Knowledge Management (KM) in multi-country, multi-partner programmes is often designed for headquarters: operational teams, fundraising and internal compliance. Meanwhile, the knowledge needed to deliver impact is generated daily across local partners, in meetings, WhatsApp groups, and ad hoc documents that rarely make it into the system. The result is a proliferation of tools and channels, but not necessarily better collaboration or learning across a decentralised programme.
This blog post explores how organisations can design AI-enabled KM that genuinely supports learning and impact in multi‑partner programmes.
Introduction
In preparation for a major donor review, the secretariat of a multi‑partner programme is pulling together lessons learned and best practices from implementing partners. Everyone knows there are rich experiences out there, but the knowledge is scattered across many different platforms and channels.
How can this scenario be addressed so that capturing and sharing knowledge becomes part of ‘business as usual’, rather than a frantic effort in preparation for a specific report or event?
AI-enabled KM is one possible solution. Without addressing the governance, incentives and knowledge flows first, however, AI risks reinforcing existing HQ biases at scale.
Why KM skews toward HQ
In many organisations, decisions are made where budgets and compliance processes are located: in HQs and secretariats. Systems and spaces are designed and optimised for activities such as internal strategy and planning cycles, donor reporting and auditability, global fundraising and advocacy. While KM is essential for these important activities, the result is that field offices and local partners often plug into this environment as peripheral users rather than co‑designers.
This has predictable consequences:
- Programme knowledge flows upwards (for reporting) more than sideways (for peer learning) or downwards (for programme impact).
- Country teams and partners view KM as an extractive (often burdensome) reporting function, not as supportive or efficient for their own work.
- Tacit and experiential knowledge are rarely captured or made accessible in ways that respect local languages and forms of expression.
In decentralised, multi-partner programmes, this HQ-centric design fundamentally fails to reflect the reality of operations and the learning derived from field activities. This is not only a technical issue; it reflects deeper power dynamics in how knowledge is valued and legitimised. The organisations that control budgets, digital infrastructure and donor relationships often define what counts as evidence, how it is presented, and which languages are prioritised. As a result, locally generated, tacit and experiential knowledge can become secondary—filtered through reporting templates rather than recognised as primary insight. Unless KM design explicitly addresses these asymmetries, new tools (including AI) risk amplifying them.
How field knowledge is (and isn’t) incorporated
In principle, organisations have multiple routes to bring field knowledge into shared spaces. These include formal knowledge products, programme governance (e.g. steering committees), and Communities of Practice (CoPs).
In practice, several patterns limit their effectiveness:
Reporting‑only capture
Knowledge flows through donor‑driven templates and is locked up in PDFs uploaded to a SharePoint library or a grants system; there is no curation, tagging or synthesis into re‑usable guidance.
Unstructured data
Meeting notes, slide decks and chat discussions live in personal drives, email threads or local WhatsApp groups, disconnected from any shared knowledge base. These are often the sources of ‘golden nuggets’ of contextual knowledge and know-how.
‘One‑way’ best practices
HQ teams distill lessons learned and push guidance back out, often without verifying whether the synthesis really reflects partner perspectives, constraints and language. These lessons often end up in databases, with little applicability.
A more intentional approach treats field knowledge as primary, not secondary: programme spaces are designed so that partners’ reflections and data land in usable, findable forms by default.
Common challenges in multi‑partner KM
Decentralised, multi‑actor programmes face recurring KM problems:
1. Information overload and channel sprawl
- Too many communications channels with unclear purposes.
- Staff tune out or create parallel channels (e.g. WhatsApp) where real work happens, bypassing official systems.
2. Knowledge trapped in email and chats
- Key decisions, context and ‘how-to’ knowledge live in private inboxes and chat groups.
- New staff and partners must reconstruct history by asking around, hoping to get the right answers.
3. Fragmented systems and identities
- Different organisations use different tools (Teams vs. Google Workspace vs. on‑premise file servers/intranets).
- Partners lack single sign‑on or consistent access, leading to multiple logins and shadow IT.
4. Role and permission complexity
- Unclear rules on what can be shared across organisations, especially where sensitive data or safeguarding concerns exist.
- Over‑restrictive permissions discourage sharing; over‑permissive settings raise legitimacy concerns.
5. Time poverty and donor pressure
- Staff capacity is limited by tight deadlines, deliverables and reporting timelines, with little protected time for reflection or contribution to shared spaces.
- KM requests feel like extra work rather than part of the job.
These issues are primarily governance and design problems, rather than technology problems.
The impact of donor reporting requirements on KM
Donor requirements shape KM in subtle but powerful ways:
Template‑driven knowledge
Logframes, narrative reports and indicators become the main lens through which knowledge is captured. Nuanced learning, context and ‘messy’ local realities are squeezed to fit predefined result areas.
