Using AI as a force for management innovation

In September, Leeds University Business School hosted a session on The Business of AI as part of Leeds Digital Festival 2024.

AI technologies are reshaping industries, disrupting traditional business models, and creating new opportunities. Organisations that fail to adapt, risk falling behind.

Companies are increasingly integrating AI into everyday practice to gain a competitive edge, so understanding its implications is crucial for survival and growth. As part of the event, we heard from a panel of Leeds University Business School academics who discussed how organisations can take advantage of this technology, radically innovating outdated management systems by using AI as a tool for optimal results.

Here are some key ideas from our panel of experts who spoke on the topic of using AI as a force for management innovation at the event:

Chee Yew Wong, Professor of Supply Chain Management: “AI is being increasingly integrated into supply chain applications, from social media analysis to high-tech marketing. One type of AI, known as machine learning, is being used as a predictive analytics tools to improve demand forecasts, gain insights into customers’ buying behaviours, and optimize inventory planning. Additionally, some start-ups are developing AI-driven solutions to identify risks across multi-tier supply chains. 

“However, many of these AI algorithms face challenges because supply chains often lack complete data, and there is sometimes reluctance among stakeholders to share information due to competitive concerns. Until supply chains become fully digitalized and more transparent, the potential for AI in supply chain management will remain limited.”

Dr Josh Morton, Associate Professor of Strategy and Technology: “While AI isn't yet a fully-fledged strategist, it is certainly bringing new capabilities that have potential to influence strategy processes in a number of ways. Drawing on my own conversations with industry experts and reflecting on recent commentaries by organisations such as McKinsey and Co, I see two notable applications: 1. how AI is freeing up time for strategists by automating tasks like data collection and environmental analysis, and 2. How AI is acting as an additional ‘voice’ in strategic discussions, providing divergent thinking that can inform both day-to-day activities and longer-term thinking, such as scenario planning.

“However, while AI offers valuable input and can support strategy, it is not infallible and is not yet ready to take on serious authority and make final decisions. Human judgment remains crucial to validate AI outputs and to discern what is feasible in strategy. Strategists therefore have a key role in orchestrating the interaction between AI systems and human teams. Ultimately, for now AI serves as an accelerator of tasks and a sounding board, rather than a predictor or ‘creator’ of future strategies, providing food for thought rather than definitive answers.”

Dr Nabi Omidvar, Associate Professor of Artificial Intelligence in Finance: “In the financial services sector, UK regulators have taken a balanced approach to AI, encouraging innovation while being mindful of the associated risks. Interestingly, financial institutions often seem more cautious than their regulators when it comes to adopting AI.

“While public fascination is largely focused on the impressive capabilities of generative AI (AI used to create new content), advanced non-generative AI techniques (those used to mainly classify or predict information based on existing data which can be used, for example, to enhance operational efficiency, improve forecasting, or refine risk assessment) are often overlooked. Ironically, these non-generative AI methods are fundamental to the success of generative AI itself.

“This hesitation to adopt non-generative AI stems partly from concerns about transparency and explainability (i.e. the ability to understand and justify how AI models make decisions, which is essential for trust, accountability, and compliance). Because of this, financial institutions tend to favour linear predictive models, which are not considered AI but are seen as easier to understand and justify.

“However, these models are less effective in handling complex data and can lead to issues such as higher false positives in credit scoring, where individuals may unfairly be labelled as high-risk. While explainability in AI is important, focusing too narrowly on it could stifle innovation and result in poorer outcomes.”

Krsto Pandza, Chee Yew Wong, Aristeidis Theotokis, Josh Morton, Nabi Omidvar and Shahla Ghobadi presenting at the Business of AI event.

Aristeidis Theotokis, Professor of Marketing: “In marketing, understanding human acceptance of AI is central, as it’s clear that AI occupies a unique, paradoxical position. It’s often too human-like for a technology yet still too technological for a human collaborator. This duality makes AI challenging to fully integrate into organizations without careful consideration of its role.

“Unlike traditional tools, AI’s autonomous intelligence enables it to make decisions and interact with consumers and employees in ways that blur the lines between machine and social actor. AI is increasingly used in customer service, managing complaints with empathy and efficiency, while also acting as a personal shopping assistant, recommending products based on individual preferences and past purchases. As a result, AI must be treated not just as a functional tool but as a social entity—one that can influence perceptions, relationships, and trust within the consumer landscape.

“Our research focuses on the factors shaping AI acceptance, from how it’s framed and marketed to individual traits like trust and technological openness. Furthermore, contextual elements such as cultural attitudes and industry standards shape responses to AI, as do specific AI characteristics, including its transparency, functionality, and perceived intelligence. By treating AI thoughtfully in marketing, we can better align its integration with consumer expectations and employee dynamics, ultimately enhancing its acceptance and effectiveness across domains.”

Krsto Pandza, Professor of Strategy and Innovation, chaired the session and concludes with the following remarks: “Business schools are uniquely equipped to engage with one of the most important questions related to AI developments - how can AI influence the way companies compete, and structure their operations to be more efficient and innovative?

“Researchers here at Leeds University Business School are investigating which industries are most susceptible to AI-driven disruption and how leading companies in these sectors are adapting their competitive strategies and business processes. The emerging insights suggest that different sectors experience different levels of disruption, and innovation ranges from incremental improvements in efficiency to more radical changes in business models.

“Our research also contributes to the discussion on whether AI augments human capabilities or substitutes them.  The research to date suggests we will certainly see some job replacement, but the most creative activities will require collaboration between humans and AI.”  

For more information about our research on AI, visit Insights on Digital Innovation.