Artificial Intelligence

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

"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

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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 students need to know when considering their future careers, and how do they ensure they're ready for the workplace transformation already underway? Here, 91��ɫ 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 students to test within the safe confines of 91��ɫ’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, students need AI literacy, but also the judgement to deploy it.

This comes from hands on experience. At 91��ɫ we embed AI directly within the curriculum and we’ll encourage students to experiment, by building an AI native company, for example.

University gives students 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 91��ɫ. Being able to sell even in a tough market will always add value.

At 91��ɫ, we embrace rather than dismiss AI, teaching students the skills necessary to navigate the turbulent contemporary workplace.

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

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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

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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 91��ɫ.
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 students), 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.

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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

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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

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26 August 2025

Professor Christopher Tucci recently took on the role of Interim Vice-Dean (Education) at 91��ɫ 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 students, 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 91��ɫ’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 students) – 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 students for a world where AI is rendering some jobs obsolete?

We’ve always had job replacement, and we always will – preparing students 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 students can use to their advantage, rather than something that rivals them.

Along the same lines, we can do more to help students 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 students rounded skill sets rather than learning just one way of doing something.

As long as we’re teaching students 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.

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Interim Vice-Dean (Education) at 91��ɫ Business School reveals how technology is driving a more personalised and impactful student experience.

Main image: Professor Christopher Tucci | 91��ɫ

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How AI avatars are revolutionising the student experience at 91��ɫ Business School

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

How AI avatars are revolutionising the student experience at 91��ɫ Business School

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

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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 91��ɫ 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 students hands-on experience with applied AI in a business context. Feedback was overwhelmingly positive, with many students 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 students 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, students 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, students 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 students 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 students in a blended or fully online programme.

Supporting flexible and on-demand learning

For students 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 students 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 students.

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 91��ɫ 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 students. These aren’t anonymous chatbots; they are extensions of the educator’s presence. This sense of familiarity encourages engagement and helps students 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 students across different time zones, schedules, and diverse learning needs.

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

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How AI can guide educators in embedding sustainability into curriculum

Esther Canonico-Martin — Wed, 15 Jan 2025 12:14:02 +0000

How AI can guide educators in embedding sustainability into curriculum

Esther Canonic… Wed, 15/01/2025 - 12:14

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9 June 2025

As educators and as a university, we have an overarching responsibility to equip students to become responsible members of society, who can contribute to a healthier, more inclusive world. Achieving this requires us to embed sustainability meaningfully into our learning and teaching.

However, academics often face significant barriers to achieving this, including time pressures, limited guidance on how to go about it, uncertainty about how sustainability relates to their discipline, or a narrow understanding of what sustainability entails.

And it’s important to note, in relation to this last point, that the United Nations Sustainable Development Goals (UN SDGs) make it clear that sustainability is a broad and interconnected concept. It extends beyond climate and biodiversity to include quality education, gender equality, reduction of inequalities, and more.

Beyond box-ticking: creating ‘aha’ moments

So, how can we truly integrate sustainability into learning and teaching at 91��ɫ Business School? One starting point is through direct messaging – at student inductions or in programme introductions, for example – positioning sustainability as a core university value.

While useful, however, this approach can fall flat in a world saturated with sustainability messaging, where students may experience ‘sustainability fatigue’. For this reason, instead of focusing on overt principles, we may actually be able to achieve greater impact by guiding students towards making their own realisations – those ‘aha’ moments where a deeper understanding of sustainability clicks into place through the learning experience itself.

For example, in my own teaching, I ask management students to complete personality tests and explore different conflict management styles. These activities expose students to a range of valid approaches to work, helping them appreciate perspectives different from their own.

This kind of experiential learning fosters a deeper understanding of the value of individual differences in the workplace, highlighting how inclusion can enhance performance. It is an essential step towards reducing inequalities and promoting inclusion, which is directly related to sustainability.

To help educators to work in this way, however, we need to link sustainability with our learning outcomes. UNESCO offers a framework that maps the UN SDGs to learning outcomes, suggesting how to integrate them across programmes. But, even with this support, embedding a holistic approach to sustainability takes time and effort, particularly at the outset.

A framework for deeper integration

This is where generative AI tools can be useful, enabling academics to map and audit programme content for alignment with the UN SDGs. That said, generative AI has its limits. It can only interpret what's explicitly stated in the content, often missing subtler connections.

In the teaching situation I discussed earlier, for example, activities such as completing personality assessments and exploring different conflict management styles can help students develop awareness of individual differences, which is a crucial step towards inclusion and reducing inequalities. However, AI platforms would likely not flag them as sustainability-related, in spite of the fact that they absolutely are, aligning directly with UN SDGs focused on equality and inclusion.

To address this, as the UNESCO framework suggests, we should consider learning outcomes at three levels: cognitive, socio-emotional and behavioural. In other words, students must first understand sustainability concepts, then connect with them personally, and ultimately apply them in practice.

In my own practice, I still use generative AI platforms as a support tool. I begin by identifying key areas of my module with shared learning goals. Then, I ask the AI to suggest relevant UN SDGs and map them to learning outcomes across the cognitive, emotional and behavioural domains. This produces a more accurate, nuanced audit of sustainability in my teaching, helping to reinforce a broader, more integrated view of what sustainability really means.

