{"id":3973,"date":"2026-05-05T00:00:49","date_gmt":"2026-05-04T17:00:49","guid":{"rendered":"https:\/\/smtechglobal.com\/thai\/?p=3973"},"modified":"2026-05-05T14:17:19","modified_gmt":"2026-05-05T07:17:19","slug":"ai-adoption-in-emerging-markets-constraints-opportunities-reality","status":"publish","type":"post","link":"https:\/\/smtechglobal.com\/thai\/2026\/05\/05\/ai-adoption-in-emerging-markets-constraints-opportunities-reality\/","title":{"rendered":"AI Adoption in Emerging Markets: Constraints, Opportunities, Reality"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3973\" class=\"elementor elementor-3973\">\n\t\t\t\t<div class=\"elementor-element elementor-element-132f70e e-flex e-con-boxed e-con e-parent\" data-id=\"132f70e\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-38414b4 elementor-widget elementor-widget-heading\" data-id=\"38414b4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.23.0 - 25-07-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\"><i style=\"white-space: normal; background-color: transparent;\">AI Adoption in Emerging Markets:&nbsp;<\/i><br><span style=\"white-space: normal; background-color: transparent;\">Constraints, Opportunities, Reality<\/span><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6d4bbb0 elementor-widget elementor-widget-text-editor\" data-id=\"6d4bbb0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.23.0 - 25-07-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>Artificial intelligence is often described as the next general -purpose technology, on par with electricity or the internet. But beneath the global hype lies a harder question: what does AI adoption&nbsp;<i>actually<\/i>&nbsp;look like in emerging markets?<\/p>\n<p>In many low- and middle-income countries, the narrative swings between extremes. On one side, there\u2019s a fear of being left behind in the \u201cAI race.\u201d On the other hand, there\u2019s breathless optimism that AI will leapfrog weak institutions, patch over poor infrastructure, and unlock rapid growth.<\/p>\n<p>The truth sits somewhere in between. Emerging markets face very real constraints\u2014but also have unique opportunities to build AI in ways that are more inclusive, efficient, and relevant to local realities.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-df53cdd elementor-widget elementor-widget-text-editor\" data-id=\"df53cdd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2 style=\"margin: 0px; --flex-direction: initial; --flex-wrap: initial; --justify-content: initial; --align-items: initial; --align-content: initial; --gap: initial; --flex-basis: initial; --flex-grow: initial; --flex-shrink: initial; --order: initial; --align-self: initial; align-self: auto; flex: 0 1 auto; order: 0; place-content: normal; align-items: normal; flex-flow: row; gap: 20px; --widgets-spacing: 20px 20px; --widgets-spacing-row: 20px; --widgets-spacing-column: 20px; --kit-widget-spacing: 0px; max-width: 100%;\" data-id=\"46845f6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\"><span style=\"color: #eaa159; letter-spacing: 0px; background-color: transparent;\">1. Constraints: Why AI Is Not a Magic Wand<\/span><\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-79dbdb9 elementor-widget elementor-widget-text-editor\" data-id=\"79dbdb9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4><strong>1.1 Data: Scarce, Fragmented, and Messy<\/strong><\/h4>\n<p>AI systems are only as good as the data that trains them. In emerging markets, the data problem shows up in three ways:<\/p>\n<p><strong>\u2013 Limited digitisation:<\/strong>&nbsp;Large portions of economic and social activity remain analogue or informal &#8211; cash transactions, paper records, unregistered businesses. If it isn\u2019t captured digitally, it can\u2019t power an AI system.<\/p>\n<p><strong>\u2013 Fragmented systems:<\/strong>&nbsp;Where data is digital, it is often scattered across incompatible systems &#8211; different ministries, banks, telcos, NGOs \u2013 each with their own formats, standards, and incentives to hoard rather than share.<\/p>\n<p><strong>\u2013 Low-quality and biased data:<\/strong>&nbsp;Missing values, errors, and inconsistent labels are common. Some groups&nbsp; rural residents, informal workers, women are often underrepresented, which can bake structural bias into AI models.<\/p>\n<p>This doesn\u2019t mean AI is impossible \u2013 it means that a big part of AI work in emerging markets is actually foundational data work: digitizing, cleaning, standardizing, and governing.