Why peer learning is critical to survive the Age of Artificial Intelligence

Reda Sadki July 16, 2025Artificial intelligence, Global health

María, a pediatrician in Argentina, works with an AI diagnostic system that can identify rare diseases, suggest treatment protocols, and draft reports in perfect medical Spanish. But something crucial is missing. The AI provides brilliant medical insights, yet María struggles to translate them into action in her community. What is needed to realize the promise of the Age of Artificial Intelligence?

Then she discovers the missing piece. Through a peer learning network—where health workers develop projects addressing real challenges, review each other’s work, and engage in facilitated dialogue—she connects with other health professionals across Latin America who are learning to work with AI as a collaborative partner. Together, they discover that AI becomes far more useful when combined with their understanding of local contexts, cultural practices, and community dynamics.

This speculative scenario, based on current AI developments and existing peer learning successes, illuminates a crucial insight as we ascend into the age of artificial intelligence. Eric Schmidt’s San Francisco Consensus predicts that within three to six years, AI will reason at expert levels, coordinate complex tasks through digital agents, and understand any request in natural language.

Understanding how peer learning can bridge AI capabilities and human thinking and action is critical to prepare for this future.

Collaboration in the Age of Artificial Intelligence

The three AI revolutions—language interfaces, reasoning systems, and agentic coordination—will offer unprecedented capabilities. If access is equitable, this will be available to any health worker, anywhere. Yet having access to these tools is just the beginning. The transformation will require humans to learn together how to collaborate effectively with AI.

Consider what becomes possible when health workers combine AI capabilities with collective human insight:

AI analyzes disease patterns; peer networks share which interventions work in specific cultural contexts.
AI suggests optimal treatment protocols; practitioners adapt them based on local resource availability.
AI identifies at-risk populations; community workers know how to reach them effectively.

The magic happens in integration of AI and human capabiltiies through peer learning. Think of it this way: AI can analyze millions of health records to identify disease patterns, but it may not know that in your district, people avoid the Tuesday clinic because that is market day, or that certain communities trust traditional healers more than government health workers.

When epidemiologists share these contextual insights with peers facing similar challenges—through structured discussions and collaborative problem-solving—they learn together how to adapt AI’s analytical power to local realities.

For example, when an AI system identifies a disease cluster, epidemiologists in a peer network can share strategies for investigating it: one colleague might explain how they gained community trust for contact tracing, another might share how they adapted AI-generated survey questions to be culturally appropriate, and a third might demonstrate how they used AI predictions alongside traditional knowledge to improve outbreak response.

This collective learning—where professionals teach each other how to blend AI’s computational abilities with human understanding of communities—creates solutions more effective than either AI or individual expertise could achieve alone.

Understanding peer learning in the Age of Artificial Intelligence

Peer learning is not about professionals sharing anecdotes. It is a structured learning process where:

Participants develop concrete projects addressing real challenges in their contexts, such as improving vaccination coverage or adapting AI tools for local use.
Peers review each other’s work using expert-designed rubrics that ensure quality while encouraging innovation.
Facilitated dialogue sessions help surface patterns across different contexts and generate collective insights.
Continuous cycles of action, reflection, and revision transform individual experiences into shared wisdom.
Every participant becomes both teacher and learner, contributing their unique insights while learning from others.

This approach differs fundamentally from traditional training because knowledge flows horizontally between peers rather than vertically from experts. When applied to human-AI collaboration, it enables rapid collective learning about what works, what fails, and why.

Why peer networks unlock the potential of the Age of Artificial Intelligence

Contextual intelligence through collective wisdom

AI systems train on global data and identify universal patterns. This is their strength. Human practitioners understand local contexts intimately. This is theirs. Peer learning networks create bridges between these complementary intelligences.

When a health worker discovers how to adapt AI-generated nutrition plans for local food availability, that insight becomes valuable to peers in similar contexts worldwide. Through structured sharing and review processes, the network creates a living library of contextual adaptations that make AI recommendations actionable.

Trust-building in the age of AI

Communities often view new technologies with suspicion. The most sophisticated AI cannot overcome this alone. But when local health workers learn from peers how to introduce AI as a helpful tool rather than a threatening replacement, acceptance grows.

In peer networks, practitioners share not just technical knowledge but communication strategies through structured dialogue: how to explain AI recommendations to skeptical patients, how to involve community leaders in AI-assisted health programs, how to maintain the human touch while using digital tools. This collective learning makes AI acceptable and valuable to communities that might otherwise reject it.

Distributed problem-solving

When AI provides a diagnosis or recommendation that seems inappropriate for local conditions, isolated practitioners might simply ignore it. But in peer networks with structured review processes, they can explore why the discrepancy exists and how to bridge it.

A teacher receives AI-generated lesson plans that assume resources her school lacks. Through her network’s collaborative problem-solving process, she finds teachers in similar situations who have created innovative adaptations. Together, they develop approaches that preserve AI’s pedagogical insights while working within real constraints.

The new architecture of collaborative learning

Working effectively with AI requires new forms of human collaboration built on three essential elements:

Reciprocal knowledge flows

When everyone has access to AI expertise, the most valuable learning happens between peers who share similar contexts and challenges. They teach each other not what AI knows, but how to make AI knowledge useful in their specific situations through:

Structured project development and peer review;
Regular assemblies where practitioners share experiences;
Documentation of successful adaptations and failures;
Continuous refinement based on collective feedback.

Structured experimentation

Peer networks provide safe spaces to experiment with AI collaboration. Through structured cycles of action and reflection, practitioners:

Try AI recommendations in controlled ways;
Document what works and what needs adaptation using shared frameworks;
Share failures as valuable learning opportunities through facilitated sessions;
Build collective knowledge about human-AI collaboration.

Continuous capability building

As AI capabilities evolve rapidly, no individual can keep pace alone. Peer networks create continuous learning environments where:

Early adopters share new AI features through structured presentations;
Groups explore emerging capabilities together in hands-on sessions;
Collective intelligence about AI use grows through documented experiences;
Everyone stays current through shared discovery and regular dialogue.

Evidence-based speculation: imagining peer networks that include both machines and humans

While the following examples are speculative, they build on current evidence from existing peer learning networks and emerging AI capabilities to imagine near-future possibilities.

The Nigerian immunization scenario

Based on Nigeria’s successful peer learning initiatives and current AI development trajectories, we can envision how AI-assisted immunization programs might work. AI could help identify optimal vaccine distribution patterns and predict which communities are at risk. Success would come when health workers form peer networks to share:

Techniques for presenting AI predictions to community leaders effectively;
Methods for adapting AI-suggested schedules to local market days and religious observances;
Strategies for using AI insights while maintaining personal relationships that drive vaccine acceptance.

This scenario extrapolates from current successes in peer learning for immunization in Nigeria to imagine enhanced outcomes with AI partnership.

Climate health innovation networks

Drawing from existing climate health responses and AI’s growing environmental analysis capabilities, we can project how peer networks might function. As climate change creates unprecedented health challenges, AI models will predict impacts and suggest interventions. Community-based health workers could connect these ‘big data’ insights with their own local observations and experience to take action, sharing innovations like:

Using AI climate predictions to prepare communities for heat waves;
Adapting AI-suggested cooling strategies to local housing conditions;
Combining traditional knowledge with AI insights for water management.

These possibilities build on documented peer learning successes in sharing health workers observations and insights about the impacts of climate change on the health of local communities.

Addressing AI’s limitations through collective wisdom

While AI offers powerful capabilities, we must acknowledge that technology is not neutral—AI systems carry biases from their training data, reflect the perspectives of their creators, and can perpetuate or amplify existing inequalities. Peer learning networks provide a crucial mechanism for identifying and addressing these limitations collectively.

Through structured dialogue and shared experiences, practitioners can:

Document when AI recommendations reflect biases inappropriate for their contexts;
Develop collective strategies for identifying and correcting AI biases;
Share techniques for adapting AI outputs to ensure equity;
Build shared understanding of AI’s limitations and appropriate use cases.

This collective vigilance and adaptation becomes essential for ensuring AI serves all communities fairly.

What this means for different stakeholders

For funders: Investing in collaborative capacity

The highest return on AI investment comes not from technology alone but from building human capacity to use it effectively. Peer learning networks:

Multiply the impact of AI tools through shared adaptation strategies;
Create sustainable capacity that grows with technological advancement;
Generate innovations that improve AI applications for specific contexts;
Build resilience through distributed expertise.

For practitioners: New collaborative competencies

Working effectively with AI requires skills best developed through structured peer learning:

Partnership mindset: Seeing AI as a collaborative tool requiring human judgment.
Adaptive expertise: Learning to blend AI capabilities with contextual knowledge.
Reflective practice: Regularly examining what works in human-AI collaboration through structured reflection.
Knowledge sharing: Contributing insights through peer review and dialogue that help others work better with AI.

For policymakers: Enabling collaborative ecosystems

Policies should support human-AI collaboration by:

Funding peer learning infrastructure alongside AI deployment;
Creating time and space for structured peer learning activities;
Recognizing peer learning as essential professional development;
Supporting documentation and spread of effective practices.

