CEO have done differently? And what lessons should other new CEOs or business leaders tasked with addressing a period of organisational change learn from this experience? I would suggest four key actions that could help avoid or mitigate going through the same turbulent emotional phases that the KleanCo team experienced.

1. Beware the desire to lead

A new leader doesn’t just want a compliant workforce, even if that might sound like a dream scenario. Rather than just take the lead and hope others follow, you need to make sure you have active buy-in for any plans for renewal. This is especially true when it comes to your top management team and key stakeholders. That doesn’t necessarily mean getting everyone on board for every decision. Not only is this challenging, it can risk slowing down the process and may even hint that you lack confidence in your decisions.

Make sure that you build coalitions of relevant individuals around critical issues. Ensure that the key people are on board with those decisions that directly impact them and their authority. In KleanCo, the management team was happy to hail the CEO as a saviour when things were going well. However, they were never really engaged with the underlying strategy and quickly became ambivalent towards his policy of cost-cutting and centralisation when results started to decline. He eventually became the scapegoat for the shared problems.

2. Read the room

While this might sound obvious, it’s certainly not a given. The fact that a new leader is taking over can often hint at the need for organisational change. However, while renewal might be required, it’s important that a leader doesn’t simply push ahead and impose their own solutions. They should take the time to listen to all the relevant stakeholders and get an understanding of what are the most pressing issues to fix.

What’s more, by listening first, new CEOs can better assess how the renewal process is going to impact the authority and emotions of individuals (and especially those in the C-suite). In the KleanCo case, the CEO failed to properly understand the emotional impact of his restructuring of authority. It caused fragmentation and infighting in the management team, and ultimately brought about his resignation. By taking the time at the start of the process, you can hopefully reduce the chance of anxiety and tension arising among the team later on.

3. Harness team diversity

One of the challenges of having a strong or “heroic” leader is that they tend to take on the responsibility for all the decisions made and all the outcomes. This can cause individuals, even those in top management, to become ambivalent to the strategies being implemented. They become passive and silent bystanders to the transformation taking place.

In such cases, collective leadership becomes more salient, especially during periods of disruption and uncertainty. Diverse team membership brings novel ideas, different experiences and better social regulation of the stresses caused by change where a single leader cannot. Collective responsibility can create a network of relationships – a wider emotional safety net – than a single point of authority.

4. Celebrate milestones

Many organisations – especially multinationals, where teams are separated geographically as well as by work function – run the risk of falling back into self-organisation. This is especially true if there is conflict or resistance to the strategy being pushed by a leader they deem as remote or not serving their specific needs. This was the case at KleanCo, where the regional managing directors began to ignore the authority of the CEO. They shifted focus to their own siloed strategies and projects – sometimes to the detriment of the wider organisation.

This is a natural response to bad leadership (whether real or perceived) and can be seen happening again and again when there is resistance to proposed changes. However, celebrating milestones allows a leader to cultivate a sense or spirit of collective achievement and engagement with the changes taking place. These milestones can be directional – for example, celebrating the first steps towards a bigger transformational goal. The important thing is that they should occur on a relatively regular basis to retain that spirit of engagement.

In the same vein, it is also important to invest in real and symbolic celebrations. The truth is that small things matter a lot to people and cost very little to achieve. Giving positive feedback helps build confidence and create a sense of belonging, which are essential for a leader wanting to bring their organisation, and their people, with them through a period of transformation.

Effective leaders understand the importance of engaging with their staff and creating a positive work environment. That’s why I always tell leaders that the best thing they can do is to walk around and listen to staff. Showing that you care and respect them helps them care about and respect the organisation.

Rather than commanding from the front and imposing changes, true heroic leaders focus on building relationships and trust within their team. They make sure that people are empowered and engaged through every step of the transformational journey.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: Must AI Accuracy Come at the Cost of Comprehensibility?
Artificial intelligence is constantly pushing boundaries and making complex decisions better and faster in ever more diverse aspects of our lives, from credit approvals, online product recommendations to recruitment. Companies are jumping onto the AI bandwagon and investing in automated tools to keep up with the times (and technology) – even if they are not always able to explain to customers how their algorithms arrive at decisions.

In 2019, Apple’s credit card business was accused of sexism when it rejected a woman’s request for credit increase while her husband was offered 20 times her credit limit. When she complained, Apple representatives reportedly told her, “I don’t know why, but I swear we’re not discriminating. It’s just the algorithm.”

