--- id: kn-e8-non-ai-alternatives title: Non-AI alternatives bucket: E8 Problem definition and AI alternatives item: null topic: non-ai-alternatives question: Would a simpler, non-AI or non-digital option meet the need as well, and why is AI the better choice here? type: question audiences: - all evidence: practitioner-consensus status: draft reviewed_by: [] updated: 2026-10-17 url: /problem-alternatives/non-ai-alternatives sources: - id: src-001 pages: - 1 - id: src-004 pages: - 16 - id: src-006 pages: - 25 - 39 - id: src-007 pages: - 18 - id: src-012 pages: - 22 - 24 - id: src-015 pages: - 20 - 21 - 43 - 44 - id: src-016 pages: - 3 - id: src-024 pages: - 38 - 43 - 62 - 63 - id: src-025 pages: - 18 - id: src-028 pages: - 13 - 15 - 18 - id: src-030 pages: - 6 - 29 - 30 - 44 - id: src-032 pages: - 10 - id: src-035 pages: - 12 - id: src-038 pages: - 24 - 28 - id: src-039 pages: - 29 --- # Non-AI alternatives > [!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:** Would a simpler, non-AI or non-digital option meet the need as well, and why is AI the better choice here? Evidence: practitioner-consensus · Audiences: all · Type: question · Status: draft · Updated: 2026-10-17 ## Basics Before funding an AI tool, find out what else could meet the same need. Options include changing how work is done, a basic digital tool, more training, more staff, a policy change, or doing nothing new [src-007 p18; src-015 p43]. A team should list these options with the benefits, limits and risks of each, and say with evidence why AI does better [src-001 p1]. If it cannot, the project may work as well or better without AI [src-004 p16; src-012 p22]. The separate page on problem definition covers how to state the problem itself. ## Why it matters for AI tools AI added where it does not fit can waste time, bring new risks and make services more fragile, and low-resource settings feel this most [src-007 p18]. Many AI tools need digital and AI literacy that poorer and marginalised groups may lack, so the people with most need may be the least able to use them [src-028 p15]. AI tools also cost more to keep running than fixed content such as standard text messages, because models, data and user behaviour change and the system needs updating [src-015 p44]. Where a tool scores or models people, as in credit scoring, the case for it should be strongest, including a reason why no reasonable alternative exists [src-035 p12]. Interest from donors or the media in new technology can push a programme towards tools that do not suit its users [src-038 p24]. ## What good evidence looks like - The proposal lists several ways to solve the problem, including a no-technology option, with the benefits, limits and risks of each [src-001 p1; src-030 p6]. - It names measures showing AI beats the other options, not only that AI can do the task [src-001 p1; src-015 p44]. - It describes how the problem is handled today and what the current process or tool fails to do [src-025 p18; src-030 p29]. - It states what the team would do without AI, such as a simple decision rule or more staff [src-015 p43]. - It counts the full cost of each option: data, dealings with mobile operators, upgrades, total cost of ownership and time spent supporting users [src-030 p30]. - It shows whether adding features to an existing tool would be cheaper than building a new one [src-030 p30]. - It shows that the people who need the service have the devices and skills the AI option assumes, or explains how others will be reached [src-028 pp15, 18]. - It shows the AI part supports existing workers or services rather than replacing them, or gives reasons where it replaces them [src-015 p21; src-039 p29]. - It describes a low-tech version of the service, such as a feature phone or human route, that users can fall back on [src-012 p24]. ## Red flags in a proposal - AI is chosen before other options are compared [src-001 p1; src-007 p18]. - The case for AI rests on what the technology can do, with no comparison against the current way of working [src-030 p29]. - The proposal assumes target users will soon own smartphones without evidence on cost, literacy, power or airtime [src-032 p10]. - A tool that scores or ranks people gives no reason why a traditional method would not do [src-035 p12]. - The budget covers building but not upkeep, updates or user support [src-015 p44; src-030 p30]. - AI replaces a human role, such as coaching, where trust and feedback matter, with no evidence it works as well [src-015 p21]. ## How to find out Map existing services and what already works for the target group, then ask which parts could move to a digital channel [src-016 p3; src-028 p13]. Run a stop-and-review point after the context work and before choosing any technology: ask whether the technology will help meet the goals, save time or money, do new or