Introduction – Why AI Governance Is (Truly) Strategic in 2025

With the enforcement of the European AI Act (Regulation EU 2024/1689) and the rapid adoption of global frameworks such as the NIST AI RMF, AI governance has become mandatory for companies of all sizes and sectors.

Penalties for non-compliance can reach up to 7% of global revenue or €35 million (Art. 71 AI Act, effective from 2025–2026), making it essential to implement solid, traceable, and up-to-date governance structures.

Key Frameworks for Robust Governance

NIST AI RMF (Risk Management Framework)

• Focused on the identification, assessment, and mitigation of AI-related risks.
• From 2025, includes recommendations for bias detection, explainability, and continuous model monitoring (NIST, 2023).

Best Practices:

• Conduct an initial risk assessment to classify AI systems by risk and impact.
• Map data flows and decision points, involving cross-functional stakeholders.
• Implement monitoring dashboards for audit trails, drift detection, alerts, and performance metrics.

ISO/IEC 42001:2023 – AI Management System Standard

• The ISO standard that structures policies, roles, responsibilities, and processes for responsible AI management.
• According to Deloitte, ISO 42001 adoption reduces operational and compliance risks by up to 35% (Deloitte, 2024).

Best Practices:

• Develop modular policies and update technical documentation at least annually (AI Act, Annex IV).
• Train teams on roles and responsibilities, with regular internal audits.
• Align policies with key AI Act articles: Art. 9 (risk management), Art. 14 (human oversight), Art. 61 (post-market monitoring).

Operational Best Practices for 2025–2026

• Cross-functional integration: establish an AI governance committee including IT, Legal, Compliance, and Business to avoid decision-making silos.
• Continuous monitoring: use tools like Prometheus, MLflow, or cloud-native solutions (AWS, Azure Monitor) for real-time tracking of performance and drift, in line with Art. 61 AI Act (mandatory from August 2, 2026, for high-risk systems).
• Automation and scalability: adopt compliance checking tools and workflow automation to support growth and reduce operational costs by 20% (McKinsey benchmark, 2024).

Common Challenges and Effective Solutions

• Cultural resistance: overcome skepticism through practical workshops, demos, and internal quick wins (e.g., audit simulations, incident response exercises).
• Documentation and transparency: standardize processes and use ISO/AI Act templates for audits and reporting, reducing compliance preparation time.
• Change management: integrate AI governance into training plans and performance evaluations of teams and managers.

Conclusion and Next Steps

Robust AI governance is not just a regulatory requirement, but a value driver for competitiveness, risk management, and corporate reputation.

Try our free assessment to evaluate your company’s AI maturity level and contact our experts for tailored consulting support.

Sources and Further Reading

• AI Act (EU 2024/1689) – Official Text and Timeline
• NIST AI Risk Management Framework (2023)
• ISO/IEC 42001:2023 – Artificial Intelligence Management System

Why 2025 Is a Turning Point for AI Governance

2025 marks a critical shift in AI compliance, with the EU AI Act coming into force and the rapid adoption of global frameworks like the NIST AI RMF. Companies, driven by strict regulations and the need to protect their reputation and business continuity, are evolving their strategies.

According to Forrester (2024), 70% of European companies will invest over €1 million in AI governance within the next 12 months.

AI Act Timeline: Key Dates and Upcoming Obligations

• August 1, 2024 — Regulation enters into force; no immediate obligations
• February 2, 2025 — Mandatory AI literacy (Art. 4) and ban on “unacceptable AI” (e.g., social scoring, biometric ID)
• May 2, 2025 — Code of conduct for General Purpose AI (GPAI) ready for adoption
• August 2, 2025 — GPAI obligations apply (transparency, safety, copyright), national authorities designated, EU/national governance in place
• August 2, 2026 — Application to all high-risk AI systems (Annex III: HR, healthcare, credit, recruiting, etc.)
• August 2, 2027 — Specific rules for AI embedded in regulated products (e.g., medical devices, vehicles)

