AI, Work and Organisations

 

 

Artificial intelligence is fundamentally reshaping how we work, how organisations operate, and how citizens interact with employers, government services, and civic institutions. With 75% of workers now using AI in their roles and UK government projections indicating 10 million workers will have AI integrated into their jobs by 2035, the transformation is already underway. Yet critical questions remain unanswered: How can workers maintain meaningful agency in workplaces that are increasingly managed by algorithms? What new skills will people need, and how do we prevent widening inequalities? How should the hidden labour sustaining AI systems be valued and protected? When AI systems make consequential decisions about hiring, performance, or service delivery, how do we ensure fairness, accountability, and respect for human dignity? 

Your research could explore these urgent challenges at the intersection of AI, work, and organisational life. Potential approaches might draw on participatory design methods to centre worker voices in AI development, ethnographic studies to understand lived experiences of algorithmic management, or design research to create tools that enhance rather than undermine human autonomy. You might investigate how different regulatory frameworks affect workplace AI adoption, design contestability mechanisms for algorithmic decision-making, or examine the long-term impacts of AI on skills, careers, and professional identity across diverse sectors and communities.

Place-based Context 

The North East presents distinctive opportunities for researching AI's impact on work and organisations. The region's diverse economic landscape spans traditional industries undergoing digital transformation (manufacturing, logistics, public utilities), growing tech sectors, and extensive public services including NHS trusts, local authorities, and emergency services. Regional initiatives such as the North East Local Enterprise Partnership's digital strategy and the North of Tyne Combined Authority's innovation programmes are actively exploring AI adoption whilst grappling with skills gaps and the need for inclusive transformation.

Your research could engage with organisations facing real challenges: how might Newcastle City Council deploy AI in citizen services whilst maintaining trust and accountability? How could manufacturing employers in the region's industrial base implement AI to enhance rather than displace worker capabilities? What approaches might support gig economy workers in the North East to exercise greater agency over algorithmic management systems? The region's strong tradition of social partnership and its diverse communities, from urban Newcastle to post-industrial towns, offer rich contexts for investigating how AI can serve workers and citizens equitably.

Relevant Partner Organisations

Potential collaborations span public sector organisations navigating AI adoption in citizen-facing services (Newcastle City Council, North of Tyne Combined Authority, NHS North East and North Cumbria, Northumbria Police, Department for Work and Pensions), technology organisations developing workplace AI (Nokia Bell Labs, Google, Thoughtworks, Yoti), policy and regulatory bodies shaping AI governance (Ofcom, Cabinet Office, National Cyber Security Centre, DSIT), and civic organisations supporting workers and communities (VONNE, Demos, Sunderland Software City). These partnerships could provide access to real organisational contexts, enable co-design with workers and managers, support policy-relevant research, and ensure findings reach practitioners facing immediate decisions about workplace AI implementation.

Related Articles and Reading 

• Algorithmic Management and Worker Agency: Zhang et al. (2022) on algorithmic management reimagined for workers; Zhang et al. (2023) on stakeholder-centred AI design and co-designing worker tools with gig workers; Do et al. (2024) on designing gig worker sousveillance tools; Wood et al. (2019) on autonomy and algorithmic control in the global gig economy. 
• Fairness, Bias and Justice in Workplace AI: Park et al. (2022) on designing fair AI in human resource management; Yurrita et al. (2023) on disentangling fairness perceptions in algorithmic decision-making; Lee et al. (2021) on human-AI interaction in HR and employee resistance to algorithmic evaluation; Sloane et al. (2023) on bias and fairness concerns of artificial intelligence as a service. 
• Generative AI and Knowledge Work Transformation: Woodruff et al. (2024) on how knowledge workers think generative AI will transform their industries; Weisz et al. (2024) on design principles for generative AI applications; He et al. (2024) on AI and the future of collaborative work; Drosos et al. (2024) on understanding generative AI-assisted data analysis workflows. 
• Hidden Labour and Data Work: Fox et al. (2023) on the hidden human labor of AI integration within essential work; Sambasivan et al. (2021) on data cascades in high-stakes AI; Wang et al. (2022) on aspiration in data annotation; Gray and Suri (2019) on ghost work and the new global underclass. 
• Workplace Surveillance and Worker Wellbeing: Roemmich et al. (2023) on emotion AI at work and implications for workplace surveillance and emotional privacy; Constantinides et al. (2024) on sensible and sensitive AI for worker wellbeing. 
• Explainability and Transparency: Liao et al. (2020) on questioning the AI and informing design practices for explainable AI user experiences; Ehsan et al. (2021) on expanding explainability towards social transparency in AI systems. 
• UK Government Policy and Strategy: UK Government AI Opportunities Action Plan (2025); UK Government Pro-Innovation Approach to AI Regulation Government Response (2023); UK National AI Strategy (2021). 
• European Policy and Regulation: European Parliament and Council Regulation on Artificial Intelligence, AI Act (2024); McKinsey Global Institute (2024) on the race to deploy AI and raise skills in Europe and beyond. 
• International Policy and Labour Market Analysis: OECD (2023) on AI and the labour market policy considerations; International Labour Organization (2021) on the role of digital labour platforms in transforming the world of work. 
• Industry Research and Analysis: McKinsey and Company (2025) on the agentic organization; Microsoft Work Trend Index (2024) on AI at work. 
• Foundational Texts: Zuboff (2019) on surveillance capitalism; Eubanks (2018) on automating inequality; O'Neil (2016) on weapons of math destruction.