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Ragvir Sabharwal on building strong analytical foundations for the next generation

Shamita Harsh — Wed, 15 Jan 2025 12:14:02 +0000

Ragvir Sabharwal on building strong analytical foundations for the next generation

Shamita Harsh Wed, 15/01/2025 - 12:14

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As a senior teaching fellow at Imperial Business School, Dr Ragvir Sabharwal teaches probability, statistics, and econometrics to ��ý on the BSc Economics, Finance and Data Science (EFDS) and MSc Business Analytics programmes. He also serves as Head of Year for the EFDS programme, guiding ��ý through one of Imperial’s first-of-its-kind degree with a uniquely modern curriculum.

From high-dimensional data to classroom impact

Combining methods from statistics and economics, Dr Sabharwal’s research focuses on developing models that help analyse complex, high-dimensional data over time. His doctoral work in the Department of Statistics at the London School of Economics and Political Science (LSE), funded by the Economic & Social Research Council, advanced methods for estimating dynamic factor models and detecting structural instabilities in large datasets.

This work contributes both to the theoretical foundations of econometrics and to practical applications in macroeconomic and financial analysis. For example, his research demonstrates how sudden shifts in economic or financial systems can be detected in real-time, enabling models to quickly recalibrate and forecasts to remain accurate even through periods of macroeconomic instability.

Before moving into academia, Dr Sabharwal worked for several years as a competition economist at a leading economic consultancy, advising on antitrust cases in the UK and Europe. This professional experience grounds his teaching in real-world applications, giving ��ý insight into how econometric and statistical tools are used in practice.

Dr Sabharwal has developed and delivered a wide range of courses in statistics, econometrics, machine learning, and causal inference, both in-person and online. He has taught ��ý at BSc, MSc and PhD levels. His consistently outstanding teaching evaluations and multiple teaching awards highlight his ability to make complex quantitative concepts accessible and engaging for ��ý from diverse educational backgrounds.

What excites him most is shaping how ��ý approach data-driven questions early in their studies: “Helping ��ý build strong analytical foundations right from the outset is incredibly rewarding. Rigorous early training will be invaluable in shaping how they’ll approach data-driven decisions throughout their professional lives.”

An academic journey through economics and statistics

Dr Sabharwal’s path reflects a deep commitment to both economics and statistics. He obtained two MSc degrees, one in Economics and another in Financial Statistics from LSE, before completing his PhD in Statistics there. His doctoral thesis developed new tools for handling high-dimensional time series data, tackling challenges that traditional models cannot easily address.

At Imperial, Dr Sabharwal combines his research expertise with his passion for teaching, shaping the next generation of data-savvy economists and analysts. By fostering strong foundations in statistics and econometrics, he aims to prepare ��ý not only to succeed academically but also to become thoughtful, rigorous decision-makers in business, policy and beyond.

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Dr Sabharwal’s teaching focuses on equipping ��ý to think rigorously about data and its role in decision-making

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How to use analytics to play darts like a pro

Martin Haugh — Mon, 21 Oct 2024 11:47:42 +0000

How to use analytics to play darts like a pro

Martin Haugh Mon, 21/10/2024 - 11:47

21 October 2024

For the first time, new research from Imperial Business School demonstrates how to calculate the optimal darts strategy.

From expected goals in football to biomechanics in cricket, sport is undergoing an analytics revolution. No discipline is untouched by a newfound focus on statistics, and this includes darts – a game which has undergone a rapid journey from pub activity to global primetime.

In darts, two players alternate taking turns of three successive throws. The goal is to get from 501 points to zero before your opponent, with the final throw hitting a double. To date, however, analysis of darts has focused on the individual getting from 501 to zero in as few turns as possible, which isn’t quite the same thing. Our research is the first to account for this subtle difference by modelling darts as a dynamic zero-sum game.

Strategic play

In other words, when computing a player’s optimal strategy we ought to take the opponent’s score into account. This is particularly important towards the end of a game, when a player could choose to aim for the double bullseye – a region worth 50 points that counts as a double, but is considerably more difficult to hit than other doubles.

Whether choosing this high-risk, high-reward option is optimal depends on the opponent’s score. If the opponent is likely to win next turn, it’s more likely to be worthwhile. Conversely, if the opponent has a high score remaining and is unlikely to win next turn, it might be better to take a safer option and aim for an easier double on the next turn.

By analysing data from professional players, we were able to develop a model to calculate a player’s optimal strategy for this type of situation. To illustrate this, we compared two hypothetical situations. In both situations Player A is on a score of 50 with one throw left in their turn. In the first situation Player B is also on 50 and in the second Player B is on 150.