Trends and Strategies with Deadlines in Mind

1. Proactive Risk Management (Art. 9 — Risk Handling)

📅 Obligations apply to GPAI systems from August 2, 2025

Recommended actions:

• Integrate ex-ante risk assessments and regular audits aligned with ISO 42001 / NIST AI RMF
• Use tools like RiskWatch or LogicManager
• Goal: reduce potential fines by up to 50% through a preventive approach

2. ESG in AI Governance

ESG focus is rising, with 55% of boards demanding AI-ESG reports

Recommended actions:

• Align AI policies with UN SDGs
• Implement dashboards to track environmental and social impact
• Example: an energy company cut IT consumption by 18% using sustainable models

3. AI Audit Tools

📈 +40% growth in the AI audit market (Gartner)

Recommended actions:

• Adopt tools like IBM AIF360, Arthur AI, Google What-If
• Integrate automated logs from cloud providers (AWS, Azure, GCP)
• Choose scalable and compliant solutions

4. Global Harmonization vs Local Regulation

The EU, US, and G7 are aligning on standards, while APAC follows different paths

Recommended actions:

• Map jurisdictions using a compliance matrix
• Base internal processes on NIST AI RMF and ISO 42001
• Monitor guidelines via IAPP, WEF, EU AI Alliance

5. Human-AI Collaboration (Art. 14)

📅 Meaningful oversight required from August 2, 2025

Recommended actions:

• Design hybrid workflows with human validation of critical outputs
• Provide training on AI literacy and ensure continuous oversight
• Track KPIs to measure improvement in accuracy and efficiency

Conclusions and Next Steps

2025 and the 2025–2027 biennium will be pivotal for:

• Complying with requirements for AI literacy, GPAI transparency, high-risk AI
• Implementing risk management, ESG integration, and continuous auditing
• Establishing real collaboration between humans and AI
• Preparing for the full regulatory rollout by 2027

Try our free assessment to evaluate your company’s AI maturity and contact our experts for tailored support.

Sources and Further Reading

• EU AI Act — Official Text and Timeline
• NIST AI RMF
• ISO/IEC 42001:2023 Standard for AI Management System

Introduction — The AI Act Gets Real: What Changes for Companies

The EU AI Act (Regulation EU 2024/1689), fully applicable between 2025 and 2026, is the world’s first comprehensive legal framework for the responsible and safe use of artificial intelligence. Its risk-based approach (Articles 6 and 7) defines tiered obligations depending on the system's impact.

Here are the most frequently asked questions we receive from Italian companies — with updated answers based on the latest EDPB guidelines.

1. Which Systems Are Classified as "High-Risk"?

According to Annex III of the AI Act, high-risk systems include those used in:

• HR/Recruiting (selection, evaluation, promotions)
• Finance (credit scoring, fraud detection, KYC)
• Healthcare (diagnosis, triage, medical devices)
• Education, law enforcement, critical infrastructure
• Note: Compliance for existing systems is required starting August 2, 2026 (Source).

2. What Are the Penalties for Non-Compliance?

Administrative fines can reach up to €35 million or 7% of global annual turnover (Art. 71). These are among the highest in EU regulations, exceeding those under the GDPR (Text).

3. How Should Companies Start Preparing?

• Gap analysis against the AI Act, mapping all AI systems in use
• AI governance policies: appoint a compliance officer, update data processing registers, activate internal audits
• Track key deadlines: obligations on AI literacy and GPAI start February–August 2025; high-risk systems from August 2026

4. What Is Post-Market Monitoring?

It means continuously monitoring the performance, safety, and potential incidents of AI systems after deployment (Art. 61). As of August 2, 2026, logging incidents, anomalies, and technical updates becomes mandatory (Source).

5. Is Specific Training Required? Who Should Be Involved?

Yes. Article 4 mandates training on AI literacy and risk awareness for all roles involved in managing AI systems (e.g., Data Owners, IT, Legal, Compliance, HR). This becomes mandatory starting February 2025.

