Research AI Landscape

cmi-strategy-2026

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University of Sheffield: Centre for Machine Intelligence Strategy 2026

Source: Centre for Machine Intelligence — Strategy 2026 (brochure PDF, “Centre for Machine Intelligence Strategy 2026”) Published: 2026 Publisher: University of Sheffield, Centre for Machine Intelligence (CMI)

Overview

The Centre for Machine Intelligence (CMI) is one of the University of Sheffield’s Centres of Excellence, backed by a £3.6m investment. Its mission is to “forge AI research, education, innovation, and training, for the benefit of society,” bringing together hundreds of researchers across the University to pursue interdisciplinary, responsible AI research while addressing the global challenges and social harms associated with it.

This is Sheffield’s own institutional AI strategy — the local anchor against which sector-wide reports in this vault (ukcori-uk-institutional-ai-guidance-2026, ukri-ai-strategic-framework, unesco-hei-ai-survey-2025) are compared. It sits alongside, and above, Sheffield’s existing GenAI use-principles (uos-genai-principles-research, uos-genai-principles-productivity) as the whole-of-institution strategic frame those principles operate within.

The CMI’s cross-disciplinary strengths span six areas:

  • AI and Research Software Engineering
  • Development of open source AI software and resources
  • Human-Centric AI in society and culture, including ethics and inequalities
  • AI-Enabled Research, including Secure Data Environments
  • AI for Engineering and Manufacturing, including materials
  • AI and Robotics

The strategy is structured around the University’s four strategic pillars: Research, Innovation, Education, and One University.

Research

Goal: Drive up the quality of research outputs across all areas (University KPI 1) by facilitating access to cutting-edge AI technology, datasets, and skills appropriate to each discipline, while following UKRI’s Principles of Responsible and Trustworthy AI. Increase the number and success rate of large AI grant applications, including by early-career researchers (ECRs) (University KPI 2).

How: improving awareness of opportunities and cross-disciplinary connections; cementing Sheffield’s reputation as an AI-intensive institution; ensuring AI expertise and tools are capitalised on in grant applications; shaping funding calls through engagement with funding bodies.

Delivery plan:

  • Improve utilisation of the RSE AI team, and AI training generally, across institutional grant bids and research outputs
  • Improve awareness of, and access to, cutting-edge AI technologies and curated datasets — including Sheffield’s in-house technologies — by establishing an internally-facing “AI Sandbox”
  • Participate in collaborative research proposals at all scales, from seed funding to large-scale centres of excellence
  • Conduct cutting-edge interdisciplinary research into, and using, AI, across all disciplines
  • Embed humanities and social sciences perspectives and approaches in all AI research and policy activity

Innovation

Goal: Improve the breadth of the pipeline of AI-enabled and AI-enabling spinouts through the Commercialisation Journey. Increase engagement with policymakers, industry, and regional/national/international actors, resulting in collaborative research, knowledge exchange, policy improvements, and wider impact (University KPI 4).

Delivery plan:

  • Contribute materials and expertise on AI-enabled spinouts to Commercialisation Journey cohorts
  • Raise awareness of commercialisation opportunities across the University, from undergraduate to Professor
  • Lead development of strategic partnerships with industrial partners, local government, and policymakers
  • Provide knowledge-exchange opportunities across Faculties and with external partners, including advisory roles and policy contributions
  • Identify and foster potential REF impact case studies

Education

Goal: Cement Sheffield’s reputation for AI-positive pedagogy coupled with critical AI literacy, and as a leader in research-based AI teaching across academic and vocational routes and all subjects (University KPIs 5, 6 & 7). Identify opportunities and risks of AI for student recruitment and diversity (University KPI 8).

Delivery plan:

  • Externally signpost routes to study AI and AI-enabled subjects at Sheffield
  • Act as ongoing critical advocates for AI education innovation
  • Contribute to programme reviews across Faculties where invited
  • Identify and pair external guest lecturers hosted by CMI with taught programmes
  • Identify and share AI-related student placement opportunities
  • Facilitate engagement with national and international critical AI literacy initiatives

One University

Goal: Build a world-leading environment for responsible AI research and learning, including a sustainable and collaborative researcher community (University KPI 10). Improve CMI’s financial sustainability through research income, industry partnerships, and philanthropy (University KPI 11). Explore AI’s role in improving the University’s environmental sustainability, while honestly communicating the environmental costs of scaling current AI approaches (University KPI 12). Support open, constructive conversation about AI’s impacts on research, learning, and society, and improve working practices and AI literacy.

Delivery plan:

  • Act as a hub for AI activity — a gateway to AI expertise, promoting activities and achievements internally and externally
  • Convene regular community events across the University’s AI community, plus specialist Interest Groups
  • Convene an External Advisory Board of regional, national, and international stakeholders, complementing the existing Academic Advisory Board
  • Work with the Research, Partnerships, and Innovation Major Partnerships Team to deepen strategic industry relationships
  • Work with Campaigns and Alumni Relations to ensure AI research/education visibility to potential donors
  • Provide generous AI leadership, fostering staff development and the Centre’s long-term sustainability

Implications for this project

CMI strategy elementCatalogue implication
Internal-facing “AI Sandbox” giving curated access to cutting-edge AI technologies and datasetsClosest CMI analogue to this project’s catalogue; a natural integration point or complementary initiative — the catalogue could feed the Sandbox’s tool inventory, or vice versa
Explicit commitment to UKRI’s Principles of Responsible and Trustworthy AIReinforces the alignment already noted in ukri-ai-strategic-framework; Sheffield’s own centre is formally committing to the same national framework this catalogue’s governance fields are designed around
Human-Centric AI strength area (ethics, bias, inequalities in society and culture)Matches the capability-area gap for qualitative/non-STEM tooling flagged in ukcori-uk-institutional-ai-guidance-2026; CMI is a potential internal partner/reviewer for that gap
”Embed humanities and social sciences perspectives in all AI research and policy activity”Supports broadening the catalogue’s assessment panel and capability-area coverage beyond STEM-framed tools
External Advisory Board (proposed) / Academic Advisory Board (existing)Potential stakeholder or governance route for presenting/endorsing the catalogue institutionally
AI and Research Software Engineering as a named key strengthRSE AI team is a candidate source of technical review capacity for tool assessments
Honest communication of AI’s environmental costs (KPI 12)Reinforces the case (also raised in ukcori-uk-institutional-ai-guidance-2026) for an environmental-impact field or notes in tool assessments
REF impact case studies from AI-enabled spinoutsTools with clear translation-to-impact stories are candidates for cross-referencing with Innovation-pillar case studies

Suggested citation

University of Sheffield, Centre for Machine Intelligence (2026). Centre for Machine Intelligence Strategy 2026.

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