Key Takeaways
- A recent study of 200 women micro‑entrepreneurs in Bengaluru found that 32.5 % reported using at least one “AI‑enabled” tool, but the tools listed (KhataBook, WhatsApp Business auto‑replies, Canva, Facebook posting‑time suggestions) are essentially rule‑based or template‑driven applications, not genuine artificial intelligence.
- The paper’s true contribution is that sustained, peer‑based NGO training—not the technology itself—drives confidence and adoption; women receiving >5 hours of support reported confidence scores of 7‑9, whereas those with <2 hours scored 2‑4.
- Labeling ordinary digital tools as “AI” exemplifies a broader trend of “AI washing” in the development sector, where the buzzword attracts donor funding without delivering algorithmic innovation.
- Without clearer definitions, rigorous peer review, donor specifications, and implementer scrutiny, the sector risks building an evidence base on rebranded literacy programs, obscuring the real barriers (fear, lack of confidence) and leaving it unprepared to evaluate actual AI systems when they fail.
Study Overview and Misleading AI Label
The International Journal of Research and Innovation in Social Science (IJRISS) published a survey of 200 women micro‑entrepreneurs in Bengaluru that reported “32.5 % of them are ‘actively using at least one AI‑enabled tool’ in their businesses.” The authors listed KhataBook (a digital ledger), WhatsApp Business auto‑replies, Canva, and Facebook’s “best time to post” suggestions as the AI tools in question. As the article notes, “None of those, as the women in the study experience them, are artificial intelligence.” The tools are essentially digital versions of paper notebooks or scripted templates, lacking any machine‑learning or generative components that the women directly interact with.
What the Research Actually Shows
Despite the misleading label, the study uncovers a robust and well‑established finding: sustained, peer‑based NGO training drives tool adoption and confidence. Women who received more than five hours of NGO support reported confidence scores between 7 and 9 on a self‑assessment scale, while those with less than two hours scored between 2 and 4. The paper itself states, “The strongest predictor of confidence in the study is not the tool, it is the training hours.” This aligns with the ICT4D consensus established by Heeks in 2010, confirming that human relationships, not the technology, are the decisive factor.
The Rise of “AI Washing” in Development
Labeling basic digital aids as AI exemplifies a phenomenon the article calls “AI washing”—the practice of exaggerating or misrepresenting AI capabilities to gain reputational or financial advantage. The piece draws a parallel to the SEC’s 2024 action against Delphia and Global Predictions, which paid $400,000 for falsely claiming AI use. In contrast, the development sector lacks a comparable enforcement mechanism, allowing the inflation to continue unchecked. As the article warns, “Call something, anything ‘artificial intelligence’ and donor money rains down from the sky.”
Consequences of the Mislabeling
Three likely outcomes arise when donors fund programs under the “AI for women’s empowerment” banner based on inflated evidence:
- Repackaging old work – Implementers simply rebrand existing digital‑literacy interventions as AI projects, securing funding without altering the underlying activity.
- Misattributing impact – Future algorithmic tools (credit‑scoring engines, generative health chatbots, fraud‑detection systems) will cite studies like the Bengaluru paper as proof that “AI empowers women,” even though the original evidence never tested such technologies.
- Obscuring real barriers – The study’s genuine insight—that 35 % of non‑adopters cite fear and lack of confidence as their primary obstacle—gets buried under the AI narrative, diverting attention from needed social‑infrastructure investments (trust‑building, confidence‑coaching).
Who Must Act to Restore Meaning
The article assigns responsibility across four stakeholder groups:
- Researchers should either justify the AI label with a technical definition or adopt more accurate terms such as “digital tools” or “mobile business apps.” Peer reviewers must reject manuscripts that conflate rule‑based features with AI.
- Journals need to slow down the review process. A six‑day turnaround, as seen with the Bengaluru paper (received 26 Feb 2026, accepted 3 Mar 2026), is insufficient to catch definitional drift. Editors should require authors to specify the model class and technical mechanism for any tool labeled AI.
- Donors ought to demand transparency: every grant proposal using “AI” must disclose the model class, training‑data provenance, and decision‑making locus. If the intervention is merely template‑based messaging, fund it honestly as such.
- Implementers should push back on vendor hype. When offered an “AI‑powered” version of a tool they already use, they must ask what specifically is AI about it. If the answer rests on pre‑transformer heuristics, they can craft more honest proposals and produce more useful evaluations.
The Broader Pattern and a Path Forward
The development sector repeatedly latches onto the technology du jour—mobile money, blockchain, now AI—attaching it to populations that actually need sustained human support. The Bengaluru paper itself acknowledges this dynamic: “AI tools alone do not empower, people do.” The sector has known this for fifteen years, yet the vocabularic shift to “AI” allows old findings to be recycled into new funding cycles, eroding the ability to distinguish genuine algorithmic interventions from repackaged digital literacy.
If the trend continues, when real AI systems inevitably falter—whether through biased credit scores or erroneous health advice—the field will lack the conceptual vocabulary and evidence base needed to investigate failures. The SEC’s enforcement actions demonstrate that clarity and accountability are possible; researchers, journals, donors, and implementers in development must adopt similar rigor before the term “AI” loses all meaning.
Quoted excerpts from the original piece:
- “None of those, as the women in the study experience them, are artificial intelligence.”
- “The strongest predictor of confidence in the study is not the tool, it is the training hours.”
- “Call something, anything ‘artificial intelligence’ and donor money rains down from the sky.”
- “AI tools alone do not empower, people do.”
These verbatim highlights illustrate the article’s central argument: the label, not the technology, is driving funding narratives, and the sector must reclaim precision to serve both entrepreneurs and future AI innovations responsibly.
https://www.ictworks.org/we-have-an-artificial-intelligence-definition-problem-in-ict4d/