Innovative digital wholesalers are offering an opportunity to improve last-mile health services, including companies like mPharma in Ghana, SwipeRx in South-East Asia, Dawa Mkononi in Tanzania and PillTech in Cambodia, among others. Their models and geographical focuses differ, but they all have two things in common: First, they leverage digitization to improve the quality, availability and affordability of medicines at the largely private points of sales LMIC patients typically rely on; and second, they improve the level of services of their clients (i.e., private care providers) by offering value-added support that ranges from accredited training to inventory management.

Since these wholesaler companies represent a unique lever to increase the availability of quality medicines for low-income patients, Sanofi has targeted them in its Impact Fund, supported by the Hystra team, which acts as operating partner. By combining patient capital and technical expertise, the Fund aims to support entrepreneurs who are improving access to healthcare, particularly essential medicines, at the last mile in LMICs. The Fund started to invest in “digitally enabled wholesalers” in LMICs over four years ago, and currently has around 10 ventures in its portfolio.

We’ll discuss our learnings from these investments below, highlighting several challenges these businesses are facing, exploring how funders can help overcome them, and sharing a practical tool that outlines key priorities and performance targets across the digital wholesaler business model in the health sector.

Understanding the Struggles of Health-focused Digital Wholesalers in LMICs

Working closely with these entrepreneurs, we have observed that even well-managed digital wholesalers struggle under a flood of complex choices, driven by limited market information and constrained management capacity. This leads to overstretched teams, along with ill-timed and costly decisions in an increasingly difficult financing environment.

Senior management faces a near-impossible balancing act: Their clients expect reliable service across a huge range of medical and over-the-counter SKUs (Stock Keeping Units, the alphanumeric codes retailers use to track their inventory). Meanwhile, their investors push them for profitability and demand competing strategic initiatives (ranging from customer acquisition through credit programs to international expansion), while their operations teams grapple with daily crises with little middle management support or market information.

Under these pressures, wholesaler entrepreneurs often make decisions that seem reasonable in isolation, but that end up overwhelming staff, accelerating cash burn and, more often than not, frightening away potential investors.

These mistakes undermine the funds they have painstakingly raised. The following examples illustrate the types of issues we’ve seen these wholesalers face:

• Some ventures dramatically increased their inventory across hundreds of SKUs before securing consistent availability of their top 100 products, the products that their clients, such as pharmacies, depend on. The result? Their cash was tied up in slow-moving stock while their core products still faced stockouts.
• Some launched credit schemes to acquire new customers before their operations were stable enough to manage the additional complexity generated by the credit. They spent months chasing overdue payments, burning cash and energy that could have been invested in strengthening their core service.
• Others ordered large shipments from abroad at attractive prices, only to watch their goods, and their liquidity, sit for months in delayed and unpredictable overseas transportation they hadn’t fully anticipated.
• In all these scenarios, we have seen motivated employees being put under pressure, and in many cases, working till exhaustion. This has led to high churn in wholesalers’ teams, which ultimately impacts the venture’s performance. For instance, we have seen ventures struggling to recruit and retain commercial sales managers, in a context where the best candidates are more interested in other, more attractive job positions.

Making matters worse, these mistakes are being made at a time when cash is harder to raise than ever. Ventures in LMICs are caught in a pincer effect: Financial institutions are reluctant to provide them with funding due to heightened risk, while macroeconomic volatility further strains their operations. These dynamics have manifested in multiple ways: Equity fundraising has become more difficult as investors perceive higher risks; local currency loans are scarce; the cost of US dollar-denominated debt has soared (for example, with the naira depreciation, loans taken in US dollars have seen their real cost in Nigerian naira triple between May 2023 and February 2024); and grant funding has declined due to European budget constraints and shifts in US foreign policy.

How Investors Can Unlock the Growth of Digital Wholesalers

In our Fund’s efforts to support digital wholesalers, we’ve faced a structural paradox: Most of them need intensive support to unlock their growth, yet their limited management teams can only contend with a handful of the technical assistance (TA) offerings we provide to companies. For the Fund to be sustainable, we have had to address these issues in a cost-effective way, which has proven to be difficult.

These ventures typically operate with lean leadership teams that must simultaneously drive strategy, fundraise and manage day-to-day operations in a highly hands-on manner, often due to limited middle management capacity. While they would benefit from support across a wide range of operational areas, their bandwidth constraints mean they can only address a few priorities at any given time, making rigorous alignment and prioritization essential.

Given their early stage, most of the ventures we invest in also have a limited capacity to absorb large investment tickets (typically ranging from $100,000 to $1 million). This creates another structural challenge: The limited upside potential of small tickets, combined with ventures’ constrained ability to co-finance the technical support we provide (a requirement for our portfolio companies), continues to make cost-effective TA a difficult equation to solve.

Nevertheless, playing a truly transformational role in these companies’ growth requires more than capital and high-level strategic guidance. It requires us to help entrepreneurs identify the top current priorities for their team, and to provide tailored and targeted support to ensure that they are able to achieve them.

To that end, we have supported our portfolio companies with hands-on technical assistance across critical aspects of their business models. These assignments have taken many forms (varying in duration, scope and intensity), but they have consistently shared a focus on addressing core drivers of growth by looking at what is truly happening “under the hood” at these enterprises. This support has included:

• A six-month in-person engagement in Cambodia helping PillTech’s team strengthen procurement, warehousing and sales practices to expand their rural reach.
• A hybrid assignment (a mix of in-person and remote support) to implement sales and operations planning for Viebeg in Rwanda.
• An assignment we launched in Tanzania in early 2026, to support Dawa Mkononi in turning around sales performance.

A Practical Tool for Supporting Digital Wholesalers in LMICs

Building on these field engagements, alongside some ongoing coaching we’ve provided to CEOs and leadership teams across our portfolio (including to the most mature ventures), we have consolidated all the insights we’ve gathered into a practical tool: “The  Journey of Health Digital Wholesalers in LMICs.” Designed with and for entrepreneurs, the tool aims to help ventures identify and focus on what matters most at each stage of their growth, while also enabling the Fund to take a more cost-effective approach to delivering technical support.

This tool outlines key priorities and performance targets across the digital wholesaler business model (including areas like the value proposition, operational model, financial and IT model, and team) tailored to each stage of a venture’s maturity.

The journey is split into three stages of maturity, which should be mastered sequentially to ensure that no capital is wasted, and that staff know where to focus:

• Stage 1: Build Your Foundation. Prove that your value proposition has traction by winning over 100+ repeat customers, thereby confirming your “right to play” in this business space. Here, the goal is to establish a minimal value proposition, with a small but repeat customer base. For example, at this stage, a venture is expected to have a client interface working and 200-300 SKUs in stock to confirm traction.
• Stage 2: Nail it, Then Scale it. Consolidate for profitability, becoming a trusted wholesaler for a customer segment in a given geography thanks to deepened relationships and high reliability (i.e., aiming to achieve 99% availability for your top 100 SKUs). Then scale to new geographies/segments while maintaining service quality and product availability, with the goal of deepening your share of your pharmacy clients’ merchandise to up to 70-80% (ideally).
• Stage 3: Grow Beyond Drug Supply. Develop innovative services that help private points of care transform their operations and serve their communities better. For example, these services could help pharmacists better manage their inventory, validate their licenses (a difficult task in some LMIC markets), or broaden the services they offer to patients (e.g., adding diagnostic services to their existing offerings).

Our ambition in codifying this knowledge was threefold:

• First, to give CEOs a cost-effective way to assess where they stand and prioritize the goals they must accomplish to grow their businesses, by making actionable operational insights directly accessible to them and their team.
• Second, to conduct diagnostics in parallel to our due diligence, translating findings into a Value Creation Plan co-developed with the venture. This ensures that TA funds go towards the areas they are most needed.
• Third, to foster knowledge-sharing across the broader ecosystem of practitioners.

Since its inception, this tool, tested with several portfolio companies as part of a diagnostic exercise, has proven to be valuable. It has helped not only ourselves as investors, but also the companies and their top management to align on which of the three stages of maturity the enterprise is at — a key prerequisite to seeking alignment on strategic priorities. When using the tool with these portfolio companies, we often started by identifying unsuspected misalignment on maturity among top management, which needs to be solved before moving forward.

For example, when running the tool with top management teams, we realized that some initially did not agree on how well they were segmenting customers, or how well their sales teams were delivering the sales pitch. These leaders also realized that some of their planned initiatives — e.g., geographic expansion, warehouse openings, and complex technology features or product importation — were premature. They needed to focus on the fundamentals first: from improving customer segmentation to ensuring reliable availability for their top 100 products. These were not the most exciting priorities, but they were the right ones for their stage of growth.

This tool continues to evolve, and we expect it to grow in line with experience and external contributions, as part of a broader set of solutions developed by the Sanofi Impact Fund. Clarifying the journey of health digital wholesalers is one of many components of the Fund’s TA approach, and we expect the insights generated by these efforts to evolve as we learn more from ventures, co-investors and the ecosystem.

Indeed, this tool was generated from the start with the support of entrepreneurs: Back in 2024, when we first started the codification of our learnings, we organized a peer-learning session between all ventures of the portfolio (at the time), where the more mature ones could reflect back on their journey while the early-stage ones reflected forward. It was a key moment that generated rich insights and helped us refine the tool to match entrepreneurs’ perspectives.