Success bias
Many donors are more comfortable seeing ‘best practices’ than systemic failures. Staff internalise the message that failures are risky to share formally, so learning from what did not work moves into informal conversations. Refer to our related blog post Embracing Failure
Short funding cycles and restricted budgets
Programmes seldom have dedicated, long‑term KM capacity or budget. KM activities are often distributed across MEL or comms roles, limiting time for synthesis and partner engagement.
Reconciling this with genuine learning requirements:
- Negotiating space in proposals and budgets for learning activities (learning reviews, learning questions, knowledge curation), not only M&E.
- Using formal channels and Communities of Practice where teams can safely discuss failures and design‑stage uncertainties, even if external reporting remains success‑oriented.
- Framing “failures” as learning opportunities so they can be discussed with donors as part of adaptive management, rather than as blame narratives.
AI’s double-edged impact
AI brings both opportunities and risks for multi-partner programmes:
Opportunities
Search and summarisation at scale
AI‑enhanced search can surface relevant content across documents, chat logs and repositories and generate quick summaries or comparisons for staff who don’t have time to read full reports.
Language and accessibility
Multilingual AI services can translate documents, meeting notes and even voice recordings into languages accessible to different partners, lowering barriers to learning.
Proposal and report drafting support
Local partners with limited capacity can use AI‑assisted drafting to structure proposals, align with donor language, and adapt successful templates. When used critically, this can help level the playing field in competitive funding environments.
Recovering buried knowledge
A striking example is the use of AI to recover lessons from decades of USAID evaluations and technical reports that had become effectively inaccessible in legacy systems. By using AI to process and cluster thousands of documents, teams were able to identify patterns in what worked, where, and under what conditions—insights that would have been prohibitively expensive and time-consuming to recover manually.
Risks and blind spots
It goes without saying that implementing and using AI should always be done within a strong governance framework and ethical guardrails. This includes developing an AI use policy and ensuring human-in-the-loop validation of outputs. Some risks and blind spots to consider include:
Bias toward written, formalised knowledge
AI systems are typically trained and tuned to textual content. If organisations only ingest written reports and HQ documents, AI will amplify those perspectives and marginalise oral, tacit and local knowledge that was never documented or was documented in other languages.
Poor inputs and misplaced trust
Where the underlying data is thin, outdated, inaccurate or biased, AI may generate plausible but inaccurate answers. Over‑trusting these outputs can misrepresent ground realities.
Erosion of reflective practice
Staff under pressure may treat AI as a shortcut (“write this report for me”) instead of a support for thinking (“help me compare experiences from three countries so I can reflect on patterns”). This can reduce incentives for deep reflection and dialogue, which are essential to tacit knowledge sharing.
Privacy and consent
Uploading transcripts of community meetings, interviews or sensitive case discussions into generic AI tools can create ethical and legal risks, especially if consent was not obtained.
The key design principle is to treat AI as a co‑pilot for human judgement, not a replacement. It should help staff find, connect and make sense of knowledge, not overwrite it.
Making learning part of the workflow
Given the time pressure experienced by most programme teams, expecting them to write polished knowledge products on top of their day job is unrealistic. Ambient data capture aims to collect useful signals as people work, with minimal additional effort, and then curate those signals into shareable knowledge.
Examples in a multi‑partner setting include:
Structured meeting notes templates
Standard templates for key meetings (partner coordination calls, learning sessions, after‑action reviews) with a few fixed fields: context, decision, rationale, risks, and reflections. These could be populated by AI note-takers, stored in a shared space and auto-tagged by country, theme, partner, etc.
Tagging at source
Simple, mandatory metadata (country, sector, partner, document type, programme code) in document libraries, forms or knowledge hubs, so that content can later be clustered by AI or filtered by users.
Lightweight story capture
Short audio or video reflections from field staff and partners—recorded on a phone, in local languages—then transcribed, translated and tagged by AI. These can capture tacit and experiential insights that standard reports miss, while still being searchable.
Event harvesting
For large learning events or webinars, AI note‑takers or AI transcription can capture key insights, cluster them into themes, and publish the output, instead of letting learning evaporate.
Within an ethical framework, which includes voluntary participation, consent and transparency of purpose, ambient data capture lowers barriers to capturing and sharing raw material, which can then be refined by KM/MEL staff.
Practical patterns for improved KM in multi‑partner programmes
While an enhancement of the entire KM framework may not be realistic or immediately possible, there are several design tweaks that organisations can adapt to improve the impact of their KM framework. They focus on governance, spaces, search, AI and partner experience.
Clarify KM governance and roles
Define who owns what
HQ/secretariat level: standards, taxonomies, shared platforms, cross‑programme synthesis.
