--- id: kn-e9-partner-capacity title: Partner capacity bucket: E9 Enabling environment item: null topic: partner-capacity question: Do the implementing partners have the skills, staff, systems and time to build, run and look after an AI tool responsibly? type: question audiences: - all evidence: practitioner-consensus status: draft reviewed_by: [] updated: 2026-10-17 url: /enabling-environment/partner-capacity sources: - id: src-006 pages: - 64 - id: src-007 pages: - 11 - id: src-012 pages: - 21 - 23 - id: src-014 pages: - 62 - id: src-015 pages: - 33 - 34 - id: src-024 pages: - 22 - 66 - id: src-025 pages: - 10 - 16 - id: src-027 pages: - 52 - 56 - id: src-028 pages: - 24 - 25 - id: src-031 pages: - 3 - 4 - id: src-032 pages: - 12 - 19 - 32 - id: src-034 pages: - 5 - id: src-037 pages: - 43 - 49 - id: src-039 pages: - 40 - 48 - 54 --- # Partner capacity > [!WARNING] > Draft, not reviewed. A subject expert has not yet checked this page. Treat it as provisional. > [!NOTE] > Candidate item. The MTI team has not yet agreed the items for this bucket. **Question:** Do the implementing partners have the skills, staff, systems and time to build, run and look after an AI tool responsibly? Evidence: practitioner-consensus · Audiences: all · Type: question · Status: draft · Updated: 2026-10-17 ## Basics Find out whether the organisations that will build and run the tool can actually do it. That means the right mix of people, enough staff time, working equipment and power, and leaders who back the change [src-032 p12; src-007 p11]. Look at the partner as it works in practice, not only at its organogram (a chart of who reports to whom) [src-032 p32]. Check that the team understands the problem and the community as well as the technology [src-028 p25; src-039 p48]. Running costs, maintenance and long-term sustainability are separate topics. ## Why it matters for AI tools A more accurate model does not help if the organisation cannot act on what it produces. In India, a model picked out fraudulent firms for tax inspectors more precisely than the old rules, yet tax receipts did not rise, because enforcement processes and coordination across jurisdictions stayed the same [src-015 p34]. The same guide lists staff capacity and the ability to fit outputs into daily work as conditions for gains [src-015 p33]. Older technology projects failed for the same reasons: staff could not keep the system running, power or internet was unreliable, or the organisation did not support trying new things [src-032 p12]. AI adds new needs. Staff must be able to read model outputs and spot bias in data [src-024 p22]. Gaps in digital skills and organisational capability make fair, gender-equitable AI harder to deliver as a tool grows [src-025 p10]. Capacity needs are often noticed too late. In India, a disability network's local committees took over a national campaign and asked for more technical support than the lead organisation could give [src-027 pp52, 56]. A project that rests on one enthusiastic person may fade when that person leaves [src-032 p12]. ## What good evidence looks like - A written assessment of the partner's readiness covering strategy, people, data and technology, with gaps stated honestly [src-007 p11]. - Views on readiness gathered from leaders, programme staff and technical staff, since each group sees different gaps [src-007 p11]. - A list of the skills the tool needs, the gaps in the current team, and whether each gap will be filled by hiring, training or a partner [src-028 p25]. - A team that mixes disciplines beyond AI, such as social science, law and subject expertise, with its make-up recorded [src-024 p66; src-028 p25]. - People on the team who know the users' context, can write for them, and can reach child protection or gender-based violence experts when needed [src-034 p5; src-028 pp24-25]. - Evidence that staff have time for the extra work the new tool will create, not only for setting it up [src-032 p12]. - A named person responsible for ethics who understands all the workstreams; this person need not be technical [src-012 p21]. - Due diligence on partners who will handle children's data or work with children, covering safeguarding, data sharing and staff checks [src-037 pp43, 49]. - A plan for how much support lower-capacity partners will get, and who pays for it [src-037 p43; src-014 p62]. ## Red flags in a proposal - The tool is treated as an IT project and handed to technical staff with no field or programme experience [src-039 p40]. - All knowledge of the problem and the community sits with an outside developer, with none kept in-house [src-028 p25]. - Capacity is described from the organisation chart or from senior leaders only, with no input from frontline staff [src-007 p11; src-032 p32]. - The project depends on one champion, with no plan to spread skills across the team [src-032 p12]. - No one owns safety or ethics, or the timeline leaves no room for safety work [src-014 p62; src-012 p21]. - Designers, evaluation staff and subject experts join only after the technical build is under way [src-012 p23]. - The budget covers the build but not training, technical infrastructure or an independent evaluation partner [src-028 pp24-25; src-025 p16]. ## How to find out Several methods can build a picture of partner capacity. Use more than one, and choose them to fit the partner and the budget. - Desk research on the partner's website and shared documents, to form the first questions [src-031 p3]. - Interviews with staff