If you are keen to learn more about this topic, or interested in contributing, we’d love to continue the conversation and share the diagnostic tool derived from these insights — a ready-to-deploy methodology delivered through 3-5 day in-person operational assessments. Feel free to reach out to the Sanofi Impact Fund (via Laura Collet, Head of Sanofi Impact Fund) or Hystra teams (via Alice Magand, Project Manager).

François Lepicard is a Senior Partner, Alice Magand is a Project Manager and Guillaume Massot is a Project Manager at Hystra; Laura Collet leads the Global Health Unit Impact Fund at Sanofi.

Photo credit: PeopleImages

According to Africa: The Big Deal – Startup Deals Database, a database listing funding deals secured by startups in Africa, since 2019, there were 208 equity exits across the continent. And while agriculture made up 10% of investment flow (436 out of 4,173 deals), it accounted for just 3% of exits (seven out of 208). To put it another way, for every 62 announced ag deals, there has been one ag exit. In comparison, the energy and water sector saw one exit for every 33 deals, while fintech saw one exit for every 16 deals. The chart below shows the ratio of exit to deals for each sector, expressed as a percentage. Agriculture has, by this measure, the lowest ratio of any sector in Africa.

It’s not just the lack of a steady flow of exits that raises a red flag. A healthy investment ecosystem is also characterized by a mix of exit types — including strategic acquisitions, initial public offerings (IPOs) and secondary transactions — at consistent or rising multiples that create reasonable returns. In US venture capital, according to Pitchbook’s VC Exit data, 38% of exits in 2025 came via acquisitions, 33% via IPOs, and the remaining 29% from secondary sales of shares in private companies: a nice, healthy mix.

In agriculture, we see much more concentration. According to Pitchbook, 23 out of 26 exits in smallholder agriculture across emerging markets were via merger and acquisition (M&A). The remaining three were via two buyouts and one IPO. That overreliance on M&A decreases both the likelihood of an exit (since there are only so many acquirers out there), as well as the value returned from those exits (since acquirers are well aware that companies do not have many other options available).

Impact investors are at a crossroads, with two possible paths before us. On one path, investors simply stay the course, maintaining the hope that big exits for smallholder-focused agri-SMEs are around the corner, and that when these exits finally come they will enable companies that are achieving rapid scale to proceed through the traditional venture capital and commercial market routes.

The other, less-traveled road requires us, as investors, to build the exit pathways that we’ve been hoping for. Taking this path would be more difficult, but it would give us the freedom to explore exit strategies that are more appropriate for early-stage companies that work directly with smallholder farmers, while strengthening their resilience and improving their livelihoods.

Rather than continuing to operate under existing market structures, emerging models, such as impact secondary funds and impact buybacks, show that there are still untapped opportunities for impact investors to build innovative funding solutions that are better suited for smallholder-focused agri-SMEs. In the article below, I’ll discuss the challenges of achieving exits in smallholder-focused agriculture, highlighting the limitations of current investing models and exploring some of these emerging alternatives.

A trickle of exits, rather than a steady stream

According to Africa: The Big Deal – Startup Deals Database, between 2019 and 2025, 74 of the 208 equity exits (36%) from companies across Africa were in fintech, while only seven were in agriculture. Four of these companies were grocery delivery platforms, two were dinner kit/meal plans, one operated in the food and beverage sector, and one was a seed potato farm. None of these companies demonstrated a focus on smallholder farmers.

Cross-checking this with data from Pitchbook, since 2006, there have been 385 exits in the agriculture industry across East Africa, West Africa, India and Latin America. Yet agriculture comprised only 4% of exit deal flow, while sectors like financial services (35%), healthcare (20%) and energy (16%) dominated. Of these agriculture exits, only 72 companies had a focus on smallholder farmers. Compare that number to over 2,500 and 1,500 exits in financial services and healthcare, respectively.

The exits we have seen in smallholder agriculture can make it tempting to believe that more exits are coming, but these few successful cases do not necessarily demonstrate scalable or replicable exit solutions. Mercy Corps Ventures, for example, saw successful exits or partial exits from its investments in Pula, Umoja and Topl. Pula is a microinsurance firm serving over 20 million farmers across 22 countries in Africa, Latin America and Asia. Umoja provides Web3-native hedging solutions for currency and other financial risk assets. Topl’s blockchain solution links smallholder farmers with value-add service providers, like weather information, insurers, lenders and input providers. In addition to these heavy tech play companies, recent years have also seen Truvalu’s exit from GrowPact and Pamoja Farms’ full acquisition of Ten Senses Africa.

But while these examples are promising indicators of the potential of agri-SMEs, they are the exception rather than the norm. The common challenges of working in agriculture help to explain why there have been such sporadic and limited exits to date.

The unique challenges of exits in agriculture

Agriculture is a notoriously difficult sector to invest in, given the asset-heavy nature of the companies, alongside their mostly low margins, and long research and development, sales, and cash conversion cycles. ISF Advisors found that offtakers sourcing from smallholders, like traders and processors, often lend to these farmers at a loss, hoping to either secure farmer harvests, or help enable greater productivity or other impacts. These losses are driven by low loan pricing, high cost of funds, and a high cost of risk when a climate shock hits: Offtakers’ lending losses increase from 13% when farmers don’t face any climate shocks, to 42% if a shock occurs. Businesses within the sector must also deal with a wide range of value chains, perishability of their primary goods, slow farmer adoption of new practices, and susceptibility to climate change and seasonal variations. One of Acumen’s portfolio companies, Kentaste, experienced this firsthand back in 2021-2023, when a two year drought significantly impacted the coconut harvest, with supply falling by 38%. The impact of climate change, combined with its debt obligations, led to a drastic shift in the company’s unit economics.

In addition to the economics of agribusinesses, the operational complexity of these companies’ business models requires Patient Capital and more creative financing. For example, vertically-integrated models — wherein a smallholder-focused company offers farmers a combination of inputs, hands-on training and direct market access — demand precise coordination. These companies often need to establish regional hubs where trained agents have the resources to pay farmers, aggregate and store product, and deliver or process the raw material on time. Typical investment products and timelines are usually not suitable for their needs.

Agriculture needs both debt and equity

Considering the factors laid out above, it’s reasonable to wonder whether or not equity is the right tool for investing in agriculture. If debt can reliably get investors their money back and still help agribusinesses achieve their objectives, why continue to invest equity?

It’s because, as Acumen has seen, equity capital can be catalytic for early-stage agri-SMEs.

Equity is appropriate for companies that demonstrate significant revenue growth and potential to scale. From Acumen’s own equity portfolio, S4S Technologies — which sells solar dryers to dehydrate fruits and vegetables, restoring the value of smallholders’ rejected produce — more than doubled its revenues two years after our initial investment. Equity capital plays a role in a blended instruments approach that can provide critical support to agri-SMEs that serve smallholders, while also being pragmatic about investors’ liquidity needs. Impact investors want to support the needs and work of agri-SMEs, but they also need to see some level of return on their investments. Ensuring that companies have a healthy mix of debt and equity can help achieve both.

I spoke with two agriculture impact investors to get their thoughts on why we should continue to invest equity and not just debt into agri-SMEs. According to Tamer El-Raghy, Managing Director of the Acumen Resilient Agriculture Fund, Acumen’s later-stage agriculture investment initiative, “Companies require equity to maintain a healthy balance sheet. Excessive debt can lead to over-leveraging, substantially elevating the risk of survival during both macroeconomic and microeconomic fluctuations.” Similarly, Chris Isaac of AgDevCo stated, “For early-stage agri enterprises, equity is essential as a buffer for inevitable shocks. Relying on senior debt only is high risk, because a bad season or a market shock could knock you off course.”

Equity still plays a critical role in scaling agri-SMEs, and so the need for more potential exit pathways remains.

Current exit approaches are limited for impact investors in agriculture

We believe that impact investors cannot continue on their current path, hoping that agriculture exits are just around the corner and believing that traditional financial structures can sufficiently serve the needs of smallholder-focused agri-SMEs. That long-expected increase in exits will never arrive as long as these exits continue to occur on a one-off, sporadic basis, with both investors and entrepreneurs tasked with identifying potential buyers.

Common exit strategies in Africa involve one of three routes: strategic acquisition, a secondary sale, or management buybacks. AVCA’s 2025 African Private Capital Activity Report found that 38% of exits across sectors were sales to strategic buyers, followed by secondary sale transactions at 26% and management buybacks at 19%. But while these are feasible strategies, there are limitations to each.

In agriculture, local and multinational corporations are often considered the most likely strategic buyers. However, corporate expectations around investment and scale can make it challenging to pitch a smallholder-focused company.

One example is Olam, one of the largest commodity trading firms in the world. Olam had previously backed a digital farmer services platform, Jiva Ag, but ultimately closed the company “in light of the expected continuing investment required to sustain its operations in these challenging times and difficult market conditions.”

Similarly, when Acumen asked another large multinational corporation about their level of interest in acquiring or investing in agri-SMEs, they expressed their hesitations: “We would partner with an ‘ESG’ company in a heartbeat. It’s a nice story. But it’s a different story of wanting to invest. Is there a financial ROI? Is there AI? How would a[n investment] partnership make our unit economics more favorable?” Given these concerns, a smallholder agri-SME would need to achieve significant scale, volume and profitability to be attractive to a large corporation.

