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Many AI initiatives end at technical deployment. Often, however, it is after the technical barriers of compute, models, data, and infrastructure are overcome—or at least mitigated—that the real challenge of AI adoption begins. The findings of our case study analysis bear this out: three of the top five barriers are nontechnical, including the second most-cited obstacle (“Social and Institutional Embeddedness,” discussed below). Tobi Olatunji, an African AI entrepreneur, this way: “The assumption we had to kill earliest was that the technical problem was the hardest one. It is not.” 

AI adoption is a multidimensional process, one that implicates society, institutions, policy, and human behavior as much as code or hardware. The barriers we discuss below are more diffuse and harder to define or categorize than the technical parameters above, and the list is no doubt incomplete. Precisely because technology is so deeply embedded in human and social life, a wide variety of nontechnical variables shape its success or failure. We are guided here by what came up most frequently and most prominently in our research.

Across our investigations, one finding recurred with striking consistency: Technically capable AI systems fail when they are dropped into human and organizational contexts without regard for existing habits, workflows, and cultures. In our structured case study analysis, the challenge of embedding AI into social and institutional life—rather than expecting users and institutions to adapt to AI—places just behind data as one of the most commonly identified challenges (Table 1). It sits at the intersection of technology and society, involving both technical design decisions at the user experience and product level and harder social and institutional questions about how to work within long-standing systems and cultures. 

This challenge is a contemporary expression of a long-recognized reality. Scholars of technology and organizations have long argued that new systems are absorbed into, and constrained by, what Ole Hanseth and Eric Monteiro call the “”—the existing configuration of tools, work practices, and institutional arrangements into which any new technology must be fitted. A have highlighted the particular complexity of adapting technologies largely developed in the Global North to on-the-ground realities in the Global South. 

Solutions: Two broad aspects of the embeddedness challenge, and the workarounds organizations have developed, emerged from the research. The first is the need to meet individual users where they already are. The second is the need to integrate AI within existing organizations and their workflows, processes, and cultures. 

When it comes to individuals, we found that AI adoption efforts encounter immediate and often insurmountable friction when they require users to learn new interfaces or develop new digital habits. Across geographies and sectors, one of the most-cited factors for successful adoption in our interviews was integration of AI into platforms users already know and trust. WhatsApp emerged as one of the most common solutions. As of more than 100 Indian AI deployments put it, “The friction of downloading a new app is high while the friction of chatting with a bot on WhatsApp is near zero.” (a company building AI for health care) and (an Indian language tool) are among the initiatives that have integrated their AI offerings into WhatsApp. It is worth noting that the reliance on a single foreign platform sits uneasily with the dependency and lock-in concerns raised earlier in this report (for example, at the model level).

In addition to platform choice, input and output modes matter greatly as well—especially in a deployment context where many users have low literacy or limited comfort with text-based interfaces. Many of the deployments we researched demonstrated that voice in particular is a critical interface layer. Digital Green found that multimodal input—allowing farmers to ask questions by voice and image rather than just text—dramatically increased usability. Crisis Cognition’s platform enables voice queries and spoken responses specifically to address literacy and digital literacy barriers. The Agricultural Information Exchange Platform (AIEP) initiative, piloting AI advisory services for smallholder farmers in Kenya and India, similarly identified voice-based interaction as essential for reaching low-literacy users. 

At the organization level, we found that the landscape of AI deployment is littered with tools that are technically functional but rarely used because they are seen as distractions, inconveniences, or threats. In India, a functioning AI diagnostic tool for neuroradiology cultural resistance from the specialists it was designed to support; studies seeking to understand why found that up to 90 percent of radiologists perceived AI as a threat to their job security.

Conversely, actively integrating new AI tools into existing workspaces and community processes can spur adoption: , a chatbot that provides sexual and reproductive information, worked with Indigenous Quechua organizations to expand its reach among underserved populations. Many of the organizations we encountered made similar targeted efforts to serve women in particular. For instance, Digital Green with the Swayam Sampurna Farmer Producer Organisation, a community of women farmers in India, to spur adoption of its chatbot by local women. 

The institutional integration challenge can have policy dimensions, too. In India, one legal AI platform, by all accounts highly technically capable, has struggled to achieve meaningful impact without integration into official judicial workflows. To do so, however, would require both cultural change within legal institutions and policy frameworks to nudge or require adoption—a challenge that points to the role of public sector mandates, procurement standards, and regulatory signaling in shaping which AI tools actually reach scale.

