Artificial Intelligence

Management & Entrepreneurship Conversations conference explores novelty in research

Evie Burrows-Taylor — Tue, 14 Jul 2026 13:37:20 +0000

Management & Entrepreneurship Conversations conference explores novelty in research

Evie Burrows-Taylor Tue, 14/07/2026 - 13:37

8 July 2026

This year's event, organised by Business School academics Anne ter Wal, Chengwei Liu, Ibrat Djabbarov, Luigi Mosca and Mark Kennedy, focused on research about how novelty is generated, framed and implemented in a technology-driven world.

Discussions explored several topics under the umbrella of how AI is affecting society, such as the future of work, generative AI and the creative process and what happens when human reasoning meets AI.

The conference included two debates, with Professor of Technology & Innovation Management Anne ter Wal challenging Professor of Management and Head of the Management & Entrepreneurship department Kevin Corley on whether the theoretical paradigms in management have become an insurmountable obstacle to novelty in management research.

The second, between Assistant Professor of Creativity & Innovation Ozum Demir-Caliskan and Professor of Strategy & Organisational Behaviour Mark Kennedy, explored whether increased adoption of AI will inevitably lead to the demise of human creativity.

Other sessions looked at collaborative climate innovation in smart homes, how layered architectural framing shapes innovation pathways, and enabling outsider innovation in mature research areas.

Artificial Intelligence

Entrepreneurship

Management

Academic area

Management & Entrepreneurship

Off

External

Off

Renowned global academics came together at Imperial Business School for the latest Management & Entrepreneurship Conversations conference.

1 minute read

Book launch on Tencent and its rise to being a global tech giant

Lou Lee — Mon, 14 Apr 2025 10:15:24 +0000

Book launch on Tencent and its rise to being a global tech giant

Lou Lee Mon, 14/04/2025 - 10:15

22 July 2026 News

Exploring new frontiers at the Africa Innovates Conference 2026

15 July 2026 News

Business School academic nominated for second term on UK's fiscal watchdog

14 July 2026 News

Celebrating the Dean’s Community Award winners

1 July 2026

To mark the publication of their new book, China's 'Everything Online' Company: The Unique Innovation Model of Tencent, Professor George Yip, Emeritus Professor of Marketing & Strategy at Imperial Business School, and co-author Professor Xiaolan Fu, Professor of Technology and International Development at the University of Oxford, shared insights from their research into one of the world's leading technology companies.

Introduced by Lord Clement-Jones, Liberal Democrat House of Lords spokesperson for Science, Innovation and Technology, the event traced Tencent's evolution from its early instant messaging platform QQ to the creation of WeChat and its vast digital ecosystem spanning social media, gaming, payments, cloud computing and AI.

Drawing on years of research, Professor Yip and Professor Fu explained that Tencent's success stems not only from technological excellence, but also from the way it encourages new ideas to emerge. Their book introduces the company's distinctive OCEAN framework: Openness, Coopetition, Empowerment, Autonomy and Attentiveness as a way of managing stakeholder needs. Together, these principles help Tencent operate more like a connected ecosystem than a traditional corporate hierarchy, encouraging experimentation while allowing new ideas to scale quickly.

“I hope people left with fresh insights into how Tencent combines technology, organisation and ecosystem thinking to create innovation at scale, and why those lessons matter well beyond China.”

Professor George Yip

Emeritus Professor of Marketing & Strategy

Reflecting on the event, Professor Yip said: "The enthusiasm and engagement from the audience showed just how much interest there is in understanding one of the world's most innovative companies. I hope people left with fresh insights into how Tencent combines technology, organisation and ecosystem thinking to create innovation at scale, and why those lessons matter well beyond China, even to the UK's next Prime Minister."

The evening concluded with a lively audience Q&A exploring the future of innovation, digital ecosystems and China's rapidly evolving technology landscape.

Professor Yip and Professor Fu's book offers fresh insights for business leaders, policymakers, researchers and ��ý interested in innovation, digital platforms and the future of technology.

Subscribe for research and events from the Business School

Artificial Intelligence

Big Tech

Brand

Consumers

Digital Transformation

Digital transformation

Entrepreneurship

Academic area

Management & Entrepreneurship

Marketing

Off

External

Off

Imperial Business School welcomed business leaders, academics and alumni for the launch of a new book offering a rare insight into how Chinese technology giant Tencent became one of the world's most innovative companies.