AI-human transformation through collaboration: A comparative view

Working with AI individually	Working with AI through structured peer networks
Powerful tools but limited adaptation Insights remain isolated Success depends on individual skill	Continuous adaptation through structured sharing Insights multiply across network through peer review Collective wisdom enhances individual capability
AI recommendations may miss local context Trial and error in isolation Slow spread of effective practices	Context-aware applications emerge through dialogue Structured experimentation with collective learning Rapid diffusion through documented innovations
Overwhelmed by rapid AI changes Struggling to keep pace alone Uncertainty about appropriate use	Collective sense-making through facilitated sessions Shared discovery in peer projects Growing confidence through structured support

The collaborative future

As AI capabilities expand, two paths emerge:

Path 1: Individuals struggle alone to make sense of AI tools, leading to uneven adoption, missed opportunities, and growing inequality between those who figure it out and those who do not.

Path 2: Structured peer networks enable collective learning about human-AI collaboration, leading to widespread effective use, continuous innovation, and shared benefit from AI advances.

What determines outcomes is how humans organize to learn and work together with AI through structured peer learning processes.

María’s projected transformation

Six months after her initial struggles, we can envision how María’s experience might transform. Through structured peer learning—project development, peer review, and facilitated dialogue—she could learn to see AI not as a foreign expert imposing solutions, but as a knowledgeable colleague whose insights she can adapt and apply.

Based on current peer learning practices, she might discover techniques from colleagues across Latin America and the rest of the world:

Methods for using AI diagnosis as a conversation starter with traditional healers;
Strategies for validating AI recommendations through community health committees;
Approaches for using AI analytics to support (not replace) community knowledge.

Following the pattern of peer learning networks, Maríawould begin contributing her own innovations through structured sharing, particularly around integrating AI insights with indigenous healing practices. Her documented approaches would spread through peer review and dialogue, helping thousands of health workers make AI truly useful in their communities.

Conclusion: The multiplication effect

AI transformation promises to augment human capabilities dramatically. Language interfaces will democratize access to advanced tools. Reasoning systems will provide expert-level analysis. Agentic AI will coordinate complex operations. These capabilities are beginning to transform what individuals can accomplish.

But the true multiplication effect will come through structured peer learning networks. When thousands of practitioners share how to work effectively with AI through systematic project work, peer review, and facilitated dialogue, they create collective intelligence about human-AI collaboration that no individual could develop alone. They transform AI from an impressive but alien technology into a natural extension of human capability.

For funders, this means the highest-impact investments combine AI tools with structured peer learning infrastructure. For policymakers, it means creating conditions where collaborative learning flourishes alongside technological deployment. For practitioners, it means embracing both AI partnership and peer collaboration through structured processes as essential to professional practice.

The future of human progress may rest on our ability to find effective ways to build powerful collaboration in networks that combine human and artificial intelligence. When we learn together through structured peer learning how to work with AI, we multiply not just individual capability but collective capacity to address the complex challenges facing our world.

AI is still emergent, changing constantly and rapidly. The peer learning methods are proven: we know a lot about how humans learn and collaborate. The question is how quickly we can scale this collaborative approach to match the pace of AI advancement. In that race, structured peer learning is not optional—it is essential.

Language as AI’s universal interface: What it means and why it matters

Reda Sadki July 16, 2025Artificial intelligence

Imagine if you could control every device, system, and process in the world simply by talking to it in plain English—or any language you speak. No special commands to memorize. No programming skills required. No technical manuals to study. Just explain what you want in your own words, and it happens.

This is the transformation Eric Schmidt described when he spoke about language becoming the “universal interface” for artificial intelligence. To understand why this matters, we need to step back and see how radically this changes everything.

The old way: A tower of Babel

Today, interacting with technology requires learning its language, not the other way around. Consider what you need to know:

To use your smartphone, you must understand apps, settings, swipes, and taps
To search the internet effectively, you need the right keywords and search operators
To work with a spreadsheet, you must learn formulas, functions, and formatting
To program a computer, you need years of training in coding languages
To operate specialized software—from medical systems to industrial controls—requires extensive training

Each system speaks its own language. Humans must constantly translate their intentions into forms machines can understand. This creates barriers everywhere: between people and technology, between different systems, and between those who have technical skills and those who do not.

The new way: Natural language as universal interface

What changes when AI systems can understand and act on natural human language? Everything.

Instead of learning how to use technology, you simply tell it what you want:

“Find all our customers who haven’t ordered in six months and draft a personalized re-engagement email for each”
“Look at this medical scan and highlight anything unusual compared to healthy tissue”
“Monitor our factory equipment and alert me if any patterns suggest maintenance is needed soon”
“Take this contract and identify any terms that differ from our standard agreement”

The AI system translates your natural language into whatever technical operations are needed—database queries, image analysis, pattern recognition, document comparison—without you needing to know how any of it works.

Why a universal interface changes everything

1. Democratization of capability

When language becomes the interface, advanced capabilities become available to everyone who can explain what they want. A small business owner can perform complex data analysis without hiring analysts. A teacher can create customized learning materials without programming skills. A farmer can optimize irrigation without understanding algorithms.

The divide between technical and non-technical people begins to disappear. What matters is not knowing how to code but knowing what outcomes you want to achieve.

2. System integration without friction

Today, making different systems work together is a nightmare of APIs, data formats, and compatibility issues. But when every system can be controlled through natural language, integration becomes as simple as explaining the connection you want:

“When a customer complains on social media, create a support ticket, alert the appropriate team based on the issue type, and draft a public response acknowledging their concern”

The AI handles all the technical complexity of connecting social media monitoring, ticketing systems, team communications, and response generation.

3. Context that travels

Unlike traditional interfaces that reset with each interaction, language-based AI systems can maintain context across time and tasks. They remember previous conversations, understand ongoing projects, and track evolving situations.

Imagine telling an AI: “Remember that analysis we did last month on customer churn? Update it with this quarter’s data and highlight what’s changed.” The system knows exactly what you’re referring to and can build on previous work.

4. Coordination at scale

When AI agents can communicate through natural language, they can coordinate complex operations without human intervention. Schmidt’s example of building a house illustrates this—multiple AI agents handling different aspects of a project, all coordinating through language:

The land-finding agent tells the regulation agent about the plot it found
The regulation agent informs the design agent about building restrictions
The design agent coordinates with the contractor agent on feasibility
Each agent can explain its actions and reasoning in plain language

Real-world implications

For business

Companies can automate complex workflows by describing them in natural language rather than programming them. A marketing manager could say: “Monitor our competitor’s pricing daily, alert me to any changes over 5%, and prepare a report on their promotional patterns.” No need for programmers, database experts, or data analysts.

For healthcare

Doctors can interact with AI diagnostic tools using medical terminology they already know, rather than learning proprietary interfaces. “Compare this patient’s symptoms with similar cases in our database and suggest additional tests based on what we might be missing.”

For education

Teachers can create personalized learning experiences by describing what they want: “Create practice problems for my students who are struggling with fractions, make them progressively harder as they improve, and let me know who needs extra help.”

For government

Policy makers can analyze complex data and model scenarios using plain language: “Show me how proposed changes to tax policy would affect families earning under $50,000 in rural areas versus urban areas.”

Five challenges ahead

This transformation is not without risks and challenges:

Accuracy: Natural language is ambiguous. Ensuring AI systems correctly interpret intentions requires sophisticated understanding of context and nuance.
Security: If anyone can control systems through language, protecting against malicious use becomes critical.
Verification: When complex operations happen through simple commands, how do we verify the AI did what we intended?
Dependency: As we rely more on AI to translate our intentions into actions, what happens to human technical skills?

The bottom line

Language as a universal interface represents a fundamental shift in how humans relate to technology. Instead of humans learning to speak machine languages, machines are learning to understand human intentions expressed naturally.

This is not just about making technology easier to use. It is about removing the barriers between human intention and digital capability. When that barrier falls, we enter Eric Schmidt’s “new epoch”—where the distance between thinking something and achieving it collapses to nearly zero.

The implications ripple through every industry, every job, every aspect of daily life. Those who understand this shift and adapt quickly will find themselves with almost magical capabilities. Those who do not may find themselves bypassed by others who can achieve in minutes what once took months.

The universal interface is coming. The question is not whether to prepare, but how quickly you can begin imagining what becomes possible when the only limit is your ability to describe what you want.

What does AI reasoning revolution mean for global health

What does AI reasoning mean for global health?

Reda Sadki July 16, 2025Artificial intelligence, Global health

When epidemiologists investigate a disease outbreak, they do not just match symptoms to known pathogens. They work through complex chains of evidence, test hypotheses, reconsider assumptions when data does not fit, and sometimes completely change their approach based on new information. This deeply human process of systematic reasoning is what artificial intelligence systems are now learning to do.

This capability represents a fundamental shift from AI that recognizes patterns to AI that can work through complex problems the way a skilled professional would. For those working in global health and education, understanding this transformation is essential.

The difference between answering and reasoning

To understand this revolution, consider how most AI works today versus how reasoning AI operates.

Traditional AI excels at pattern recognition. Show it a chest X-ray, and it can identify pneumonia by matching patterns it learned from millions of examples. Ask it about disease symptoms, and it retrieves information from its training data. This is sophisticated, but it is fundamentally different from reasoning.

Consider this scenario: An unusual cluster of respiratory illness appears in a rural community. The symptoms partially match several known diseases but perfectly match none. Environmental factors are unclear. Some patients respond to standard treatments. Others do not.