6. How to Address Bias and Fairness?

• Regular audits of models (technical and legal)
• Use fairness metrics (e.g., disparate impact, equalized odds)
• Implement logging and explainability (Art. 13, 15)
• Recommended tools: IBM AIF360, Google What-If Tool (IBM)

7. What’s the Impact on SMEs?

The regulation provides simplified regimes and dedicated support for SMEs and startups (Art. 53–55). These include partial exemptions, regulatory sandboxes, and supporting guidelines (EDPB).

8. How Does It Relate to the GDPR?

They are fully integrated: the AI Act requires applying privacy-by-design principles (Arts. 5 and 25 GDPR) in high-risk AI systems. Each AI system must also be evaluated for data protection impact assessments (DPIAs).

9. Compliance Timelines: By When Must You Be Ready?

• Prohibited practices and AI literacy: from February 2, 2025
• GPAI and transparency obligations: from August 2, 2025
• High-risk systems: from August 2, 2026
• AI embedded in regulated products: from August 2, 2027
• (Official timeline)

10. How to Verify AI Vendors' Compliance?

• Request updated technical documentation (Art. 28)
• Demand audit trails, performance metrics, impact assessments
• Assess certifications like ISO/IEC 42001, NIST AI RMF, or the EU AI Trust mark

Conclusion & Resources

The AI Act is more than a compliance checklist: it’s a framework to strengthen governance, transparency, and reliability in AI systems — bringing both risks and competitive opportunities.

Try our free assessment to evaluate your company’s AI maturity and contact our experts for tailored support.

Sources & Further Reading

• AI Act – Official Text
• Implementation Timeline (artificialintelligenceact.eu)
• EDPB – Guidelines & Q&A
• ISO/IEC 42001:2023 -- AI Management Systems

Why AI Governance Is a Business Priority (Not Just a Tech One)

Artificial intelligence offers competitive advantages, but poor governance can quickly turn it into a legal, operational, and reputational risk. According to McKinsey (2024), 45% of companies adopting AI face compliance issues within the first year.

Here are the five most frequent mistakes—and how to build a robust governance system inspired by the AI Act and NIST standards.

Mistake 1: Leaving Governance Solely to IT

Delegating AI governance exclusively to the IT department while ignoring legal, compliance, and ethical aspects creates organizational silos and increases exposure to systemic risks and undetected biases.

How to avoid it:

• Establish a cross-functional committee including IT, Legal, Compliance, Risk, and Business stakeholders.
• Schedule regular meetings to align internal policies with the AI Act (e.g., risk management under Art. 9).
• Best practice: A retail leader reduced non-compliance risk by 30% by embedding compliance by design from the development phase.

Mistake 2: Ignoring Data Drift (and Model Degradation)

Many organizations underestimate the risk of model performance deterioration over time (model drift), leading to operational failures and potential compliance violations.

According to Gartner, by 2026, 75% of AI models will fail due to unmonitored drift.

How to avoid it:

• Implement advanced monitoring systems (e.g., MLflow) to track real-time performance.
• Schedule regular model retraining and quarterly dataset audits.
• Set up automatic alerts for abnormal (>5%) variations in key metrics.

Mistake 3: Neglecting Documentation and Traceability

Lack of detailed logs and decision process traceability makes it impossible to prove compliance during audits. The AI Act (Annex IV) requires complete and auditable documentation for all high-risk systems.

How to avoid it:

• Apply frameworks like ISO/IEC 42001 for document management.
• Use structured templates that include training data, decision logs, and model versioning.
• Tangible benefit: According to Deloitte, structured documentation cuts audit and inspection times by 40%.

Mistake 4: Underestimating Continuous Team Training

Insufficient training on AI ethics, bias, and regulatory requirements raises the risk of mistakes and sanctions. 62% of companies report critical training gaps (PwC, 2024).

How to avoid it:

• Run workshops on bias detection, AI compliance, and AI Act requirements.
• Use certified platforms (Coursera, Udemy, etc.) for ongoing training.
• Monitor training effectiveness through KPIs and pre/post-training assessments.