2 minute read

Listen: The Standard’s Tech and Science Daily podcast interviews Dr Mark Kennedy about Chinese AI company DeepSeek

Evie Burrows-Taylor — Mon, 03 Feb 2025 09:58:57 +0000

Listen: The Standard’s Tech and Science Daily podcast interviews Dr Mark Kennedy about Chinese AI company DeepSeek Evie Burrows-Taylor Mon, 03/02/2025 - 09:58

28 January 2025

Digital transformation

Artificial Intelligence

Digital Transformation

Technology

ITV News interviews David Shrier about DeepSeek’s emerging dominance in the technology market

Evie Burrows-Taylor — Mon, 03 Feb 2025 09:49:09 +0000

ITV News interviews David Shrier about DeepSeek’s emerging dominance in the technology market Evie Burrows-Taylor Mon, 03/02/2025 - 09:49

27 January 2025

Digital transformation

Artificial Intelligence

Digital Transformation

Technology

Science’s biggest AI challenge isn’t discovery – it’s proof

Ryan Cory-Wright — Wed, 15 Jan 2025 12:14:02 +0000

Science’s biggest AI challenge isn’t discovery – it’s proof

Ryan Cory-Wright Wed, 15/01/2025 - 12:14

More blogs

17 July 2026 Knowledge

Science’s biggest AI challenge isn’t discovery – it’s proof

15 July 2026 Knowledge

How to survive as an entrepreneur: insights from Steve Blank

6 July 2026 Knowledge

What business leaders can learn from the erosion of democracy

17 July 2026

Article at a glance

AI can now generate scientific hypotheses faster than traditional research systems can test them
Scientific progress still depends on verification: testing whether promising claims hold up against evidence, theory and domain expertise
AI-driven science will deliver on its promise only if we can test ideas as quickly and rigorously as we generate them

Scientific breakthroughs look sudden only in retrospect: a new theory, a medical advance, a technology that changes how we see the world. But before an idea becomes a discovery, it must survive a slower and less glamorous process of verification.

AI-driven science needs scalable verification methods. This is because AI systems can analyse vast amounts of data, identify patterns and generate new hypotheses faster than ever before. But verifying those hypotheses remains slow, costly, and highly domain-specific.

Ideas need proof

Science begins with a hypothesis: a claim precise enough to be tested, challenged and potentially proven wrong. Verification asks whether that hypothesis survives contact with what we already know by testing it against against theory, evidence, experiments, simulations, or formal reasoning. It is this process that separates genuine discoveries from ideas that merely appear plausible.

Scientific advances such as germ theory, thermodynamics and modern agricultural practices transformed society not simply because scientists proposed new ideas, but because those ideas were rigorously tested and repeatedly validated. Verification, then, is the dividing line between a promising idea and a discovery that science can trust.

The age of abundant ideas

For centuries, generating new scientific ideas was one of the most time-consuming parts of research. Over the past decades, major discoveries have become harder to achieve as scientific problems have grown more complex and interconnected.

This is what makes AI so exciting for science: it changes the speed at which researchers can move from data to possible explanations.

Machine learning systems can scan vast datasets for patterns that humans might miss, while large language models (LLMs) can help connect those patterns to possible explanations, making hypothesis generation dramatically faster.

That changes the economics of discovery. When machines can produce candidate explanations faster than researchers can test them, the bottleneck shifts from generating ideas to verifying and filtering them.

Science’s new bottleneck

In many areas of science, the pace of hypothesis generation has historically been constrained by human expertise, data collection and theory-building. AI weakens that constraint: it can propose many more candidates than existing verification systems, such as peer review, can comfortably absorb.

While the number of hypotheses has increased dramatically, verification remains comparatively slow, expensive and often dependent on human expertise. Laboratory experiments, clinical trials and peer review cannot easily be scaled at the same rate as AI-generated ideas. The result is a growing verification bottleneck.

“Generating ideas is no longer the primary constraint. Determining which ideas stand up to scrutiny is becoming the harder problem.”

Without effective verification, scientific progress risks becoming overwhelmed by plausible yet untested results. Some hypotheses may fit existing data while failing to hold up under further scrutiny. Others may appear convincing but remain disconnected from established scientific knowledge.

In other words, generating ideas is no longer the primary constraint. Determining which ideas stand up to scrutiny is becoming the harder problem.

History shows the cost of inadequate verification. Scientific claims that initially appeared convincing have later been overturned because measurements were flawed, assumptions proved incorrect, or results could not be replicated. Without rigorous verification, minor errors can compound, leading to years of wasted effort and resources.

