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Indonesia Launches AI Talent Factory to Develop Advanced Experts and Local-Language LLMs

Indonesia's Komdigi launches AI Talent Factory to train advanced builders for national needs and local-language AI. Cohorts ship pilots with ministries and track service impact.

Categorized in: AI News Government Human Resources IT and Development
Published on: Sep 21, 2025
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Indonesia Launches AI Talent Factory to Develop Advanced Experts and Local-Language LLMs

AI Talent Factory: Indonesia's push to build applied AI expertise

The Ministry of Communication and Digital Affairs (Komdigi) is launching AI Talent Factory to develop innovators who can build and deploy AI for national priorities. The goal is clear: produce advanced practitioners who can solve real problems, not just beginners or passive users.

"AI Talent Factory is not just about producing beginner or intermediate talents, but also advanced-level experts ready to tackle the nation's complex issues," said Bonifasius Wahyu Pudjianto, Head of Komdigi's Human Resource Development Agency.

Deputy Minister Nezar Patria called for AI that reflects Indonesia's identity and scale. "It would be great if we could develop our own Large Language Models (LLMs) using our own languages. Our regional languages are rich, our ethnic groups are diverse, and our cultural heritage is extraordinary."

Why this matters

Indonesia has 9.3 million digital talents today. Demand is projected to reach 12 million by 2030. That gap won't close if people only consume tools-they must build them.

As part of President Prabowo Subianto's national development agenda, AI Talent Factory supports stronger digital infrastructure and a workforce ready to deliver public value at scale.

What AI Talent Factory should produce

  • Machine learning engineering: modeling, evaluation, and deployment with clear performance and cost targets.
  • Data engineering: pipelines, data quality, metadata, and secure access for training and inference.
  • MLOps: CI/CD for models, monitoring, drift detection, rollback, and incident response.
  • NLP and localization: tokenization, fine-tuning, and evaluation for Bahasa Indonesia and regional languages.
  • AI product management: problem framing, ROI cases, user testing, and adoption in public services.
  • Policy, risk, and ethics: safety-by-design, fairness testing, documentation, and procurement standards.
  • Security for AI: data governance, PII protection, red-teaming, supply chain integrity.

Local-language AI as a national asset

  • Prioritize corpora in Bahasa Indonesia and regional languages with clear licensing and provenance.
  • Stand up evaluation benchmarks for translation, summarization, Q&A, and speech-to-text in local contexts.
  • Fund fine-tuning and retrieval systems that reflect local knowledge, law, and public service workflows.
  • Build public-private data partnerships with transparent safeguards and citizen trust mechanisms.

Program delivery model

  • Cohort-based training: beginner-to-advanced tracks with rapid specialization after core fundamentals.
  • Capstones tied to ministries/SOEs: each team ships a working pilot addressing a live use case.
  • Placement and apprenticeships: rotate graduates into agencies and state enterprises for on-the-job impact.
  • Shared services: common MLOps platform, data catalogs, and governance playbooks to reduce duplication.

KPIs to track

  • Number of pilots deployed in production and months-to-value.
  • Service-level impact: processing time reduced, accuracy improvements, citizen satisfaction scores.
  • Model safety: red-team findings resolved, incidents, and rollback rates.
  • Talent pipeline: graduation rates, certifications earned, placements within government and industry.
  • Local-language coverage: tasks supported, benchmark scores, and usage by agencies.

What leaders can do now

  • Government agencies: nominate high-impact use cases with clear data access, privacy boundaries, and success metrics.
  • HR leaders: run a skills inventory and map people into the tracks above; set learning paths and certification goals.
  • IT and development teams: prepare data pipelines, labeling standards, and a secure environment for experiments.
  • Policy owners: adopt lightweight model governance-documented datasets, evaluations, and approvals before go-live.

"We need around nine million digital talents, but we must think beyond users-we need talents who can be both deployers and developers," Patria emphasized. That mindset shift is the core of AI Talent Factory.

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