Mistake 5: Failing to Scale Policies with Business Growth

Static or outdated policies fail to cover new use cases, markets, or technologies, exposing the business to penalties and operational issues. The AI Act requires ongoing review and dynamic adaptation.

How to avoid it:

• Design modular policies with annual reviews.
• Automate compliance checks with dedicated tools.
• Real case: A fintech avoided fines in new markets by promptly adapting its AI governance policies.

Conclusions and Next Steps

Avoiding these mistakes means shifting from reactive governance to a proactive, scalable, and certifiable AI governance model.

Try our free assessment to evaluate your company’s AI maturity level and contact our experts for tailored consulting.

The widespread adoption of chatbots and AI systems for customer service is revolutionizing client relations, offering continuous availability, cost reductions, and faster responses. However, careless management of these tools can expose companies to unprecedented legal, financial, and reputational risks. The recent Air Canada case is a clear wake-up call for any organization delegating its official voice to AI.

The Air Canada Case: When the Chatbot Becomes a Source of Corporate Liability

In February 2024, a grieving Canadian customer contacted Air Canada’s chatbot to inquire about bereavement fare discounts. The AI system, with no filters, promised a retroactive refund not foreseen by company policy. When Air Canada refused the refund, the company invoked standard disclaimers and the "separate" nature of the chatbot from official policies.

However, the British Columbia Civil Resolution Tribunal ruled that:

• The chatbot officially represents the company.
• Information provided by the AI is as binding as that of a human employee.
• Generic disclaimers do not exempt the company from liability for specific errors.

Outcome: Air Canada was ordered to compensate the customer. But the real impact extends far beyond the $812 in damages.

What's Changing: Legal Precedent and the New Standard for Corporate Accountability

• Direct liability: Companies are responsible for information provided by their chatbots, with no possibility to deflect blame to developers or vendors.
• Equivalence to human staff: AI “hallucinations” are treated as official company statements.
• Reversed burden of proof: It’s not enough to claim a technical error; companies must prove the existence and effectiveness of safeguards.

In short: AI is no longer an "experimental channel" but an official representative in customer service and public communications.

The Problem of AI “Hallucinations”: Why Active Governance Is Necessary

Hallucinations in large language models are not exceptions, but consequences of their predictive nature:

• Spurious pattern matching: Plausible but incorrect information generation.
• Confidence bias: Highly confident answers even when factually unfounded.
• Poor uncertainty signaling: Fluent language masks the lack of fact-checking.

Most exposed sectors:

• Travel and Hospitality: Constantly evolving policies and high expectations of precision.
• Financial Services: Strict regulations, risk of incorrect advice.
• Healthcare and Insurance: Medical advice, insurance coverage—high legal exposure.

Regulatory Landscape: From the AI Act to Customer Protection Practices

The EU AI Act already imposes several key requirements for AI systems interacting with the public:

• Transparency (Art. 52): Mandatory disclosure, explanation of limitations, and alternative channels for accurate information.
• Accuracy and robustness (Art. 13): Rigorous testing, ongoing monitoring, and correction mechanisms.

Governance Strategies and Risk Mitigation

Architectural Safeguards

• Validated knowledge bases integrated with official databases, approval workflows, and version control.
• Automated fact-checking and discrepancy flagging for human review.
• Template responses and fallbacks for out-of-scope queries.

Process-Based Safeguards

• Human-in-the-loop escalation for high-risk queries.
• Quality assurance via sampling and customer feedback.
• Complete logging and audit trails of all AI interactions.

Legal & Compliance

• Continuous alignment of AI responses with official policies.
• Regular audits of content and templates.
• Contractual SLAs, liability clauses, and audit rights over vendor systems.

Best Practices and Success Cases

Emirates Airlines: Hybrid AI-human architecture, automatic escalation for policy-sensitive queries, real-time fact-checking.

• Result: 97% accuracy, 60% cost reduction, zero legal incidents.

USAA: Multi-level validation, unified source of truth, prioritization of accuracy over speed.

