Pakistan’s National AI Policy 2025: A Comparative Appraisal, Advantages, Risks, Execution Pathways, and Regional Benchmarks
Year: 2025
Type: article
Abstract: Pakistan’s National Artificial Intelligence (AI) Policy articulates a coherent scaffold for nationwide AI adoption built on four pillars: ring-fenced financing through a National AI Fund (NAIF); a geographically distributed network of Centers of Excellence in AI (CoE-AI); ambitious human-capital targets (e.g., nationwide awareness by 2026; ~1M trained learners and ~10k trainers by 2027); and a trust framework centered on an AI Regulatory Directorate with sectoral sandboxes. Using qualitative document analysis, we examine these instruments’ funding architecture, institutional design, skills pipeline, and governance, and assess internal coherence, feasibility, and delivery risks against the policy’s own baselines and timelines. On July 30, 2025, Pakistan’s federal cabinet approved the National AI Policy 2025; approval announcements highlighted an AI Council, a master plan/action matrix, AI Innovation and Venture Funds, and a headline target to train one million AI professionals by 2030. To contextualize feasibility and ambition, we benchmark key instruments against regional peers India (IndiaAI Mission), China (Generative AI Measures), Sri Lanka (National AI Strategy and 2024 AI White Paper), Bangladesh (National AI Policy 2024), and Iran (National AI Plan 2025), and incorporate these comparators throughout the manuscript. (Dawn.com, 2025) Our appraisal finds notable strengths: predictable capital that can crowd-in private and multilateral co-funding; a distributed, demand-driven CoE topology that couples R&D with incubation and workforce development; time-bound, measurable targets that create accountability; and an explicit “pro-innovation with guardrails” posture via sandboxes and rights-respecting oversight. Critical risks include: (1) NAIF portfolio governance (absence of stage-gate disbursement criteria, portfolio-mix guidance, and enhanced conflict-of-interest firewalls); (2) trainer capacity versus timelines (10k master trainers as the throughput bottleneck); (3) regulatory overlap between the AI directorate, sectoral regulators, and higher-education bodies; (4) under-specified data/compute reference architectures and access standards; and (5) ambiguous measurement of “awareness” and public-sector upskilling without protected training time. We propose execution-ready remedies: stage-gated NAIF disbursements tied to outcomes (certifications, sandbox graduates, IP/startups); a funded national Train-the-Trainer corps anchored at CoEs; a published sandbox rulebook (eligibility, risk tiers, pre-deployment testing/red-teaming, exit-to-market); a national data reference architecture (metadata schemas, APIs, consent patterns, access tiers); and independent, pre/post awareness and skills surveys with disaggregated reporting. If implemented, these adjustments materially raise implementation fidelity and the likelihood that Pakistan’s AI ambitions translate into durable economic and social value.
Source: INNOVAPATH
DOI: 10.63501/2p23r912
Author: Sohail RAO
Institution: Dow University of Health Sciences
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4413550800