In the case of management buybacks, three things need to happen: First, the company needs to generate enough cash that a buyback is even an option, and buying back shares needs to be seen as a reasonable use of this cash, which likely means the company needs to be intensely profitable and/or have reached a slower-growth steady state. Additionally, the company must have decided to not raise significant amounts of additional equity, which would counteract their repurchasing of company shares.

Finally, when it comes to secondary sales, a lack of buyers makes this a rare option. A key barrier here is the unwillingness of development finance institutions (DFIs) and multinational development banks (MDBs) (which provide much of the capital to potential acquirers) to support secondary transactions. As a recent report from Third Way Capital, sponsored by British International Investment (BII), said: [DFIs’] mandates emphasise primary capital deployment and [their] impact frameworks include a use of proceeds lens, which often excludes transactions that buy out existing investors rather than deploying capital into new assets.”

However, a 2024 sale of BII assets to Blue Earth Capital is a hopeful sign. More openness to these types of vehicles among DFIs and MDBs is essential to building the kinds of consistent and appropriate exit pathways that are ripe for exploration in agriculture.

We need to start building alternative, more appropriate exit pathways

In response to these challenges, alternative ideas and vehicles are beginning to emerge and take shape in the agriculture sector. These include:

• Impact secondary buyers, such as Blue Earth Capital and Coller Capital, which offer exit opportunities for impact investors. 
• Approaches like Planetary Impact Ventures’ evergreen capital structure with no carried interest, which enables individual investors to exit after five years while the fund continues. 
• Entities like Africa Eats, which foregoes exit expectations altogether via an impact holding company modeled after Berkshire Hathaway.   
• Impact buybacks, another sustainable exit pathway wherein a company buys back investor shares by meeting impact milestones. 
• Revenue-based buybacks, which could create a more financially feasible alternative for companies by allowing them to buy out investor stakes over time based on the revenue they’ve generated. 
• Employee ownership models, as explored extensively by Transform Finance and the Predistribution Initiative, which have been primarily based in the United States but offer room for further research and application in emerging markets.

There are certainly more questions than answers when it comes to exploring these alternative pathways: Where will the capital come from and how can investors raise it? What kind of fund structure would be needed to deploy them? What happens to the companies and their capital needs once a secondary buyer or evergreen holding company invests? What would it take for investors to be willing to accept impact milestones as a way for companies to buy back investor shares?

Unlike the current exit pathways that are common across the sector, these solutions are more financially feasible for small agri-SMEs, and some of them — particularly impact secondary buyers and holding companies — have potential to scale. If these practices received wider uptake, investors and companies alike would not need to keep searching or hoping for the next buyer.

The path where we wait for traditional exits to happen and solely seek investments that fit existing market structures leads to nowhere. The growth, return and timeframe expectations of traditional market structures were not designed with smallholder farmers or climate resilience in mind. It’s time for us to start forging the less-traveled path.

What other exit pathways are out there? We’d love to hear about other ideas and solutions that you’ve heard about or are interested in exploring. Share your thoughts with us here.

Coco Lim is Manager of Insights at Acumen.

Photo credit: Peter Irungu for Acumen

But there is another number worth looking at: Out of nearly 95.2 million enterprises registered in the country, over 99% are micro.

As of September 9, 2026, the Ministry of MSME Dashboard reported 95,236,393 enterprises across the Udyam Registration Portal and Udyam Assist Platform, which aim to give MSMEs a formal identity and registration number, along with access to lending and other benefits. Of these, 94,651,631 were classified as microenterprises — about 99.4% of the total. 

However, the word “micro” needs some care here. Under the revised MSME classification effective from April 1, 2025, a microenterprise can have investment of up to ₹25 million (~US $265,000) and annual turnover of up to ₹100 million (~$1.06 million). So being classified as micro does not, by itself, tell us that a business is tiny. What the numbers do tell us is that the overwhelming majority of registered enterprises (about 73 million) are still at (or technically “under”) the first rung of the MSME (micro, small and medium) classification. They are classified as “nano,” a subset of “micro” that includes businesses with less than ₹10 million in annual turnover. These enterprises are mainly cash-based and family-run, and the majority of them are rural. They have the capacity and aspiration to grow, but no dedicated ecosystem to help them transition and scale.

India has clearly become very good at bringing enterprises into the formal system. What we have not become equally good at is helping viable enterprises move beyond micro and into their next stage of growth: becoming large enough to attract formal investment.

The Gap Between Nano and Investable

At the end-point of that journey sits the Self-Reliant India Fund. The fund was set up to channel growth equity to MSMEs through a Fund-of-Funds structure. According to government data from July 30, 2026, 766 MSMEs had been assisted between the fund’s launch in 2021 and June 30, 2026, with more than ₹30 billion invested ($316 million) by the government. An additional $900 million of government funding is yet to be deployed.

Put these two numbers next to each other and the gap becomes hard to miss: Tens of millions of enterprises have entered the formal system. But just hundreds have grown large enough to obtain equity funding from a government platform designed to provide them with easy access to this support.

What happens to everyone in between?

That question has stayed with us through much of our work with rural and semi-urban businesses. At Quiver Catalyst, a rural enterprise scaling platform, we work with growth- oriented entrepreneurs who are ready to move beyond day-to-day survival. Our support combines practical mentorship, business support and advisory, investment readiness, and growth capital in the form of micro-equity — because we have found that capital alone rarely addresses the barriers that emerge as a business grows. 

We conducted a year of fieldwork with more than 850 enterprises across four states of India, combining the findings with national data to produce our latest white paper, “Beyond Survival: Unlocking Growth Pathways for India’s MSMEs,” which examines the transition from microenterprise survival to sustained growth. In the process, we’ve seen that many Indian enterprises are not struggling to survive. They have customers. They sell something people are willing to pay for. Some have been around for years. The entrepreneur has already done the difficult work of getting the business off the ground.

And yet, when the time comes to grow beyond the nano stage, things begin to get complicated. The books that were perfectly adequate for running a nano business may not be enough for an investor trying to understand its margins, working capital or cash flow. The entrepreneur may know exactly how to sell within her district but have no idea whom to call when she wants to expand into the next one. A large order can look like an opportunity until the business realises it does not have enough working capital to fulfil it.

Then there is the question of the money itself: The business may have outgrown the microloan that helped it get started, but that doesn’t mean it is unbankable. It may just be too large for the smallest loans and still too small, informal or unstructured for many institutional investors. For instance, it may need $5,000 or $25,000 to make the next jump, not $50,000 and not $1 million.

This is where a lot of businesses get stuck.

Other Obstacles Faced by Growing MSMEs

These MSMEs also face an obstacle less visible than finance. In a small enterprise, the founder is often doing almost everything: speaking to customers in the morning, buying raw material in the afternoon, checking production, following up on payments and somehow maintaining the accounts in between.

As the business grows, the decisions this entrepreneur must consider start to change:

• Should I hire someone now or wait?
• Can I afford more equipment?
• If I sell through a distributor, what margin do I give up?
• Can I take this loan if my sales drop for three months?
• How much stock should I hold before the main selling season begins?

In a larger company, there are people whose entire jobs are built around answering questions like these. For a first-generation entrepreneur, they can become decisions that determine whether the business grows at all. And where the entrepreneur is located changes how difficult it is to find those answers.

Ambition is not a Mumbai privilege — Access is

For instance, we have repeatedly seen that MSMEs located outside of major population centres face unique challenges that aren’t shared by their urban counterparts. This is not because entrepreneurs in smaller towns lack ambition or capability. It is because the networks that support business growth are far less dense and often much harder to access outside India’s major commercial centres. These enterprises are hampered by the three Ls: location, missing market linkages and lack of leverage.

Consider what an entrepreneur’s journey can look like in a major city like Mumbai (India’s financial capital) compared to a small, remote Indian town. At different points in this journey, a growing business needs a banker who understands it, an accountant who can help put the numbers together, a distributor willing to take a chance, a mentor who has solved the same problem before, and a network that can provide connections to investors and buyers.

In places like Mumbai, many of these people are relatively close. In a small town like Gadchiroli, the entrepreneur may have to travel much further — both socially and physically — to cover each of these needs.

Supporting MSME Growth in India Requires More than Just Capital

The question then is: What would make that distance shorter?

Expanding access to incubators and accelerators is clearly one part of the answer. Jagriti Enterprise Centre – Purvanchal, PRIME Meghalaya, Villgro and a growing number of incubators use different models, but all point to a simple need: Entrepreneurs outside the large business centres need access to mentors, markets, knowledge and networks, not only classroom training.

But incubation cannot end with a workshop and a certificate. An entrepreneur trying to grow a business needs someone to turn to when the distributor does not pay, when the margins change, when new equipment suddenly looks necessary, or when the loan that looked affordable on paper begins to feel very different in an off-season.

Finance needs some rethinking too. A rural agricultural processor can have a perfectly healthy business over the course of a year, and still have very uneven cash flows within those 12 months. But a fixed installment payment does not care whether it is peak season or lean season: The monthly repayment amount remains the same.

Government programmes have done a great deal to expand formal credit and credit guarantees for small businesses. But access to credit and access to the right kind of capital are not always the same thing. For some businesses, revenue-linked or cash-flow-linked structures may make more sense. For others, debt may still be the right answer. The larger point is that the financing has to understand the business rather than forcing every business into the same repayment pattern.

