Türkiye launches AI directorates to centralize tech governance and boost innovation

Türkiye's New AI Directorates: What Public-Sector Leaders Should Do Next

Türkiye is signaling a clear intent: make AI governance a national capability, not a scattered effort. New AI directorates will likely coordinate policy, set standards, and push adoption across ministries while keeping risks in check.

If you work in government, this is your window to align projects, budgets, and talent with a single, coherent plan. Below is a concise playbook to set direction, build momentum, and show measurable outcomes in 90 days.

Why this matters

• Central coordination reduces duplicated spend, conflicting rules, and slow approvals.
• Clear standards help agencies deploy AI safely in public services: health, justice, tax, transport.
• Confidence grows when risk controls, audits, and transparent reporting are in place from day one.

Likely mandate for the directorates

• Policy and standards: National AI policy, model governance, data-sharing rules, and security baselines.
• Public-sector enablement: Shared platforms, model catalogs, MLOps guidance, and procurement frameworks.
• Risk and ethics: Impact assessments, bias testing, incident reporting, and independent oversight.
• R&D and industry: Grants, sandboxes, public-private pilots, and startup pathways for GovTech.
• International alignment: Benchmarking with OECD principles and the EU's approach to AI.

First 180 days: priorities that move the needle

• Standards set: Publish a national AI policy guide with a simple risk tiering framework and approval workflow.
• Data ready: Classify datasets, define access rules, and stand up a secure data exchange for government.
• Model governance: Approve a shortlist of models/providers with security, privacy, and uptime thresholds.
• Pilot portfolio: Launch 10-20 ministry pilots tied to clear service KPIs (processing time, cost per case, accuracy).
• Procurement tools: Create framework contracts and a rapid call-off process for AI services and support.
• Training: Roll out role-based training for policy leads, product managers, engineers, and auditors.

Governance mechanics that actually work

• Inter-ministry council: Monthly decisions on standards, budgets, and cross-cutting risks.
• Risk tiers: Minimal, limited, high, and prohibited use cases with matching controls and approvals aligned to OECD AI Principles and the EU's AI approach.
• Model registry: Track model versions, owners, datasets, evaluation results, and deployment status.
• Audit trail: Log prompts, outputs, interventions, human-in-the-loop actions, and incident handling.

Funding and procurement

• Budget pools: Central fund for cross-government platforms; ministry funds for domain pilots.
• Challenge grants: Problem-first briefs; award by measurable outcomes and service impact.
• Framework contracts: Pre-approved suppliers for models, integration, red-teaming, and audits to cut cycle times.

Talent and capacity

• Core roles: Product leads, data engineers, ML engineers, applied researchers, and risk/audit specialists.
• Upskill existing teams: Short, role-specific paths for policy, procurement, and service delivery teams.
• Where to start: See focused options by role at Complete AI Training - Courses by Job.

Trust, safety, and oversight

• Security: Integrate with national SOCs; mandate isolation for sensitive workloads; encrypt data in transit and at rest.
• Fairness: Require bias testing and representative datasets for public-facing models.
• Red-teaming: Stress-test models for misuse, prompt injection, data leakage, and harmful outputs.
• Public transparency: Plain-language model cards and service notices for any AI-assisted decisions.

International alignment (practical steps)

• Map national risk tiers to OECD principles and the EU approach to increase interoperability and trust.
• Join cross-border pilots for digital identity, customs, and health data exchanges where standards exist.
• Adopt shared incident taxonomies to simplify reporting and lessons learned.

Metrics to report quarterly

• Service time saved per case and total staff hours reallocated.
• Accuracy against human baseline and error rates by segment.
• User satisfaction and complaint resolution time.
• Incidents by severity and time to remediate.
• Cost per transaction and infrastructure utilization.
• Fairness metrics across priority demographics.
• Training completion and certification rates by role.
• Share of systems meeting audit and logging requirements.

Common pitfalls to avoid

• Buying tools before defining the problem and success metrics.
• Ignoring data quality; model performance will suffer no matter the vendor.
• Shadow AI projects without security reviews or legal bases for processing.
• One-off pilots with no plan for scaling and maintaining.

A simple operating model

• Plan: Define service outcome, risk tier, data sources, and KPIs.
• Build: Start small; instrument for telemetry and auditing from the start.
• Evaluate: Run A/B tests against a human baseline; check fairness and security findings.
• Approve: Independent review for high-risk use cases; document mitigations.
• Scale: Roll out with training, support, and ongoing monitoring.

30/60/90-day checklist

• Day 30: Publish risk tiers, approval workflow, and procurement templates. Name accountable owners in each ministry.
• Day 60: Launch first wave of pilots with signed KPIs. Stand up the model registry and audit logging.
• Day 90: Report early results, incidents, and fixes. Expand successful pilots; sunset those that miss targets.

Bottom line

Centralized AI governance gives Türkiye a path to safer, faster, and more efficient public services. Start with clear standards, a focused pilot portfolio, and measurable outcomes. Build trust through transparency and strong controls, and keep the loop tight: plan, test, learn, scale.