Verification isn’t one-size-fits-all

Verification is not the same in every scientific field. In physics, it often relies on mathematical consistency and reproducible experiments. In biology, it may require laboratory testing and observation. In medicine, verification can involve clinical trials and statistical evidence.

There is no universal framework that can verify every scientific claim. What unites these disciplines is the principle that scientific knowledge advances only when ideas are tested against evidence. Although the methods differ, verification remains fundamentally driven by structured, iterative reasoning.

Trust is the new competitive advantage

AI is changing scientific discovery and may ultimately require us to rethink aspects of the traditional scientific method.

For much of human history, ideas were scarce and difficult to generate. Today, AI is making ideas abundant. The scarce resource is increasingly becoming the ability to determine which discoveries are robust enough to trust, invest in and build upon.

“In an age of abundant AI-generated ideas, trust becomes the scarce resource, and verification will be how science and business earn it.”

As AI continues to accelerate scientific discovery, the greatest opportunities may lie not in generating ever more hypotheses, but in developing better ways to verify them. That means building verification into AI-driven science itself through:

Automated checks wherever possible
Stronger links to established theory
Better benchmarks
Human oversight where scientific judgement matters

For businesses, universities and investors, the main lesson is not that AI makes scientists less important. It is that AI makes scientific judgement more valuable. As machines make it cheaper to generate hypotheses, the competitive advantage will shift to organisations that can test claims quickly, rigorously and carefully before building products, making investments or changing practice. In an age of abundant AI-generated ideas, trust becomes the scarce resource, and verification will be how science and business earn it.

Discover insights by Dr Cory-Wright

Read 'The need for verification in artificial intelligence-driven scientific discovery'.

Artificial Intelligence

Data

Digital Transformation

Innovation

Technology

Digital transformation

Knowledge Categories

Technology

Academic area

Analytics & Operations

AI is making scientific ideas abundant. The hard part is knowing which ones to trust.

4 minute read

Off

"No jobs are safe from AI, but some are safer than others": Professor of Practice David Shrier on the future of work

David Shrier — Wed, 15 Jan 2025 12:14:02 +0000

"No jobs are safe from AI, but some are safer than others": Professor of Practice David Shrier on the future of work

David Shrier Wed, 15/01/2025 - 12:14

More blogs

17 July 2026 Knowledge

Science’s biggest AI challenge isn’t discovery – it’s proof

15 July 2026 Knowledge

How to survive as an entrepreneur: insights from Steve Blank

6 July 2026 Knowledge

What business leaders can learn from the erosion of democracy

11 May 2026

The rapid advancement of artificial intelligence (AI) has left the workplace in flux. Many of those already in the workforce fear for the future of their roles, while those about to enter it are told they need to be skilled at using the latest technology to make themselves attractive to employers.

Amid all this career anxiety, research shows that while 85 million jobs will be displaced by 2025, 97 million new roles will simultaneously emerge, representing a net positive job creation of 12 million positions globally.

Across the entire economy, generative AI will have a profound impact, leveraging 32 per cent of each job’s function on average to boost productivity.

So, what do business school ��ý need to know when considering their future careers, and how do they ensure they're ready for the workplace transformation already underway? Here, Imperial Business School's Professor of Practice, AI & Innovation, David Shrier reveals all.

The sectors that need a human touch

Unsurprisingly, safer sectors include those that require a talent for building relationships, as well as professions where you need to show empathy, judgement, communication and creativity. Or else, where some level of physical expertise is necessary.

Both hospitality and events are projected to grow globally in the next few years, driven in part by a reaction to the rise in technology and the desire for real life experiences.

As energy security looms large, the transition to clean energy and soaring energy demands from data centres will fuel a long-lasting need for qualified energy experts, technicians and engineers – particularly those who can work with systems to optimise the electricity grid and secure supply. Similarly, jobs in defence technologies will continue to expand amid turbulent geopolitics.

Meanwhile, there’s an enduring and worldwide dearth of cybersecurity skills, with nearly five million jobs unfilled at a global level. The complex and highly specialised nature of this field means these are hard posts to fill.

It's also worth considering manufacturing sectors, which are less exposed to AI disruption than professional services.

The jobs under threat

Work that requires high-level thinking and intelligence appears to be the most prone to upheaval thanks to AI, according to an analysis of some 800 occupations across 160 million jobs.

These are typically the professions that pay more than $100,000 a year, such as high value-added roles within professional services including law, finance and business consultancy.

As an example, logistics used to be a high paying, valued profession that required heavy cognitive lifting, but today it can be done more easily and cheaply by a computer. But while backroom functions can be easily automated, the hands-on labour of a builder or warehouse worker is often too complex to replace.