And even the right capital will do very little if the enterprise has nowhere new to sell.

Market access sounds like a broad development-sector phrase until you sit with an entrepreneur and realise it can mean something as simple as finding the first reliable distributor outside her district.

Government channels have opened useful doors for businesses navigating the challenges of growth. The Public Procurement Policy for Micro and Small Enterprises sets a 25% annual target for the amount of goods procured from MSEs across Central Ministries, Departments and CPSEs, while the MSME Ministry’s Procurement and Marketing Support Scheme is intended to help MSMEs improve marketability and reach new markets. But getting through those doors still requires documentation, quality control, working capital, competitive pricing, fulfilment capability and relationships.

That is why we have slowly come to believe that the transition from nano to investable cannot be solved by capital alone.

Sometimes the business needs money. Sometimes it needs six months of better record-keeping before taking on that money. Sometimes it needs a distributor, someone to sit with the entrepreneur and work out whether buying that new equipment will actually improve the business. And sometimes the most useful thing for an entrepreneur is simply helping her understand how an investor or lender is likely to perceive her business before she sits across the table from one. We have therefore started one step earlier than the question, “How much capital does this business need?” We ask: “What is actually preventing this business from growing? What needs to change before more capital can genuinely help?”

The answers to these questions are different for every enterprise. That is precisely the point. Every MSME faces its own challenges in moving from nano to small and medium-sized. India needs to do a better job of addressing those obstacles if it hopes to see its successes in formalising MSMEs translate into actual, sustainable business growth and job creation.

Quiver has embarked upon a journey to develop a new capital stack led by risk capital, along with an enabling system to help transition these enterprises. But India needs tens (or hundreds) of like-minded organisations to join us, if we hope to scale the country’s millions of nano enterprises. 

Read our full white paper at https://www.quiver.in/insights

Pranshu Chhabra is a development professional working with Quiver, Rudra Midhun Kumar is part of Quiver’s Diligence Team, and Chiropriya Dasgupta is the Director of Investments at Drishtee Foundation and Co-Founder of Quiver.

Photo credit: Quiver

However, scaling these innovations could also provide NGOs with a solution to that funding challenge. The development sector has reached a firm consensus that it needs to depend less on grants, so hybrid business and operational models are becoming increasingly important for NGOs seeking to remain sustainable, relevant and adaptable as the aid landscape evolves. There’s a growing need for these organisations to explore approaches like rethinking their funding architecture, claiming a clearer niche, and evolving the support systems they can draw on. Strategies to generate earned revenue could play an important role in that ongoing evolution toward diversified funding.

But this transition often costs money before it makes money. And this “messy middle” — the period where early support falls away before a business model can stand on its own — is where social innovations stall. This is the stage the Enabel Innovation Hub focuses on. Enabel is the Belgian Agency for International Cooperation, and its Innovation Hub supports mission-driven organisations in Africa and the Middle East in scaling innovative solutions to complex societal issues. The Hub’s research looked at what has to change from within for NGOs to execute this transition — an organisational and mindset shift the sector has been far quicker to call for than to fund. And what we learned is that the transition between grant dependency and financial sustainability goes beyond finding a different type of payer. Instead, it may require an organisation to change what it provides and to whom, how it is governed, who it employs, and what it chooses to say “no” to.

There is no clean path from grants to revenue 

Since 2018, the Enabel Innovation Hub has provided “transition-to-scale” support to over 50 non-profit social innovations across 18 African countries through grant funding and capacity-building technical assistance. 

For a long time we worked on the assumption that scale — funded by a larger grant with more ambitious targets — was the next natural step after a successful pilot. But our grantees’ lived experience showed us that doing more of the same thing in more places is rarely what the job requires. The harder task is changing what an organisation is and where its money comes from.

Those changes were easier to contemplate when grant budgets were growing. Now they are shrinking while expectations rise, and NGOs are being told to diversify as though there were a clear route from grant money to a sustainable model, in markets where programme beneficiaries cannot afford the full cost of the service or product being provided.

To better understand how NGOs are successfully diversifying, we undertook a study involving 18 mission-driven organisations operating across Africa, with varying degrees of financial diversification and at different stages of their journey. Our goal was to better understand how NGOs can scale their innovations while pursuing financial sustainability.

We found that as organisations scaled, financial sustainability was often a condition they continuously managed, with diversification often being the first visible shift. Of all the organisations we spoke to, almost none had moved neatly from grants to earned revenue. They layered and rebalanced income streams as circumstances changed, whether due to funding volatility, policy shifts, affordability pressures or operational demands. Shocks such as losing a grant often prompted diversification and model evolution more than strategy alone.

Financial diversification is not the same as scaling, but it brings many of the tensions involved in scaling to the surface. Deciding to sell something forces an organisation to figure out what it is uniquely good at, who will pay for it, and whether it is prepared to stop some of its regular programming to make space for this new focus (or to hand over its innovations to external partners that will take them to market).

Diversification did not relieve short-term financial pressure for the NGOs we spoke with, because it introduced complexity and demanded capabilities the organisation did not yet have. However, we did find that those NGOs that were more financially diverse were often the most resilient.

The Anchoring-Balancing-Compounding Archetypes

To make sense of the patterns we were seeing, we developed the Anchoring–Balancing–Compounding (ABC) archetype model. It describes three archetypes that non-profits occupy and move between as they diversify their funding sources. And crucially, it identifies the different demands each one places on the organisation and its backers — e.g., showing funders the different types of support required.

Anchoring organisations are predominantly grant-led, bringing in a small share of earned income that helps subsidise costs while grants remain their main source of revenue. Their most pressing need is room to experiment: unrestricted or flexible capital to test revenue models, support to work out pricing for users who cannot pay the full cost, and permission to fail without a restricted grant’s deliverables pulling them back toward premature scaling.

Balancing organisations run grants and earned revenue in rough parity and are actively holding mission and commercial logic together, often through dual legal structures. Here the constraints shift from experimentation to management, creating a need for other types of support — e.g.: governance and legal support for a hybrid structure, working capital to run several income streams at once, and assistance with the harder, less fundable task of managing the tension between mission and revenue as both grow.

Compounding organisations are led by earned income, retaining grants for what markets cannot or should not fund, and they often present as social enterprises with a strong core mission. Their needs are different from those of the other two archetypes, and they include: strategic, catalytic grants ring-fenced for any non-commercial work, the partnership and government-relations capacity to embed into public programmes or procurement systems at scale, and the evidence infrastructure that performance-linked or outcome-based finance demands.

These archetypes raise two key questions for non-profit organisations: Which financial model best serves the organisation’s mission, and what would it need to change to operate with that model?

The ABC Archetype Model, developed from Enabel’s Pathway to Sustainable Scale research and refined through Enabel’s implementation with the Fit4Scale portfolio.

The capacity limbo that no one pays for

An organisation moving into Anchoring, Balancing or Compounding brings assets a new commercial entrant would need years to build. Most have spent a long time working in contexts where markets are thin and public budgets are constrained. They’ve also built relationships a newcomer simply doesn’t have. They know how to design with users rather than for them, and how to translate between what is happening on the ground and what a donor’s reporting template will accept.

However, none of that transfers automatically to a commercial model. Knowing a community well will not tell you how to run three income streams at once, price a service that has never had a price before, or judge when another organisation is better placed to carry an innovation forward. Those are different jobs, and they usually need different people.

This challenge can be seen in the insights we learned from Aflatoun, a global education organisation that empowers children and young people through social and financial education. It set up AflaVentures as a profit-making arm to generate earned income, and then could not staff it with the parent organisation’s existing team. According to a representative at the organisation: “AflaVentures has mixed success, partly because as an NGO … you really need to have commercial people to be successful in that, and finding a person who is interested in doing commercial activities for a nonprofit isn’t easy … at the moment we are not doing fantastic in that area but it’s purely a staffing issue more than anything else.”

Hiring is one part of the transition. A commercial function also has to sit inside an organisation whose systems, incentives and culture were built for non-profit delivery, and that can require a second legal entity with its own board.

In situations where the current team cannot stretch far enough, the grant work and the commercial work compete for the same people. For instance, Green Farmlands has a core team small enough that staff double up across roles. Yet it cannot resource everything at once, so it chooses between its non-profit activities and its revenue-generating ones month by month.

The organisations we interviewed described this as capacity limbo: more demand and opportunity than the team and its systems can carry. This situation is most challenging when an organisation is moving from founder-led delivery to formal operations, or running an innovation alongside core programmes with no dedicated capacity for either. At myAgro, for instance, roles shifted every few months and process knowledge sat informally in people’s heads, which made it difficult to bring in new staff.

This is why the form of support offered to these organisations matters as much as the amount. NGOs at this point need funding that’s flexible enough to let them build capability and change course when a model fails. They also need backers who behave as learning partners rather than compliance monitors checking whether fixed project deliverables have been achieved.

The hidden challenges behind financial diversification

To help address these interrelated challenges, Enabel launched its Fit4Scale initiative earlier this year, which aims to build the capabilities NGOs actually need as they pursue financial diversification and scale, working directly with the leaders who have the decision-making power to make these calls.

We’re supporting organisations’ efforts to: test new commercial business lines which bring in earned revenue, establish commercial enterprises, set up governance mechanisms that can subsidise their non-profit activities, and explore partnerships and alternative funding models to scale their innovations.

