Artificial intelligence now appears in almost every development discussion. In Member Countries of the Islamic Development Bank (IsDB), it is usually presented as part of …
Artificial intelligence now appears in almost every development discussion. In Member Countries of the Islamic Development Bank (IsDB), it is usually presented as part of …
Artificial intelligence now appears in almost every development discussion. In Member Countries of the Islamic Development Bank (IsDB), it is usually presented as part of the answer to familiar challenges: low productivity, weak service delivery, financial exclusion, and institutional capacity gaps. That is understandable. But the phrase “affordable AI” can easily become too broad to mean much.
It is better to ask a narrower question. What changes, in practical terms, when AI becomes cheap enough to use at scale?
That is where the subject becomes interesting. In many Member Countries, the main needs are already clear. Farmers need timely advice. Small businesses need support with language, paperwork, and routine customer communication. Public agencies need ways to handle repeated requests more quickly and more consistently. None of these needs is new. What has changed is that some of the tasks involved in meeting them can now be carried out at much lower cost.
That is the real shift.
A translated document, a basic screening, a routine response, a farming recommendation, these are not headline-grabbing uses of technology. But they are precisely the kind of functions that matter when access depends on whether a service can be delivered repeatedly and cheaply. Stanford’s 2025 AI Index reports that the cost of running a GPT-3.5-level query fell more than 280-fold between November 2022 and October 2024. For development policy, that matters less as a technical achievement than as a cost story. In some cases, the unit economics are no longer what they were.
Still, it is worth keeping the point in proportion. Lower AI cost matters only where cost was part of the original barrier. If the real problem lies elsewhere, then cheaper AI will not do very much.
This is why smallholder agriculture stands out.
In many rural areas, extension systems have long struggled with the same issues: not enough staff, too much distance, limited continuity, and high delivery costs. The outcome is predictable. Many farmers receive little support, or receive it too late to be useful.
Digital delivery does not solve every part of that problem, but it can change the reach. Precision Development (PxD), for example, reached more than 18 million smallholder farmers across Asia and Africa in 2024, including in Ethiopia. Evidence from India suggests meaningful profit gains relative to program cost. The broader lesson is straightforward. When advice can be delivered through voice, SMS, or messaging tools at very low marginal cost, the scale of outreach starts to change.
A similar pattern can be seen in rural finance and insurance. Apollo Agriculture has used machine-learning-based credit scoring in Kenya and Zambia to serve farmers who do not fit traditional lending models. Farmerline’s Darli AI delivers advisory in 27 African languages, which is significant because language remains one of the most practical barriers in digital service delivery. Pula has extended climate insurance to millions of smallholders across Africa and Asia through technology-enabled platforms. These are not identical models and they should not be treated as if they were. But they do illustrate the same basic point: once delivery costs fall far enough, serving neglected users becomes more viable.
The case for microenterprises is also persuasive, although it is less tidy.
Very small businesses are often held back by a buildup of ordinary frictions. Responding to customers takes time. Translating information takes time. Organising stock takes time. So does handling simple compliance tasks. None of this sounds dramatic. Yet taken together, these demands consume time and attention that small firms do not really have to spare.
This is one area where low-cost AI can genuinely help. The Wasoko-MaxAB platform, for example, combines logistics and inventory optimisation, local-language interfaces, and credit assessment to support informal retailers in a number of African markets. One should be careful not to claim too much from a single example. Even so, it does show how businesses that were previously too small to access structured support can begin to use tools that improve day-to-day operations in practical ways.
But this is also where caution matters.
If a business still runs mainly on cash, paper records, and limited digital familiarity, then AI cost is probably not the main issue. The bigger issue is that the business is not yet digitally ready. In that case, the higher-return intervention may be something much simpler: onboarding, payment tools, or basic record-keeping. This sounds obvious, but it is often forgotten when AI enters the discussion.
That same caution should apply more broadly.
There are sectors where the affordability of AI is not the central constraint. In primary education, the bigger problems are often teacher quality, curriculum, and classroom conditions. In urban management, municipal finance and institutional fragmentation may matter far more. In public administration, weak workflows and weak accountability can be more damaging than limited analytical capacity. AI may still be useful in these areas, but it is unlikely to be decisive unless those more basic constraints are addressed first.
For IsDB, this leads to a fairly practical conclusion.
The institution’s value does not lie in promoting AI as a symbol of modernisation. That would be too superficial. The more useful role is to identify where lower-cost AI genuinely changes the economics of inclusion or service delivery, and then to support the conditions that make those gains real.
Some of those conditions are immediate. Connectivity matters. Access matters. Trusted delivery channels matter. For rural users and women microentrepreneurs especially, these are not secondary issues. They are part of whether the service works at all. Public agencies may also need support to access affordable local-language tools and to pilot them in areas where the development case is already credible, such as agricultural advisory, basic health triage, and MSME support.
Other conditions are more structural. Member Countries will need stronger Arabic- and French-language data ecosystems, more local or regional hosting capacity, and workforce adaptation in occupations that sit close to citizens and end users. This includes not only technical specialists, but also extension officers, administrators, teachers, and SME support professionals. That side of the agenda may attract less attention than frontier-model discussions, but it is probably more important.
There is also a specific opening in Islamic finance. As AI-enabled financial services expand, questions of Shariah compliance, certification, governance, data control, and cross-border interoperability are likely to become harder, not easier. A fragmented approach would slow progress. A more coordinated one could reduce uncertainty and make useful adoption more realistic.
The wider conclusion is modest, but that is part of its strength.
Affordable AI matters where it lowers the cost of delivering services that people already need and that institutions have struggled to provide at scale. That is where the developmental case is strongest. Where the necessary complements are missing, the technology alone is unlikely to deliver much.
For IsDB Member Countries, then, the serious question is not whether AI is impressive. It is whether lower-cost AI becomes useful in settings where it can extend access, improve consistency, and reduce delivery cost in a meaningful way. That is a narrower claim than much of the current rhetoric suggests. It is also the one most likely to hold up.