A pattern-matching AI might list possible diseases based on symptom similarity. But a reasoning AI would approach it like an epidemiologist:

“Let me examine the symptom progression timeline.”
“The geographic clustering suggests environmental or infectious cause. Let me investigate both paths.”
“Wait, these treatment responses do not align with any single pathogen. Could this be co-infection?”
“I need to reconsider. What if the environmental factor is not the cause but is affecting treatment efficacy?”

The AI actually works through the problem, forms hypotheses, recognizes when evidence contradicts its assumptions, and adjusts its approach accordingly.

How reasoning AI thinks through problems

Advanced AI systems now demonstrate visible thinking processes. When analyzing complex health data, they might:

“First, let me identify the key variables affecting disease transmission in this population.”
“I will start by calculating the basic reproduction number using standard methods.”
“These results seem inconsistent with the observed spread pattern. Let me check my assumptions.”
“I may have overlooked the role of asymptomatic carriers. Let me recalculate.”
“This aligns better with observations. Now I can project intervention outcomes.”

This is not scripted behavior. The AI works through problems, recognizes errors, and corrects its approach—much like a researcher reviewing their analysis.

Why reasoning requires massive computational power

Reasoning AI systems require thousands of times more computational resources than traditional AI. Understanding why helps explain both their power and limitations.

Think about the difference between recognizing a disease from symptoms versus investigating a novel outbreak. Recognition happens quickly: an experienced clinician identifies malaria almost instantly. But investigating an unusual disease cluster requires sustained analysis, exploring multiple hypotheses, checking each against evidence.

The same applies to AI. Traditional pattern-matching AI makes a single pass through its neural network. But reasoning AI must:

Explore multiple hypotheses simultaneously;
Check each reasoning step for logical consistency;
Backtrack when evidence contradicts assumptions;
Verify conclusions against all available data; and
Consider alternative explanations.

Each step requires intensive computation. The AI might explore hundreds of reasoning paths before reaching sound conclusions.

Matching expert performance

AI systems in mid-2025 perform at the level of graduate students in mathematics and other fields. For global health, this means AI that can:

Design epidemiological studies with appropriate controls;
Identify confounding variables in complex datasets;
Recognize when standard statistical methods do not apply; and
Develop novel approaches to emerging health challenges.

This is not about calculating faster—computers have done that for decades. It is about understanding concepts, recognizing which analytical techniques to apply, and working through novel problems.

Applications in global health

Reasoning AI transforms multiple aspects of global health work:

Outbreak investigation: AI that can integrate diverse data sources—clinical reports, environmental data, travel patterns, genetic sequences—to identify outbreak sources and transmission patterns.

Treatment optimization: Systems that reason through drug interactions, comorbidities, and local factors to recommend personalized treatment protocols.

Resource allocation: AI that understands trade-offs between prevention and treatment, immediate needs and long-term capacity building, to optimize limited resources.

Research design: Systems that can identify weaknesses in study designs, suggest improvements, and recognize when findings may not generalize to other populations.

Policy analysis: AI that reasons through complex interventions, anticipating unintended consequences and identifying implementation barriers.

What makes AI reasoning different

Five capabilities distinguish reasoning AI from pattern-matching systems:

Working memory: Reasoning AI holds multiple pieces of information active while working through problems, like a human tracking several hypotheses simultaneously.
Logical consistency: Each conclusion must follow logically from evidence and prior reasoning steps.
Error recognition: When results do not make sense, the system recognizes the problem and adjusts its approach.
Abstraction: The AI recognizes general principles and applies them to specific situations, not just memorizing solutions.
Explanation: Reasoning AI can explain its logic, making its conclusions verifiable and trustworthy.

The path forward

The reasoning revolution does not replace human expertise but augments it in powerful ways. For global health professionals, this means:

AI partners that can work through complex epidemiological puzzles;
Systems that help design culturally appropriate interventions;
Tools that identify patterns humans might miss while respecting local knowledge.

Understanding reasoning AI is no longer optional for those shaping global health. These systems are becoming intellectual partners capable of working through complex problems alongside human experts. The question is not whether to engage with this technology but how to use it effectively while maintaining human agency, judgment, and values in decisions that affect human lives.

The ability to reason—to work systematically through complex problems—has always been central to advancing human health and knowledge. Now that machines are learning this capability, we must thoughtfully consider how to harness it for global benefit while ensuring human wisdom guides its application.

Agentic AI revolution and workforce development

The agentic AI revolution: what does it mean for workforce development?

Reda Sadki July 16, 2025Artificial intelligence

Imagine hiring an assistant who never sleeps, never forgets, can work on a thousand tasks simultaneously, and communicates with you in your own language. Now imagine having not just one such assistant, but an entire team of them, each specialized in different areas, all coordinating seamlessly to achieve your goals. This is the “agentic AI revolution” —a transformation where AI systems become agents that can understand objectives, remember context, plan actions, and work together to complete complex tasks. It represents a shift from AI as a tool you use to AI as a workforce that you collaborate with.

Understanding AI agents: More than chatbots

When most people think of AI today, they think of ChatGPT or similar systems—you ask a question, you get an answer. That interaction ends, and the next time you return, you start fresh. These are powerful tools, but they are fundamentally reactive and limited to single exchanges.

AI agents are different. They work on a principle of “language in, memory in, language out.” Let’s break down what this means:

Language in: You describe what you want in natural language, not computer code. “Find me a house in California that meets these criteria…”
Memory in: The agent remembers everything relevant—your preferences, previous searches, budget constraints, past interactions. It maintains this memory across days, weeks, or months.
Language out: The agent reports back in plain language, explains what it did, and asks for clarification when needed. “I found three properties matching your criteria. Here’s why each might work…”

But here is the crucial part: between receiving your request and reporting back, the agent can take actions in the world. It can search databases, fill out forms, make appointments, send emails, analyze documents, and coordinate with other agents.

The house that agentic AI built

The example of building a house perfectly illustrates how agents transform complex projects. In the traditional approach, you would:

Spend weeks searching real estate listings yourself.
Hire a lawyer to research zoning laws and regulations.
Work with an architect to design the building.
Interview and select contractors.
Manage the construction process.
Deal with disputes if things go wrong.

Each step requires your active involvement, coordination between different professionals, and enormous amounts of time.

In the agentic model, you simply state your goal: “I want to build a house in California with these specifications and this budget.” Then:

Agent 1 searches for suitable lots, analyzing thousands of options against your criteria.
Agent 2 researches all applicable regulations, permits, and restrictions for each potential lot.
Agent 3 creates design options that maximize your preferences while meeting all regulations.
Agent 4 identifies and vets contractors, checking licenses, reviews, and past performance.
Agent 5 monitors construction progress and prepares documentation if issues arise.

These agents do not work in isolation. They communicate constantly:

The lot-finding agent tells the regulation agent which properties to research.
The regulation agent informs the design agent about height restrictions and setback requirements.
The design agent coordinates with the contractor agent about feasibility and costs.
All agents update you on progress and escalate decisions that need human judgment.

Why agentic AI changes everything

This workflow example is true of every business, every government, and every group human activity. In other words, this transformation has universal relevance.

Every complex human endeavor involves similar patterns:

Multiple steps that must happen in sequence;
Different types of expertise needed at each step;
Coordination between various parties;
Information that must flow between stages; and
Decisions based on accumulated knowledge.

Today, humans do all this coordination work. We are the project managers, the communicators, the information carriers, the decision makers at every level. The agentic revolution means AI agents can handle much of this coordination, freeing humans to focus on setting goals and making key judgments.

The memory advantage

What makes agents truly powerful is their memory. Unlike human workers who might forget details or need to be briefed repeatedly, agents maintain perfect recall of:

Every interaction and decision;
All relevant documents and data;
The complete history of a project; and
Relationships between different pieces of information.

This memory persists across time and can be shared between agents. When you return to a project months later, the agents remember exactly where things stood and can continue seamlessly.

Agentic AI from individual tools to digital teams

The revolutionary aspect is not just individual agents but how they work together. Like a well-functioning human team, AI agents can:

Divide complex tasks based on specialization;
Share information and coordinate actions;
Escalate issues that need human decision-making;
Learn from outcomes to improve future performance; and
Scale up or down based on workload.

But unlike human teams, they can:

Work 24/7 without breaks;
Handle thousands of tasks in parallel;
Communicate instantly without misunderstandings;
Maintain perfect consistency; and
Never forget critical details.

The new human role as co-worker to agentic AI

In this world, humans do not become obsolete—our role fundamentally changes. Instead of doing routine coordination and information processing, we:

Set goals and priorities;
Make value judgments;
Handle exceptions requiring creativity or empathy;
Build relationships and trust;
Ensure ethical considerations are met; and
Provide the vision and purpose that guides agent actions.

Challenges and considerations

The agentic revolution raises important questions:

Trust: How do we verify agents are acting in our interest?
Control: What happens when agents make decisions we did not anticipate?
Accountability: Who is responsible when an agent makes an error?
Privacy: What data do agents need access to, and how is it protected?
Employment: What happens to jobs based on coordination and information processing?

What can agentic AI do in 2025?

Early versions of these agents already exist in limited forms. Organizations and individuals who understand this shift early will have significant advantages. Those who continue operating as if human coordination is the only option may find themselves struggling to compete with those augmented by agentic AI teams.

Where do we go from here?