No discipline is untouched by a newfound focus on statistics

We can calculate Player A’s win-probability as a function of where on the dartboard they target the final dart in their turn. In one situation we assessed, for example, it is much more advantageous for Player A to be aggressive and aim for the bullseye, as it gives a win probability of 46.8 per cent compared with much smaller win probabilities for any other region of the board.

In the second situation we looked at, however, a defensive approach offers a superior win percentage of 78.6 per cent if Player A aims for single 10, as this leaves them with three throws at the easier-to-hit double 20 on the next turn. This is because Player B is far less likely to win on their next turn, so Player A is likely to have another turn and therefore another chance to win.

We found that playing strategically in this way gives the typical professional player an increased chance of victory of between 0.2 per cent and 0.6 per cent in a single game (also known as a "leg"), compared to using a strategy that ignores the opponent’s score. Over a longer match (e.g. best of 35 legs), however, this can be as high as two to three per cent – a significant edge for professional players.

Skill factor

However, this does not take into account variations in skill between different players. In practice, skill levels vary with the target region (e.g. players practise targeting triple 20 more, so can hit it more easily than other triples) and the individual player (e.g. one player might be better at hitting certain regions than others).

As we did not have data for every player–target combination, we applied empirical Bayesian methods to fill the gaps, allowing us to fit a skill model for each player. We then used this to analyse real-world situations.

For example, in a 2018 match between Gerwyn Price and Peter Wright, Wright was on 18 and Price was on 20. It was the beginning of Wright’s turn and the most obvious strategy was to aim for double nine to win the leg. Instead, he aimed (successfully) at single two, then needed to hit double eight with one of his next two throws. He missed both of those, and Price then won the leg.

When computing a player’s optimal strategy we ought to take the opponent’s score into account

Commentators questioned Wright’s decision not to use all three throws to aim at double nine. However, according to our skill model, what he did was indeed the optimal strategy for him, with a win percentage of 71.4 per cent when targeting single two with his first dart versus 70.5 per cent if he targeted double nine.

Interestingly, when we applied the same situation to a different player – Michael Smith – our model recommended the double nine approach, with a win percentage of 77.8 per cent compared to 70.3 per cent. This is because Smith is more adept at hitting double nine than double eight, while the opposite is the case for Wright.

This suggests experienced players are well aware of their own relative strengths and weaknesses. Our skill model effectively makes this instinctive knowledge visible, unlocking one of the key mysteries of the game – how and why the top players play the way they do.

This article draws on findings from "Play Like the Pros? Solving the Game of Darts as a Dynamic Zero-Sum Game and An Empirical Bayes Approach for Estimating Skill Models for Professional Darts Players" by Martin B. Haugh (��ý) and Chun Wang (Tsinghua University).

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Can managers really learn anything from sport?

Sporting metaphors are everywhere in business but aren't always liked. Dr Jan-Michael Ross joins the game to show such language is not down and out, if you keep your eye on the ball

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How to create more effective marketing strategies with data

Stephan Seiler — Thu, 13 Jun 2024 11:05:22 +0000

How to create more effective marketing strategies with data

Stephan Seiler Thu, 13/06/2024 - 11:05

14 June 2024

For a long time, academics and marketers have struggled to directly observe the consumer's "journey". But all that's changing, thanks to the availability of pre-purchase data.

Our understanding of pre-purchase behaviour changed almost overnight with the advent of the internet and the growth of online retailing. Since then, the search processes of consumers have become increasingly observable, from the virtual stores and review sites they visit to the products they browse, all in seconds from the comfort of their own homes.

Search data can even drill down into the detail of the clicks they made and the length of time they spent in making their purchase decision. This has opened the door to a far better understanding of consumer behaviour and targeted marketing strategies based on this data.

Our research takes stock of how pre-purchase data has been used by academics and marketers and highlights opportunities for its future use as the quality of data improves.

Why data matters

There are two important areas of marketing decision-making where search data is particularly useful. Firstly, it tells us a lot more about a consumer’s preferences than just observing the purchase. Most consumers compare at least a few products before making their decision and search data allows us to see specific pairings which are clearly viewed as similar products.

It also allows us to see the key product features that assume the most importance in the purchase decision and other aspects of the process, for example the outlets customers considered and to what extent price or other factors played a part. Search engine data can even show us when consumers visited review sites to aid their decision-making. This is important information for recommendation systems or pricing decisions.