But these structural model shifts are only part of the decision. Underneath lie harder challenges.

One involves a change of mindset. Much of the NGO world is built around a particular idea of social mission: doing good for its own sake, delivering projects, meeting donor commitments, etc. But earning revenue requires a different mindset that enables the marketing and selling of products and services — one that holds a commercial logic alongside the social one. This not only changes what an organisation does, it also changes the kinds of people it needs to do it. As Mark Thomson, Director of Business Strategy and Delivery at IWMI, a global research organisation addressing water challenges in developing countries put it: “The hardest shift for IWMI hasn’t been strategic … it is cultural. We had to stop thinking of our digital tools as project outputs and start thinking of them as products that need to be maintained, improved and sustained over time to deliver ongoing impact. That reframe sounds simple, but it cuts against decades of how research institutes are funded and how our people are incentivised. Grant cycles reward novelty. Sustaining what works well requires a completely different mindset.”

The other challenge is often more emotional: balancing mission alignment. Financial diversification forces NGOs to confront questions around mission drift — e.g., as you bring in earned revenue and new capabilities to scale your innovation, does this protect your mission or shift you further away from your original purpose? As Ian Pringle, Executive Director of Farm Radio International described it: “Our expertise in participatory communication, audience engagement, radio, digital platforms and data, for example, can be viewed not only as tools for delivering development programmes, but as capabilities that can solve problems for other organisations. The difficult part is knowing what to commercialise without commercialising our purpose. For Farm Radio, the test is not simply whether something can generate revenue. It is whether we can build a viable offering around what we do exceptionally well, while continuing to reach the people and communities who are unlikely to be served by a purely commercial model.”

Scaling can also mean partnering with others to bring in the skillsets the NGO doesn’t have, sometimes handing them a piece of what the NGO built. And that’s often where the discomfort sits. Handing an innovation to someone else to scale can feel like losing it, even when another organisation is better placed to carry it forward. As Martin Jacobs, Global Rehabilitation Specialist at Handicap International put it: “For the first time, we are looking at our eHealth application through a scaling lens. Instead of simply asking how to do more with more inputs, such as replicating a pilot elsewhere, we are examining our unit cost of delivery and our long-term financial model. This puts our mission as an NGO into question: Where does our mandate end? When and how should we let go and hand the solution to others better equipped to commercialise it? And how do we ensure those partners prioritise impact over profit?”

Commercialisation can pull financial resilience and organisational values into direct conflict. Earned revenue can be less reliable than a long-term grant, particularly in thin markets with volatile demand. And diversification only leads to scale when there is a market to diversify into, which can rule out a substantial share of the work the development sector exists to do. Additionally, though deciding to spin off a commercial activity is perfectly valid (and often represents the most sustainable route to scale), this is not an outcome that is typically rewarded by grant funders.

So the financial support for commercialisation efforts has to reach further than revenue. NGO leaders need the space and the support to decide how their impact should scale, and to shift both skillsets and mindsets across the organisation in line with that pathway.

What funders would have to change

The overarching question is: What types of support are funders prepared to provide, to help organisations implement these changes? We see three core areas where this support could focus:

Funding the transition itself: As mentioned above, moving to new financial models generates costs before it creates returns. These organisations need transition capital: temporary funding that enables them to learn what type of organisation they need to become, and then funds their efforts to become that organisation.

None of this requires the sector to spend more than it already does. Most grant budgets allow a small amount for capacity building, and it is usually spent on compliance training to meet the donor’s reporting standards. The work that moves an organisation from one funding model to the next is different: It involves everything from pricing and financial modelling, to the slow business of learning to sell something. It produces no immediate beneficiary numbers, and it’s difficult to attribute to a funder’s intervention, which is why it is often overlooked.

Designing for learning, not only delivery: How money is designed matters just as much as the amount. Field realities move faster than reporting chains, so a leader who discovers in month eight that the business model does not work has two bad options: Acknowledge that the attempt has failed, and risk being seen as a poor planner. Or keep struggling to deliver the original deliverables across the original grant period and lose two more years.

Funding arrangements shape whether organisations have the space to make these choices well. As one leader at myAgro, which helps smallholder farmers save for agricultural inputs, put it: “If you come up with a list of requirements and deliverables, we’re just going to steer towards those requirements, and not towards finding a real solution.”

Timing matters as well. An organisation moving towards the “Balancing” stage of the ABC archetype takes on cost and complexity well before it sees any return, which is exactly the profile a one to two-year grant cycle punishes. Judging its finances at the moment the grant ends catches it at its worst point.

The organisations that shifted the furthest across archetypes in our study shared one thing: leadership that had accepted that the organisation needed to become something different. That mattered more than market opportunity did, and it strengthens the case for funders to work with NGO decision-makers themselves to get shared buy-in for full organisational change, rather than treating this diversification like another small-scale innovation project within the organisation’s broader programming. But this is as much a behavioural shift for funders as it is for organisations. It requires a new psychology of grantmaking, where the goal stays fixed, but the ways to get there can change based on what you learn.

Redefine what scaling success looks like: Funders also need to broaden what counts as progress. During a transition to new revenue streams, most of an NGO’s progress is invisible on a standard grant report. This reporting isn’t designed to capture the capabilities and systems an organisation needs to deliver at scale, such as a viable pricing model, new commercial capability, stronger governance, a partnership that establishes who carries the work, or the evidence that a business model should be abandoned before more is spent on it. Sometimes the end result of this process is that the NGO decides that it should continue to be predominantly grant-funded, or that an external partner is better placed to take an innovation forward.

The ABC model makes that conversation more specific. Instead of assuming every organisation should travel from grants toward earned revenue, it encourages funders to ask three questions: Where is this organisation now, where does it need to go to achieve greater impact, and what capabilities, structures and funding will it need to get there?

If funders are serious about scaling impact, they have to pay for the transition itself, not just the pilot that precedes it and the scale they hope will follow. That means transition capital: flexible funding, committed for the years an organisation spends becoming something different, that pays for the partnerships, governance, structures and skills the ABC process demands. Until that money exists, the sector will keep asking NGOs to cross a gap it refuses to fund.

Disclosure: Enabel is the Belgian Agency for International Cooperation. The Innovation Hub is Enabel’s innovation unit, funded by Belgium and the EU, supporting mission-driven organisations active in Africa and the Middle East in scaling innovative solutions to complex societal issues. The Enabel Innovation Hub is supporting the organisations mentioned in this article, IMWI and Farm Radio International, with grant funding and capacity building technical assistance. It also commissioned and published a mixed methods study which interviewed Aflatoun, Green Farmlands and myAgro. 

Toon Driesen is the manager of the Enabel Innovation Hub, funded by Belgium and the EU; Alex Losneanu is Innovation Director and Pritika Kasliwal is Innovation Lead at Brink, which is a partner to the Innovation Hub.

Photo credit: cagkansayin

That realisation made us rethink everything. My co-founders and I saw that, if we wanted children to arrive at school ready to learn, we had to reach them earlier, which meant supporting their parents from the time of the child’s birth. To help meet that need, in 2016 we founded ToguMogu, an AI-powered digital parenting platform in Bangladesh that has reached more than 476,000 registered users over the past six years, at parenting stages ranging from pregnancy through age 10. Alongside its AI assistant, it connects parents to a network of experts, including paediatricians available through teleconsultation and a community of hundreds of thousands of other parents, while also offering growth tracking, expert-reviewed content and other services. 

Light of Hope and ToguMogu were not separate ideas. They emerged from the same insight: that a child’s development is continuous and starts at birth. We simply applied that insight to two different stages of childhood: the early years from 0-5, and the primary school years from 4-12.

After nearly a decade of building services for parents navigating both of these stages of childhood, we’ve developed a clear view of where parenting technology is failing families, not just in Bangladesh but globally. Despite being worth hundreds of millions of dollars and growing at double-digit rates, the parenting technology industry has three structural blind spots that are getting more serious the faster the sector grows. These blind spots exist in wealthy and emerging markets alike, but their consequences are most severe in low- and middle-income countries where families have the fewest options for child development specialists or other alternative professionals to turn to.

The First Blind Spot: Parenting Apps Are Built for Mothers, Not Families

Walk through the onboarding flow of almost any parenting app and you will notice something immediately: The language, the content and the community features assume one user, the mother. This reflects a real imbalance in caregiving, where mothers worldwide perform 2.5 times more unpaid care work than fathers. But by designing only for mothers, these platforms reinforce that imbalance rather than helping to close it.

Research from developed markets reveals how this gender bias is often built into popular parenting apps, as shown in a 2021 study of digital parenting guidance in Canada. This design choice not only reduces the chances of fathers getting involved, it also often gives mothers the added responsibility of “curating” digital parenting guidance for their partners — a dynamic that doesn’t serve the interests of either parent (or their children).

We saw this pattern in our own platform and set out to change it. Of ToguMogu’s nearly half a million registered users, over 164,000 are men, roughly 35% of our user base. We deliberately designed the app to include fathers, framing them as active participants in a shared parenting journey instead of secondary observers of the mothers. Once we designed for fathers, they showed up.

This matters far beyond app design. When fathers are engaged and supported in early parenting, children do measurably better. Research on parenting programmes indicates that engaging fathers, not mothers alone, improves both the effectiveness of the programme and the outcomes for the child. A parenting platform that engages the whole family is not just being inclusive; it is investing in better developmental outcomes for the child, which is the entire point.