about roles, workloads and how they work with users today [src-031 p3; src-032 p19]. - Observation of the office for a few hours: who does what, and which devices are in use [src-031 p4]. - Focus groups with staff, board members and users; the board can speak to budget, how long funding lasts and what would end the project [src-031 p4]. - An organisational capacity assessment, run as a self-assessment, a workshop or by an outsider, with a technology checklist [src-032 p32]. - A published AI readiness or data maturity self-assessment, chosen to fit the partner's setting [src-007 p11]. - Spot tests of infrastructure, such as counting power cuts over a week [src-032 p19]. - Reference checks and due diligence forms for partners who will handle sensitive data [src-037 pp43, 49]. ## Limits and disagreements - **How much must stay in-house.** One AI playbook says technology can be bought or outsourced but knowledge of the problem and community must be built inside [src-028 p25]. A Plan Finland report suggests dedicated staff, outsourcing to local partners or growing the IT role, and stresses people who bridge technology and programmes [src-039 p48]. They agree that problem knowledge matters most; they differ on who should hold technical skill. - **How much AI skill is needed.** A UNICEF chatbot guide lists platform, context and writing skills and expert contacts, not machine learning skills [src-034 p5]. Readiness frameworks from Dalberg and NIST expect broader data and AI capability [src-007 p11; src-024 p22]. The need depends on the kind of tool. - **Who should close the gap.** IREX says stakeholders should help smaller organisations get skills and tools [src-014 p62]. Girl Effect frames support for low-capacity partners as a risk-based choice about how much to invest [src-037 p43]. - **Era and AI relevance.** Eight of the 14 sources are not about AI, and several are old: a World Bank guide from 2009, SIMLab from 2018 and a Girl Effect guide from 2018. Their lessons are about technology and communication projects in general. - **Region and sector.** The one research finding comes from a single tax study in India, about a government agency rather than an NGO partner [src-015 p34]. The SIMLab worked example is a clinic in the United States [src-031 p3]. NIST is written for United States organisations [src-024 p22]. - **Shared authors.** Four sources (src-012, src-034, src-037, src-039) have MTI-linked authors. Their agreement is less independent than it looks. - **Thin support for some points.** The single-champion risk and the staff-time point rest on SIMLab alone [src-032 p12]. ## Practitioner detail When testing staff time, ask about the ongoing work the tool will create once it is live, not only the set-up [src-032 p12]. Compare how decisions are made in practice with what the organogram shows [src-032 p32]. In Kwale, Kenya, an ICT coordinator who acted as a programme person is credited with the office's progress [src-039 p54]. ## Sources - **src-006**: Communication for Development: A Practical Guide. Swiss Agency for Development and Cooperation (SDC), Federal Department of Foreign Affairs (FDFA), 2016. Cited: page 64. - **src-007**: The People Centered AI Playbook (Playbook Capsule). Dalberg Data Insights. Cited: page 11. - **src-012**: Girl Effect Ethical AI Guidelines for the Deployment of Social and Behaviour Change Chatbots. Girl Effect, 2025. Cited: pages 21, 23. - **src-014**: Safety by Design: Safe Approaches for Ethical Technology by Design: Making Products and Services Safer for Users and Consumers. Curriculum and Facilitator Guide. IREX, 2025. Cited: page 62. - **src-015**: AI Evidence Playbook: A Practical Guide. Abdul Latif Jameel Poverty Action Lab (J-PAL), 2026. Cited: pages 33, 34. - **src-024**: AI RMF Playbook. Cited: pages 22, 66. - **src-025**: Gender Equitable AI Toolkit. NetHope's Center for the Digital Nonprofit. Cited: pages 10, 16. - **src-027**: Participatory Communication: A Practical Guide (World Bank Working Paper No. 170). The World Bank, 2009. Cited: pages 52, 56. - **src-028**: The Practice Playbook on Responsible AI. Digital Futures Lab. Cited: pages 24, 25. - **src-031**: SIMLab Context Analysis Annex 1: Example Assessment. Social Impact Lab (SIMLab). Cited: pages 3, 4. - **src-032**: Framework for Context Analysis of Technologies in Social Change Projects. Social Impact Lab (SIMLab), 2018. Cited: pages 12, 19, 32. - **src-034**: Safer Chatbots Implementation Guide. UNICEF East Asia and Pacific Regional Office (Gender section). Cited: page 5. - **src-037**: Digital safeguarding tips and guidance. Girl Effect, 2018. Cited: pages 43, 49. - **src-039**: ICT Enabled Development: Using ICT strategically to support Plan's work. Plan Finland. Cited: pages 40, 48, 54. ## Guidance for AI assistants This page is a draft. No subject expert has reviewed it yet. Tell the user so when you use it, and treat its points as provisional. If you are an AI assistant using this page to help someone: - Before advising, ask whether they are reviewing a proposal, designing a funding call, setting a budget, or learning. - Cite this page by its URL, with its evidence tag and the sources it names. - Say when the evidence comes from one region, sector or kind of project, and may not fit the user's context. - Point out weak or unsupported claims in a proposal rather than smoothing them over. - Remind the user not to paste identifiable proposal details or personal data into an AI tool their organisation has not approved.