A note by EkStep Foundation, March 2026
100 AI DIFFUSION PATHWAYS BY 2030
A race to deploy the most powerful technology in human history to benefit the billions who need it most.
Shouldn't technology reach those who need it the most, fastest?
“Productivity gains from AI follow a progression—first at the level of the individual, then the team, then the organisation, and finally society. The last step—societal productivity—is the hardest.”
Nandan Nilekani, Raisina Dialogue 2026
The most transformative technology in human history should reach the people whose lives it can transform the most, and soon, but deploying AI in the real world is extraordinarily difficult. It is expensive, complex, and risky at scale. A successful deployment journey goes well beyond technology - it involves assembling the resources and know-how, making choices, and acting within resource constraints to advance towards a desired outcome.
A diffusion pathway packages the lived experience of deploying AI at scale into playbooks and toolkits for other adopters, dramatically reducing their learning costs, time, and risk. Since AI is a general-purpose technology that could benefit billions—touching agriculture, health, education, governance, livelihoods, and dozens more—a single pathway isn’t enough. Each sector, country and organisational context carries its own data and resource constraints, trust relationships, and failure modes.
A diverse group of eleven organisations with overlapping self-interests and united by shared beliefs recently launched an effort to achieve 100 AI diffusion pathways by 2030. Diffusion pathways work. Here is the evidence, the beliefs and an invitation to join.
The Proof
Shared deployment knowledge compressed adoption time by over 90% across three successive adopters:
The Government of Maharashtra (India) built MahaVISTAAR1, an AI agricultural advisory service, from scratch: institutional architecture, data pipelines, government alignment, and 30 partner organisations. Over 3 million farmers (of the 16 million) now use it.
Learn more9
months
Maharashtra — MahaVISTAAR1
Built from scratch: institutional architecture, data pipelines, government alignment, and 30 partner organisations. 3M+ farmers.
Ethiopia’s ATI2 , drawing on Maharashtra’s pathway, built a comparable deployment in one-third the time, without rediscovering which architectures work in the field.
Learn more3
months
Ethiopia — ATI2
Drew on Maharashtra's pathway to build a comparable deployment in one-third the time
67% fasterAmul’s Sarlaben3 launched, serving 3.6 million farmers, 40 million cattle, and 2 billion milk transactions a year—drawing on two full deployment cycles of shared learning already available.
Learn more3
weeks
Amul — Sarlaben3
Two deployment cycles of shared learning available 3.6M farmers, 40M cattle, 2B milk transactions/year.
97% fasterWhat was shared across deployments was specific and actionable: reusable technical components, data governance models, documented failure modes, operational playbooks for institutional alignment, safety guardrails, training approaches refined in the field, all of which cut deployment costs in subsequent deployments. And the compression accelerated as the shared knowledge base grew, because each new deployment added to the stock of reusable assets available to subsequent adopters. The later adopters were better informed, not necessarily better resourced.
What a Pathway Is, and Isn’t
A diffusion pathway is different from a case study, a white paper, or a best-practice guide.
It’s written from the next adopter’s perspective, not the builder’s. A case study tells you what someone did. A pathway gives you know-how - it tells you what you would need to do, in a specific context, and what assets you can take off the shelf instead of building from scratch.
Two components
A Deployment Playbook:
the documented lived experience of deploying AI, which goes well beyond models and hardware - Who needed to be in the room, which design choices mattered and which didn’t, how institutional trust was preserved, onboarding stakeholders and end users etc
A Reusable Toolkit:
technical assets, data pipeline governance templates, safety guardrails, procurement strategies, evaluation benchmarks, training materials, scale-up and stakeholder onboarding strategies that another adopter can use directly.
Why AI Deployment Is So Hard Without Shared Pathways
Three bodies of evidence confirm that AI’s real difficulty is institutional, not technological - a live deployment, a cross-sector study, and the historical record.
01 A Live Deployment
Building MahaVISTAAR required designing sustainable data pipelines that farmers and government institutions could trust, ensuring the system works across Marathi dialects, and catching failure modes (e.g., wrong dosage advice, misread crop conditions) before deployment. Several government bodies, data providers, and research institutions, as well as farm extension workers, had to align before a single farmer could use it. The AI model was the smaller part of a nine-month effort. MahaVISTAAR didn’t happen because one organisation got it right. It happened because thirty organisations decided that getting it right mattered more than getting it done quickly.