The Second Blind Spot: The Age-Five Cliff

Parenting apps tend to be heavily concentrated on children’s early years. This focus is well founded: Around 90% of a child’s brain development happens before age 5, and early connections lay the groundwork for later cognitive ability. But treating age 5 as a finish line is a mistake.

Development does not stop when a child starts school. Research on middle childhood, which spans the ages of 6-12, often describes this period as the “forgotten years,” since it is overlooked by most education research despite being a time of rich cognitive, social and emotional growth. Middle childhood is when children consolidate learning habits, self-regulation and social skills, yet it is precisely the stage at which most parenting apps disengage from the family.

When a child enters school, the parenting technology sector largely hands the family over to the education technology sector. Some edtech tools connect to families through school-to-parent messaging and home engagement features, but that is a different job from supporting a parent in the developmental work of raising a 7-year-old at home: managing big emotions, building independence, and navigating a child’s first real social conflicts. Currently, few tools serve that need, which leaves parents of primary-school-aged children with less support than they had when their child was an infant.

At ToguMogu, our usage data showed that families did not stop seeking guidance once their child started formal school; questions simply shifted from topics like feeding and sleep, to issues like behaviour, learning and schooling. In response, we expanded ToguMogu to serve children up to age 10, adapting our content and expert network in recognition of the fact that guidance for ages 0-5 does not transfer neatly to the school years. However, the broader sector has largely not made that realisation, and families in emerging markets, where school systems are least able to fill the gap, pay the price.

The Third Blind Spot: AI Without Accountability

As the head of a digital platform that integrates artificial intelligence into its service delivery, I’m familiar with both the upsides and downsides of the widespread incorporation of AI into parenting apps. ToguMogu’s AI assistant provides parents with insights based on their child’s developmental progress, and I have seen firsthand how useful this can be, and how easily it can go wrong. UNICEF’s guidance for child-centred AI calls for regulatory oversight, safety, fairness, transparency, explainability and accountability. Yet no consistent standard, either regulator- or industry-led, holds parenting tools to those principles, even when they leverage AI to offer developmental assessments, behavioural guidance and health recommendations for children — areas where the wrong advice can have significant negative consequences.

The risk is real, and it is particularly relevant in the Global South. Studies show that while parenting programs are generally successful in improving children’s behaviour, their effectiveness may not be easily replicated in low- and middle-income countries, where ignoring unique cultural contexts can worsen existing inequalities and cause harm. The difference between developed and emerging countries here is cultural, not technological: Many parenting interventions in the Global North are based on values like individuality and independence, which run contrary to the collectivism, emotional restraint and joint-family child-rearing that characterise parenting across South Asia and other emerging regions. A tool that treats one culture’s parenting norms as universal therefore risks judging another culture’s practices as deficient. That’s why parenting technologies must be tested locally, built on local data and kept under human oversight: As research has found, this sort of deep local adaptation yields better results than surface-level changes, like simply translating an interface from one language to another.

Education technology already offers a cautionary tale. UNESCO has warned that generative AI is advancing faster than national regulation and that many educational institutions remain unprepared to validate the tools they adopt. Parenting platforms risk repeating that pattern with even higher stakes. The children affected include infants and preschoolers, and in many emerging markets, families have no clinician, health visitor or specialist to turn to for a second opinion when an app gets it wrong. The app may be the only source of guidance available.

What Funders and Development Organisations Should Do

Over a decade of building ToguMogu and Light of Hope, we have fallen prey to each of these three blind spots ourselves, and we know they are fixable. But they will not fix themselves. The funders, governments and development organisations that support and partner with these platforms should put their leverage to use to advocate for the following changes.

Require father-inclusive design as a condition of support: Any platform receiving development funding or an institutional partnership for early childhood outcomes should have to show active engagement from fathers and co-parents, not just mothers. Measure it. Fund it. Expect it.

Fund the 5-8 window deliberately. The move into school should not be a drop-off point in support. Development organisations working on education should actively seek out and back platforms that bridge parenting technology and school-age support. That infrastructure barely exists today, and it needs to be built.

Set accountability standards for AI in early childhood before a crisis forces them. Funders and institutional partners should require, at a minimum, that AI-driven parenting tools disclose the make-up of their training data, undergo independent clinical validation before deployment, and tell parents clearly when guidance is AI-generated rather than reviewed by a clinician. The sector will not do this quickly on its own. External pressure is what will move it.

None of this requires punishing innovation or demanding costly trials for every feature. It requires that the organisations funding this sector attach basic expectations to their support. The families using these platforms in low- and middle-income countries are not beta testers. For many, a parenting app is the closest thing to professional early childhood guidance they will ever receive. That responsibility deserves more care than the sector currently brings to it. At ToguMogu, partners, including the United Nations Population Fund and Bangladesh’s Directorate General of Family Planning, have pushed us toward higher standards than we would have set alone. That kind of institutional pressure works. The development community should apply it far more widely.

Zillul Karim is Co-Founder and Strategic Advisor at ToguMogu, and serves as Board Director at Light of Hope Limited.

Photo credit: Pressmaster

Staff are regularly using free, publicly available AI tools to summarise evaluation reports, translate documents, draft proposals, tidy up their field notes and prepare donor updates. Some do this carefully. Others do it with little sense of how outputs should be checked, where their own human judgement must be used, and what types of training data should never be uploaded.

Leadership responses to this off-the-books AI use vary. Some leaders I’ve worked with know it is happening and look away. Some suspect it and hope the question resolves itself, or may even embrace staff experimentation with AI tools in the absence of an organisational policy. A few others have restricted or banned staff use of AI tools.

That last response deserves attention. In a sector rightly alert to technology’s risks, restricting or even banning staff use of AI tools may feel like the responsible position for many development and impact organisations. In practice, it is often the opposite. Saying no does not keep AI out of an organisation; instead, it keeps AI use invisible, and invisibility is the one condition under which none of AI’s risks can be managed.

The AI ‘Governance Vacuum’ in Global Development

This issue isn’t merely anecdotal. In a major survey by the Humanitarian Leadership Academy and Data Friendly Space, covering 2,539 humanitarian workers across 144 countries, seven in 10 reported using AI daily or weekly, while fewer than a quarter of their organisations had any formal AI policy in place. The same survey found that almost two-thirds of organisations offered staff little or no AI training, even as most staff used the tools regardless. The researchers referred to the resulting misalignment as a “governance vacuum”: individual use running well ahead of the institution’s capacity to guide it.

The timing makes this lack of guidance harder to ignore. Official development assistance from DAC members fell by 23.1% in 2025 to US $174.3 billion, the largest annual contraction on record, with core contributions to the UN system down 27% and bilateral programming cut deeply. Organisations are being asked to protect service delivery with fewer people and tighter budgets, as well as to justify every cost while doing so. Meanwhile, AI tools keep getting cheaper, more capable and easier to access. Anyone with a browser can use one, and many staff already do.

So the question of whether these organisations will adopt AI was settled some time ago, without fanfare, one prompt at a time. What remains unsettled is the governance that should underpin this growing AI use. On one side sit staff with heavier workloads and rising expectations. On the other sit boards and senior leadership teams that still tend to treat AI as a future strategy question, a procurement decision or a reputational risk to be contained. Between them sits the real-world organisation: people using AI tools informally because they help, while policy, training and accountability trail behind.

The Three Downsides to Restricting AI

None of this is an argument for rushing in, no holds barred, to adopt AI. Organisations operating in the international development and social impact sectors handle sensitive data, work with vulnerable communities and operate in charged political contexts. Badly governed AI usage can result in exposed data, fabricated evidence, biased analysis, and weak conclusions concealed by false confidence. These risks are real. But addressing them will require clear safeguards and informed oversight rather than disengagement.

That’s because AI restrictions and bans are weak forms of protection and carry costs of their own. Three stand out.

The first falls on staff. When an organisation avoids the issue, every judgement call devolves to individuals. The programme officer using AI to summarise a 90-page impact evaluation must decide alone whether the summary can be trusted. The fundraiser drafting a concept note must decide alone what AI outputs are safe to paste in. The monitoring officer experimenting with qualitative analysis must work out, unaided, whether the AI tool has flattened what respondents actually said. These are hard questions even for specialists. Organisations that refuse to engage with them are not sparing staff the burden; they are transferring that burden to them, without providing the rules, training or backup that could help staff navigate it.

There is an equity dimension here too. When organisations provide neither practical guidance nor safe routes to experiment, staff are left to make their own calculations about AI’s risks and rewards. More confident users may save time, but they may also expose sensitive information or rely on plausible-sounding but erroneous outputs; more cautious colleagues may avoid those risks, but also miss legitimate opportunities to work more effectively. The result is neither fair nor controlled. Individual confidence and risk tolerance rather than organisational priorities and consistent safeguards determine who uses AI, how they use it and who bears the consequences.

The second cost involves organisational learning. The most useful applications of AI in social impact and global development work are rarely the controversial ones. They sit in the unglamorous middle of these organisations’ work: synthesising lessons across projects, making institutional knowledge findable, drafting first versions of routine documents, comparing proposals, and pressure-testing assumptions before a decision is made. These workflows are precisely the ones many organisations struggle to resource, and precisely where AI can help without displacing expertise or automating anything sensitive.