There is a real risk when organisations have little or no insight into how their AI tools are making decisions. Research has shown that a lack of explainability is one of executives’ most common concerns related to AI. It also has a substantial impact on users’ trust in and willingness to use AI products. But many organisations continue to invest in AI tools with unexplainable algorithms on the assumption that they are intrinsically superior to simpler, explainable ones. This perception is known as the accuracy-explainability trade-off.

Does the trade-off between accuracy and explainability really exist?

To understand the dilemma, it is important to distinguish between so-called black box and white box AI models: White box models typically include a few simple rules, possibly in the form of a decision tree or a simple linear model with limited parameters. The small number of rules or parameters makes the processes behind these algorithms more easily understood by humans.

On the other hand, black box models use hundreds or even thousands of decision trees (known as “random forests”), with potentially billions of parameters (as deep learning models do). But humans can only comprehend models with up to about seven rules or nodes, according to cognitive load theory, making it practically impossible for observers to explain the decisions made by black box systems.

Contrary to common belief that less explainable black box models tend to be more accurate, our study shows that there is often no trade-off between accuracy and explainability. In a study with Sofie Goethals from the University of Antwerp, we conducted a rigorous, large-scale analysis of how black and white box models performed on nearly 100 representative datasets, or what is known as benchmark classification datasets. For almost 70 percent of the datasets across domains such as pricing, medical diagnosis, bankruptcy prediction and purchasing behaviour, we found that a more explainable white box model could be used without sacrificing accuracy. This is consistent with other emerging research exploring the potential of explainable AI models.

In earlier studies, a research team created a simple model to predict the likelihood of loan default, which was just less than 1 percent less accurate than an equivalent black box model and simple enough for the average banking customer to understand. Another high-profile example relates to the COMPAS tool that is widely used in the United States justice system for predicting the likelihood of future arrests. The complex black box tool has been proven to be no more accurate than a simple predictive model that considers only age and criminal history.

Understand the data you are working with

While there are some cases in which black box models are ideal, our research suggests that companies should first consider simpler options. White box solutions could serve as benchmarks to assess whether black box ones in fact perform better. If the difference is insignificant, the white box option should be used. However, there are also certain conditions which will either influence or limit the choice.

One of the selection considerations is the nature and quality of the data. When data is noisy (with erroneous or meaningless information), relatively simple white box methods tend to be effective. Analysts at Morgan Stanley found that simple trading rules worked well on highly noisy financial datasets. These rules could be as simple as “buy stock if company is undervalued, underperformed recently, and is not too large”.

The type of data is another important consideration. Black box models may be superior in applications that involve multimedia data replete with images, audio and video, such as image-based air cargo security risk prediction. In other complex applications such as face detection for cameras, vision systems in autonomous vehicles, facial recognition, image-based medical diagnostics, illegal/toxic content detection and, most recently, generative AI tools like ChatGPT and DALL-E, a black box approach may sometimes be the only feasible option.

The need for transparency and explainability

Transparency is an important ingredient to build and maintain trust, especially when fairness in decision-making, or when some form of procedural justice is important. Some organisations learnt this the hard way: A Dutch AI welfare fraud detection tool was shut down in 2018 after critics called it a “large and non-transparent black hole”.  Using simple, rule-based, white box AI systems in sensitive decisions such as hiring, allocation of transplant organ and legal decisions will reduce risks to both the organisation and its users.

In fact, in certain jurisdictions where organisations are required by law to be able to explain the decisions made by their AI models, white box models are the only option. In the US, the Equal Credit Opportunity Act requires financial institutions to be able to explain why credit has been denied to a loan applicant. In Europe, according to the General Data Protection Regulation (GDPR), employers must be able to explain how candidates’ data has been used to inform hiring decisions and candidates have the right to question the decision. In these situations, explainability is not just a nice-to-have feature.

Is your organisation AI-ready?

In organisations that are less digitally developed, employees tend to have less understanding, and correspondingly, less trust in AI. Therefore, it would be advisable to ease employees into using AI tools by starting with simpler and explainable white box models and progressing to more complex ones only when teams become accustomed to these tools.

