OP-ED: UNIDO
Case studies show the way forward A concrete example is found in Tajikistan, where a Centre of Excellence integrates AI into several industries, aiming to modernize production through advanced digital tools, for example, via AI-virtual prototyping for garment manufacturers. But technology was only one part of the intervention. Structured training programs upgraded workforce skills, while institutional support strengthened local innovation capacity. The result: firms moved beyond basic assembly toward higher-value production, expanding exports and employment. In collaboration with IBM’s Impact Accelerator program, UNIDO is co- developing an AI-powered solution to help countries assess their capacity to harness digital and AI technologies for sustainable industrial and economic transformation, while strengthening supply chain resilience. The model assesses readiness across five critical pillars – infrastructure, data ecosystems, innovation capacity, capital access and governance frameworks. Pilot deployments in Brazil and Mexico will apply the framework to identify and outline country-specific industrial transformation pathways, generating actionable insights for policy, investment and workforce development. This
approach builds lasting institutional capacity, moving beyond one-off assessments to sustained transformation. Looking ahead, new Centres of Excellence, including in China with technology leaders in intelligent manufacturing and robotics, aim to further integrate advanced digital manufacturing solutions into national industrial ecosystems. The emphasis remains consistent: Co-development, skills transfer and long-term institutional strengthening. Scaling solutions, not just technology A key lesson is clear: AI does not automatically drive development. Access to models or platforms alone does not guarantee transformation. Impact depends on the ability to deploy AI across real economic systems, supported by skilled talent, robust digital infrastructure, regulatory clarity and effective governance. Strong frameworks for data governance, competition,
ethics, cybersecurity and transparent procurement are essential to build trust and reduce risk. International alignment on standards through multilateral cooperation can prevent fragmentation and ensure that developing countries are not excluded from emerging AI value chains. Sustainability and inclusivity must be core design principles. AI should prioritize productivity gains in sectors that employ large populations such as agriculture, manufacturing and logistics, while also investing in reskilling to mitigate displacement. Public-private partnerships can expand digital access and education reform can close skills gaps. The challenge now is scale: Proven solutions must move beyond pilots, infrastructure must reach underserved communities and governments and industry must collaborate to build enabling, responsible ecosystems that distribute benefits broadly rather than concentrate them among a few.
“Looking ahead, new Centres of Excellence... aim to further integrate advanced digital manufacturing solutions into national industrial ecosystems.”
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