Leadership teams that either avoid AI or respond to staff AI usage mainly by restricting tools are unlikely to discover this middle ground. They also lose the chance to build the judgement and safeguards needed for higher-stakes uses through supervised practice on low-risk work. If organisations defer that learning, they may eventually face a consequential AI decision with no institutional experience to draw on.

The third cost lands on partners and communities. All social impact organisations — from large UN agencies to grassroots NGOs — worry, rightly, about extractive or carelessly used technology. But unmanaged AI is more dangerous than visible, bounded AI. When staff paste a partner’s internal documents into public tools with no data rules, when community feedback is summarised without anyone checking for lost meaning, or when AI-polished language smooths the uncertainty out of a report, the risk is carried by the people least able to see or challenge the process. Workplace AI bans prevent none of this. They simply guarantee that nobody is watching for it.

Some organisations have grasped this. Mercy Corps developed internal generative AI chatbots grounded in its own digital library, giving staff a safer way to use organisational knowledge while reducing the risk of sensitive data being pasted into public tools. The United Nations Development Programme (UNDP) took a more specialised approach: Its Artificial Intelligence for Development Analytics platform gives staff and the public multilingual, source-linked access to evidence from nearly 7,000 evaluation reports. Launched in 2022 and substantially upgraded since, more than three quarters of users reported that it made evaluation evidence easier and quicker to use, although UNDP acknowledges that uptake remains uneven.

Guidance for Responsible AI Adoption

Responsible adoption starts from a different premise: AI is already inside the organisation, so leaders first need to understand how it is being used. Strategy, policy and procurement still matter, but they should respond to real workflows and risks rather than preclude any investigation of them.

It begins with mapping real staff use. This should be a practical exercise to establish where staff already use AI: which tools they prefer, what information they’re feeding in, and which outputs they’ve come to rely on. This exercise will highlight both where staff are successfully using AI, and where they feel out of their depth. The aim is not to shame experimentation. Rather, it is to make invisible practice visible enough to govern.

The next step is to select a small number of existing or proposed workflows for formal, supervised use. Strategy, policy and procurement should inform that choice, but they should not substitute for examining the work itself. Good starting points are low-stakes, internal tasks grounded in source material and easy for a person to verify — for example, summarising public documents, preparing internal meeting notes, compiling donor information from published sources, or checking a human-written draft against agreed-upon criteria such as factual accuracy, required content and donor rules. These are useful entry points because staff can practise source discipline, verification and human review before using AI with sensitive data or consequential decisions.

Alongside this, staff need simple data boundaries. Public information, internal operational material, personal data, partner documents and community-level data should not be treated alike. Most teams can begin with plain-language categories, concrete examples of what staff may and may not upload in common situations, and a clear route for advice when the answer is uncertain. They also need defined human review points. The higher the stakes, the stronger the requirement that a person checks sources, tests assumptions and makes the final decision. AI should be treated as a tool that can accelerate parts of a workflow while producing errors plausible enough to escape casual review — not as a colleague whose judgement can be trusted.

Finally, boards and senior leaders need enough AI literacy to govern its use. They do not need to become technologists, but they must be able to: distinguish between controlling access to tools and controlling the risks within particular workflows; understand how data protection, model error and human review requirements vary by use case; and judge whether staff have the training and approved tools to work safely. They should be asking: Do we know where AI is already used? Which tools and data are permitted? Where must a person verify sources or make the final decision? And who owns the quality, compliance and consequences of each AI-assisted workflow?

This is a manageable agenda. But it requires leaders to move from avoidance to stewardship.

The central question for impact-driven organisations is no longer whether or not AI should be used — that ship has sailed at most organisations across the sector. The challenge now is to determine where AI can responsibly improve their work, where it should be kept out, and what staff and leadership capability is needed to tell the difference. Saying no may feel prudent, given the organisational risks this technology can bring. But if prohibiting AI has the practical effect of increasing unmanaged use, fostering uneven staff practices and weakening oversight, it is not prudence. It is risk in another form, borne by the people the sector exists to serve.

Loksan Harley is the Founder and Principal of Homelands AI.

Photo credit: wildpixel

So how big an impact is this challenge having on women entrepreneurs, and what can be done to address it?

We already know that fraud is among the well-documented barriers women face when accessing digital financial tools and services. What’s missing is data on the unique risks women experience once they have already gained access to formal financial services or adopted digital tools — particularly in terms of their exposure and response to digital financial fraud, and their ability to recover from it.

Both CARE and Innovations for Poverty Action (IPA) have recently collected more data on this issue, via the Midline Survey from Strive Women, a Mastercard Strive program implemented by CARE, and from IPA’s Financial Consumer Protection Surveys. We combined our datasets to answer a few questions: Is there a specific story for women around digital risk and fraud? Are they more vulnerable? And what can we do to improve their resilience?

Are women more likely to be victims of fraud?

IPA’s surveys from seven countries find that women, on average, report fraud attempts 6 percentage points less often than men. But this gap shrinks once you account for differences in education, country, employment and financial literacy — factors closely tied to digital and financial platform usage. In other words, once you compare women and men with similar levels of education, employment and financial literacy, they report fraud attempts at about the same rate. Women aren’t exposed to fraud less because they’re women; they’re exposed less, in part, because they use formal digital and financial tools less regularly.

Similarly, data from the Strive Women program shows this pattern directly. Surveying over 1,000 entrepreneurs from Peru, Pakistan and Vietnam, it found that their reported rates of encountering fraud (attempted or successful) in the past 12 months are low (21% in Vietnam, 19% in Peru and 12% in Pakistan). A key determinant of fraud exposure is the entrepreneur’s country context and their usage of digital platforms: Entrepreneurs who used more digital tools for their business were more likely to experience fraud, regardless of gender. In other words, the very tools driving business growth are the same ones driving fraud exposure.

However, CGAP research shows that once contact from a fraudster happens, women are more likely to be affected by fraud: less equipped to identify a scam or resist social engineering tactics that exploit trust or urgency, and less supported by the systems meant to help when something goes wrong.

How does fraud affect women and men differently? 

Our findings reveal several areas where fraud has different effects on women and men.

Being prepared: Women may be slightly more attuned to digital risk than men. Data from Strive Women shows that 75% of women rate cybersecurity as important to their business, compared to 68% of men. But that added awareness doesn’t translate into more action: About a third of both women and men still take zero protective steps, and when entrepreneurs do act, they almost always default to the same single habit — a strong password — with far fewer using two-factor authentication, backing up their data or using antivirus software.

Knowing what to do next: Twice as many women as men in the Strive Women sample said they would not know what steps to take if their digital account were compromised, despite reporting high confidence using digital tools day-to-day. IPA’s data across several countries tells a more mixed story on formal complaints: Women were somewhat more likely than men to file complaints in Ethiopia and Pakistan (note: the data from these countries is not yet publicly available), roughly on par in the Philippines and Kenya, and less likely in Tanzania, Bangladesh and Uganda, where the gap reached about 17 percentage points. This spread suggests that the broader cultural contexts and financial systems women are navigating may affect their ability to seek help. Evidence from India points to one manifestation of this challenge: Women are more likely to turn to family or friends before pursuing a formal complaint, and fear of family backlash can discourage them from going further on their own — meaning the decision to seek redress is often a social one, not just a practical one. And even when a woman does clear that hurdle, the system doesn’t always meet her halfway. As one Strive Women entrepreneur in Peru put it: “There was money taken from my card. I went to the bank. They told me I had made that withdrawal, but I had not.”

Bouncing back: The Strive Women sample shows that men also have a larger financial runway to cope with a shock or emergency: 22% of men have enough capital to run their business for six months or more if something goes wrong, compared to only 14% of women. Without that cushion, a single fraud event threatens the sustainability of their business. IPA data also finds that women across the surveyed countries are 8 percentage points less likely to have capital reserves than men.

What actually helps women avoid fraud

Consumer education can help women detect and avoid fraud, depending on how it is designed and delivered, but education alone can’t solve the problem. In rigorous evaluations of fraud education programs in Kenya and Nigeria, IPA found that standalone tip sheets and training increased women’s confidence in recognizing fraud, bringing it closer to men’s. However, these initiatives did not similarly improve women’s ability to accurately identify fraudulent messages. In other words, participants felt more able to spot fraud but were not measurably better at it.

The exception was a program in Uganda that used interactive, story-based lessons built around realistic fraud scenarios. Users navigated common scams by making decisions at key moments, allowing them to practice how to respond before encountering fraud in real life. The program reduced the share of users who lost money to fraud by 1.4 percentage points overall and by 2.6 percentage points among women. Beyond training, a well-timed nudge or notification at the right point in a user’s digital journey can build awareness just as effectively. Better still is to build security into the product itself, so that safety doesn’t hinge on a behavior change — e.g., through default protections like automatic multi-factor authentication, transaction alerts, safer onboarding and role-based account permissions.

Redress mechanisms also need to be offered through channels women already trust. Many women still prefer an in-person agent to a chatbot, and routing support through community structures such as women’s groups and peer networks can reduce the stigma that keeps fraud victims from reporting. Peer networks and endorsements can also support women’s financial safety. In Ghana, an IPA evaluation found that training group leaders in mobile banking and encouraging peer endorsement were especially effective at building women’s confidence to handle fraud. IPA research also finds that offering free, easy-to-access legal aid can increase redress resolution rates for women slightly more than men.

