From the Research Universities edition of November 9, 2026
Using AI in research funding: guidance for funders and reviewers (Association of Medical Research Charities)
Association of Medical Research Charities (AMRC) · Research funding (medical research charities) · United Kingdom (applicable to funders and reviewers more widely)
- Publisher
- AMRC
- Original publication
- 2 November 2026 (updated 4 November 2026)
- Source retrieved
- 2026-11-10
What happened
The UK association for medical research charities says generative AI could help funders and reviewers with some tasks but must not replace human judgment, and states that it must not be used to make research funding decisions. Listed possible funder uses include drafting application-form structures and email replies, an applicant question chatbot, identifying potential expert reviewers, drafting meeting minutes and applicant feedback, lay summaries and translation. Guidance says to consider tool appropriateness, protection of personal, confidential and scientific information and intellectual property, whether to set funder staff policies, and how to tell applicants, reviewers and supporters about AI use. It recommends confidential in-house or enterprise data-protected tools assessed for security and says confidential applications must not be entered in publicly available tools. Reviewers should be told not to enter applications into generative AI tools, though some funders may allow limited uses such as language refinement under stated conditions. Included now because it was published 2 Nov (updated 4 Nov), was not archived, and is funder-side guidance that complements last week's archived Springer review of funder AI policies.
Why it matters
Relevant to university sponsored-programs and research offices that advise applicants and reviewers, and to research funders: it states what a sector body expects for confidentiality, human accountability and disclosure, which shapes tool choices and internal policy.
Evidence and measured results
Sector-association web guidance (not empirical research). It sets expectations and considerations; it reports no measured outcomes, sample or evaluation method. Author and dates are stated on the page.
Limitations and uncertainty
Guidance, not evidence of effectiveness; UK medical research charity context; not binding on members or other funders; no measured results; the page says 'Published: November 2026' in its summary and 2 November in the footer, with an update on 4 November.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-11-09; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Funders and research offices lack clear rules on where AI may be used with confidential applications and review processes.
- Stakeholders
- Vice president for research
- Sponsored programs director
- Research compliance and integrity officers
- Funder program and grants managers
- Discovery questions
- Which funder AI rules apply to our applicants and reviewers?
- Which AI tools currently touch proposals?
- Who approves tools for confidential material?
- How do we communicate AI use to applicants?
- Value hypothesis
- A clear tool-and-policy framework may reduce confidentiality risk and confusion for applicants and reviewers; the guidance does not measure benefits.
- Potential engagement
- A short policy and tooling assessment mapping current AI use in proposal support and review, funder rules and approved data-protected tools.
- Unsupported claims
- Efficiency or cost savings
- Compliance with any specific funder's rules
- That limited reviewer uses are permitted everywhere
Pre-sales engineering
Role takeaway
- Fit
- Relevant wherever proposals or reviewer material may reach AI tools; it is policy guidance rather than a technical specification.
- Architecture and integration
- Route any AI assistance for grants through enterprise or in-house tools with confirmed non-reuse of submitted data; block public tools for applications through network or endpoint controls.
- Prerequisites
- Inventory of AI tools in use
- Data-handling terms for each tool
- Data classification for proposals
- Constraints
- Guidance gives no technical controls
- Funder rules vary
- Reviewer limits depend on each funder
- Security
- Confidential applications, personal data and unpublished ideas must stay out of public tools; evaluate retention and reuse terms.
- Proof of value
- Data-flow review of each AI tool touching proposals
- Test that public tools are blocked for application content
- Confirm no training or reuse in vendor terms
Delivery
Role takeaway
- Work
- Draft staff and reviewer rules, approve tools, publish disclosure language and train applicants, staff and reviewers.
- Dependencies
- Funder policy review
- Security assessment of tools
- Leadership approval of policy
- Ownership
- Research administration or sponsored programs owns policy; IT security owns tool assessment.
- Skills and adoption
- Staff and reviewers need short training on what may and may not be entered into AI tools and how to disclose use.
- Governance checkpoints
- Policy approval before tool enablement
- Periodic review against funder rule changes
- Incident process for confidential data entered into public tools
- Acceptance criteria
- Proposed: approved-tool list published
- Proposed: reviewer guidance issued before the next review cycle
- Proposed: disclosure wording available to applicants
- Risks
- Applicants or reviewers using public tools
- Conflicting funder rules
- Policy lagging tool changes
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Where AI supports funding workflows, use confidential in-house or enterprise data-protected tools assessed for security, review how each tool retains and could reuse submitted information, and keep tools separate from systems that hold unpublished proposals.
Governance
Who approves, reviews and stays accountable for outcomes?
People stay responsible for decisions; AI use needs appropriate trialing and safeguards, funder-defined rules for staff and reviewers, and transparent communication to applicants and reviewers. Reviewer entry of applications into generative AI tools should be prohibited, with any limited allowed uses specified.
Security and privacy
What data, permissions and controls need testing?
Confidential funding applications, personal data and scientific ideas must not go into publicly available tools; intellectual-property and confidentiality protections depend on the tool's data handling terms.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Limited language-refinement uses may help non-native English speakers, which funders can allow under stated conditions; guidance asks funders to communicate expectations to reviewers.
Procurement
What should contracts, pricing and exit terms secure?
Specify confidential or enterprise data-protected tools, assess security and safety, and review retention and reuse before purchase for any grant-support use.
Operating model
Which teams own the service once it runs?
Keeps humans accountable for funding decisions and places tool assessment, reviewer rules and applicant communication with the funder or research office.
Publication history
- 2026-11-09Research Universities · Issue 623 resources
Stable resource ID: amrc-using-ai-in-research-funding-guidance-funders-human-decisions-20261102