• Result: +40% customer satisfaction, -85% legal risk, +35% operational efficiency.

Recommendations for Management

• Risk Assessment Framework: Audit all AI-customer touchpoints, conduct gap analysis, and map risks across jurisdictions.

• Implementation Roadmap:

  • Immediate: Review disclaimers, set up emergency procedures, train staff.
  • Medium-term: Implement technical safeguards and redesign human-AI processes.
  • Long-term: Build a compliance-ready architecture and lead in accuracy and reliability.

• Vendor Management: Select vendors based on track record, liability sharing, compliance support, audit rights, and performance guarantees.

Conclusion: AI Customer Service Is (Already) a Matter of Corporate Governance

The Air Canada case marks a turning point: chatbot management is no longer a tech issue but one of governance, legal, and risk oversight.

Organizations that want to protect themselves—and unlock the value of AI—must invest in control architectures, training, audits, and strategic legal partnerships.

GenComply supports companies in building AI governance frameworks that protect brands, customers, and stakeholders.

Main sources:

• AI Act – Official documentation

Artificial intelligence is transforming medicine, enabling more accurate diagnoses, resource optimization, and personalized treatments. However, poor AI governance can lead to overlooked clinical and compliance risks. Model drift—the gradual loss of model accuracy—can result in serious and sometimes silent consequences, as demonstrated by recent international cases.

The JAMA Case: When Model Drift Becomes a Clinical (and Regulatory) Problem

A study published in JAMA analyzed the impact of the COVID-19 pandemic on AI models used to predict in-hospital mortality in oncology. Among 143,049 patients evaluated in U.S. facilities, AI models lost up to 20% in accuracy (True Positive Rate), with no monitoring system alerting clinicians to the anomaly.

The cause? Invisible changes in input data—fewer diagnostic tests, changes in triage protocols, demographic shifts—made the model less reliable, while aggregate metrics (e.g., AUROC) failed to detect the issue.

Why It Happens: Anatomy of Model Drift in Healthcare

• Covariate shift: changes in the input data distribution (new diagnostic technologies, altered protocols, different data collection methods).
• Label shift: changes in the prevalence of target conditions (new epidemics, demographic changes).
• Concept drift: changes in the relationship between input and clinical output (new disease variants, introduction of unforeseen therapies).

Aggravating factors:

• Extreme biological complexity and variability.
• Operational pressure on clinical teams limits time for monitoring.
• Lack of specific governance processes and a culture of timely reporting.

Impacts on Governance, Safety, and Liability

• Direct clinical risk: errors in patient prioritization and allocation of critical resources.
• Loss of trust: clinicians become less likely to rely on AI for strategic decisions, impacting innovation.
• Regulatory risk: potential non-compliance with stringent AI Act requirements (Art. 61 – post-market monitoring, Art. 9 – risk management, Annex III – high-risk healthcare systems).
• Shared liability between technology providers and healthcare institutions: post-market surveillance and anomaly response readiness become essential for compliance and risk management.

Case Study Lessons: Algorithmic Governance Makes the Difference

Mayo Clinic
Implemented continuous learning architectures with real-time clinical feedback, adaptive retraining, and outcome monitoring.
Result: -40% false positives, +25% early diagnosis accuracy.

NHS Trust (UK)
Tested multi-hospital federated learning, preserving privacy and performance robustness in high-variability contexts.
Result: performance stability above 95%, even during crises.

Prevention: Strategies and Best Practices for AI Governance in Healthcare

• Multilevel and continuous monitoring: input-level, performance-level, and output-level monitoring (beyond aggregate metrics).
• Integrated explainability: use of SHAP values, attention mechanisms, and counterfactual analysis to detect anomalies.
• Early warning systems: stratified alerts for immediate escalation and fallback to manual protocols during critical degradation.
• Audit trail and documentation: structured logs and periodic audits to demonstrate AI Act compliance.
• Continuous retraining and validation, involving clinical staff, data scientists, and compliance officers.
• Clear contractual requirements with tech vendors: post-market surveillance, SLA for drift management, regulatory compliance evidence.