The big rewards for future business leaders

If businesses can deploy AI in the most effective way, the return on investment is huge – far higher than if they simply replace humans with AI, which is the worst way to use it.

Companies that can figure out how to augment employees’ performance using of AI tools could see a return on investment of up to 200 per cent. This requires often painful changes but avoids large scale layoffs.

There’s an even greater prize, and one we encourage ��ý to test within the safe confines of Imperial’s classrooms. This is working alongside AI as an equal to create something we call symbiotic intelligence, which can give a massive return on investment of up to 2,000 per cent.

If that sounds improbable, consider an example of a business founder who leveraged a host of AI agents and tools to create telehealth business Medvi, which generated around $400 million in its first year with just two employees. While questions have been raised about some of Medvi’s business practices, that shouldn’t distract from the proof of concept it offers around operating leverage on AI systems. We will see more of this. Rather than treating AI as a tool for automation or efficiency, the founder can use creativity and judgement to build a business around the emerging capabilities of the new technologies.

Can you really future proof your career?

Students can actively seek to be "AI ready": to be an employee who uses AI to improve their performance rather than replaced by it. If they can learn to work symbiotically with AI, they will dramatically outperform those who can’t. Just as a novelist requires language literacy, ��ý need AI literacy, but also the judgement to deploy it.

This comes from hands on experience. At Imperial we embed AI directly within the curriculum and we’ll encourage ��ý to experiment, by building an AI native company, for example.

University gives ��ý the freedom and space to experiment beyond the glare of performance reviews. Much has been made of the high failure rate of AI pilots within businesses – up to 95 per cent, according to one report. In my view, that’s not high enough. You need a reframe: this is innovation exploration, not "failure". Thomas Edison famously said he’d never failed but had instead discovered "10,000 ways that didn’t work" before successfully creating a lightbulb.

Tips for success

Efforts to build your innovation skills will never be wasted. Even if you’re not starting a business, you’ll need to reskill and reorient yourself rapidly to stay current.
Purposefully lean in to the skills that distinguish our humanity such as emotional intelligence, teamwork, empathy – the kind of skills AI doesn’t do well.
Experiment with AI tools to find more than productivity gains.
Build your fundamental sales skills. Neglected from many top tier MBA programmes, these are offered at Imperial. Being able to sell even in a tough market will always add value.

At Imperial, we embrace rather than dismiss AI, teaching ��ý the skills necessary to navigate the turbulent contemporary workplace.

Subscribe to IB Knowledge

Artificial Intelligence

Jobs and Labour

Technology

Knowledge Categories

Technology

Academic area

Analytics & Operations

What business school ��ý need to know about choosing a career in the AI workplace

Main image: pixdeluxe / iStock via Getty Images

5 minute read

Off

Why business leaders should force employees to use AI

Jiankun Sun — Wed, 15 Jan 2025 12:14:02 +0000

Why business leaders should force employees to use AI

Jiankun Sun Wed, 15/01/2025 - 12:14

More blogs

17 July 2026 Knowledge

Science’s biggest AI challenge isn’t discovery – it’s proof

15 July 2026 Knowledge

How to survive as an entrepreneur: insights from Steve Blank

6 July 2026 Knowledge

What business leaders can learn from the erosion of democracy

27 April 2026

Article at a glance:

Forcing staff to use AI helps them overcome instinctive mistrust of the technology, finds Dr Jiankun Sun in new research from ��ý.
When staff are forced to use AI for a short trial period, they tend to continue using it even after the trial ends.
Learning is key to sustained use of AI among employees, as it allows them to develop a comprehensive, unbiased understanding of its performance.

Artificial intelligence (AI) is sweeping through the commercial world, with 88% of companies reporting regular use in at least one function. This is, though, a wave of enthusiasm largely driven by business leaders. Among employees there remains significant scepticism and a lack of enthusiasm about adopting AI.

However, this is something of a chicken-and-egg problem. AI’s impact on productivity can only increase if the technology develops and improves. But this improvement relies on user-generated data through broad adoption and sustained usage, which employees are reluctant to embrace. So how can businesses and leaders encourage employees to make more use of AI and overcome what is known as ‘algorithm aversion’?

Negative bias

Our research explores this question, in particular the potential benefits of mandating the use of AI – in other words, forcing employees to use AI tools in order to help them understand their benefits, learn how to use them productively, and embed continued use to help AI improve. To test this, we worked with an online education company using an AI algorithm to help salespeople select candidates for leads (i.e. matching teachers with ��ý), aiming to maximise post-trial conversion.