We expect data quality in offline retail to increase soon, allowing managers and researchers to study offline pre-purchase behaviour

Secondly, the new data on search behaviour confirms that consumers search very few products before making a purchase, typically only two or three. This not only allows firms to shape which products consumers discover through marketing activity such as product rankings online or preferential shelf placement in a physical store, but it makes this activity crucial in bringing relevant products to a consumer's attention.

If consumers do less research before their purchase, product visibility assumes greater significance; if a firm's product isn't seen quickly, it may not be seen at all. This type of data allows marketers to put their products in front of those consumers most likely to buy them, and establishes how strongly individual marketing actions impact search behaviour, as well as purchases.

More recently, regulators have also taken an interest in such data and analysis because they worry that retailers will display their own products more prominently on their webpage. Pre-purchase data, and information on how consumers react to changes in product rankings, can show us how detrimental this type of retailer behaviour is to consumers.

Tracking consumer behaviour in physical stores

Off-line retailing presents more of a problem as it's far more challenging to track a customer's search and purchase journey through what could be a number of physical stores, reviewing comparable products, then checking their requirements are met, before making a final purchase. In the future, stores might be able to track consumers using their cell phones, but this does not yet provide accurate enough location data to see the route customers took through a store, where they stopped to browse products or which products caught their attention.

If consumers do less research before their purchase, product visibility assumes greater significance

Eye-tracking technology (both on- and off-line) can also provide important data for marketers, but this can only be done in a research environment where the customer is fully aware that their behaviour is being analysed, which in turn can skew results. However, we expect data quality in offline retail to increase soon, allowing managers and researchers to study offline pre-purchase behaviour in an equally comprehensive way as is currently possible online.

Pre-purchase behaviour has already become a key tool in the retailer's armoury and an important factor in building an effective marketing strategy. In the online world it's likely that this will become even more sophisticated as technology continues to evolve and the proportion of customers shopping online increases.

The same is true in the off-line world, as the tracking data on mobile phones improves – although it will always lag behind. Perhaps those retailers – supermarkets in particular – that straddle both worlds will benefit the most.

This article draws on findings from "Consumer search: What can we learn from pre-purchase data?", by Elisabeth Honka (UCLA), Stephan Seiler (��ý) and Raluca Ursu (NYU Stern).

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Toxic customers and how to handle them

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The Fifth Industrial Revolution and finance: insights from Naveed Sultan

Michael Mills — Fri, 12 Jan 2024 14:01:24 +0000

The Fifth Industrial Revolution and finance: insights from Naveed Sultan

Michael Mills Fri, 12/01/2024 - 14:01

22 January 2024

Naveed Sultan, Professor of Practice at Imperial Business School and former Chair of Citi’s Institutional Clients Group, shares his insights into how data and technology are transforming finance and business, and what challenges and opportunities this presents

With over 30 years of experience in global banking and finance, including as Chair of Citi's Institutional Clients Group, Naveed Sultan has been at the forefront of digital transformation in the financial sector. Now a professor of practice at Imperial Business School, he sat down with us to discuss how technology is changing finance and business.

What opportunities does financial digital transformation offer to businesses?

The world is going through a major transition, which some observers are calling a fifth industrial revolution. Large parts of the economy are being reorganised, new business models, ecosystems and platforms are emerging, and lifestyles are changing.

Digital transformation, as the deployment of a range of technologies to create better economic and societal outcomes, is part of this, with data at its core. For the finance function within a business, if you can get data promptly and accurately through technology, with the right analytics, you can make better decisions and create better outcomes.

In time, these processes can make organisations more productive, efficient and accurate, and provide a greater level of confidence in their underlying activity, particularly from a compliance and risk perspective. The greater the confidence organisations have in their core processes and their operating and business models, the greater the confidence they have in doing business with clients.

How is this playing out in the financial sector?

We can split the sector broadly into two camps: new fintech players such as Stripe, Square and Revolut, and established banks. On the consumer side, there has been growing competition between the two, but on the institutional side there’s actually greater collaboration.

Fintech firms need access to banks’ customers, while banks themselves see that fintech solutions can deliver better results for their clients. This has led to incumbents in the sector taking equity positions on emerging fintech players or buying them outright.

This allows banks to offer a better, more innovative and efficient proposition to their clients. And, as fintech firms become more successful, their valuations go up and banks benefit from investing in them.

What should businesses be aware of when planning digital transformation?

Whenever new technologies come up, there is a natural tendency to deploy them in the current operating model or on top of legacy systems, without considering the underlying core processes, or the organisational, cultural and talent implications. Generally, this is unlikely to be successful.