The agentic revolution represents something humanity has never had before: the ability to multiply our capacity for complex action without proportionally increasing human effort. It is as if every person could have their own team of tireless, brilliant assistants who understand their goals and work together seamlessly to achieve them.

This is not about replacing human intelligence but augmenting human capability. When we can delegate routine coordination and information processing to agents, we can focus on what humans do best: creating meaning, building relationships, making ethical judgments, and pursuing purposes that matter to us.

The world we imagine—where building a house or running a business or navigating healthcare becomes as simple as stating your goal clearly—represents a fundamental shift in how complex tasks get accomplished. Whatever the timeline for this transformation, understanding how AI agents work and what they make possible has become essential for anyone trying to make sense of where our societies are heading.

The concept is clear: AI systems that can understand goals, remember context, and coordinate actions to achieve complex outcomes. What we do with this capability remains an open question—one that will be answered not by the technology itself, but by how we choose to use it.

Peer learning outperforms technical assistance

The great technical assistance disruption: How peer networks outperform experts at a fraction of the cost

Reda Sadki July 16, 2025Writing

“If health workers do not share their challenges and solutions, we are bound to fail.” This declaration from a participant in the Teach to Reach initiative facilitated by The Geneva Learning Foundation (TGLF) cuts to the heart of a crisis that has long plagued global health technical assistance: the persistent gap between what external experts provide and what practitioners actually need.

At the annual meeting of the American Society of Tropical Medicine and Hygiene (ASTMH), TGLF’s Reda Sadki presented evidence of a quiet revolution taking place in how global health organizations approach capacity building and technical assistance. His research and practice demonstrate that digitally-enabled peer learning can overcome fundamental limitations that have constrained traditional models for decades. The implications challenge not just how we train health workers, but the entire infrastructure of expert-driven technical assistance that dominates global health.

Why we resist learning from screens

To understand why this revolution has been so long in coming, Sadki traced our resistance to digital learning back to philosophical roots that run deeper than most global health practitioners realize. The skepticism, he argued, stems from a fundamental assumption about how real learning occurs — an assumption that shapes everything from how we design training programs to how we structure technical assistance.

“Plato initiated our traditional negative view of the written word,” Sadki explained, describing how the ancient philosopher believed that writing “detaches the message from its author and transforms it into a dead thing, a text.” For Plato, authentic learning required direct interaction between teacher and student. Anything mediated — whether by writing or, by extension, digital technology — was considered a pale imitation of real knowledge transfer.

This ancient skepticism persists in modern global health, where the dominant assumption is that learning means recalling information and teaching means transmitting that information through direct instruction. Face-to-face workshops and expert-led training sessions are considered “real” technical assistance, while digital alternatives are viewed as convenient but inferior substitutes.

“It is a false dichotomy to distinguish between, to oppose our lived reality to the digital one,” Sadki argued. “The digital one is lived also. It is also reality.” Yet this dichotomy continues to shape technical assistance models that prioritize flying experts around the world to deliver content in person, even when evidence suggests digital approaches may be more effective.

Indeed, the evidence is striking. Two major meta-analyses comparing learning modalities found that “distance learning results have been consistently better” than traditional face-to-face approaches, “and that has been the case since 1991.” Yet global health technical assistance remains largely wedded to what Bill Cope and Mary Kalantzis call a “didactic learning architecture” — the familiar setup where external experts deliver content to passive recipients arranged “in rows, they do not speak to each other, the teacher sits at the front.”

When information transmission fails

The inadequacy of information transmission models becomes clear when considering the nature of challenges that health workers actually face. Most global health training assumes that the problem is a lack of information — that if practitioners simply knew more facts or protocols, they would perform better. This assumption drives technical assistance focused on delivering standardized content through lectures, presentations, and workshops.

But research in learning science reveals a more complex reality. “When knowledge is a river, not a reservoir, process, not a product,” expert-led information transmission breaks down, Sadki observed. Modern knowledge workers have “around 10 percent” of the knowledge they need “right there in your brain,” with “90 percent of what you need to know going to come from other humans, or increasingly from machines.”

This insight challenges the foundation of traditional technical assistance. If practitioners need to access knowledge through connections rather than storage, then the goal should not be filling their heads with information but connecting them to networks where knowledge flows. Yet most capacity building programs continue to focus on what Sadki called “content-driven learning” rather than connection-driven learning.

The shift required is profound. Rather than positioning external experts as the primary source of knowledge, effective technical assistance must create what Connell Foley described as “a fundamental shift from being an expert who provides answers, to being a facilitator who, through critical thought, can develop questions that prompt others to analyze and develop strategies to address their own needs.”

Digital technologies as technical assistance disruptors

The breakthrough comes when digital technologies “enable you to defy distance and boundaries in order to connect with others and learn from them.” This represents more than technological innovation — it challenges the basic economics and power structures of traditional technical assistance.

Consider the conventional model: international organizations identify capacity gaps, hire external experts, and deploy them to deliver training. This approach assumes that valid knowledge flows primarily from international experts to local practitioners. It requires significant funding for travel, venues, and expert fees, limiting both reach and frequency of interaction.

Digitally-enabled peer learning turns this model on its head. “Peer learning has always been there,” Sadki noted. “Learning from others, learning from people who are like yourself has always been important, but it has been limited to those within your physical space.” Digital technologies remove that spatial limitation, enabling practitioners facing similar challenges across different contexts to learn directly from each other.

Cristina Guerrero, an emergency health doctor who leads a helicopter rescue team in Cadiz, Spain, experienced this transformation through the foundation’s #Ambulance! programme with the International Federation of Red Cross and Red Crescent Societies (IFRC) and the International Committee of the Red Cross (ICRC). “I thought I already knew how to face violence,” she reflected. “Then I heard how they do things in other parts of the world. I learned how I can do my work differently. I became mindful in new ways.”

Her experience illustrates what traditional technical assistance models struggle to achieve: not just information transfer, but genuine transformation of practice. Sadki noted that peer learning produced “changes in mindfulness” — higher-order learning that most would consider “impossible to achieve by digital means.” Yet “digital combined with social and peer learning made it possible.”

Evidence of a new technical assistance model

TGLF’s collaboration with the World Health Organization, implementing 46 cohorts of peer learning initiatives focused on immunization and other technical areas, provided rigorous evidence that peer learning can replace traditional expert-led technical assistance. The first impact evaluation of this collaboration in January 2019 found that “these are more than just courses. These are interventions designed to foster and improve practice at every level.”

This approach represents what researcher Alexandra Nastase and colleagues would recognize as a fourth model of technical assistance, beyond their three categories of capacity substitution, supplementation, and development. This model challenges fundamental assumptions about who holds valid knowledge and how capacity building should occur.

The most dramatic validation came through TGLF’s Impact Accelerator mechanism. When 644 alumni signed a pledge to achieve impact in July 2019, something remarkable happened. “‘We are together’ became a slogan for the individuals involved,” Sadki observed. The measurable results were astonishing: participants who engaged in peer learning showed seven times higher rates of project implementation compared to a control group that did not engage in peer learning activities to support and learn from each other.

The scale of subsequent initiatives has been even more striking. The Movement for Immunization Agenda 2030, launched in March 2022, grew to 6,185 participants in its first two weeks. In the first four months, more than 1,000 developed action plans, and over 4,000 joined a new Impact Accelerator. Within this period, 30 percent of participants reported successful implementation of their local projects — implementation rates that far exceed what traditional technical assistance typically achieves.

Beyond the expert monopoly

Perhaps most significantly, the Geneva Learning Foundation’s model has enabled practitioners to transcend traditional power structures and drive their own capacity building agendas. Rather than waiting for external technical assistance, practitioners began forming organic learning networks that generate solutions from the ground up.

A collective of women who deliver vaccines formed naturally and began leading discussions on making HPV vaccination work for women and girls at successive Teach to Reach peer learning events facilitated by TGLF.
More than 1,300 health practitioners co-authored a manifesto for global health “with no international agencies or international consultants involved.”
When the foundation surveyed 1,234 health professionals about climate change threats to the health of their communities, nearly half identified it as the highest threat level — and began making connections to learn from and support each other.

These examples illustrate a fundamental shift in the locus of knowledge creation. Traditional technical assistance assumes that solutions flow from international experts to local implementers. The foundation’s model demonstrates that practitioners facing similar challenges often hold the keys to solutions, and that the role of technical assistance should be creating conditions for them to learn from each other.

Transforming the technical assistance paradigm

The evidence points toward what Sadki called “an opportunity for transformation that may be much harder to achieve [than what we already know how to do], but with a far greater return on the investment.” The transformation involves “empowering health professionals to drive improvement from the ground up, connecting them to their peers, and linking to global guidance.”

This requires fundamentally different approaches to capacity building. Instead of the traditional model where external experts deliver knowledge to passive recipients, effective peer learning creates what Sadki described as “circular, interactive configurations” where practitioners engage directly with each other’s experiences. The facilitation may be digital, but the knowledge exchange is profoundly collaborative.

By systematically applying insights from social learning, networked learning, and digital learning, the foundation has created what amounts to “a human knowledge network” that “unites practitioners and those who support them in a shared pledge to turn knowledge into action.”

The fact that these “recent advances in learning science remain largely unknown in global health, at least in some quarters” remains a challenge.