Prior to our experiment the company found that the algorithm, despite being designed to maximise sales conversion rates, was underutilised by staff. They tended to use it for low-stakes leads that were less likely to be converted, trusting their tried-and-tested manual processes for higher-importance leads.

We found that a key driver for this was employees’ inherent negative bias against AI, especially at the early stage when the technology is newly introduced to them and they lack knowledge about its capabilities. This creates a vicious cycle, which often goes unnoticed in practice, where negativity means AI is used selectively for low-importance work, which makes its performance appear poor, which reinforces the negative beliefs. This is at the heart of why algorithm aversion persists and deepens over time.

Forcing and learning

We found that forcing workers to use the AI tool increased their post-experiment use of it by almost 16% compared to the control group – a significant rise. What’s more, this held true across staff members with different pre-experiment usage levels, and did not drop over time.

We also observed that among those who were forced to use the AI tool, the ones that experienced a greater uptick in sales conversions during the experiment tended to be more likely to use it going forward, which was not the case in the control group. After the experiment, those in the forced group were also more likely to use AI for high-quality leads compared to beforehand.

Why did this happen? We found that learning is a key mechanism to break the vicious cycle. Forcing workers to use AI across all types of leads allows them to develop a more comprehensive and less biased understanding of its performance. They then realise that rather than performing worse than manual selection, AI performance is actually comparable or possibly even superior. This builds trust and encourages employees to continue using AI voluntarily even after the forced trial period is over.

Lessons for leaders

Our findings suggest that staff tend to have a negative bias against using AI tools from a cold start, as they lack faith in their capabilities, and are more likely to use them for low-stakes work. Forcing staff to make use of AI tools for more important, high-stakes tasks can help to overcome this bias.

In the long term, this can help to move from a vicious cycle of negativity to a positive loop through data network effects. Broader adoption generates more diverse usage data, which enables AI to improve, which encourages further adoption. This is important because AI requires usage data to evolve, and – as AI is capable of improving at a pace faster than humans – this creates the possibility of genuinely AI-integrated organisations.

In turn, this is important for leaders concerned about the return on investment in AI technology. Sinking funds into functionality that nobody takes advantage of is a nightmare scenario for businesses. Understanding that a forced-use trial period can start a sector on the road to high-level AI integration removes this problem and opens up a future of ever-increasing efficiencies through improved AI tools.

Read insights from Dr Sun's paper

Discover her latest findings

Artificial Intelligence

Jobs and Labour

Marketing

Knowledge Categories

Strategy & Leadership

Technology

Academic area

Analytics & Operations

Employees may be willing to use AI for everyday tasks, but many are resistant to adopting it for important work when there is more to lose. New research shows a short period of forced use can help overcome this reticence

Main image: FG Trade / iStock via Getty Images

4 minute read

Off

Professor Christopher Tucci on AI, innovation and the next generation of learners

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

Professor Christopher Tucci on AI, innovation and the next generation of learners

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

More blogs

17 July 2026 Knowledge

Science’s biggest AI challenge isn’t discovery – it’s proof

15 July 2026 Knowledge

How to survive as an entrepreneur: insights from Steve Blank

6 July 2026 Knowledge

What business leaders can learn from the erosion of democracy

26 August 2025

Professor Christopher Tucci recently took on the role of Interim Vice-Dean (Education) at Imperial Business School. This will see him focus on pedagogical development and innovation, including leading on how the business school adapts its offer in an increasingly AI-driven world. We spoke to Professor Tucci to find out more about what this involves, and how his experience feeds into the role.

What does your new role as Interim Vice-Dean (Education) involve?

Broadly, the role involves responsibility for all educational programmes at the Business School, including our 18 master’s programmes, four PhD programmes, and current and upcoming bachelor’s programmes.

This includes thinking strategically about the whole portfolio of educational activities to deliver a great experience for ��ý, faculty, and programme managers. For example, we position courses into suites—the MBA suite, the finance suite, the management suite, and the specialised suite. And we’re constantly evaluating how much overlap there is between and within them, and whether our groupings match changes in the real business world.

That requires us to understand changing employer needs, evolving student interests, and shifts in cohort demographics. I know from my experience working on Imperial’s I-X initiative that there’s a rapidly growing interest in digital technologies, especially AI. Employers want people who understand these technologies and the ability to use them, not just as tools to boost productivity, but on a strategic level.