Businesses have to first reorganise how they work, in a holistic way. They need strategic commitment from senior management, the right talent to manage data and deploy new technology, and plans in place to change culture and staff mindsets. Only when these key organisational and cultural requirements are met will a business be ready for technology-led change.

And from a society-wide perspective too, we need to start seeing digital transformation not as a technology issue, but as a policy, strategic, cultural and organisational issue. All economic actors need to operate in alignment for an optimal transition, both economically and societally.

What role are specific technologies such as blockchain and AI playing in financial digital transformation?

At the surface level, they are already increasingly important. For example, blockchain provides smart contracts, improved supply chain management and greater transparency in transactions, while AI offers better risk management, fraud detection, algorithmic trading and personalised customer experiences.

However, there is often an inflection point when technologies work in combination. So, when AI and blockchain are combined, we can see, for example, cross-border tokenisation of securities with smart contracts and enhanced fraud protection – something that combines the technologies as more than the sum of their parts.

Mr Sultan, thank you for your time.

About the expert

Professor of Practice at Imperial Business School since 2023, Naveed Sultan is Co-Director of the School’s Centre for Financial Technology and co-founded the Centre for Responsible Leadership.

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How data science can boost your chances of finding a date

Fanyin Zheng — Tue, 05 Dec 2023 14:28:42 +0000

How data science can boost your chances of finding a date

Fanyin Zheng Tue, 05/12/2023 - 14:28

6 December 2023

Online dating can be frustrating and time-consuming, but not anymore. We’ve developed new algorithms that make finding your perfect match easier and more enjoyable. Here’s how we did it.

Finding love is difficult enough without having to fathom how well dating services are doing their job of providing the perfect match. Today, this delicate task is one often left to algorithms – the “secret sauce” of hundreds of dating platforms serving millions of people around the world. But match rates across the online dating sector are often dismal. Luckily, for the many individuals who bemoan their unsuccessful sorties into the dating market, there is good news.

We’ve delved into the inner workings of algorithms used by a leading dating app and overhauled the technology – with happy results. By creating new algorithms more tailored to the individual, we’ve succeeded in increasing the number of matches by close to 30 per cent. This has wider implications for any businesses that rely upon algorithms to match customers in a two-sided market place.

How we cracked the code of dating app algorithms

Online dating is today worth $12 billion worldwide, and it’s one of the most common ways people meet their partners: in the US, some 65 per cent of same sex couples, and 39 per cent of heterosexual couples met online, research from 2018 shows. After lockdowns eased, dating app downloads soared.

But companies guard the finer details of the proprietary technology used to suggest potential dates. On the leading US platform with whom we collaborated, both users must declare they like each other for a match to be made. But most platforms don’t run every potential candidate past every other user – that would simply exhaust them. Instead, they limit the number of profiles a user sees; our partner shows a maximum of nine a day to each of its 800,000 users.

The more matches individuals have achieved in the recent past, the less likely they are to like profiles in the near future

By unpicking data held by our industry partner, we were able to understand the workings of their algorithm. This company uses profiles created by the individuals when they sign up – giving information about their age, gender, height, religion, education, location and more. They can also declare their preferences. When tastes converge, there’s a match. Note this model differs from the decentralised algorithms deployed by apps such as Tinder, which learn from users’ online behaviour.

The three keys to better dating matches

What we’ve done is to rejig the workings of this algorithm to bring in three major changes.

Firstly, we have succeeded in tailoring choices so that they are more personalised and granular. Currently, this platform applies a filter based on the user’s stated preferences and the platform’s broad assumptions – people with a degree will only be interested in others with a similar education, or of a similar height, or ethnicity, or interest in music for instance. But love may be hiding in unexpected places. Our algorithm allows for more nuance and different possibilities.

We’ve succeeded in increasing the number of matches by close to 30 per cent

We’ve also taken into account whether any "like" will be reciprocated. Half a match isn’t enough. Because our algorithm can also account for the recipient’s tastes, it makes a more judicious daily selection, making better use of the limited slots available for potential profiles, and increasing the chances of a match. Remember that individual users only see a limited number of profiles a day.

Our third innovation has been to understand and account for what we call "the history effect", which looks at a user’s recent activity on the app. The more matches that individuals have achieved in the recent past, the less likely they are to like profiles in the near future. What they experienced on the platform in their previous session will affect how they behave in the next, and we adjust for that. This may be down to feeling overwhelmed by the number of potential matches, or indeed there may be other factors at play. The more likes they get, for instance, the more attractive they might feel, and the more picky they may become.