better things, or increase equity [src-004 p16]. Test each problem against a set of conditions for AI fit with a mixed group of staff, including frontline workers [src-007 p18]. Use a decision tree that sends the team back to other options when it cannot say how AI is better [src-001 p1]. Compare costs, using cost-benefit or cost-effectiveness analysis to set a technology route beside a traditional one [src-030 p44]. Ask whose job it is to fill a service gap, since advocacy may suit better than a new service [src-039 p29]. Plan to pilot the AI tool against a non-AI option before scaling [src-015 p20]. ## Limits and disagreements Support is broad: the core point appears across about a dozen publisher families. Most of these sources, though, are toolkits that state the principle rather than test it. Only J-PAL reports trial evidence, and its findings come from specific settings: teacher coaching in South Africa and labour courts in Mexico [src-015 pp21, 44]. J-PAL also shows AI can add value, but sometimes only when the process around it is right [src-015 p44]. Seven of the fifteen sources do not discuss AI at all; they speak about ICT (information and communication technology) in general [src-004 p16; src-006 p39; src-030 p6; src-038 p28]. Their lessons carry over, but they were not written about AI. Sources differ on how high to set the bar. Pollicy asks for the strongest proof only where tools model human behaviour and asks less of tools that model crops or weather [src-035 p12]. NIST treats non-AI options as part of routine risk work for any AI system [src-024 pp38, 43]. NIST writes from a United States risk management setting [src-024 p38]. Several sources share MTI-linked authors (src-012, src-030, src-038, src-039), so their agreement is less independent than it looks. Plan Finland supplies two sources (src-004, src-039) and SIMLab two (src-030, src-032). ## Practitioner detail NIST describes non-AI options as including semi-automated or mostly manual ways of gathering and judging information, and asks teams to weigh them during context mapping and again when choosing how to respond to risks [src-024 pp43, 62-63]. Cost-effectiveness analysis compares the cost of two or more routes to the same countable outcome, without putting a money value on that outcome [src-030 p44]. SDC asks, before producing a video or brochure, how many people will use it, how it will reach them, and whether a cheaper means would do [src-006 pp25, 39]. SDC does not apply this to digital tools; the same questions fit them. Girl Effect suggests starting AI on one small part of the user journey, such as onboarding, rather than the most obvious or exciting part [src-012 p24]. UNICEF advises combining new channels with radio, print and face-to-face outreach where access is uneven [src-038 p24]. ## Sources - **src-001**: Decision Tree for the Responsible Application of Artificial Intelligence (v1.0). AAAS Center for Scientific Responsibility and Justice (CSRJ). Cited: page 1. - **src-004**: CONNECT! A practical guide to using ICTs in Plan projects. Plan Finland. Cited: page 16. - **src-006**: Communication for Development: A Practical Guide. Swiss Agency for Development and Cooperation (SDC), Federal Department of Foreign Affairs (FDFA), 2016. Cited: pages 25, 39. - **src-007**: The People Centered AI Playbook (Playbook Capsule). Dalberg Data Insights. Cited: page 18. - **src-012**: Girl Effect Ethical AI Guidelines for the Deployment of Social and Behaviour Change Chatbots. Girl Effect, 2025. Cited: pages 22, 24. - **src-015**: AI Evidence Playbook: A Practical Guide. Abdul Latif Jameel Poverty Action Lab (J-PAL), 2026. Cited: pages 20, 21, 43, 44. - **src-016**: Gender Formative BIG Research questions. Cited: page 3. - **src-024**: AI RMF Playbook. Cited: pages 38, 43, 62, 63. - **src-025**: Gender Equitable AI Toolkit. NetHope's Center for the Digital Nonprofit. Cited: page 18. - **src-028**: The Practice Playbook on Responsible AI. Digital Futures Lab. Cited: pages 13, 15, 18. - **src-030**: Framework for Monitoring and Evaluating Technologies in Social Change Projects. SIMLab (Social Impact Lab), 2018. Cited: pages 6, 29, 30, 44. - **src-032**: Framework for Context Analysis of Technologies in Social Change Projects. Social Impact Lab (SIMLab), 2018. Cited: page 10. - **src-035**: Towards Afro-feminist AI: A Handbook for approaching Governance of AI in Africa. Cited: page 12. - **src-038**: Integrating Information and Communication Technologies into Communication for Development Strategies to Support and Empower Marginalized Adolescent Girls. UNICEF (C4D Section, Programme Division), 2013. Cited: pages 24, 28. - **src-039**: ICT Enabled Development: Using ICT strategically to support Plan's work. Plan Finland. Cited: page 29. ## 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.