Academics are being squeezed on a number of fronts, so we’re working out how we can automate some of the back-office tasks such as organisation of marking, allocating teaching assistants, and tracking project supervision requests. These are often time-consuming, manual processes that could be simplified, giving academics more time to focus on face-to-face teaching, student support, and research.

I am a big proponent of active learning and project-based learning (for our ��ý) – learning to deal with the ambiguities of what you’re exposed to and trying to solve problems as they come up.

What experience will you bring to the role?

I recently worked at NEOM University (in Saudi Arabia), helping to develop the curriculum for a brand-new institution that hasn’t even opened its doors yet. That gave me the freedom to focus on cutting-edge business needs, thinking about the skills and experience employers want from graduates now and in the future.

Things are very different at an established institution that already has a strong reputation, but the experience gave me valuable insight into some of the less tangible things that are important in the modern business world – soft skills, curiosity, creative mindsets, problem-solving abilities.

I also have industry experience in computer science research, which fuelled my interest in R&D management and innovation management. I think that gives me a lot of empathy for employers, how they think, how they work, and what they want from graduates.

How are you future-proofing ��ý for a world where AI is rendering some jobs obsolete?

We’ve always had job replacement, and we always will – preparing ��ý for this has always been part of our responsibility, and it’s part of my role now. For example, in response to current trends such as AI, we need to ensure that we impart critical thinking skills: how to evaluate and question AI output rather than just relying on it, so it’s a tool ��ý can use to their advantage, rather than something that rivals them.

Along the same lines, we can do more to help ��ý understand the weaknesses of these technologies, not just the strengths, so they have a rounded, critical view. This is important because in business, if you’re managing a portfolio of products and services, and you’re trying to come up with something new that would resonate with your target market, that’s not something that can be easily delegated to AI. Computers aren’t very good at running companies!

Instead, I am a big proponent of active learning and project-based learning – learning to deal with the ambiguities of what you’re exposed to and trying to solve problems as they come up. This gives our ��ý rounded skill sets rather than learning just one way of doing something.

As long as we’re teaching ��ý in this way, helping them see the bigger picture, and we’re teaching them to think critically and understand the pros and cons of emerging digital technologies, then I think our graduates are in a strong position in terms of vulnerability to job replacement.

Sign up to our newsletter

Artificial Intelligence

Education and Learning

Management

Student Experience

Digital transformation

Knowledge Categories

Strategy & Leadership

Academic area

Management & Entrepreneurship

Interim Vice-Dean (Education) at Imperial Business School reveals how technology is driving a more personalised and impactful student experience.

Main image: Professor Christopher Tucci | ��ý

3 minute read

Off

How AI avatars are revolutionising the student experience at Imperial Business School

Nai Li — Wed, 15 Jan 2025 12:14:02 +0000

How AI avatars are revolutionising the student experience at Imperial Business School

Nai Li Wed, 15/01/2025 - 12:14

More blogs

17 July 2026 Knowledge

Science’s biggest AI challenge isn’t discovery – it’s proof

15 July 2026 Knowledge

How to survive as an entrepreneur: insights from Steve Blank

6 July 2026 Knowledge

What business leaders can learn from the erosion of democracy

18 August 2025

As GenAI continues to reshape the educational landscape, business schools are exploring new ways to deliver teaching that is both scalable and human-centred. One promising development is the use of AI-powered faculty avatars, digital twins, that can support student learning while preserving the relational core of education. At the crossroads of projected growth in the AI Avatar market and ethical debates, we are beginning to recognise their potential to enhance the learning experience.

In January 2024, the IDEA Lab at Imperial Business School launched a small-scale pilot project in collaboration with David Shrier, Professor of Practice (AI & Innovation), developing an AI avatar named Davatar as an experimental element of his AI Ventures module. The pilot was designed to give ��ý hands-on experience with applied AI in a business context. Feedback was overwhelmingly positive, with many ��ý identifying Davatar as the standout feature of the module.

Building on this success, in spring 2025, we extended the use of AI avatars to support teaching modules like marketing management, entrepreneurship and innovation, and organisational behaviour on the Global Online MBA and Executive MBA programmes, and piloted three more avatars, MarkBot, OmarBot, and BartBot, which were developed in collaboration with the Touchcast Mentor Platform and Dr Ayelet Segal.

These avatars were not intended to replace human educators, but to supplement and enhance faculty presence in online and blended learning environments. Initial feedback suggests they have the potential to transform the way we support ��ý across different time zones, schedules, and diverse learning needs.