How our algorithm performed in the dating market

Testing our algorithm in the real world is fraught with difficulties. These are real people seeking real relationships and we must tread carefully. We can’t dictate how they will react, and there’s no way to conduct a precise controlled experiment with like-for-like individuals.

But we were able to put the algorithm through its paces in a number of ways: firstly with a simulation using the platform’s data from activity of selected users based in Houston, Austin and Dallas – the three largest cities in Texas – over six months. It must be noted that all our experiments took place among heterosexual daters, who represent more than nine in 10 users in these environments. By simulating how individuals would react by using our algorithm, we were able to show a marked rise in the number of matches – enough to convince the dating platform to try it out in the real world.

Our algorithm allows for more nuance and different possibilities

And there, results were even more pronounced. We were able to test the new algorithm on individuals who used the app over a week in Houston. To gauge its performance, we compared the dating activity in very similar conditions in Dallas and Austin over the same time – and we saw a significant rise in the number of matches between Houston daters. In all, these experiments revealed a rise of at least 27 per cent.

Not only is this great news for those trying their luck in the dating market, it’s good news for companies seeking to improve their search and recommendation options. Our algorithmic experiments are broad enough to allow this innovation to be deployed in other two-sided markets, such as ride hailing apps and online work platforms. More personalised technology, better knowledge of the other party, and more oversight of recent online activity makes for success in dating, and elsewhere.

This article draws on findings from "Improving Match Rates in Dating Markets Through Assortment Optimization" by Ignacio Rios (The University of Texas at Dallas), Daniela Saban (Stanford University) and Fanyin Zheng (��ý).

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Can anyone topple X (the platform formerly known as Twitter)?

Andre Veiga — Tue, 29 Aug 2023 10:12:18 +0000

Can anyone topple X (the platform formerly known as Twitter)?

Andre Veiga Tue, 29/08/2023 - 10:12

29 August 2023

Elon Musk's controversial reign at Twitter, renamed X after his takeover, has been well documented – and yet, the brand survives. Here, we take a look at why competitors like Threads are struggling to match its success.

X has two fundamental advantages that maintain its position as the incumbent king of its corner of the social media jungle. Firstly, it delivers a valuable service that users can't find anywhere else. It's where you go to find out what happened seconds ago on the other side of the world. It's a live news feed, harnessing the views of a community as diverse as Barack Obama and Mrs Smith from Dagenham.

Secondly, it demonstrates the power of networks. Any successful social media platform needs a large, active community to generate content and engagement. Like the best parties, once it is able to attract enough users, it becomes an irresistible magnet with easy subsequent growth.

The first platform to achieve such scale often then becomes an incumbent that is hard to dislodge. Many have tried to oust X from its top spot, most notably and recently Threads, but incumbency advantage is a strong hand.

The social media dilemma

What did Threads do wrong, or what should it have done to guarantee success? It had the makings of a solid rival to X, particularly at a time when many Twitter users were feeling alienated and the site was losing advertising revenue. Threads had the opportunity to exploit Meta’s user base and related data. It had the funds to develop an effective competitive platform.

Yet Threads has been failing miserably and fast, going from tens of millions of users in the first week to a paltry eight million users within a month. Of course, externally validated user figures are at best an approximation – what counts is time on the platform and engagement, not how many Instagram users have a Threads icon on their account (these were added by default and don't necessarily reflect engagement).

Musk has behaved like an engineer, experimenting wildly with staff, features, and anything else he can get his hands on

Mark Zuckerberg saw an opportunity to launch Threads as an antidote to the perceived toxicity of X, promoting it as a positive and creative space that would foster community. Brands were encouraged to adopt a personable, casual, relatable tone in their posts, making them fun and human. It's a sad indictment of human nature that people seemingly don't want that.

Many early users of Threads described the experience as stilted and boring. The fact is that the often controversial environment of X keeps users engaged, ultimately adding to the platform’s bottom line. Therein lies a social dilemma – how do we, as a society, create a business model that provides all the benefits of social media, with none of the toxicity, addiction, depression and disinformation.

Strategic mistakes

Threads also failed when it came to implementing the features that made Twitter so successful in the first place. There were no hashtags allowing users to follow topics, it launched in the US before sorting out European GDPR compliance (thereby reducing its potential user base), and users were unable to direct message one another. It also failed to scale quickly enough (entering a platform market is a sprint not a marathon!), or to create a limited window for users to switch from X to Threads – paradoxically, the longer the door remains open for individuals to switch, the less likely they are to do so and the greater the incumbency advantage.