02 A Cross-Sector Study
The Use Case Adoption Framework 4 (UCAF) examined hundreds of AI use cases across agriculture, health, education, and government and found a consistent pattern- technology accounted for only roughly 30% of what reaching scale required. Factors like data readiness, language support, workforce adaptation, accountability guardrails, and organisation-wide alignment accounted for the remaining 70%, and this proved consistent across sectors and continents.
03 The Historical Record
Jeffrey Ding’s research on general-purpose technologies shows that the electric dynamo took fifty years to produce widespread economic gains, not because the technology was unavailable, but because adopters independently rediscovered how to organise around it. Michael Mazarr’s RAND study and Sangeet Paul Choudary’s Reshuffle reach the same conclusion about AI: the competitive challenge is social and institutional, not technological. The fifty-year lag is a failure of knowledge sharing. Diffusion pathways compress this lag.
Why diffusion pathways matter beyond the evidence
Technology does not distribute and transform lives by itself. Smallholder farmers, under-resourced health workers, and teachers in underfunded schools are rarely the first to benefit from a new capability and often the last. Reaching them requires deliberate effort.
No single actor is enough
Deployment knowledge spans organisations, sectors, and geographies. It only becomes useful when it comes together in a real-world deployment — and is shared for the next one.
The cost of waiting is real
It is measured in decisions made today without knowledge that already exists somewhere in the world. Populations that never see a benefit from AI become resistant to it — constraining markets, trust, and political will.
AI must add to agency
The goal is not algorithms that replace people. It is people empowered by knowledge that was previously out of reach — who understand more and judge better because of it.
3.6M
farmers
Amul System — A Concrete Example
Across the Amul system, 3.6 million farmers manage livestock with access to only 1,400 vets. The goal is not an algorithm that replaces the vet — it is a farmer who understands more and judges better because knowledge that was previously out of reach is now available to her.
A farmer who becomes a better farmer. A teacher who becomes a better teacher. A health worker who makes better judgements. Not people made dependent on technology — people empowered by it.
Who Has Committed
Eleven founding organisations committed in February 2026:
Anthropic, Google Foundation, the Gates Foundation, UNDP, the governments of Ethiopia, Italy, and Kenya, Qhala, Carnegie India, the Observer Research Foundation, and EkStep Foundation came together, on the sidelines of the AI Impact Summit in Delhi, and set a target of 100 pathways by 2030. Several are already live.
What brought them together isn’t a common mandate. It overlaps with self-interest: organisations that have each concluded, through their own work, that AI serving the people who need it most is also in their interest. AI can benefit billions of people in ways that are good for AI, too. For-profit companies, philanthropies, governments, not-for-profits and multilaterals each arrived through a different door.
Deployment now carries political weight alongside invention, following the AI Impact Summit: More than 89 countries signed the New Delhi Declaration and the Charter for the Democratic Diffusion of AI was endorsed by 20 countries and four international organisations.
Videos & Articles
AI Diffusion & the 100by30 Commitment
INDIA AI IMPACT SUMMIT 2026
ATLANTIC COUNCIL
Feb 12
BABL AI
Feb 20
BW BUSINESSWORLD
2026
Use case adoption framework
Published with Carnegie India
AI Adoption Journey for Population Scale
Shalini Kapoor & Tanvi Lall · Carnegie Endowment for International Peace · January 2026
Why AI adoption at scale remains elusive. Introduces the Use Case Adoption Framework (UCAF) — moving AI out of pilot purgatory and into scalable deployment. Identifies cross-sector horizontal enablers: data readiness, language, voice, workforce reimagination, and accountability guardrails.
Read at Carnegie EndowmentThe Invitation
There are four ways to contribute to the effort:
Illustrate a pathway
If you’ve deployed AI at scale or know of a deployment, document the lived experience - the architectural decisions, the institutional work, the failure modes that only surfaced in the field.
Use a pathway
If you’re ready to deploy, use what exists. Your starting point will be far ahead if you do so.
Strengthen a pathway
If your expertise is in AI safety, workforce adaptation, data governance, or language support, contribute to and strengthen a pathway that benefits all deployments built on it.
Build a new pathway
If you see a sector, country or community where no pathway exists yet, build it there. Document the journey and share. The knowledge you create will travel to contexts you can’t anticipate, serving people you’ll never meet.