Enhancing faculty presence through digital twins

One of the ongoing challenges in online learning is the reduced sense of human connection. As per an article in Theory of Transactional Distance, ��ý often feel isolated, especially when studying asynchronously or across different time zones, and their success on the programme can be impacted by the degree of “transactional distance” between themselves and their teachers. The AI avatars were created to address this by replicating the voice, tone, and visual likeness of real faculty members, effectively becoming “digital twins.”

This was significant. In feedback surveys, ��ý consistently reported that it felt more natural and reassuring to interact with an avatar that resembled their lecturer. As one student said, “It’s him, just in another form. It makes it feel more like he’s here with us.” This was especially important during periods of high stress, such as when approaching assignment deadlines, when ��ý often seek last-minute reassurance or clarification.

By embedding academic identity into the avatar experience, we were able to foster trust and sustain the continuity of the learning relationship. Our research in this area will continue to explore whether digital twins can contribute to a reduction in the perceived transactional distance for ��ý in a blended or fully online programme.

Supporting flexible and on-demand learning

For ��ý juggling work, family, and studies, often across different time zones, the traditional model of office hours and scheduled tutorials can be limiting. One of the main benefits of the avatars was their 24/7 availability. Students could engage with them whenever they needed support, whether during a lunch break in Singapore or late at night in London.

Usage data from March to June 2025 showed clear spikes in avatar engagement just before major assignment deadlines. Students used them not for casual conversation, but for structured academic queries, whether seeking help with frameworks, clarification of learning outcomes, or guidance on assessments. This on-demand support reduced barriers to learning and made academic support more equitable and responsive.

In 1984, educational psychologist Benjamin Bloom reported that ��ý tutored one-to-one performed two standard deviations better than peers taught only in a group environment. Our research may help us understand whether AI avatars might one day help us solve the problem of scaling personalised one-to-one tutoring to make it available on-demand for all ��ý.

Strategic implications for educators and institutions

The pilot raises broader questions about how AI can be integrated into teaching practice. First, any introduction of AI must begin with pedagogy, not technology. It is Imperial Business School’s belief that tools like avatars should be designed to enhance – never to weaken or replace - the thoughtful dialogue and interaction between student and teacher that underpins effective learning.

Secondly, the identity of the educator is important. Avatars that resemble real faculty members, both visually and vocally, help build stronger bonds with ��ý. These aren’t anonymous chatbots; they are extensions of the educator’s presence. This sense of familiarity encourages engagement and helps ��ý see AI as an ally, not a threat.

Third, professional development will be crucial. To ensure faculty feel confident using these tools, we must offer opportunities to improve both technical skills and pedagogical understanding. This isn’t just about learning to operate a new platform; it’s about rethinking how we design and deliver teaching in an AI-enhanced environment.

Where do we go from here?

This was an initial pilot phase involving three modules and a small group of early-adopting faculty. The next step is to expand the initiative more broadly, testing AI avatar use across various disciplines, delivery modes, and levels of AI familiarity. In parallel, we will continue to research student and academic attitudes towards the use of AI tools in education, and to explore the ethical and environmental aspects of this technology.

Ultimately, our goal is not to replace the human elements of teaching, but to explore the responsible use of AI to enhance them. Digital twins offer a way to extend faculty presence, provide responsive support, and foster more inclusive learning experiences. When used thoughtfully, they may help us reimagine what personalised education can be like in an increasingly digital world.

Moving beyond FAQs: designing for dialogue

Many AI tools in education often fall short because they are designed to deliver content passively, primarily answering frequently asked questions, and do little to support critical thinking or deeper understanding. Our aim was different. We wanted to explore whether AI avatars could play a meaningful pedagogical role by facilitating a more interactive and reflective learning process.

To achieve this, we designed the avatar conversations based on Laurillard’s Conversational Framework, a model that sees learning as a dialogue between teacher and student. Students weren’t simply given answers. Instead, the avatars guided them back to key programme concepts and helped them explore different interpretations and applications.

For example, a student might ask, “Is this a good example of emotional intelligence in the team task?” or “Should I apply Porter’s Five Forces to the group case or the individual one?” Rather than giving binary responses, the avatars encouraged the student to reflect and engage more deeply with the material.

Initial feedback [on the AI-powered faculty avatars] suggests they have the potential to transform the way we support ��ý across different time zones, schedules, and diverse learning needs.

