Why this matters now: Croatia’s innovation community is racing to ship AI-powered experiences—from compliance automation to media production. The EU AI Act is entering force in phases, and investors now check whether teams can prove responsible development. This manual shows how to operationalise the Act without suffocating creativity.
1. Start with an AI Product Charter
- Purpose statement: Document what the model helps users achieve, and where it must not be used.
- Target users & context: Capture industries, risk scenarios, and expected human oversight.
- Success metrics: Include accuracy thresholds, fairness metrics, sustainable compute goals.
- Governance cadence: Quarterly reviews with product, legal, security, and an external advisor.
Use the charter to filter feature requests. If a request introduces a prohibited use case (e.g., emotion recognition in employment), decline it early and record the decision.
2. Data Strategy: From Sources to Stewardship
2.1 Data Inventory
- Map each dataset: origin, licensing, personal data classification, special categories, minors.
- Record lawful basis for scraping or purchasing datasets; document due diligence reports.
2.2 Quality & Bias Controls
- Define representativeness goals (gender, language, region). For Croatian-language models, combine public corpora with curated sector data.
- Run bias detection scripts every sprint; plot trends in dashboards reviewed during stand-ups.
2.3 Retention & Security
- Link datasets to retention schedules; automatically flag when data approaches end-of-life.
- For high-risk systems, encrypt both at rest and in transit, monitor access with anomaly detection.
3. Classification Under the AI Act
| Risk Tier | Examples | Obligations |
|---|---|---|
| Prohibited | Social scoring, subliminal manipulation | Stop development immediately |
| High-Risk | Credit scoring, HR screening, biometric identification | Register system, implement QMS, log events, provide human oversight, submit conformity assessment |
| Limited Risk | Chatbots, creative assistants | Provide transparency notices, record human handover options |
| Minimal Risk | Content tagging, spell-check | Voluntary codes, ethical guidelines |
Create a Risk Dossier for each model with classification rationale, user impact analysis, mitigation plan, and sign-offs.
4. Build the Compliance Delivery Lane
Product & Engineering Checklist
- Embed AI Act requirements into product tickets: risk tier, dataset ID, evaluation plan.
- Implement feature flags with audit logs for activation/deactivation & reason codes.
- Instrument monitoring to capture drift, hallucination rates, safety trigger activation.
Legal & Policy Checklist
- Draft clear terms explaining AI assistance, limitations, and user responsibilities.
- Maintain a public-facing model factsheet (purpose, data sources, performance, human fallback).
- Prepare a regulator-ready technical documentation pack (Annex IV of AI Act): architecture diagrams, logs, test reports.
Content & UX Checklist
- Design progressive disclosure: short in-product notice + link to full transparency page.
- Offer explicit opt-out or human review request.
- Provide examples of suitable prompts and red lines directly in the interface.
5. Human Oversight in Practice
- Assign oversight owners per risk scenario (e.g., compliance analyst for regulatory scoring, editor for AI text suggestions).
- Build dashboards that highlight anomalies and allow one-click review/rollback.
- Run quarterly drills simulating incorrect outputs and regulator inquiries.
- Log interventions as part of conformity evidence.
6. Launch Readiness Review
Before GA, run a structured review covering:
- Risk classification confirmed by legal and documented.
- Technical robustness validated (stress tests, adversarial checks, fallback paths).
- Data governance signed-off (retention, provenance, re-training triggers).
- Transparency surfaces live (UI cues, knowledge base, help centre).
- Customer enablement ready (tutorials, changelog, pricing, support scripts).
- Incident response updated for AI-specific scenarios (model pause, notification matrix).
Deliverables should include demo recording, signed conformance checklist, and an executive "go/no-go" memo.
7. Measure & Improve Post-Launch
- Track user satisfaction with and without AI assistance.
- Monitor false positive/negative ratios; set thresholds that trigger retraining.
- Analyse DSAR volumes related to AI decisions and adjust consent management accordingly.
- Publish quarterly transparency reports summarising usage, incidents, and remediation.
8. Funding & Partner Readiness
Investors and enterprise buyers now require:
- Evidence of compliance roadmap tied to product OKRs.
- Board minutes documenting AI risk discussions.
- Supplier assessment responses (due diligence questionnaires, security attestations).
- Independent audits or expert opinions on high-risk systems.
Prepare a lightweight "AI Governance Portfolio" PDF covering these artefacts—update it after each quarter.
9. Creative Team Enablement
- Run skill tracks: prompt engineering, bias detection, regulatory horizon scanning.
- Provide reusable content blocks with compliance-approved messaging for marketing and sales.
- Build feedback loops from customer success to product for misalignment or edge cases.
- Document do/don’t guidelines for generated content (copyright, sensitive topics, tone).
10. Sustainability & Ethics Layer
- Log compute usage for each training iteration; set efficiency goals.
- Evaluate environmental impact and include in corporate ESG reporting.
- Engage external ethicists or user councils twice per year for feedback.
- Align with national AI guidelines (Croatian AI Strategy) to unlock incentives and partnerships.
Final Word
Responsible AI is now a commercial advantage. Teams that can show governance discipline win bigger contracts, attract better partners, and avoid the costliest rework. Vision Compliance can co-drive your AI Act readiness sprint—with templates, workshops, and regulator-tested documentation frameworks.