The future of technical assistance

As global health faces increasingly complex challenges — from climate change to pandemic preparedness to health system resilience — the ability to harness collective intelligence through peer learning may prove essential. The evidence suggests that effective solutions emerge not from more sophisticated expert-driven interventions, but from better systems for enabling practitioners to learn from each other.

The implications extend beyond individual capacity building to systemic change. When health workers share challenges and solutions across contexts, they create what Sadki called “a river of knowledge” that practitioners can dip into when they need to solve a problem. This enables rapid adaptation and innovation at scales that traditional technical assistance cannot achieve.

The revolution in global health technical assistance may ultimately be less about technology and more about recognition — acknowledging that expertise is distributed rather than concentrated, and that the future lies not in perfecting systems for delivering knowledge from experts to practitioners, but in creating conditions for practitioners to take action by combining what they know because they are there every day with the best available global knowledge – reshaping global knowledge in the process.

References

Feenberg, A., 1989. The written world: On the theory and practice of computer conferencing, in: Mason, R., Kaye, A. (Eds.), Mindweave: Communication, Computers, and Distance Education. Pergamon Press, pp. 22–39.

Foley, C., 2008. Developing critical thinking in NGO field staff. Development in Practice 18, 774–778. https://doi.org/10.1080/09614520802386827

Jurgenson, N., 2012. The IRL Fetish. The New Inquiry 6.

Kalantzis M, Cope B. Didactic literacy pedagogy. In: Literacies. Cambridge University Press; 2012:63-94.

Means, B., Toyama, Y., Murphy, R., Bakia, M., Jones, K., 2010. Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. U.S. Department of Education Office of Planning, Evaluation, and Policy Development Policy and Program Studies Service.

Nastase, A., Rajan, A., French, B., Bhattacharya, D., 2020. Towards reimagined technical assistance: the current policy options and opportunities for change. Gates Open Res 4, 180. https://doi.org/10.12688/gatesopenres.13204.1

Neumann, Y., Shachar, M., 2010. Twenty Years of Research on the Academic Performance Differences Between Traditional and Distance Learning: Summative Meta-Analysis and Trend Examination. MERLOT Journal of Online Learning and Teaching 6.

Sadki, R. (2022). Learning for Knowledge Creation: The WHO Scholar Program. Reda Sadki. https://doi.org/10.59350/j4ptf-x6x22

Sadki, R. (2023). Learning-based complex work: how to reframe learning and development. Reda Sadki. https://doi.org/10.59350/7fe95-1fz14

Sadki, R. (2024). Knowing-in-action: Bridging the theory-practice divide in global health. Reda Sadki. https://doi.org/10.59350/4evj5-vm802

Watkins, K.E., Sandmann, L.R., Dailey, C.A., Li, B., Yang, S.-E., Galen, R.S., Sadki, R., 2022. Accelerating problem-solving capacities of sub-national public health professionals: an evaluation of a digital immunization training intervention. BMC Health Services Research 22. https://doi.org/10.1186/s12913-022-08138-4

The funding crisis solution hiding in plain sight

Reda Sadki June 27, 2025Global health

“I did not realize how much I could do with what we already have.”

A Nigerian health worker’s revelation captures what may be the most significant breakthrough in global health implementation during the current funding crisis. While organizations worldwide slash programs and lay off staff, a small Swiss non-profit, The Geneva Learning Foundation (TGLF), is demonstrating how to achieve seven times greater likelihood of improved health outcomes while cutting costs by 90 percent.

The secret lies not in new technology or additional resources, but in something deceptively simple: health workers learning from and supporting each other.

Nigeria: Two weeks to connect thousands, four weeks to change, and six weeks to outcomes

On June 26, 2025, representatives from 153 global health and humanitarian organizations gathered for a closed-door briefing seeking proven solutions to implementation challenges they knew all too well. TGLF presented evidence from the Nigeria Immunization Agenda 2030 Collaborative that sounds almost too good be true to senior leaders who have to make difficult decisions given the funding cuts: documented results at unprecedented speed and scale – and at lower cost.

Working with Gavi, Nigeria’s Primary Health Care Development Agency, and UNICEF, they facilitated connections among 4,300 health workers and more than 600 local organizations across all Nigerian states, in just two weeks. Not fleeting digital clicks, but what Executive Director Reda Sadki calls “deep, meaningful engagement, sharing of experience, problem solving together.”

The challenge was reaching zero-dose children in fragile areas affected by armed conflict. The timeline was impossible by traditional standards. The results transformed many skeptics into advocates – including those who initially said it sounded too good to be true.

A civil society organization (CSO) volunteer reported that government staff initially dismissed the initiative: “They heard about this, thought it was just another CSO initiative. Two weeks in, they came back asking how to join.”

What happened next addresses the most critical question about peer learning approaches: do health workers learning from each other actually improve health outcomes?

TGLF’s comparative research demonstrated that groups using structured peer learning are seven times more likely to achieve measurable health improvements versus conventional approaches.

In Nigeria, health workers learned the “five whys” root cause analysis from each other. Many said no one had ever asked them: “What do you think we should do?” or “Why do you think that is?” The transformation was both rapid and measurable.

For example, at the program start, only 25 percent knew their basic health indicators for local areas. “I collect these numbers and pass them on, but I never realized I could use them in my work,” participants reported.

Four weeks in, they had produced 409 root cause analyses. Many realized that their existing activities were missing these root causes. After six weeks, health workers began credibly reporting attribution of new activities that led to finding and vaccinating zero-dose children.

Given limited budget, TGLF had to halt development. But here is the key point: more than half of participating have maintained and continued the peer support network independently, addressing sustainability concerns that plague traditional capacity-building efforts.

The snowball effect at scale

The breakthrough emerged from what Sadki describes as reaching “critical mass” where motivated participants pull others along. “This requires clearing the rubble of all the legacy of top-down command and control systems, figure out how to negotiate hierarchies, especially because government integration is systematically our goal.”

Nigeria represents one of four large-scale implementations demonstrating consistent results. In Côte d’Ivoire, 501 health workers from 96 districts mapped out 3.5 million additional vaccinations in four weeks. Global initiatives are likely to cost no more than a single country-specific program: the global Teach to Reach network has engaged 24,610 participants across more than 60 countries. The global Movement for Immunization Agenda 2030, launched in March 2022, grew from 6,186 to more than 15,000 members in less than four months.

The foundation tracks what they call a “complete measurement chain” from individual motivation through implementation actions to health outcomes. Cost efficiency stems from scale and sustainability, with back-of-envelope calculations suggesting 90 percent cost reduction compared to traditional methods.

Solving the abundance paradox

“You touched upon an important issue that I am struggling with—the abundance of guidance that my own organization produces and also guidance that comes from elsewhere,” noted a senior manager from an international humanitarian network during the briefing. “It really feels intriguing to put all that material into a course and look at what I am going to do with this. It is a precious process and really memorable and makes the policies and materials relevant.”

This captures a central challenge facing global health organizations: not lack of knowledge, but failure to translate knowledge into action. The peer learning model transforms existing policies and guidelines into peer learning experiences where practitioners study materials to determine specific actions they will take.

“Learning happens not simply by acquiring knowledge, but by actually doing something with it,” Sadki explained.

For example, a collaboration with Save the Children converted a climate change policy brief into a peer learning course accessed by more than 70,000 health workers, developed and deployed in three days with initial results expected within six weeks.

Networks that outlast the funding crisis

The foundation’s global network now includes more than 70,000 practitioners across 137 countries, with geographic focus on nations with highest climate vulnerability and disease burden. More than 50 percent are government staff. More than 80 percent work at district and community levels.

Tom Newton-Lewis, a leading health systems researcher and consultant who attended the briefing, captured what makes this approach distinctive: “I am always inspired by the work of TGLF. There are very few initiatives that work at scale that walk the talk on supporting local problem solving, and mobilize systems to strengthen themselves.”

This composition ensures that peer learning initiatives operate within rather than parallel to official health systems. More than 1,000 national policy planners connect directly with field practitioners, creating feedback loops between strategy development and implementation reality.

Networks continue functioning when external support changes. The foundation has documented continued peer connections through network analysis, confirming that established relationships maintain over time.

Three pathways forward

The foundation outlined entry points for organizations seeking proven implementation approaches. First, organizations can become program partners, providing their staff access to existing global programs while co-developing new initiatives. Available programs include measles, climate change and health, mental health, non-communicable diseases, neglected tropical diseases, immunization, and women’s leadership.

Second, using the model to connect policy and implementation at scale and lower cost. Timeline: three days to build, four to six weeks for initial results. Organizations gain direct access to field innovations while receiving evidence-based feedback on what actually works in practice.

Third, testing the model on current problems where policy exists but implementation remains inconsistent. Organizations can connect their staff to practitioners who have solved similar problems without additional funding. Timeline: six to eight weeks from start to documented results.

The foundation operates through co-funding partnerships rather than grant-making, with flexible arrangements tailored to partner capacity and project scope. What they call “economy of effort” often delivers initiatives spanning more than 50 countries for the cost of single-country projects.

Adaptability across contexts

The model has demonstrated remarkable versatility across different contexts and challenges. The foundation has successfully adapted the approach to new geographic areas like Ukraine and thematic areas like mental health and psychosocial support. Each adaptation requires understanding specific contexts, needs, and goals, but the fundamental peer learning principles remain consistent.

An Indian NGO raised a fundamental challenge: “Where we struggle with program implementation post-funding is without remuneration frontline workers. Although they want to bring change in the community, are motivated, and have enough data, cannot continue.”