Operational Recommendations for Management

For Chief Medical Officers
- Define KPIs and dashboards for AI system surveillance.
- Integrate AI risk training into clinical education programs.

For CIOs and IT Leaders
- Strengthen data pipelines and integration between AI systems and EHRs.
- Ensure incident response procedures and scalable AI deployments.

For Regulatory Affairs and Compliance
- Continuously map and update AI Act-mandated documentation.
- Involve key stakeholders in auditing and continuous improvement.

Conclusion: Govern Drift to Govern Risk

Model drift is a real clinical and regulatory threat that cannot be addressed with traditional tools.

Smart AI governance in healthcare is now a critical enabler of safety, trust, and competitiveness for the entire ecosystem.

Organizations that invest in proactive monitoring, structured audit processes, and strategic vendor partnerships will ensure not only compliance—but also the trust of doctors, patients, and regulators.

GenComply supports healthcare providers, vendors, and stakeholders in building advanced AI governance frameworks—enabling innovation without losing control.

Main Sources:

• AI Act – Official Documentation

The introduction of artificial intelligence solutions in public services marks a major shift in the digitalization and accessibility of institutions. However, the case of the MyCity chatbot launched by the City of New York in 2024 clearly highlights the concrete risks associated with unguided adoption of these tools: from compliance issues to reputational and legal consequences.

The MyCity Case: From Innovation to Systemic Failure

Designed to simplify small businesses’ access to local regulations, the MyCity chatbot quickly became a case study in failed AI deployment. Following independent tests conducted by investigative journalists, serious anomalies emerged:

• Advice that contradicted the law on dismissals and handling of harassment reports.
• Recommendations involving unlawful wage practices (e.g., illegal withholding of tips).
• Guidance on circumventing mandatory building and health codes.

These errors were not limited to isolated incidents but potentially reached thousands of users.

Root Causes and System Vulnerabilities

1. Training on unvalidated datasets:

The AI was trained on a legal corpus without validation, leading to risks such as confusing exceptions with general rules, treating outdated regulations as current, and interpreting exceptional clauses as standard practice.

2. Insufficient human oversight:

There was a lack of systematic involvement from legal experts, compliance officers, and AI governance specialists during output validation—a critical step, especially for high-impact regulatory answers.

3. Absence of proactive monitoring:

The lack of risk assessment dashboards, automatic alerts, and structured feedback/auditing processes prevented the timely identification of risks.

Legal, Reputational, and Systemic Impacts

• Risk of civil lawsuits and administrative penalties for businesses that followed illegitimate advice.
• Potential criminal liability for violations of labor laws.
• Reputational damage to the public administration and increased public distrust in institutional AI use.

Regulatory Framework: The AI Act and Decision Support Systems

In the European context, the AI Act classifies decision support systems affecting fundamental rights as “high risk,” imposing several key obligations:

• Error and anomaly logging (Art. 14): Systematic documentation of malfunctions and remediation plans.
• Quality management system (Art. 17): Structured control and escalation processes, with continuous operator training.
• Transparency obligations (Art. 52): Clear disclosure of AI usage, explanation of decision-making logic, and disclaimers about system limitations.

AI Governance and Prevention Strategies

• Advanced data curation:
  Constant validation of legal sources, database versioning, and regular reviews for accuracy and consistency.

• Human-in-the-loop:
  Automatic escalation systems for high-risk queries routed to qualified legal and compliance reviewers.

• Continuous monitoring and auditing:
  Operational dashboards with real-time metrics, alerts for anomalous patterns, and periodic audits of logs and outputs.

Operational Recommendations

For public administrations:

• Adopt procurement criteria favoring vendors with proven expertise in AI governance and legal tech.
• Invest structurally in internal competencies on AI ethics and compliance.
• Systematically involve stakeholders and establish supervised pilot programs.

For compliance officers:

• Integrate AI risks into enterprise risk management frameworks.
• Foster structured collaboration among legal, IT, and business functions.
• Define and monitor specific AI governance KPIs.