Challenges:

• Individual discomfort or lack of familiarity with tools
• Institutional resistance or fear
• Policy inertia that resists AI adoption

Solutions:

• Deploy on existing platforms (especially WhatsApp)
• Voice and image input
• Work with existing institutions and communities to reach marginalized groups (Indigenous populations, gender) 
• Policy reform, including procurement and public sector mandates

Part of the solution to integration challenges runs through developing new capacity and skills—but capacity gaps represent significant barriers to adoption in their own right (Table 1). Interviewees across regions consistently cited a lack of AI literacy, and more broadly digital literacy, as one of the most significant impediments to deployment. The problem is especially acute within government, where officials are often making consequential procurement and deployment decisions without understanding, as one interviewee put it, “each brick in the complex system.” Kallot of Amini framed the stakes as higher than just failed deployments, suggesting that governments that do not understand what they are buying are left “vulnerable to extractive models”—a considerable concern for middle power countries in particular, as they seek to navigate between Chinese and U.S. technology solutions. 

The capacity challenge extends beyond just technical skills. Carolina Glasserman Apicella of DataGénero notes the difficulty of building teams that combine technical AI expertise with domain knowledge—in her case, gender expertise and legal knowledge, which are essential for building tools that are accurate and contextually grounded. In other words, capacity building in AI is significantly more complicated than just training users how to use technology; it is about cultivating teams that can bridge technical and substantive domains and that understand the communities they are building for. It is worth mentioning that this broader notion of capacity—encompassing social and cultural knowledge as much as technical skills—is perhaps not adequately captured in our structured analysis of case studies, which nonetheless identifies a shortage of capacity and skills as the fourth most-frequent impediment to AI adoption (Table 1). 

Solutions: Organizations have taken several approaches to address capacity shortages. Some deploy their own experts directly into client agencies—Amini’s model of temporarily placing technical staff within companies and government agencies is an example. At the community level, in Malawi trained tech-savvy farmers as informal peer support agents who could introduce their Ulangizi chatbot technology to neighbors and friends; these peer support agents help 150 to 200 farmers almost every week.

University partnerships have proven particularly valuable in building capacity, as they provide both expertise and the institutional credibility needed to enter communities. Digital Umuganda, for instance, relies heavily on local academics to conduct surveys and gather contextual data in remote African villages, a model replicated by Bhashini in India and several other grassroots projects. 

Underlying the shortage of capacity and skills is a structural problem that no training program can fix: brain drain. Rodrigo Durán of CENIA cited the far higher salaries offered by large private companies in explaining the difficulty of building AI capacity, particularly in the public sector. Middle power countries, especially those located in the Global South, may be particularly vulnerable to this dynamic. , an Ethiopian engineer and investor, puts it like this: “We are simply training our best minds for export.” Perhaps ironically, rising anti-immigrant sentiment in the West may offer at least a partial solution. The emergence of the United Arab Emirates as a regional AI talent hub—facilitated by favorable immigration policies and tax incentives—offers an example of how talent-friendly environments can begin to compete with the pull of Big Tech salaries and provide an alternative destination for skilled practitioners from the Global South.

Challenges:

• Lack of technical and AI skills
• Lack of domain knowledge combined with technical skills
• Brain drain

Solutions:

• Embed technical staff in partner organizations, especially in the public sector
• Partnerships with academia 
• Peer support agents and other community interventions
• Talent-friendly policies 

AI is to inspire greater optimism in the Global South than in the United States and Europe, where pessimism about job displacement, misinformation, and political polarization has come to dominate public discourse around the technology. Our research nonetheless found a significant undercurrent of skepticism, which functions as a barrier to AI adoption. Some users cited concerns over deepfakes and hallucinations in their reluctance to use the technology. Others were worried about their jobs—local language model builders said they have a hard time enlisting human translators—and still others simply did not believe the technology works (a skepticism that only deepens when users are offered offline or edge solutions that seem to contradict their notions of what AI requires).

Trust can be particularly hard to earn among women, minorities, and other excluded populations. This issue was particularly salient in the case of women and health care. In Lebanon, of 525 young women found that 54 percent cited privacy as a reason for hesitating to use AI chatbots for gynecological and other intimate health concerns. A of an AI mental health chatbot for transgender women and men who have sex with men had similar findings: while many users valued the platform’s perceived anonymity and confidentiality, others cited concerns about providing personal information to systems whose data practices they did not fully trust. These findings are consistent with broader on gender gaps in technology adoption in the Global South as well as with gender differences in AI trust and adoption in the United States. 

Solutions: The most consistent finding across our interviews is that trust travels through people, not platforms or technology. Organizations that have succeeded in building user trust have done so by keeping humans visibly in the loop, often from the very first point of interaction. Dino Rech, from Audere, mentioned the importance of “having folks at clinics that actually talk to you the first time you use the tool.” Digital Green and many others grew in the early stages through on-the-ground partners, and Digital Umuganda’s university partners help build credibility in communities.