This approach is, hopefully, a first step toward a future where sustainability is an intrinsic part of all teaching at 91��ɫ Business School, supported by technology and grounded in deeper understanding. It may require incentives, such as recognition for good practice, but momentum is already growing towards making sustainability a natural part of how we teach and learn.

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The first step in integrating UN SDGs in education at 91��ɫ Business School

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AI-powered rental fashion: Smarter recommendations for a sustainable future

Gah-Yi Ban — Wed, 15 Jan 2025 12:14:02 +0000

AI-powered rental fashion: Smarter recommendations for a sustainable future

Gah-Yi Ban Wed, 15/01/2025 - 12:14

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6 May 2025

Fashion is one of the most polluting industries in modern times. It produces about 10 per cent of the UK's annual carbon footprint – more than all international flights and maritime shipping combined. 91��ɫ from Climatetrade, 2023, shows it also consumes vast amounts of water and generates millions of tons of waste. According to Fashion Transparency Index 2023, the constant demand for cheap clothing has made the industry a "key driver of human rights abuses". One of the largest study into clothing habits ever undertaken by climate action NGO WRAP shows the UK's wardrobes hold 1.6 billion items of unworn clothes.

To address some of these challenges, a new business model of subscription rental fashion has sprung up in recent years. Established brands like LK Bennett and Ann Taylor, along with startups such as Hurr, My Wardrobe HQ, nuuly, and Armoire, now offer rental services that allow multiple customers to “share” a much larger wardrobe, reducing the need for constant new purchases. These companies operate on a monthly subscription model, enabling members to rent and swap garments within each period. Returned garments are inspected, cleaned, and recirculated, ensuring efficient inventory use. The rental fashion business model has potential for reducing waste, lowering emissions, and extending the lifecycle of clothing, all the while keeping customers stylish and satisfied.

The challenge of keeping customers engaged

As operations researchers, my colleagues and I, together with a PhD student, Jiannan Xu, were drawn to rental fashion because of the operational complexity it presents. We partnered with a rental fashion startup and analysed its customer data. One issue stood out from the analysis: customers were not staying on the platform long enough. Many left after the initial discounted trial period, citing dissatisfaction with the style or fit of their rented items.

To improve retention and enhance the customer experience, we developed a novel approach to optimising personalised recommendations for rental fashion businesses. Our work focused on two key challenges:

Encouraging customers to subscribe – many companies offer discounted trials, yet potential subscribers hesitate because they’re unsure whether they’ll find styles they like.
Retaining customers beyond the trial period – a large percentage of cancellations stem from product fit uncertainty. Customers may select items based on images and descriptions, only to find that they don’t meet expectations in person. This mismatch leads to frustration, increased returns, and lower renewal rates.

Smarter assortment optimisation with AI

To tackle these issues, we developed an AI-powered algorithm that improves how rental companies curate product recommendations. Instead of relying on generic recommendation engines or brute-force optimisation, our model develops and optimises a stochastic decision model to account for the unique structure of rental fashion businesses. Specifically, it considers:

Initial selection uncertainty – predicting which items are most likely to encourage customers to subscribe.
Renewal uncertainty – identifying which items will maximise customer retention after the first rental cycle.

By carefully balancing these factors and using smart mathematical approximations, we developed a fast, scalable, and personalized ranking algorithm—one of the simplest and quickest methods for optimising the assortment curation process. Essentially, our algorithm gives a personalised score to each garment, whereby the score weighs in the expected consumer surplus (“signal”) and the uncertainties regarding the customer’s initial and post-wear valuations (“noise”). It thus has parallels with finance and engineering applications where good decisions are made by balancing signal-to-noise ratios.

Alternative methods to our algorithm would be either too slow for real-time use, or ineffective as they do not optimise the business objective. In extensive numerical simulations, we show our algorithm ranks and refines product assortments in milliseconds, compared to minutes using brute-force optimisation. We further show that, even under stress-testing scenarios, our algorithm can achieve results within 6.5 per cent of the best possible profit outcomes, making it both effective and scalable.

Practical benefits for rental fashion companies

Beyond technical advancements, our research has real-world implications for the rental fashion industry. By ensuring that customers receive better-matched recommendations, businesses can reduce unnecessary returns and improve overall satisfaction. This leads to:

Fewer shipments and exchanges, reducing logistics costs.
Better use of inventory, maximising item circulation.
Higher customer retention rates, strengthening long-term profitability.

The future of AI in subscription-based rentals

Beyond rental fashion, the principles behind our AI-powered optimisation model extend to subscription-based rentals of experience goods in general. This includes electronics rentals, furniture subscriptions, and toy libraries, where selecting the right assortment is key to customer satisfaction and retention. By refining product recommendations, businesses can optimise customer engagement, reduce churn, and improve profitability.

Our research is among the first to explore assortment optimisation in this space, setting the stage for further studies in inventory and subscription management. While rental businesses may not solve all the environmental challenges, smarter, AI-powered recommendations can significantly enhance business viability and the overall customer experience—paving the way for a smarter, more efficient future in rental commerce.

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