Even if an organisation chooses to implement an opaque AI model, it can mitigate the trust and safety risks due to the lack of explainability. One way is to develop an explainable white box proxy to explain, in approximate terms, how a black box model arrives at a decision. Increasing understanding of the model can build trust, reduce biases and increase AI adoption among users and help developers improve it. In cases where organisations have very limited insight into how a model makes decisions and developing white box proxies are not feasible, managers can prioritise transparency in talking about the model both internally and externally, acknowledging the risks and being open to address them.

Our research demonstrates that simple, interpretable AI models perform just as well as black box alternatives in the majority of cases and companies should first consider white box models before considering more complex solutions. But most importantly, managers can make more informed and conscious choices only when they have a sound understanding of the data, users, context and legal jurisdiction of their use case.



This is an adaptation of an article published in Harvard Business Review.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: Democracy, Defence and Conflict in the Age of AI
The rapid advancement of artificial intelligence (AI) has resulted in its proliferation across various sectors – from consulting and banking to manufacturing and healthcare, to name just a few. Generative AI has become increasingly widespread and is set to be a game changer at all levels, with far-reaching repercussions for democracy, elections and armed conflict. It is therefore critical to assess its impact on democratic principles and institutions, as well as military defence.

AI companies worldwide are racing to develop and deploy the technology as quickly as possible and attain AI supremacy. However, alarm bells have been sounded over the need to ensure that adequate safety measures are put in place to protect consumers, existing democratic institutions and society at large, and to prevent the technology from one day posing an existential threat to humanity.

What will be the impact of generative AI on our political system and in shaping public opinion and discourse? How can we guarantee that AI is utilised responsibly in conflict situations? And what is the proper role of governments in regulating the technology?

These were some of the questions posed at a recent Tech Talk X organised by digital@INSEAD, which was conducted under Chatham House Rule. Moderator Peter Zemsky, Deputy Dean and Dean of Innovation at INSEAD, was joined by panellists Antoine Bordes, vice president at Helsing, an AI and software company in defence; Judith Dada, general partner at venture capital fund La Famiglia; and Juri Schnöller, co-founder and managing director at digital political communications and campaigning firm Cosmonauts & Kings.

During the discussion, the speakers explored the intersection of AI and democracy, including the implications of AI for defence. They also proposed strategies to ensure that AI technologies foster, rather than undermine, democratic values amid the challenges posed by an increasingly volatile international security landscape.

Overview of the AI landscape

AI and big data play a key role in framing public discourse during elections, and the technology will undoubtedly affect the 2024 United States presidential race. The discussion kicked off with the panellists dissecting the evolution of how AI is used in the political realm.

Today, political parties and super PACs (political action committees, which raise and distribute campaign funds to their chosen candidates), especially in the US, are investing millions in developing and deploying AI models. These models allow them to dig deep into data points on individual voters to help facilitate more targeted campaign initiatives.

In addition to this, there is the widespread issue of bots and deepfakes being used to drive misinformation campaigns. As the technology becomes more sophisticated, it will become increasingly difficult for the average person to distinguish them from real or factual content.

Given the stresses that generative AI is putting on the political system, it is imperative for policymakers to play a key role in managing the technology appropriately. However, as this is a relatively new domain, the question is whether existing policymakers are equipped with the right knowledge and frameworks to understand the technology and enact the appropriate legislation around it.

The discussion then moved on to how AI is being used in military defence. In the Ukraine War, for instance, many AI tools that have commercial applications and are used by civilians are being harnessed to strengthen defensive capabilities, such as battlefield data analytics and drone technology. Indeed, the defence sector – and European companies in particular – saw record investment from venture capital firms in 2022, despite the wider slowdown in technology funding.

A tale of two regions

The panellists also touched on differences in the growth of AI between the US and Europe, and how European AI companies can catch up to their American counterparts. As one of the speakers pointed out, US companies have generally been a lot more strategic about investing in AI, leading to significant differences in value capture.

However, there seems to be a newfound sense of pride among European entrepreneurs who are eager to develop AI technology and shape the economic, political and regulatory perspective with a European viewpoint – one that prioritises and upholds democratic values. Generative AI, in particular, presents a big opportunity for European companies to ensure that models incorporate European data sets in their training, thereby reflecting cultural references and values in the output.

Establishing the right frameworks and regulations can nurture these seeds of progress. However, the challenge lies in designing AI regulations that help promote the creation of economic value, without putting consumers at risk. European Union lawmakers recently passed the AI Act, billed as the first law on AI by a major regulator. Although it has yet to become law, it will have major implications for the development of AI in the region.