Discover more

Artificial Intelligence

Digital Transformation

IDEA Lab

Innovation

Student Experience

Digital transformation

Knowledge Categories

Technology

Academic area

Analytics & Operations

These AI-powered digital twins are transforming learning across time zones and diverse needs

Main image: OmarBot | Imperial Business School

5 minute read

Off

Artificial Intelligence

Management & Entrepreneurship Conversations conference explores novelty in research

Management & Entrepreneurship Conversations conference explores novelty in research

Share

Renowned global academics came together at Imperial Business School for the latest Management & Entrepreneurship Conversations conference.

Book launch on Tencent and its rise to being a global tech giant

Book launch on Tencent and its rise to being a global tech giant

Exploring new frontiers at the Africa Innovates Conference 2026

Business School academic nominated for second term on UK's fiscal watchdog

Celebrating the Dean’s Community Award winners

Subscribe for research and events from the Business School

Share

Imperial Business School welcomed business leaders, academics and alumni for the launch of a new book offering a rare insight into how Chinese technology giant Tencent became one of the world's most innovative companies.

Listen: The Standard’s Tech and Science Daily podcast interviews Dr Mark Kennedy about Chinese AI company DeepSeek

Share

ITV News interviews David Shrier about DeepSeek’s emerging dominance in the technology market

Share

Science’s biggest AI challenge isn’t discovery – it’s proof

Science’s biggest AI challenge isn’t discovery – it’s proof

More blogs

Science’s biggest AI challenge isn’t discovery – it’s proof

How to survive as an entrepreneur: insights from Steve Blank

What business leaders can learn from the erosion of democracy

Article at a glance

AI can now generate scientific hypotheses faster than traditional research systems can test them

Scientific progress still depends on verification: testing whether promising claims hold up against evidence, theory and domain expertise

AI-driven science will deliver on its promise only if we can test ideas as quickly and rigorously as we generate them

Ideas need proof

The age of abundant ideas

Science’s new bottleneck

Verification isn’t one-size-fits-all

Trust is the new competitive advantage

Discover insights by Dr Cory-Wright

Share

AI is making scientific ideas abundant. The hard part is knowing which ones to trust.

"No jobs are safe from AI, but some are safer than others": Professor of Practice David Shrier on the future of work

"No jobs are safe from AI, but some are safer than others": Professor of Practice David Shrier on the future of work

More blogs

Science’s biggest AI challenge isn’t discovery – it’s proof

How to survive as an entrepreneur: insights from Steve Blank

What business leaders can learn from the erosion of democracy

The sectors that need a human touch

The jobs under threat

The big rewards for future business leaders

Can you really future proof your career?

Tips for success

Subscribe to IB Knowledge

Share

What business school ������ý need to know about choosing a career in the AI workplace

Why business leaders should force employees to use AI

Why business leaders should force employees to use AI

More blogs

Science’s biggest AI challenge isn’t discovery – it’s proof

How to survive as an entrepreneur: insights from Steve Blank

What business leaders can learn from the erosion of democracy

Article at a glance:

Forcing staff to use AI helps them overcome instinctive mistrust of the technology, finds Dr Jiankun Sun in new research from ������ý.

When staff are forced to use AI for a short trial period, they tend to continue using it even after the trial ends.

Learning is key to sustained use of AI among employees, as it allows them to develop a comprehensive, unbiased understanding of its performance.

Negative bias

Forcing and learning

Lessons for leaders

Read insights from Dr Sun's paper

Share

Employees may be willing to use AI for everyday tasks, but many are resistant to adopting it for important work when there is more to lose. New research shows a short period of forced use can help overcome this reticence

Top 10 Business School articles that you should read before the year ends

Top 10 Business School articles that you should read before the year ends

More blogs

Science’s biggest AI challenge isn’t discovery – it’s proof

How to survive as an entrepreneur: insights from Steve Blank

What business leaders can learn from the erosion of democracy

Explore articles by Imperial Business School's academics

Share

From sustainable consumption to innovation in the biopharma sector, here are 10 of the most-read IB Knowledge think pieces that shaped our understanding of globally relevant research from Imperial this year

Professor Christopher Tucci on AI, innovation and the next generation of learners

Professor Christopher Tucci on AI, innovation and the next generation of learners

More blogs

Science’s biggest AI challenge isn’t discovery – it’s proof

How to survive as an entrepreneur: insights from Steve Blank

What business leaders can learn from the erosion of democracy

What business school ��ý need to know about choosing a career in the AI workplace

Forcing staff to use AI helps them overcome instinctive mistrust of the technology, finds Dr Jiankun Sun in new research from ��ý.

How are you future-proofing ��ý for a world where AI is rendering some jobs obsolete?