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7363c85 elementor-widget elementor-widget-text-editor\" data-id=\"7363c85\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4><b>1.2 Infrastructure: Connectivity, Power, and Devices<\/b><\/h4><p>Modern AI, especially generative AI, is compute-intensive and network-dependent.<\/p><p><strong>\u2013 Connectivity gaps<\/strong><strong>:<\/strong>\u00a0Many regions still lack reliable, affordable broadband or mobile data. Even in areas with high mobile penetration, data costs can be prohibitive for always-on, cloud-based AI services.<\/p><p><strong>\u2013 Power reliability<\/strong><strong>:<\/strong>\u00a0Frequent outages, unstable grids, and dependence on diesel generators can make it hard to run data centers, edge devices, or even office networks reliably.<\/p><p><strong>\u2013 Device constraints<\/strong><strong>:<\/strong>\u00a0A significant portion of users rely on low-cost smartphones with limited memory and processing power. Heavy apps, high-latency tools, and bloated models simply won\u2019t work for them.<\/p><p>All this pushes AI builders to think\u00a0<strong>\u201coffline-first\u201d<\/strong>\u00a0and\u00a0<strong>\u201clightweight by design\u201d<\/strong>, which is both a constraint and an opportunity for innovation.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9c0d119 elementor-widget elementor-widget-text-editor\" data-id=\"9c0d119\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4><b>1.3 Talent: Shortage Across the Stack<\/b><\/h4><p>\u00a0AI adoption needs more than just PhD-level researchers. It requires:<\/p><p><strong>\u2013 Data engineers and ML engineers\u00a0<\/strong>to build and maintain the infrastructure<\/p><p><strong>\u2013 Domain experts<\/strong>\u00a0(in health, finance, agriculture, etc.) who can define real problems<\/p><p><strong>\u2013 Product thinkers and UX designers<\/strong>\u00a0who can adapt AI to local languages, behaviours, and constraints<\/p><p>In many emerging markets, there are pockets of excellent talent, but not enough to meet rising demand. Brain drain amplifies this: the best-trained professionals are often pulled toward jobs in North America, Europe, or a few regional hubs.<\/p><p>At the same time, local education systems often lag in updating curricula for AI-era skills. The result: organizations want to adopt AI but cannot find or retain the right people to do it responsibly.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-17aa730 elementor-widget elementor-widget-text-editor\" data-id=\"17aa730\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4><b>1.4 Regulation, Governance, and Trust<\/b><\/h4><p>AI sits at the intersection of data privacy, cybersecurity, consumer protection, and national competitiveness.<\/p><p><strong>\u2013 Regulatory uncertainty<\/strong>\u00a0can make companies hesitate: fearing that new rules will suddenly render their solutions non-compliant.<\/p><p><strong>\u2013 Weak enforcement<\/strong>\u00a0can make even well-written laws ineffective, especially around data protection and algorithmic accountability.<\/p><p><strong>\u2013 Low institutional trust<\/strong>\u00a0means that citizens may resist digital systems (biometrics, credit scoring, surveillance tools) if they believe they will be misused or exploited.<\/p><p>Without trusted institutions and clear rules, AI adoption risks deepening inequality and eroding public confidence.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5bf889a elementor-widget elementor-widget-text-editor\" data-id=\"5bf889a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\" style=\"color: #eaa159; font-family: Arial, sans-serif; letter-spacing: normal;\">2. Opportunities: Where Emerging Markets Can Lead or Leapfrog<\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-473ce55 elementor-widget elementor-widget-text-editor\" data-id=\"473ce55\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Constraints are only half the story. Emerging markets also have structural advantages that can powerfully shape AI adoption.<\/p>\n<h4><b>2.1 Leapfrogging Legacy Systems<\/b><\/h4>\n<p>Many established economies are constrained by expensive, entrenched legacy systems &#8211; old software, outdated processes, deeply institutionalized ways of working.<\/p>\n<p>Emerging markets sometimes have the opposite problem:&nbsp;<strong>they don\u2019t have much to rip out<\/strong><strong>.<\/strong>&nbsp;That can be an advantage.<\/p>\n<p><strong>\u2013 Mobile-first everything<\/strong><strong>:<\/strong>&nbsp;The rise of mobile money in East Africa is a classic example. Instead of replicating card-based systems from the West, these markets built payment rails on mobile phones.