While the panellists were all in agreement on the necessity of regulations, one point that was raised emphasised that these regulations should not curtail AI development by start-ups or smaller companies in Europe. The concern was that such restrictions would indirectly benefit Big Tech, US-based firms and similar start-ups in China. These hurdles could come in the form of heavy reporting burdens, restrictions, paperwork and time lost as companies adapt to new legislation and ensure that they are not running afoul of the law.

Ideally, these regulations will help mitigate consumer risks while also creating the conditions to build a flourishing European AI ecosystem. One of the panellists suggested that a multi-stakeholder approach to this complex issue could be more effective than leaving it in the hands of politicians.

Upholding democratic values

Much has been said about AI’s role in stoking populism and threatening the democratic process. One of the speakers framed democracy as a conversation that breaks down if it gets overwhelmed by bots and deepfakes. How, then, can we better protect the democratic process from the risks of AI, especially with crucial elections taking place in the US and Europe in the next few years?

As one of the panellists stressed, it will be crucial to have systems that verify AI-created content and clearly label it as being generated by AI. As political parties build customised large language models to serve their interests, it could be necessary to mandate the disclosure of the specific AI tools they are using and for what purpose, and how they train their data sets. This approach would be similar to disclosures required for political funding.

Of course, there are many cases of the technology being used for good. As a panellists commented, some NGOs are leveraging AI to help stateless individuals by getting real-time information to them in their language. The World Food Programme has also used AI to improve its ability to respond to emergencies caused by natural disasters.

Another panellist emphasised that this could potentially be the biggest technological shift humankind has ever seen. While politicians play an outsized part in shaping AI regulations, it is equally important for citizens to voice their opinions on how AI is developed, deployed and governed. This engagement is vital to ensure the preservation of democratic values in society.
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Re: INSEAD Knowledge: Expert opinion and management insights [#permalink]
FROM Insead Admissions Blog: What Businesses Can Learn From Humanitarian Operations
Supply chain disruptions have become commonplace in recent years due to the increasing occurrence of natural disasters, the Covid-19 pandemic, the Ukraine war and various forms of geopolitical tension. In an increasingly volatile business environment, organisations that are used to operating in structured environments can look to the humanitarian sector for insights on navigating this new reality.

In this podcast, INSEAD Knowledge speaks to Luk Van Wassenhove, Emeritus Professor of Technology and Operations Management and academic director of the INSEAD Humanitarian Research Group (HRG). He is not only well-known in the field of operations management, but also recognised as one of the pioneers in humanitarian operations.

In the past, humanitarian organisations have benefited from adapting best practices from the commercial sector, but businesses can also gain insights from how humanitarian organisations operate in extreme conditions. How do they overcome uncertainty, time pressure, challenging physical conditions and the lack of resources and information?  Van Wassenhove’s serendipitous engagement with the humanitarian world – including the Red Cross in Geneva and the UN World Food Programme for the UN Joint Logistics Centre – 25 years ago made him realise that operations management professionals have much to learn from humanitarian organisations.

In fact, learning can be a two-way process. This is where the INSEAD HRG has a role in bridging knowledge between the two seemingly unrelated sectors. Companies need to look beyond their current and immediate boundaries and be open to learning from others. They should also learn to work with diverse stakeholders, be agile in dealing with unexpected situations, decentralise and localise where possible and empower employees to increase responsiveness.

Among the themes discussed, “co-opetition” – where companies in the same industry not only compete but also cooperate to advance the sector – is particularly interesting and relevant. The emergence of new paradigms such as circularity and the energy transition have necessitated the rethinking and reestablishment of systems, infrastructure and supply chains. New business ecosystems that have taken shape as a result have made cooperating with new stakeholders – including competitors – inevitable and necessary.

Beyond operational agility and resilience, humanitarian operations are driven by purpose – a holy grail that many companies seek but often in vain. The absence of profit as an incentive strengthens the purpose of humanitarian organisations to deliver aid to those in need.

Moreover, principles of humanitarian work are increasingly relevant in a world with a growing divide. The inequitable access to vaccines and medical services seen at the height of the pandemic clearly shows the need for more equitable and resilient supply chains. In particular, if companies follow the example of humanitarian organisations by embracing the principle to “do no harm” and take responsibility for the impact of their business, society and the environment will benefit greatly from this change.
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