But in many cases, providers do not need new programs or policies: They just need to make better use of the data they already collect. IPA’s work with a Ugandan telecom regulator found that categorizing and segmenting existing complaint data by sex could reveal where to focus next in fraud prevention policy.

Let’s stop treating digital safety as an afterthought 

Women entrepreneurs do not need to be persuaded to use digital tools. They already are. But if we want them to continue reaping the benefits of digitization, what they need are systems that make those tools safe.

That means moving beyond consumer education as the primary line of defense. Instead, providers should embed protections into products from the outset through secure defaults, clear transaction alerts, intuitive reporting channels, and recovery mechanisms that are free and easy to navigate. Regulators should require providers to collect and analyze sex-disaggregated fraud and complaints data, and set institutional standards for delivering effective consumer protection — not just access — across genders.

The success of digital financial inclusion should no longer be measured only by how many women adopt digital tools, but by whether they can use those tools safely, with confidence — and whether they have the ability to recover when something goes wrong.

For more information about the research in this article, contact us by email at financialinclusion@poverty-action.org and entrepreneurship@care.org.

Tanvi Jaluka is the Research and Learning Lead for CARE USA’s Women’s Entrepreneurship team; Lauren Perlik is an Associate Program Manager for Innovations for Poverty Action’s Financial Inclusion Program.

Photo credit: AndreyPopov

But laying the groundwork alone will not deliver transformation. As AI moves from a research frontier to a practical tool for governments and service providers across the Global South, MDBs are confronted with a harder challenge: What will it take to actually deploy AI at scale in LMICs, and are they set up to finance that?

Investing in components rather than systems

The AI investment landscape within MDBs today is wide but thin. There are pilots, such as an AI tool for tax administration in Armenia, responsible AI experiments with municipalities in Chile, and innovation challenges for road safety in Asia. There is infrastructure, such as data centers in Thailand and Azerbaijan, broadband expansion across West Africa, and digital identity programs in the Philippines. There are enabling environments, such as cybersecurity support across dozens of countries, regulatory sandboxes, and AI policy advisory.

Each of these initiatives is valuable, and they span across the “AI stack,” including AI applications themselves; enablers like policies, regulations and digital public infrastructure; and foundational technologies like internet access, data and compute infrastructure, and digital devices. Taken together, however, they show that MDBs are still in the discovery phase, investing in components rather than systems, and in pilot programs intended to demonstrate feasibility rather than programs that aim to scale.

What’s largely missing is a financing approach that connects AI use cases to the underlying foundations they depend on (i.e., the enabler and foundational technology components of the full AI stack), and that moves from proof-of-concept to national-scale deployment within a coherent MDB program.

What scaling AI actually requires 

It will take more than funding individual use cases and building data infrastructure to achieve large-scale AI deployment in an LMIC context. Large-scale AI roll-out will require MDBs to get five things right simultaneously, spanning across multiple sectors and working with government ministries that rarely plan together. These include:

• Use cases that are technically sound, locally adapted and designed for the realities of end-users in often under-resourced environments — e.g., community health workers with basic smartphones or smallholder farmers with intermittent connectivity.

• Data foundations or interoperable platforms that connect to existing government systems, with governance arrangements that determine how data is collected, stored and used.

• Compute and connectivity calibrated to the deployment context. Consider a national deployment of AI in healthcare: If it can only run in urban environments, it hasn’t solved the problem of access across the country.

• Devices such as smartphones and tablets in the hands of frontline workers. This remains the most consistently underfinanced layer in the stack, despite being the final link in the chain that decides whether AI-based systems reach traditionally excluded communities or not — and despite the fact that smartphone ownership across LMICs stands at 50%, and just 24% in Africa (as of 2024).

• Enabling policies such as data protection regulations, procurement frameworks, and sector-specific guidelines that allow governments to deploy AI responsibly and at speed.

Many MDBs’ digital and AI strategies articulate these layers clearly but fall short on operationalization. In practice, ensuring that an AI strategy can be effectively executed means addressing these layers in a single, cohesive program as opposed to having them spread across separate projects, teams and financing instruments.

What an AI system with large-scale public benefits could look like 

Consider what an integrated approach might look like in primary healthcare, a sector where the development case for AI is strong, and the infrastructure gap — including both ill-equipped facilities and constraints on provider availability and capacity — is well-documented.

The goal of such an approach would be to create a system that gets ahead of illness instead of just responding to it. Every citizen, regardless of where they live, would receive continuous, personalized health support. That would include preventive care through ongoing monitoring and early detection, delivered by community health workers at people’s doorsteps.

Achieving such an ambition would mean investing across a suite of interconnected AI use cases rather than a single tool, including:

• A clinical decision-support system that helps community health workers diagnose conditions accurately in low-resource settings.
• An administrative layer that reduces the documentation burden on those same workers, enabling them to spend more time with patients and less on paperwork.
• A referral coordination tool that ensures that patients who need higher-level care actually get to the right facility, and that their records follow them.
• A patient tracking system that enables longitudinal monitoring, flagging individuals with deteriorating health or those who have missed critical follow-ups.

Individually, each of these use cases makes an interesting pilot. Taken together, they make up a transformational system.

However, building this system requires a shared infrastructure: a unified data platform that interoperates with the government’s existing health information systems to link a person’s longitudinal health data to their existing national ID, enabling continuity of care between providers and informing the government’s population-level research; data protection regulations that govern how patient data is collected and used; and devices in the hands of every frontline worker.

In addition to infrastructure, capacity building must be a core investment. Community health workers need to be genuinely equipped to use these tools on an ongoing basis, and government health teams must have the ability to manage, evaluate and own these systems without relying on external technical partners to keep them running. At both the grassroots and local governance levels, this requires structured, continuous support that reflects varying levels of digital literacy and local language needs, while accommodating the often-unpredictable realities of fieldwork.

At the same time, project timelines need to shift to ensure that solutions do not become obsolete before they are even launched. With AI performance doubling every seven months, having a six-to-12-month lead time from approval to first deployment, rather than the two-plus years typical of large MDB operations, matters enormously. And active technical support cannot end at go-live; it must continue through the scale-up phase. This can help governments avoid vendor lock-in and dependence on a single provider, manage data governance, and progressively own these systems over time.

Five shifts worth considering to scale systematic AI implementation

Moving from experimentation to systematic AI financing at scale is more an institutional challenge than a technical one. It involves rethinking how MDBs frame ambition, scope programs, structure financing, run operations and deliver advisory services, and includes the following five shifts.

From component funding to sector transformation: This shift reframes the question from “what AI investment can we structure?” to “what would it take to transform this sector through AI?” That reframing shapes everything downstream, including the scope of the program, the mix of instruments, the partners involved and the metrics of success.

Across the full stack, in a single program: This shift requires an institution to take stock of what already exists in a country (e.g., internet connectivity, data systems, workforce capability, regulatory environment) and build a curated set of investments that address the specific gaps between where things are and where they need to be for AI to work at scale.

Financing calibrated to scope: Compute infrastructure, data platforms and AI applications have fundamentally different risk profiles and cost structures — from tens of millions of US dollars for sub-national phases to billions for a national rollout — and a one-size financial instrument is unlikely to serve all three well.

Procurement and operations designed for AI realities: This shift requires procurement that: prioritizes digital public goods, reusable building blocks from comparable countries and MDB-developed tools; treats interoperability as a non-negotiable requirement; and avoids vendor lock-in. It also requires lead times under 12 months; project durations that account for active support beyond the launch date; and evaluation frameworks that capture adoption, use and deployment.

Deep technical advisory front-loaded: This shift involves helping governments: understand AI’s cost reality from the outset; make smart early decisions on model selection, dataset localization and the use of existing digital public goods; and manage the risks that are specific to AI, such as data interoperability failures, low user adoption, and systems that work in pilots but cannot sustain themselves after project financing ends. Two areas deserve particular attention:

• Supporting governments in choosing the right AI architecture: MDBs must help governments work through on-the-ground realities such as network connectivity in rural areas, the processing capability of devices used by frontline workers, power infrastructure, and available budgets. These factors inform choices between frontier, cloud-enabled large language models; offline-capable, cost-effective small language models; or AI-in-a-box solutions that may be equally or more effective depending on the context.

• Helping governments understand the sovereignty stakes of AI procurement: AI procurement carries strategic implications that physical infrastructure never did, and most governments are ill-equipped to navigate them. MDBs should help governments think through where data will be stored, where compute will reside, and who will control the models underpinning critical public services. These decisions, once locked in, are costly and difficult to reverse.

The development opportunity in AI transformation 

MDBs have been helping to build the infrastructure layer of the digital economy across the Global South for years. The next step is to ensure that the communities this infrastructure was built for actually benefit from AI transformation at scale.

AI can extend diagnostic reach in healthcare, strengthen agricultural advisory, and improve government service delivery across other sectors and societal needs, while supporting evidence-based policymaking — at a scale and cost few other interventions can match. As the 2030 deadline for the Sustainable Development Goals approaches, LMICs continue to face an estimated US $4 trillion annual SDG financing gap. AI alone won’t close that gap, but used well, it can make every dollar of development financing go further.

Kunal Walia is a Partner at Dalberg Advisors.

Photo credit: Urupong