<\/p>\n<p><strong>\u2013 Greenfield systems<\/strong><strong>:<\/strong>&nbsp;New digital ID programs, e-government portals, and digital public infrastructure can be designed from day one with AI-readiness in mind.<\/p>\n<p>Where there is flexibility, it\u2019s easier to design AI-native workflows rather than bolt AI onto old processes.&nbsp;<\/p>\n<p><br><\/p>\n<h4><b>2.2 Abundant Real-World Problems and Willingness to Experiment<\/b><\/h4>\n<p>Emerging markets face pressing challenges in healthcare access, education quality, agriculture productivity, financial inclusion, and urban planning. These are not abstract use cases &#8211; they are daily realities.<\/p>\n<p>This creates:<\/p>\n<p><strong>\u2013 Strong problem orientation<\/strong><strong>:<\/strong>&nbsp;AI initiatives can be tied directly to tangible outcomes &#8211; crop yields, maternal health, SME lending, traffic flow\u2014rather than purely speculative gains.<\/p>\n<p><strong>\u2013 Greater openness to experimentation<\/strong><strong>:<\/strong>&nbsp;In some contexts, the absence of heavy legacy bureaucracy allows for pilot projects and iterative experimentation, especially with support from multilateral organizations and philanthropic capital.<\/p>\n<p>When AI is pointed at real pain points, even modest improvements can have an outsized impact.<\/p>\n<h4><b><br>2.3 Local Language and Cultural Innovation<\/b><\/h4>\n<p>Large language models have been criticized for being Anglocentric and biased toward Global North datasets. This is a challenge\u2014but also an opportunity.<\/p>\n<p><strong>\u2013 Local language models<\/strong><strong>:<\/strong>&nbsp;There is growing momentum to build models that understand under-resourced languages and dialects, from Swahili to Bengali to Quechua. These fill genuine gaps that global players often ignore.<\/p>\n<p><strong>\u2013 Culturally aware applications<\/strong><strong>:<\/strong>&nbsp;Chatbots for farmers, AI tutors for students, or health triage systems need local context\u2014what crops are grown, how schools operate, how people explain symptoms. Local builders can embed this knowledge directly.<\/p>\n<p>This is a space where emerging markets can lead by building&nbsp;<b>AI that actually understands their societies<\/b>, rather than importing one-size-fits-all solutions.<\/p>\n<h4><b>2.4 New Business Models and Public\u2013Private Collaboration<\/b><\/h4>\n<p>AI deployment in emerging markets often depends on creative alignments between government, private sector, and development partners.<\/p>\n<p>We\u2019re seeing models like:<\/p>\n<p><strong>\u2013 Digital public goods and infrastructure<\/strong>&nbsp;(e.g., shared ID, payment rails, data exchanges) that multiple companies can build AI services on top of<\/p>\n<p><strong>\u2013 Outcome-based funding<\/strong>&nbsp;where investors or donors pay when AI-enabled interventions achieve measurable results<\/p>\n<p><b>\u2013&nbsp;<\/b><strong>Public &amp; private sandboxes<\/strong>&nbsp;where regulators and innovators test AI applications under controlled conditions before broad rollout<\/p>\n<p>This kind of collaboration can create ecosystems that are more coordinated than in some developed economies, where regulatory and commercial interests are often deeply adversarial.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d58a79e elementor-widget elementor-widget-text-editor\" data-id=\"d58a79e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2 style=\"color: #eaa159; font-family: Arial, sans-serif; letter-spacing: normal;\"><span style=\"background-color: transparent;\">3. Reality: What AI Adoption Looks Like on the Ground<\/span><\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a389839 elementor-widget elementor-widget-text-editor\" data-id=\"a389839\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>When you zoom in, AI adoption in emerging markets is neither a desert nor a utopia. It is a patchwork- advanced in some sectors, nascent in others.<\/p><h4><b><strong>3.1 Sector Snapshots<\/strong><\/b><\/h4><p><strong>Financial services<\/strong><\/p><ul><li>Banks and fintechs are using machine learning for credit scoring, fraud detection, and customer segmentation.<\/li><li>Alternative data, like mobile phone usage, utility payments, and transaction history, is used to score individuals and SMEs with thin or no formal credit records.<\/li><li>Risk: without strong oversight, these systems can entrench bias and exclude the very groups they claim to include.<\/li><\/ul><p><strong><br \/>Agriculture<\/strong><\/p><ul><li>AI-powered advisory tools provide farmers with crop recommendations, pest alerts, and weather-adaptive planting schedules via SMS, WhatsApp, or USSD.