For technology providers:

• Design with a "compliance by design" approach and transparent decision logic.
• Draft contracts that clearly define liability sharing in case of system malfunctions.

Conclusions

The MyCity case underscores the need for an integrated and responsible approach to AI adoption in both public administration and the private sector. Governance can no longer be an afterthought—it must be an integral part of the AI solution lifecycle, from design to post-deployment monitoring.

Only structured algorithmic governance can ensure sustainable innovation, protection of rights, and preservation of public trust.

For audits, assessments, and targeted training on AI compliance in public and private processes, GenComply consultants are available.

Primary sources:

• AI Act – Official Documentation

The adoption of artificial intelligence systems in recruitment processes is revolutionizing the HR world, but at the same time exposes organizations to increasingly concrete regulatory, reputational, and operational risks.

A recent legal precedent in the United States, Mobley v. Workday, marks a turning point for anyone using AI platforms for automated candidate evaluations---even in SaaS mode.

The Workday Case: Legal and Operational Implications

In May 2025, a California federal court granted preliminary certification to a collective action against Workday, one of the world's leading HR-tech providers. The lawsuit, filed by Derek Mobley, led to the class action being certified for age discrimination against candidates over 40, excluded from selection processes managed by Workday's AI from 2020 to the present.

A key point: the judge acknowledged the possibility of direct liability for SaaS vendors as well---not only for the companies running the recruitment processes. Claims regarding racial and disability discrimination remain open but have not yet been certified.

The litigation could impact hundreds of millions of applications and has already attracted the attention of regulators (including the EEOC), industry stakeholders, and specialized media.

Risks for Organizations

Legal implications:

• Legal precedent extending liability to SaaS vendors and corporate clients.

• Exposure to class actions and regulatory investigations in the absence of independent controls and audits.

Operational and reputational implications:

• Potential disruption of HR operations due to extraordinary audits and reviews.

• Negative impact on corporate reputation and employer branding.

• Increased scrutiny from investors, stakeholders, and the media.

The Causes: How Algorithmic Bias Can Spread

• Biased historical data: AI learns from datasets that often reflect past biases in hiring processes.

• Lack of independent audits: Many systems are validated only internally, with no third-party checks on fairness and non-discrimination metrics.

• Poor data drift management: Lack of oversight on data changes can worsen discriminatory behavior over time.

The AI Act: New Standards for European Compliance

The European regulatory landscape is quickly aligning:

The AI Act classifies automated hiring systems as high-risk, introducing detailed and enforceable obligations for all organizations adopting these technologies:

• Preliminary impact assessment on risks of bias and discrimination.

• Documentation and traceability of data, methodologies, and automated decision-making processes.

• Meaningful human oversight and mechanisms to contest algorithmic decisions.

• Mandatory regular audits and up-to-date fairness metrics.

Essential Compliance Checklist (AI & Recruiting)

• Is your ATS or AI system regularly subject to independent audits?

• Are training datasets balanced and free from known systemic biases?

• Are automated decisions fully traceable and documented?

• Is there a structured process for candidates to challenge exclusions?

• Does your vendor ensure compliance with the AI Act and national regulations?

Operational Recommendations

• Third-party audits and impact assessments should become recurring practices, not one-off efforts.

• Training and updates for HR, IT, and compliance teams on evolving regulations.

• Legal review of SaaS provider contracts, with focus on liability clauses.

• Incident response plans for promptly addressing reports of discrimination or algorithmic failures.

Conclusions

The Workday case represents a paradigm shift in managing AI-related risks in recruiting. In a context of rapidly evolving regulation and growing reputational exposure, algorithmic governance must be solid, proactive, and transparency-driven.

Addressing this now means protecting your organization from legal risks, ensuring compliance with emerging EU regulations, and reinforcing the trust of candidates, investors, and stakeholders.

To assess the compliance of your AI-based recruitment systems, GenComply consultants are available for audits, impact assessments, and targeted training.