Sadki’s response: “By recognizing the capabilities for analysis, for adaptation, for carrying out more effective implementation because of what they know, because they are there every day, that should contribute to a growing movement for recognition that CHWs in particular should be paid for the work that they do.”

The path forward

The Nigerian health worker’s realization—discovering untapped potential in existing resources—represents more than individual transformation. It demonstrates how peer learning unlocks collective intelligence already present within communities and health systems.

In two weeks, health workers connected with each other across Nigeria’s most challenging regions, facilitated by the foundation’s proven methodology. By the sixth week, they had begun reporting credible, measurable health improvements. The model works because it values local knowledge, creates peer support systems, and integrates with government structures rather than bypassing them.

With funding cuts forcing difficult choices across global health, this model offers documented evidence that better health outcomes can cost less, sustainable networks continue without external support, and local solutions scale globally. For organizations seeking proven implementation approaches during resource constraints, the question is not whether they can afford to try peer learning, but whether they can afford not to.

When funding shrinks, impact must grow: the economic case for peer learning networks

Reda Sadki June 17, 2025Global health, The Geneva Learning Foundation

Humanitarian, global health, and development organizations confront an unprecedented crisis. Donor funding is in a downward spiral, while needs intensify across every sector. Organizations face stark choices: reduce programs, cut staff, or fundamentally transform how they deliver results.

Traditional capacity building models have become economically unsustainable. Technical assistance, expert-led workshops, international travel, and venue-based training are examples of high-cost, low-volume activities that organizations may no longer be able to afford.

Yet the need for learning, coordination, and adaptive capacity has never been greater.

The opportunity cost of inaction

Organizations that fail to adapt face systematic disadvantage. Traditional approaches cannot survive current funding constraints while maintaining effectiveness. Meanwhile, global challenges intensify: climate change drives new disease patterns; conflict disrupts health systems; demographic transitions strain capacity.

These complex, interconnected challenges require adaptive systems that respond at the speed and scale of emerging threats. Organizations continuing expensive, ineffective approaches will face programmatic obsolescence.

Working with governments and trusted partners that include UNICEF, WHO, Gates Foundation, Wellcome Trust, and Gavi (as part of the Zero-Dose Learning Hub), the Geneva Learning Foundation’s peer learning networks have consistently demonstrated they can deliver measurably superior outcomes while reducing costs by up to 86% compared to conventional approaches.

Peer learning networks offer both immediate financial relief and strategic positioning for long-term sustainability. The evidence spans nine years, 137 countries, and collaborations with the most credible institutions in global health, humanitarian response, and research.

The unsustainable economics of traditional capacity building

A comprehensive analysis reveals the structural inefficiencies of conventional approaches. Expert consultants command daily rates of $800 or more, plus travel expenses. International workshops may require $15,000-30,000 for venues alone. Participant travel and accommodation averages $2,000 per person. A standard 50-participant workshop costs upward of $200,000.

When factoring limited sustainability, the economics become even more problematic. Traditional approaches achieve measurable implementation by only 15-20% of participants within six months. This translates to effective costs of $10,000-20,000 per participant who actually implements new practices.

A rudimentary cost-benefit analysis demonstrates how peer learning networks restructure these economics fundamentally.

Component	Traditional approach	Peer learning networks	Efficiency gain
Cost per participant	$1,850	$267	86% reduction
Implementation rate	15-20%	70-80%	4x higher success
Duration of engagement	2-3 days	90+ days	30x longer
Post-training support	None	Continuous network	Sustained capacity

Learn more: Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

Evidence of measurable impact at scale

Value for money requires clear attribution between investments and outcomes.

In January 2020, we compared outcomes between two groups. Both had intent to take action to achieve results. Health workers using structured peer learning were seven times more likely to implement effective strategies resulting in improved outcomes, compared to the other group that relied on conventional approaches.

What about speed and scale?

In July 2024, working with Nigeria’s National Primary Health Care Development Agency (NPHCDA) and UNICEF, we connected 4,300 health workers across all states and 300+ local government areas within two weeks. Over 600 local organizations including government facilities, civil society, faith-based groups, and private sector actors joined this Immunization Collaborative.

With two more weeks, participants produced 409 peer-reviewed root cause analyses. By Week 6, we began to receive credible vaccination coverage improvements after six weeks, especially in conflict-affected northern regions where conventional approaches had consistently failed. The total programme cost was equivalent to 1.5 traditional workshops for 75 participants. Follow-up has shown that more than half of the participants are staying connected long after TGLF’s “jumpstarting” activities, driven by intrinsic motivation.

Côte d’Ivoire demonstrates crisis response capability. Working with Gavi and the Ministry of Health, we recruited 501 health workers from 96 districts (85% of the country) in nine days ahead of the country’s COVID-19 vaccination campaign in November 2021. Connected to each other, they shared local solutions and supported each other, contributing to vaccination of an additional 3.5 million additional people at $0.26 per vaccination delivered.

TGLF’s model empowers health workers to share knowledge, solve local challenges, and implement solutions via a digital platform. Unlike top-down training and technical assistance, it fosters collective intelligence, enabling rapid adaptation to crises. Since 2016, TGLF has mobilized networks for immunization, COVID-19 response, neglected tropical diseases (NTDs), mental health and psychosocial support, noncommunicable diseases, and climate-health resilience.

These cases illustrate the ability of TGLF’s model to address strategic global priorities—equity, resilience, and crisis response—while maximizing efficiency. This model offers a scalable, low-cost alternative that delivers measurable impact across diverse priorities.

Our mission is to share such breakthroughs with other organizations and networks that are willing to try new approaches.

Resource allocation for maximum efficiency

Our partnership analysis reveals optimal resource allocation patterns that maximize impact while minimizing cost:

Human resources (85%): Action-focused approach leveraging human facilitation to foster trust, grow leadership capabilties, and nurture networks with a single-minded goal of supporting implementation to rapidly and sustainably achieve tangible outcomes.
Digital infrastructure (10%): Scalable platform development enabling unlimited concurrent participants across multiple countries.
Travel (5%): Minimal compared to 45% in traditional approaches, limited to essential coordination where social norms require face-to-face meetings, for example in partnership engagement with governments.

This structure enables remarkable economies of scale. While traditional approaches face increasing per-participant costs, peer learning networks demonstrate decreasing unit costs with growth. Global initiatives reaching 20,000+ participants across 60+ countries operate with per-participant costs under $10.

Sustainability through combined government and civil society ownership

Sustainability is critical amidst funding cuts. TGLF’s networks embed organically within government systems, involving both central planners in the capital as well as implementers across the country, at all levels of the health system.

Country ownership: Programs work within existing health system structures and national plans. Networks include 50% government staff and 80% district/community-level practitioners—the people who actually deliver services. In Nigeria, 600+ local organizations – both private and public – collaborated, embedding learning in both civil society and government structures.

Sustainability: In Côte d’Ivoire, 82% sustained engagement without incentives, fostering self-reliant networks. 78% said they no longer needed any assistance from TGLF to continue.

This approach enhances aid effectiveness, reducing dependency on external funding.

Aid effectiveness: Rather than bypassing systems, peer learning strengthens existing infrastructure. Networks continue functioning when external funding decreases because they operate through established government channels linked to civil society networks.

Transparency: Digital platforms create comprehensive audit trails providing unprecedented visibility into program implementation and results for donor oversight.

Implementation pathways for resource-constrained organizations

Organizations can adopt peer learning approaches through flexible pathways designed for immediate deployment.

Rapid response initiatives (2-6 weeks to results): Address critical challenges requiring immediate mobilization. Suitable for disease outbreaks, humanitarian emergencies, or longer-term policy implementation.
Program transformation (3-6 months): Convert existing technical assistance programs to peer learning models, typically reducing costs by 80-90% while expanding reach, inclusion, and outcomes.
Cross-portfolio integration: Single platform investments serve multiple technical areas and geographic regions simultaneously, maximizing efficiency across donor portfolios with marginal costs approaching zero for additional countries or topics.

The strategic choice

The funding environment will not improve. Economic uncertainty in traditional donor countries, competing domestic priorities, and growing skepticism about aid effectiveness create permanent pressure for better value for money.

Organizations face a fundamental choice: continue expensive approaches with limited impact, or transition to emergent models that have already shown they can achieve superior results at dramatically lower cost while building lasting capability.

The question is not whether to change—budget constraints mandate adaptation. The question is whether organizations will choose approaches that thrive under resource constraints or continue hoping that some donors will fill the gaping holes left by funding cuts.

The evidence demonstrates that peer learning networks achieve 86% cost reduction while delivering 4x implementation rates and 30x longer engagement. These gains are not theoretical—they represent verified outcomes from active partnerships with leading global institutions.

In an era of permanent resource constraints and intensifying challenges, organizations that embrace this transformation will maximize their mission impact. Those that do not will find themselves increasingly unable to serve the communities that depend on their work.

PanoramAI Reda Sadki artificial intelligence

The business of artificial intelligence and the equity challenge

Reda Sadki June 13, 2025Artificial intelligence, The Geneva Learning Foundation

Since 2019, when The Geneva Learning Foundation (TGLF) launched its first AI pilot project, we have been exploring how the Second Machine Age is reshaping learning. Ahead of the release of the first framework for AI in global health, I had a chance to sit down with a group of Swiss business leaders at the PanoramAI conference in Lausanne on 5 June 2025 to share TGLF’s insights about the significance and potential of artificial intelligence for global health and humanitarian response. Here is the article posted by the conference to recap a few of the take-aways.