<\/li><li>Satellite imagery combined with ML models can estimate yields, identify crop stress, and structure index-based insurance products.<\/li><li>Constraints remain around last-mile connectivity, trust in digital advice, and local language support.<\/li><\/ul><p><b><br \/><\/b><strong>Healthcare<\/strong><\/p><ul><li>Computer vision tools assist in screening for diseases (e.g., tuberculosis, diabetic retinopathy) where specialist doctors are scarce.<\/li><li>\u00a0AI-enabled triage chatbots and decision-support systems help community health workers prioritize cases and standardize care.<\/li><li>Ethical concerns around data consent, misdiagnosis, and accountability are still being worked through.<\/li><\/ul><p><strong><br \/>Education<\/strong><\/p><ul><li>Adaptive learning platforms personalize content for students based on their performance.<\/li><li>Generative AI tools help teachers with lesson planning, assessment, and content translation.<\/li><li>Persistent issues with device access, teacher training, and curriculum integration limit scale.<\/li><\/ul><h4><b><br \/>3.2 Who Is Actually Adopting AI?<\/b><\/h4><p>In practice, AI adoption is strongest among:<\/p><p><b>\u2013\u00a0<\/b><strong>Larger enterprises and well-funded startups<\/strong>\u00a0that can afford talent and infrastructure<\/p><p><b>\u2013\u00a0<\/b><strong>Government agencies<\/strong>\u00a0involved in identity, security, taxation, and social protection<\/p><p><b>\u2013\u00a0<\/b><strong>Donor- backed projects<\/strong>\u00a0in health, agriculture, and education<\/p><p>Small businesses and local governments often remain on the margins, using simpler digital tools (messaging apps, spreadsheets, basic CRMs) rather than advanced AI.<\/p><p>The reality is that\u00a0<strong>\u201cAI adoption\u201d often looks like incremental automation \u2013\u00a0<\/strong>better credit scoring, smarter routing, more accurate forecasting \u2013 rather than flashy humanoid robots or fully autonomous systems.<\/p><h4><b><br \/>3.3 Risks: Inequality, Dependence, and Misuse<\/b><\/h4><p>As AI diffuses, so do its risks:<\/p><p><strong>\u2013 Widening digital divides<\/strong><strong>:<\/strong>\u00a0Those with connectivity, skills, and capital benefit first; others fall further behind.<\/p><p><strong>\u2013 Dependence on foreign tech<\/strong><strong>:<\/strong>\u00a0Many AI tools rely on proprietary models, cloud services, and platforms controlled by a handful of global firms, raising questions about digital sovereignty.<\/p><p><strong>\u2013 Surveillance and repression<\/strong><strong>:<\/strong>\u00a0Without robust safeguards, AI-powered surveillance can be used to target political opponents, journalists, or marginalized communities.<\/p><p>Emerging markets face a double burden: they must harness AI for development while guarding against harms that their institutional frameworks may not yet be equipped to manage.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-08944fe elementor-widget elementor-widget-text-editor\" data-id=\"08944fe\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2 style=\"color: #eaa159; font-family: Arial, sans-serif; letter-spacing: normal;\"><span style=\"background-color: transparent;\">4. What Needs to Happen Next<\/span><\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e5b53db elementor-widget elementor-widget-text-editor\" data-id=\"e5b53db\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>For AI to deliver broad-based benefits in emerging markets, adoption must be intentional \u2013 not just opportunistic.<\/p><h4><b>\u00a04.1 Invest in Foundations: Data, Infrastructure, Skills<\/b><\/h4><p><strong>\u2013 Data governance and interoperability<\/strong><strong>:<\/strong>\u00a0Governments and industry bodies should define standards for data sharing, privacy, and security, and invest in shared data infrastructure.<\/p><p><strong>\u2013 Digital and physical infrastructure<\/strong><strong>:<\/strong>\u00a0Expand reliable connectivity and power, especially in rural and underserved areas. Support regional cloud and compute capacity where feasible.<\/p><p><strong>\u2013 Human capital<\/strong><strong>:<\/strong>\u00a0Update curricula, fund vocational training, and support continuous upskilling\u2014not just for technical roles, but also for policymakers, regulators, and civil society.<br \/><br \/><\/p><h4><b>\u00a04.2 Build Responsible, Context-Aware AI<\/b><\/h4><p><strong>\u2013 Local problem definition<\/strong><strong>:<\/strong>\u00a0Start from real constraints and priorities \u2013 food security, healthcare access, climate resilience \u2013 not from generic AI capabilities searching for a use case.