So despite Musk’s seemingly chaotic handling of Twitter, it has survived, and the users it has lost were probably marginal users anyway, rather than those who read and post to X intensively. Musk has behaved like an engineer, experimenting wildly with staff, features, and anything else he can get his hands on. There is a chance he will find a change that improves the experience of users, or improves his bottom line. In the meantime, the chaos doesn't seem to have damaged X that much.

Many early users of Threads described the experience as stilted and boring

Looking at other social media platforms, there are few successful challenges against an incumbent market leader. Perhaps the best example is Facebook's annihilation of MySpace. Since then, Facebook has tried many times, with Lasso (a TikTok challenger), Hobbi (a Pinterest challenger) and now Threads (an X challenger), and never once matched its MySpace story.

And so we get to the nub of it. The blend of a high value product and the network effect of scale provide a giant wall of obstacles for a competitor like Threads to climb before it can successfully topple X off its social media perch. So, can it be done? One thing is for sure: it will require more than Zuckerberg's current strategy to counter the impact of X's incumbency advantage.

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5 ways to find meaning and purpose in your work

Michael Mills — Mon, 21 Aug 2023 08:57:51 +0000

5 ways to find meaning and purpose in your work

Michael Mills Mon, 21/08/2023 - 08:57

21 August 2023

Work doesn’t have to be boring or stressful. You can find meaning and purpose in your work by following these five insights from Imperial Business School experts

Finding meaning and purpose in your work is not only beneficial for your well-being, but also for your performance, creativity and innovation. Here are some insights from Imperial Business School experts on how to achieve this goal.

1. Make the most of your time in the office

Whether hybrid working is new to you or something you’ve been doing for years, it can be a challenge to feel like you’re getting the most out of the home/office split, and not just spending parts of your week in different places. Dr Omar Merlo, Associate Dean (External Relations), explains how time in the office can be used to strengthen your business’s customer focus, while avoiding the pitfalls of remote communication.

2. Learn from Japanese philosophy

But don’t you feel like you should be getting something out of your work too? Customer-centricity is great, but it can’t be your sole source of joy and satisfaction. If you recognise that feeling, you might want to learn about ikigai, a Japanese philosophy that can help you achieve personal and professional fulfilment. Frank Brueck, Leadership Lab Lead at Imperial Business School’s Leonardo Centre on Business for Society, explains how to apply it to your own career and organisation, so you can connect your passion and skills with market needs.

3. Learn from sport

For all that sporting metaphors can be a business cliché, sport can genuinely offer some lessons on how to find meaning and purpose in your work. In this article, Dr Jan-Michael Ross, Associate Professor of Strategy, shows how to use sports data effectively and rigorously to develop and test new management theories. He also highlights the opportunities and challenges of using sport as a research context, and the potential benefits for academics and practitioners alike.

4. Develop general skills

Innovation is a key driver of economic growth, but it also involves significant risks and uncertainties. According to Dr Cláudia Custódio, Associate Professor of Finance, CEOs with more general and transferable skills are likelier to invest in innovation and take advantage of new opportunities, as they have more outside options and are less scared of failure. She has also looked at the implications of this for the labour market for executives and the design of CEO contracts.

5. Recognise when you’re most creative

Creativity is an asset for any organisation, but it can be hard to foster and manage. In this article, Professor Paola Criscuolo and Dr Anne ter Wal explain the phenomenon of bootlegging, or underground innovation, where employees work on their own ideas without official approval or support. They argue that bootlegging can be a source of radical innovation, as it allows employees to pursue novel and risky ideas without fear of criticism or reprisal.

If you want to dive deeper into these topics, or explore other areas of interest, keep up to date with IB Knowledge, the site that features the latest research and thought leadership from Imperial Business School experts. You’ll find a wealth of content on topics such as finance, entrepreneurship, sustainability, health and more. Click here to find out more.

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Expert Q&A: Is AI going to save us or kill us?

Evie Burrows-Taylor — Mon, 12 Jun 2023 13:50:05 +0000

Expert Q&A: Is AI going to save us or kill us?

Evie Burrows-Taylor Mon, 12/06/2023 - 13:50

12 June 2023

World Economic Forum Fellow and Visiting Lecturer at Imperial Business School Dr Catherine Mulligan explores what AI means for society

A new dawn or the beginning of the end of human civilisation? Artificial intelligence (AI) has been called each of these. And in the months since the AI-powered chatbot ChatGPT was released, it’s been impossible to avoid articles about the technology’s significance and likely impact on our everyday lives, as well as the future of the human species.