The Global Equity Challenger

At the Panoramai AI Summit, Reda Sadki, leader of The Geneva Learning Foundation, delivered provocative insights about AI’s impact on global equity and the future of human work. Drawing from humanitarian emergency response and global health networks, he challenged comfortable assumptions about AI’s societal implications.

The job displacement reality

Reda directly confronted panel optimism about job preservation: “One of the things I’ve heard from fellow panelists is this idea that we can tell employees AI is not coming for your job. And I struggle to see that as anything other than deceitful or misleading at best. ”

Eliminating knowledge worker positions in education

“In one of our programmes, after six months we were able to use AI to replace key functions initially performed by humans. Humans helped us figure out how to do it. We then refocused a smaller team on tasks that we cannot or do not want to automate. We tried to do this openly.”

What’s left for humans to do?

“These machines are already learning faster and better than us, and they are doing so exponentially. Right now, what’s left for humans currently is the facilitation, facilitating connections in a peer learning system. We do not yet have agents that can facilitate, that can read the room, that can help humans understand.”

Global access inequities

Reda highlighted three critical equity challenges: geographic access restrictions (‘geolocking’), transparency expectations around AI usage, and punitive accountability systems that discourage innovation in humanitarian contexts. “Somebody who uses AI in that context is more likely to be punished than rewarded, even if the outcomes are better and the costs are lower. ”

Emerging markets disconnect

“Even though that’s where the future markets are likely to be for AI, ” Reda observed limited engagement with Africa, Asia, and Latin America among attendees, highlighting a strategic blindness to global AI market evolution.

Organizational evolution question

Reda posed fundamental questions about future organizational structures, questioning whether traditional hierarchical models with management layers will remain dominant “two years or five years down the line. ”

Network-based innovation vision

“We’ve nurtured the emergence of a global network of health workers sharing their observations of climate change impacts on the health of communities they serve. This is already powerful for preparedness and response, but we’re trying to find ways to weave in and embed AI as co-workers and co-thinkers to help health workers harness messy, complex, large-volume climate data.”

Exponential learning challenge

“These machines are already learning faster and better than us and that, and they’re doing so exponentially better than us. It’s pretty clear what, you know, what keeps me awake at night is what what’s left for humans. ”

Key Achievement: Reda demonstrated how honest assessment of AI’s transformative impact requires abandoning comfortable narratives about job preservation, positioning global leaders to address equity challenges while identifying uniquely human capabilities in an AI-augmented world.

Reda Sadki serves as Executive Director of The Geneva Learning Foundation (TGLF), a Swiss non-profit. Concurrently, he maintains his position as Chief Learning Officer at Learning Strategies International (LSi) since 2013, where he helps international organizations improve their change execution capabilities. TGLF, under his guidance, catalyzes large-scale peer networks of frontline actors across 137 countries, developing learning experiences that transform local expertise into innovation and measurable results.

Image: PanoramAI (Raphaël Briner).

Global health: learning to do more with less

Reda Sadki May 20, 2025Global health

In a climate of funding uncertainty, what if the most cost-effective investments in global health weren’t about supplies or infrastructure, but human networks that turn learning into action? In this short review article, we explore how peer learning networks that connect human beings to learn from and support each other can transform health outcomes with minimal resources.

The common thread uniting the different themes below reveals a powerful principle for our resource-constrained era: structured peer learning networks consistently deliver outsized impact relative to their cost.

Whether connecting health workers battling vaccine hesitancy in rural communities, maintaining essential immunization services during a global pandemic, supporting practitioners helping traumatized Ukrainian children, integrating AI tools ethically, or amplifying women’s voices from the frontlines – each case demonstrates how connecting practitioners across geographical and hierarchical boundaries transforms individual knowledge into collective action.

When health systems face funding shortfalls, these examples suggest that investing in human knowledge networks may be the most efficient approach available: they adapt to local contexts, identify solutions that work without additional resources, spread innovations rapidly, and build resilience that extends beyond any single intervention.

As one practitioner noted, “There’s a lot of trust in our network” – a resource that, unlike material supplies, grows stronger the more it’s used.

Sustaining gains in HPV vaccination coverage without additional resources

Recent analysis from TGLF’s Teach to Reach programme is providing valuable insights that both confirm and extend our understanding about what drives successful vaccination campaigns.

“Through peer learning networks, we discovered, for example, that tribal communities may show less vaccine hesitancy than urban populations, teachers could be more influential than health workers in driving vaccination acceptance, and religious institutions can become powerful allies,” explains TGLF’s Charlotte Mbuh. Other strategies include cancer survivors serving as advocates, WhatsApp groups connecting community health workers, and schoolchildren becoming effective messengers to initiate family conversations about vaccination

TGLF’s findings are based on analysis of implementation strategies shared by over 16,000 health professionals. Because they emerged through peer learning activities, participants got an immediate benefit. Now the real question is whether global partners and funders are recognize the significance and value of such field-based insights.

Most remarkably, analysis revealed that “success was often independent of resource levels” and “informal networks proved more important than formal ones” in sustaining high HPV vaccination coverage – suggesting that alongside material inputs, knowledge connections play a critical and often undervalued role.

Read the full article: HPV vaccination: New learning and leadership to bridge the gap between planning and implementation

5 years on: what the COVID-19 Peer Hub taught us about pandemic preparedness

When routine immunization services faced severe disruption in 2020, placing over 80 million children at risk, TGLF and the Bill & Melinda Gates Foundation (BMGF) supported a digital network connecting more than 6,000 frontline health workers across Africa, Asia, and Latin America. The results demonstrate why knowledge networks matter during crises.

Within just 10 days, the network generated 1,200+ ideas and developed 700 peer-reviewed action plans. Most significantly, implementation rates were seven times higher than conventional approaches, with collaborative participants achieving 30% better outcomes in maintaining essential health services.

“This approach complemented traditional models by recognizing frontline workers as experts in their own contexts,” says Mbuh. Quantitative assessment showed structured peer learning achieved efficacy scores of 3.2 on a 4-point scale, compared to 1.4 for traditional cascade training – providing evidence that practitioners benefit from both expert guidance and structured horizontal connections.

Read the full article: How can we reliably spread evidence-based practices at the speed and scale modern health challenges demand?

Peer learning for Psychological First Aid: Supporting Ukrainian children

The EU-funded programme on Psychological First Aid (PFA) for children affected by the humanitarian crisis in Ukraine reveals how peer learning creates value that enhances technical training.

During a recent ChildHub webinar, TGLF’s Reda Sadki outlined five unique benefits practitioners gain: contextual wisdom that complements standardized guidance, pattern recognition across diverse cases, validation of experiential knowledge, real-time problem-solving for urgent challenges, and professional resilience in difficult circumstances.

One practitioner, Serhii Federov, helped a frightened girl during rocket strikes by focusing on her teddy bear – illustrating how field adaptations enrich formal protocols. Another noted: “There is a lot of trust in our network,” highlighting how sharing experiences reduces isolation while building technical capacity.

With multiple entry points from microlearning modules to intensive peer learning exercises, this programme demonstrates how even in active crisis zones, structured knowledge sharing can deliver immediate improvements in service quality.

Artificial Intelligence as co-worker: Redefining power in global health

As technological tools transform global health practice, a new thought-provoking podcast (led, of course, by Artificial Intelligence hosts) examines how AI could reshape knowledge production in resource-constrained settings.

Based on TGLF’s Reda Sadki’s new article and framework for AI in global health, the podcast uses a specific case study to explore the “transparency paradox” practitioners face – navigating how to incorporate AI tools within existing global health accountability structures.

The podcast outlines TGLF’s framework for integrating AI responsibly in global health contexts, emphasizing: “It’s not about replacing human expertise, it’s about making it stronger.” This approach prioritizes local context and community empowerment while ensuring ethical considerations remain central.

As technological adoption accelerates across global health settings, frameworks that recognize existing dynamics become increasingly essential for ensuring equitable benefits.

Read the full article: Artificial intelligence, accountability, and authenticity: knowledge production and power in global health crisis

Women inspiring women: Amplifying voices from the frontlines

The “Women Inspiring Women” initiative amplifies the experiences of 177 women health workers from Africa, Asia, and Latin America through both a published book and peer learning course launched on International Women’s Day (IWD).

These women share personal stories and advice written as letters to their daughters, offering unique perspectives from cities, villages, refugee camps, and conflict zones. Dr. Eugenia Norah Chigamane from Malawi writes: “Pursuing a career in health work is not for the faint hearted,” while Kinda Ida Louise, a midwife from Burkina Faso, advises: “Never give up in the face of obstacles and difficulties, because there is always a positive point in every situation.”

The initiative follows TGLF’s proven methodology: immersion in stories, personal reflection, peer exchange, and developing action plans – transforming personal narratives into structured learning that drives institutional change. With women forming two-thirds of the global health workforce yet remaining underrepresented in leadership, this approach addresses both individual empowerment and systemic transformation.

Get the book “Women inspiring women” and enroll in the free learning course here.

As we face an era of unprecedented funding constraints in global health, these examples demonstrate a powerful truth: networked learning approaches consistently deliver remarkable outcomes across diverse contexts.