<\/p><p><strong>\u2013 Inclusion by design<\/strong><strong>:<\/strong>\u00a0Ensure that women, rural communities, and marginalized groups are involved in design, testing, and feedback.<\/p><p><strong>\u2013 Ethics and oversight<\/strong><strong>:<\/strong>\u00a0Create mechanisms for accountability when AI systems cause harm or discrimination, and ensure people have recourse.<br \/><br \/><\/p><h4><b>\u00a04.3 Strengthen Collaboration and Ecosystems<br \/><\/b><\/h4><p><strong>\u2013 Public &amp; private partnerships<\/strong>: Align incentives between governments, startups, incumbents, and development partners to support long-term infrastructure and innovation.<\/p><p><b>\u2013\u00a0<\/b><strong>Regional collaboration<\/strong><strong>:<\/strong>\u00a0Share best practices, open-source tools, and regional standards to avoid each country reinventing the wheel in isolation.<\/p><p><strong>\u2013 Support local innovators<\/strong>: Provide patient capital, regulatory clarity, and access to compute and data so that local companies can compete and collaborate globally.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf0daa8 elementor-widget elementor-widget-text-editor\" data-id=\"cf0daa8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2 style=\"color: #eaa159; font-family: Arial, sans-serif; letter-spacing: normal;\"><span style=\"background-color: transparent;\">Conclusion: Beyond Hype and Doom<\/span><\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f140b50 e-flex e-con-boxed e-con e-parent\" data-id=\"f140b50\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-04e656f elementor-widget elementor-widget-text-editor\" data-id=\"04e656f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>AI will not automatically save or doom emerging markets. Its impact will depend on the choices made now \u2013 about infrastructure, governance, education, and where to focus scarce resources.<\/p><p>The constraints are real: data gaps, weak infrastructure, talent shortages, and governance challenges all slow down or skew AI adoption. But the opportunities are equally real: the chance to leapfrog legacy systems, solve hard local problems, build culturally grounded AI, and forge new models of collaboration.<\/p><p>The reality on the ground is messy and uneven, but it is moving. Early examples in finance, agriculture, health, and education show that when AI is designed for local constraints and priorities, it can produce tangible gains.<\/p><p>For policymakers, businesses, and innovators in emerging markets, the task is not to \u201ccatch up\u201d blindly with AI trends set elsewhere, but to <strong>shape AI around their own development goals<\/strong>. That means asking not just \u201cHow do we adopt AI?\u201d but \u201cWhose problems are we solving, with whose data, under whose control, and to whose benefit?\u201d<\/p><p>The answers to those questions will determine whether AI becomes just another imported technology- or a genuine tool for inclusive, sustainable progress.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>AI Adoption in Emerging Markets:&nbsp;Constraints, Opportunities, Reality Artificial intelligence is often described as the next general -purpose technology, on par with electricity or the internet. But beneath the global hype lies a harder question: what does AI adoption&nbsp;actually&nbsp;look like in emerging markets? In many low- and middle-income countries, the narrative swings between extremes. On one [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":3977,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","two_page_speed":[],"footnotes":""},"categories":[34],"tags":[],"class_list":["post-3973","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-enterprise"],"_links":{"self":[{"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/posts\/3973","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/comments?post=3973"}],"version-history":[{"count":15,"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/posts\/3973\/revisions"}],"predecessor-version":[{"id":3991,"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/posts\/3973\/revisions\/3991"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/media\/3977"}],"wp:attachment":[{"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/media?parent=3973"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/categories?post=3973"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/smtechglobal.com\/thai\/wp-json\/wp\/v2\/tags?post=3973"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}