Here, World Economic Forum Fellow and Visiting Lecturer at Imperial Business School Dr Catherine Mulligan explores what AI means for society.

1. Some have claimed AI will have a positive impact on areas as varied as wildlife conservation, hate speech and clean energy – is this realistic?

This is not the first time we've heard technology is going to save the world: we saw it with mobile networks, broadband, the Internet of Things and blockchain. Like those, AI is a tool that can be used for good, as well as bad.

However, all digital technologies come with something that we are a little bit less prone to talk about, which is unintended consequences. AI can, and will, help us solve all the issues you mentioned, as long as we remain cognizant of the possible negative side effects – and work to solve them.

2. Where could AI make its greatest positive impact?

In an era of climate change, the greatest thing we can do with AI is reduce humanity’s environmental impact. One way that this can be done is if we use it to remove inefficiencies within industries such as the energy industry. I also believe AI can help us design and deliver more efficient solutions for education, research and industrial structures.

One thing I'm working on a lot at the moment is digital measurement, reporting and verification for carbon markets. I'm looking at how we can measure environmental impact with AI, which is essential because the models are very complex.

We're also seeing AI being used in very large-scale supply chains to reduce environmental impact, as well as to improve systems design. Another area where we’ve seen a lot of developments with machine learning, which will rapidly move into AI, is healthcare.

3. Where do you think AI’s greatest negative impact will be seen?

I'm not the sort of person who’s overwhelmed with the negative side: AI won't kill us unless we let it. Having said that, we're already seeing some of the downsides.

An obvious one is the manipulation of the social digital sphere, with things like deepfakes and the amplification of hate speech. Everyone is concerned about AI ethics, but there’s also a critical discussion to be had about whose ethics will be applied. For example, a lot of this is driven by which countries are developing the technologies, while others, who use the technology, don’t have a huge amount of say in how things are developed and implemented.

There’s a lot to consider including how to protect elections, as well as how to educate people who've never seen a world where these things don't exist. We need to think about how to explain what truth is to the next generation – as a result, AI forces us to think a lot about what truth really is.

4. Should people be worried about their jobs?

We won‘t see it immediately but jobs are going to transform rapidly. For example, you can already use ChatGPT very effectively in education, with AI able to provide a very good summary of different subjects such as leadership literature. What it isn’t able to do is build a curriculum that combines multiple disciplines together and applies them into another area such as sustainable development – yet.

So, while there will be a rapid transformation, there’s still plenty of space for humans, who will be able to evolve their careers by bringing several disciplines together effectively to solve problems – known as “boundary spanning”. This is going to challenge us to think about what it means to be human, including redefining our role in the workplace.

5. Do you think businesses are ready for the downsides of AI?

Yes and no. When you talk to senior executives behind closed doors, they all know what's going to happen as a result of AI. The really good ones will admit this to staff and work to retrain them. When I started working with one company, they had about 7,000 staff running certain parts of their system – that’s now down to 25.
I wouldn't say the business world is entirely ready, but it understands what's coming.

6.In your opinion, how should we be preparing for AI?

We could easily start to replace some of parts of the C-Suite with really good, forward-looking AI, so I think there should be honest discussions at an executive level. In the UK, most executives tend to not have digital experience: CFOs often become CEOs. Companies therefore need to get people with excellent digital expertise into their executive suite and allow them to help cope with developments in AI.

Businesses also need to consider what it means for the average employee, which means asking more than just what can be automated. Again, I think the key issue in the face of AI is boundary spanning: executives who can show they’re incorporating AI into part of their company to open up a new market, or enter adjacent markets for example, will be extremely valuable.

7. Overall, do you feel optimistic about the potential of AI?

I'm very optimistic about the future of technology but I'm increasingly worried about human beings applying it without real depth of thought.

We need to pause and think before we execute some of these digital solutions because it’s critical we get this right. The societal and the environmental implications of what we're doing are crucial – not just for companies – but for every single organisation across the world right now.

This article draws on the theme of Imperial Business School’s Social Impact Day 2023 “AI for Society: A Problem or Solution”. Dr Catherine Mulligan was a panelist at the event.