By connecting practitioners across boundaries, The Geneva Learning Foundation facilitates the transformation of individual knowledge into collective action – creating the resilience and adaptability our health systems urgently need.

The evidence is compelling: investing in human knowledge networks may be among the most efficient pathways to sustainable health impact.

Equity matters: A practical approach to identify and eliminate biases

Patterns of prejudice: Connecting the dots helps health workers combat bias worldwide

Reda Sadki April 12, 2025Global health

English | Français

“I noticed that every time he went to appointments or emergency services, he was often met with suspicion or treated as if he was exaggerating his symptoms,” shared a community support worker from Canada, describing how an Indigenous teenager waited three months for mental health services while non-Indigenous youth were seen within weeks.

This testimony was just one of hundreds shared during an unusual global gathering where frontline health workers confronted an uncomfortable truth: healthcare systems worldwide are riddled with biases that determine who lives and who dies.

“Equity Matters: A Practical Approach to Identify and Eliminate Biases,” a special event hosted by the Geneva Learning Foundation (TGLF) on 10-11 April 2025, drew nearly 5,000 health professionals from 72 countries. What made the event distinctive wasn’t just its scope, but its approach: creating a forum where community health workers from rural Nigeria could share insights alongside WHO officials from Switzerland, where district nurses from South Sudan could analyze cases with medical college professors from India.

When healthcare isn’t equal: Global patterns emerge

Despite working in vastly different contexts, participants described remarkably similar patterns of bias.

“A pregnant woman was about to deliver in the hospital, but the doctor said they need to deposit 500,000 naira before she can touch the woman,” recounted Onosi Chikaodiri Peter, a community health worker with Light Bringer’s Outreach in Nigeria. “The husband was begging, pleading, with 100,000 naira, telling the doctor that he could sell all his livestock to make sure that the wife was okay. But the doctor wouldn’t attend to the woman. Along the line, the woman gave up. The child died.”

Dr. Tusiime Ramadhan, who works with Humanitarian Volunteers International in Uganda, observed the same pattern: “People with money are referred to private clinics and hospitals for better health services often owned by the same government workers who sent them there.”

Some biases manifest in subtler ways. Hussainah Abba Ali, who works with Impact Santé Afrique in Cameroon, described seeking treatment for malaria during her university years: “Because I was a young woman, the nurse assumed I was just exaggerating. She barely examined me, gave me paracetamol and told me to rest. I later found out that several men who came in after me with similar symptoms were tested immediately for malaria.”

The stories came from everywhere—a physiotherapist in Nigeria whose expertise was ignored in favor of a male colleague; a nutritionist in DR Congo whose albino neighbor avoided vaccination clinics because of stigma; a public health specialist in Ethiopia’s Somali Region who explained how healthcare systems are designed for settled communities, leaving pastoralist populations behind.

Alina Onica, a psychologist with Romania’s Icar Foundation working with domestic violence survivors, noted: “Victims are often judged for ‘not leaving’ the abuser, as if staying means it’s not serious. This bias ignores the complex trauma and fear they live with every day.”

A framework for sense-making beyond single-issue analysis

What united these diverse testimonies was the application of the BIAS FREE Framework, a practical tool that helps identify and eliminate discriminatory patterns in health systems.

“Margaret Eichler and I started this work back in 1995 after developing some gender-based analysis tools,” explained Mary Anne Burke, the framework’s co-author. “We realized we had created something that could be applied to all social hierarchies. We’ve workshopped it on every continent but Antarctica and found it applicable everywhere.”

Unlike approaches that focus exclusively on gender, ethnicity, or disability, the BIAS FREE Framework examines how these factors intersect. Brigid Burke, a researcher who’s used and taught the framework for 15 years, explained how to identify three distinct problem types:

H problems: Where existing hierarchies are maintained
F problems: Where relevant differences between groups are ignored
D problems: Where different standards are applied to different groups

“It is easier to understand a hierarchy when you’re experiencing the oppression,” Burke told participants. “You can feel that you’re being treated in a way that takes away your dignity. It’s harder when you might be the one who is either consciously or unconsciously oppressing other people.”

During the event, participants first shared their own experiences, then began to analyze them using the framework. Abdoulie Bah, a regional Red Cross officer from The Gambia, offered his analysis: “Oppressive hierarchies suggest that certain groups experience more oppression than others, often leading to a competitive dynamic among marginalized groups.”

Solutions from the ground up

What distinguished this event from typical global health conferences was its emphasis on solutions developed by frontline workers themselves.

Dr. Orimbato Raharijaona, a medical doctor from Madagascar, described his team’s efforts to reach children in remote areas: “We prioritized areas with low vaccination coverage and strengthened birth follow-up to target zero-doses. Community dialogue helped raise awareness of the need for vaccination.”

In Mali, Bouréma Mounkoro, a public health medical assistant, discovered that simply rescheduling vaccination days to align with community availability dramatically improved coverage rates and reduced dropouts.

Dayambo Yendoukoua from Niger’s Red Cross developed an integrated approach addressing rural women’s exclusion from maternal care: “Women from villages and farming hamlets have three times less access to obstetric care than urban women. We grouped women into Mothers’ Clubs, provided literacy training, set up income-generating activities, and established traditional ambulances managed by women.”

This emphasis on community-based solutions resonated with Esther Y. Yakubu, a health worker with the Health and Development Support Programme in Nigeria: “This program will surely be of great value in the health sector. If put in place, it will make a huge difference and patients will receive quality treatment without any segregations.”

Practical action – not academic debates – to decolonize global health

The event itself embodied the principles it aimed to teach. Rather than positioning Western experts as authorities, TGLF structured the event to value diverse forms of expertise.

“Community health workers can see barriers that researchers miss. Global researchers spot patterns invisible at the local level. Policy makers understand system constraints that affect implementation,” explained Reda Sadki, TGLF’s Executive Director. “It’s when these perspectives connect that we find better solutions.”

On 24-25 April 2025, this community will reconvene to determine if there is enough interest and momentum to launch the Foundation’s Certificate peer learning programme for equity in research and practice. An inaugural course could be launched as early as June 2025.

“Your participation helps determine if we develop a full program on identifying and removing bias in health systems,” TGLF explained in its materials. “When more than 1,000 people participate, it shows enough interest to create a more comprehensive learning opportunity.”

The certificate program will bring together participants from across professional hierarchies—community health workers, district managers, national planners, and global researchers—creating a rare space where knowledge flows in all directions.

Across time zones and contexts, the conversation highlighted a shared understanding: addressing bias in healthcare isn’t just about fairness—it’s about survival. As Haske Akiti Joseph, a radiographer from Nigeria’s National Orthopaedic Hospital, reflected: “These issues are happening everywhere because governments will not provide free medical services to the people, and medical considerations come due to who you are, not based on priority.”

In a world where your chances of receiving timely, appropriate healthcare often depend on your gender, ethnicity, wealth, or location, the BIAS FREE Framework offers a practical way forward—one that begins with recognizing patterns of oppression that transcend borders and cultures.

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Collaboration in the Age of Artificial Intelligence

Understanding peer learning in the Age of Artificial Intelligence

Why peer networks unlock the potential of the Age of Artificial Intelligence

Contextual intelligence through collective wisdom

Trust-building in the age of AI

Distributed problem-solving

The new architecture of collaborative learning

Reciprocal knowledge flows

Structured experimentation

Continuous capability building

Evidence-based speculation: imagining peer networks that include both machines and humans

The Nigerian immunization scenario

Climate health innovation networks

Addressing AI’s limitations through collective wisdom

What this means for different stakeholders

For funders: Investing in collaborative capacity

For practitioners: New collaborative competencies

For policymakers: Enabling collaborative ecosystems

AI-human transformation through collaboration: A comparative view

The collaborative future

María’s projected transformation

Conclusion: The multiplication effect

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The old way: A tower of Babel

The new way: Natural language as universal interface

Why a universal interface changes everything

1. Democratization of capability

2. System integration without friction

3. Context that travels

4. Coordination at scale

Real-world implications

For business

For healthcare

For education

For government

Five challenges ahead

The bottom line

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The difference between answering and reasoning

How reasoning AI thinks through problems

Why reasoning requires massive computational power

Matching expert performance

Applications in global health

What makes AI reasoning different

The path forward

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Understanding AI agents: More than chatbots

The house that agentic AI built

Why agentic AI changes everything

The memory advantage

Agentic AI from individual tools to digital teams

The new human role as co-worker to agentic AI

Challenges and considerations

What can agentic AI do in 2025?

Where do we go from here?

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Why we resist learning from screens

When information transmission fails

Digital technologies as technical assistance disruptors

Evidence of a new technical assistance model

Beyond the expert monopoly

Transforming the technical assistance paradigm

The future of technical assistance

References

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“I did not realize how much I could do with what we already have.”

Nigeria: Two weeks to connect thousands, four weeks to change, and six weeks to outcomes

Funding crisis: How does sharing experience lead to better outcomes?

The snowball effect at scale

Solving the abundance paradox

Networks that outlast the funding crisis

Three pathways forward

Adaptability across contexts

The path forward

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The opportunity cost of inaction

The unsustainable economics of traditional capacity building

Evidence of measurable impact at scale

Resource allocation for maximum efficiency

Sustainability through combined government and civil society ownership