Using industry insight to shape University strategy

Have you ever wondered whether AI is a more a tech issue or a social one? So did we. This case study explores how the University of Birmingham, UK drew on industry insight to shape its AI teaching and research strategy, and the resulting success stories.

James Sharp
The University of Birmingham

Jack Tasker
The University of Birmingham

Background and Objectives
This presentation explores how Business Engagement (BE) at the University of Birmingham, UK helped to spearhead the organisation’s teaching and research strategy around artificial intelligence (AI) and disruptive technologies. The presentation comes at an important time when the social impact of new technologies are of ever increasing importance to industry, academia and society at large on a global scale. We focus particularly on insight gained through interaction with industry and how this helped shape a strategy that emphasised the social aspects of digital change in parallel with technological considerations. By interweaving industry cues to our approach, we were able to rapidly deploy our professional services team across multiple academic departments to establish interdisciplinary networks that have ultimately led to a series of successful research projects either funded by, or partnered with, business.

The objective of the session will be twofold: 1) to highlight the role social sciences plays in AI research and why this matters to business, and 2) to demonstrate how we used industry insight to steer our AI teaching and research strategy. Business professionals and University representatives will gain insight into the important role social sciences plays in the continuing discourse concerning technological advancement. In particular, businesses will gain first-hand insight into the ways our research is helping organisations embed new policies that consider the ethics of new technologies, and how to ensure (workplace) wellbeing at a time of unprecedented technological disruption. We will also give advice to university professionals on how to establish interdisciplinary networks in fields that had historically seen little overlap, and describe how our team’s structure and agile approach facilitated a streamlined process that continues to engage both industry and academic stakeholders.

The rapid technological advances of recent years dominate much of today’s industry and academic discourse. Firms large and small are investing more and more capital in systems, R&D and expertise to keep up with shifting market forces. But as AI and linked technologies become increasingly embedded in public life, the focus is shifting from technological breakthroughs to the social and ethical impacts new technologies are having.

The fundamental questions still prevail: how can organisations seize the power of things like AI, the Internet of Things, robotics, and additive manufacturing to carve out competitive advantage? How do companies discern the overhyped tech from the overlooked? How best to keep pace with the speed of disruption? How can organisations effectively plug skills gaps at the same pace with which change is occuring? But alongside this, the public discourse is increasingly concerned with issues such as what our jobs look like in the future, and will I be replaced by a robot? How can I gain the necessary digital skills beyond full-time education? What does ethics mean in the context of AI? And how can I safeguard my personal data from those who would manipulate it for nefarious purposes?

It is of prime importance that businesses invest smartly in new technologies, but this must be balanced with a clear commitment to ethical, human-focused practices. As witnessed in the past few years, businesses who fail to stay on the right side of these ethical boundaries can expect to pay a high price – financially, reputationally, or both. But then, who decides where these ethical boundaries lie? Because as yet, no clear legal or ethical frameworks exist concerning the use of AI in business.