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How businesses are addressing the biodiversity crisis

Maurizio Zollo — Wed, 07 Jun 2023 15:16:09 +0000

How businesses are addressing the biodiversity crisis

Maurizio Zollo Wed, 07/06/2023 - 15:16

7 June 2023

Corporations have played a key role in the world’s biodiversity crisis. Now, some business leaders are recognising this – and making amends

At the end of 2022, countries around the world reached a landmark agreement on biodiversity, acknowledging the scale of the challenge we face. This is important: since 1970, we have seen a 69 per cent decline in wildlife populations, with the WWF highlighting “the double, interlinked emergencies of human-induced climate change and the loss of biodiversity”.

Alongside nations, businesses have a key role in addressing this crisis. Biodiversity loss is inextricably connected to changes in land use, overexploitation and increasing pollution, which corporations undoubtedly play a part in causing. Business leaders are beginning to recognise this and are working to mitigate some of the impacts and trends of biodiversity loss.

Scandinavian companies are more likely to focus their biodiversity efforts on changing their business models

But this is not a simple task. Biodiversity is difficult to map to specific activities, and there is no agreed measurement framework for it. This makes it complex for businesses and regulators to set specific and quantifiable targets for mitigating the biodiversity crisis.

Our new research takes a step towards addressing this issue, exploring how companies are embedding biodiversity into their business models and reporting on it. By analysing 40,000 sustainability initiatives launched over the last two decades by over 6,000 companies across all sectors and directed at the preservation and regeneration of life on land and water (UN Sustainable Development Goals 14 and 15), we have distilled insights for driving future nature-positive corporate action.

Philanthropic action vs. business model evolution

We found around half of biodiversity initiatives are philanthropic in nature, comprising donations, funding and communication activities. Although beneficial in supporting nature protection and regeneration action by communities and stakeholders, these are not as impactful as activities targeted at evolving business models to incorporate biodiversity into strategic areas such as R&D and supply chain management.

However, the data shows the proportion of initiatives focused on donations and communication fell from 57 per cent in 2008 to 48 per cent in 2021. This is still high compared with initiatives targeting other emergencies such as pollution and climate change, but it shows businesses are moving in the right direction. The next step will be for corporations to expand and integrate their sustainability strategies to target biodiversity losses more directly.

Walmart, for example, has committed to “placing nature and humanity at the centre of [its] business practices”. As part of this, the company has pledged to help protect, more sustainably manage or restore, at least 50 million acres of land and a million square miles of ocean by 2030. This will have a real, beneficial impact on nature and biodiversity, and will require Walmart to adapt its production and sourcing processes, serving as an example to other global players of how to become a regenerative company.

Regional and sector variations

While there are positive signs emerging from business, we found there are significant variations in how much focus, and what type of focus, companies give to biodiversity depending on their region and sector.

For example, we observed that those in Spain, Portugal and Latin America focus more on biodiversity than those in Scandinavia. However, Scandinavian companies are more likely to focus their biodiversity efforts on changing their business models, so they actively regenerate the natural environment (e.g. through new product development and operational processes) – a potentially more impactful approach.

Furthermore, a cross-sector comparative analysis shows more biodiversity initiatives, and more complexity within those initiatives, in sectors where biodiversity is a more material issue. For example, companies in the utilities, energy and materials sectors focus significantly more on biodiversity issues than those in healthcare and financial services. The former are also more likely to take impactful action through the evolution of their business models.

Implications for businesses

Overall, we found an average decline in the amount of corporate effort to address the biodiversity crisis, in the sense that there were fewer biodiversity actions per sustainability report over time. However, of those biodiversity initiatives, the quality appears to be improving. Less appears to be more, with companies, particularly in certain regions and sectors, moving beyond philanthropic action to focus on nature-positive business models.

A key aspect of this, moving forward, will be finding and harnessing ways to set and monitor biodiversity impact targets. Measurement tools such as the CDP Forest Questionnaire or the Taskforce on Nature-Related Financial Disclosures’ LEAP framework can be helpful. However, given the difficulty of obtaining data from tier-three suppliers, businesses also need to find ways of measuring biodiversity impact across supply chains.

Alongside measurement and reporting models, truly regenerative organisations require leadership to drive cultural change. This can help to build organisational capacity to detect opportunities that benefit both biodiversity and the bottom line. It can also position companies to detect threats from biodiversity losses, develop strategic initiatives to address them, and scale solutions across the value chain. In time, this approach can help businesses to switch from being threats to nature to being key drivers in regenerating the natural environment.

This article draws on findings from “The Evolution of Corporate Behaviour to Tackle Biodiversity Challenges” by Livio Scalvini (��ý) and Maurizio Zollo (��ý) published in collaboration with HSBC

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2. Where could AI make its greatest positive impact?

3. Where do you think AI’s greatest negative impact will be seen?

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