Demonstrated efficacy is another way to increase trust. In Malawi, an agricultural chatbot named succeeded in part because farmers could see that recommendations worked. “After following its recommendation, the worms were completely eliminated,” one farmer was quoted as saying in an article. “Since then, I have relied solely on the chatbot’s guidance.” Actively reassuring users about privacy can have similar benefits. A study of sexual and reproductive health chatbot deployments across Kenya, Uganda, India, and Bangladesh found that engagement increased where systems explicitly assured users their information would be kept anonymous. Kenya’s , a WhatsApp-based platform included in the study, opens every interaction by reassuring young users that the conversation stays between them and the bot, building on young Kenyans’ preexisting trust in the security and safety of WhatsApp.

Even when trust is established, it remains a moving target; AI deployers must work to keep earning it. In India, an agricultural platform found that women users, isolated in their communities and with few other social outlets, became perhaps too trusting of the system, using it for personal conversations and sharing sensitive information well beyond the platform’s intended scope. The organizers realized that this created real safety risks and, over time, could erode hard-earned trust (and thus adoption) within the community. Part of the solution in this case was to constrain the model more tightly to its agricultural domain and build in escalation to human support for sensitive or complex queries. The episode also points to a wider imperative: AI builders need to be attuned to new and distinctive forms of safety risk, including those that emerge from the needs of underserved users. Unaddressed safety failures are ultimately deployment failures, too.

Challenges:

• Misinformation concerns, job anxiety, skepticism that AI works
• Privacy fears

Solutions:

• Humans in the loop
• Information accuracy
• Addressing privacy
• Trust as a “moving target”

Funding is an evident and pervasive constraint, one that shapes every other barrier discussed in this report. Most of the organizations we interviewed rely on a patchwork of multilateral, philanthropic, government, and Big Tech funding to get off the ground. The last often comes with complications. Tiago Maluta of the Lemann Foundation notes that Big Tech companies often provide ostensibly “free” AI products and solutions to public educational institutions. However, recipients who do not carefully read the terms and conditions may unwittingly take on risks that outweigh the benefits—for instance, having their data reused for purposes beyond the original scope. Others raised similar concerns about the combination of credits, grants, and in-kind support that often serve as loss leaders and lead generation for private companies. 

Public funding is an option in some countries—India’s $1.2 billion IndiaAI Mission is among the most prominent examples—but governments across the Global South face genuine and difficult prioritization decisions. Two of our interviewees noted the trade-offs governments face in choosing between investing in AI infrastructure and building roads or hospitals. Limited public funding for AI is not necessarily shortsightedness; it reflects the difficulty of weighing competing priorities in resource-constrained environments.

A less recognized but no less challenging problem stems from the realities of “.” Almost every organization we interviewed was operating at pilot stage, and the challenge of moving from proof-of-concept to sustainable deployment was cited as one of the hardest problems they faced. This is a long-standing problem within the development sector, especially when it comes to technology transfers—sometimes referred to as “” dilemma, after the (perhaps apocryphal) carcasses of heavy machinery that littered the Global South during the Green Revolution.

Solutions: Organizations are responding with a range of sustainability strategies. Amini is pursuing a build-operate-transfer model in which the company sets up a system within government, runs it for two years while building capacity, and then hands over the reins to a local team. Two companies spoke of eventually transferring their platforms directly to government, while others are actively working with private sector partners to embed their tools into existing revenue streams and build profitable—or at least sustainable—business models. 

Edge AI represents another pathway. Evert Bopp, CEO of Crisis Cognition, spoke of designing for sustainability. Its portable device, 0-LA, accrues no ongoing token fees (because it uses on-device models), no internet bills, and runs on solar energy—a direct response to the API-dependency trap and infrastructure costs that make so many AI deployments expensive to run.

A final observation: We found that funding directed primarily at technology consistently underestimates the cost of the human and institutional layers. In particular, training and workflow integration are chronically underfunded relative to the technology itself.

Community participation, vital to the process of building culturally embedded data and locally relevant models, is even less of a priority. When participants are compensated at all, they are typically paid a one-time, token amount. Royalty-based compensation models—akin to those being negotiated by rights holders in the Global North in response to AI training on their work—offer a promising alternative, creating arrangements that are both economically fairer and socially more sustainable over time. Such arrangements remain fledgling, but they are beginning to emerge in and in proposals for” that would negotiate licensing and compensation on behalf of communities. At least one Global South AI lab, South Africa’s, is also pursuing licensing frameworks and reinvestment mechanisms that would license community-held language data and, in theory at least, provide an ongoing revenue stream. 

Challenges:

• Shortcomings of private sector funding (extractive, lead-generation)
• Competing priorities for governments
• Sustainability challenges (“pilotitis”)

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• Transfer to government 
• Edge AI (to reduce costs)
• Community licensing agreements