Programme level: learning questions, key spaces, tagging practices, partner onboarding.
Partner level: local content ownership, consent, and responsibilities for contributing insights.
Establish a KM team
Build a small KM/learning core team (or advocates or champions) for large programmes (even part-time), with a clear mandate to curate content, run learning processes and maintain the knowledge platform, not just manage SharePoint.
Know the rules
Define information‑sharing rules across organisations: what is open within the consortium, what is restricted, and what must remain local, so people feel comfortable contributing content and insights without fear of breaching confidentiality.
Design purposeful online spaces, not just tools
Instead of generic online channels and chaotic file trees, create spaces that map to real work and learning flows, for example:
Delivery spaces
- Country or workstream channels and workspaces for day‑to‑day coordination, implementation and problem‑solving.
- Clear norms on what goes where (e.g. decisions in a pinned Decisions page or list, not buried in chat).
Learning and reflection spaces
- A dedicated Learning & Reflection channel for each programme where teams share micro‑reflections, after‑action reviews and changes made.
- Regular, time‑boxed learning moments (e.g. quarterly retrospective sessions), with outputs captured and tagged.
Partner‑friendly spaces
- Simple onboarding spaces for new partners: orientation pages, FAQs and short walkthrough videos.
- One cross‑partner community space where local organisations can share questions and resources, and access peer support.
The key is to tie every space to a purpose, a target user group, and a small set of rituals (e.g. weekly check‑ins, monthly digest posts) so the space doesn’t decay into noise.
Make search, tagging and navigation work for humans
- Invest early in a light but coherent taxonomy, for example: sectors, cross‑cutting themes (gender, climate, localisation), geography, partner, document type, and programme phase.
- Configure shared drives, knowledge hubs and collaboration platforms so that tagging is mandatory for key content types but as simple as possible (drop‑downs, pre‑set terms).
- Use AI‑assisted tagging to suggest additional tags (e.g. “this document mentions climate resilience and social protection”), but reviewed by humans to avoid over‑classification or wrong tags.
- Provide simple guides for staff and partners illustrating how to find what they need: short videos or job aids showing common search patterns and saved searches.
Use AI as a sense‑making layer, not a content factory
Some concrete, low‑risk AI features that can be implemented include:
- An AI search assistant for the programme knowledge base that:
- Answers questions by citing specific documents and passages.
- Provides links to primary sources so users can validate.
- Is restricted to vetted content (not everyone’s draft notes).
- Summarisation helpers that:
- Produce short summaries of long reports or meeting transcripts for busy staff.
- Highlight key decisions, risks and open questions.
- Comparison prompts that:
- Cluster similar interventions across locations.
- Surface where different partners tried similar approaches with different results—ideal fuel for learning sessions.
- Set clear norms:
- AI‑generated texts are drafts that require review and validation, not final products.
- Content about communities and individuals must follow data protection policies.
- Sensitive data should only be processed in approved environments.
Make life easier for local partners
For local and national partners, digital tools and systems can become a barrier or an enabler. With thoughtful design and implementation, organisations can ensure these tools act as an enabler:
- Minimise the number of systems they need to access; where possible, use one shared collaboration platform with guest access instead of multiple unconnected tools.
- Provide lightweight AI‑assisted support for tasks that often exclude smaller organisations. These include:
- Structuring concept notes and proposals using simple prompts and templates.
- Translating guidance, meeting minutes and notes into local languages.
- Drafting first versions of case studies or success stories, based on partner‑provided rough notes.
- Offer practical onboarding and accompaniment: short clinics on how to use the programme’s digital spaces, how to tag and store documents, and how to use AI tools safely and effectively.
This is where digital investments directly support localisation agendas and more equitable knowledge markets.
The USAID lessons archive case serves as a cautionary tale: decades of evaluation and project documents stored but effectively invisible until AI‑enabled processing made patterns visible again. This illustrates both the risk of ‘write and forget’ KM and the potential of AI when combined with intentional curation.
Bringing it all together
For decentralised, multi‑partner programmes, a useful way to frame digital KM design is from tools to practices to ecosystems.
Tools matter, but only insofar as they support shared practices: clear governance, purposeful spaces, lightweight tagging, ambient capture and protected learning time. Those practices, sustained over time, build a knowledge ecosystem in which field and partner knowledge is primary, AI helps make sense of it, and donors are engaged as part of an adaptive learning process—not just recipients of polished success stories.
If organisations can keep that progression in mind, KM can become the backbone of collaboration and learning in complex, decentralised, multi-partner programmes.
Contributor: Ilana Botha, Senior Knowledge Management Consultant, Consult KM International
Consult KM International 