The EU AI Act is in force. GDPR applies to AI systems that process personal data. NIS2 may be relevant where AI systems are used within critical digital or operational services. Most enterprise teams deploying AI have no structured governance process. GreenPilot AI delivers a structured AI Governance Readiness assessment with named deliverables — before your next deployment creates a compliance gap.
Most enterprise teams deploying LLMs, automation agents, and AI-assisted decisions have no structured process to assess risk, document decisions, or demonstrate compliance. The EU AI Act changes that.
The EU AI Act requires organisations to classify AI systems by risk level. Most teams have not done this for any of their deployments.
AI systems that process personal data require a legal basis, data minimisation, and often a DPIA. Most LLM integrations have not been reviewed against these requirements.
LLM API costs and cloud compute for AI workloads are growing fast. Most teams have no attribution model, no budget governance, and no carbon estimate for their AI usage.
AI systems making or influencing decisions need defined escalation paths, override mechanisms, and audit trails. Most enterprise deployments have none of these documented.
The EU AI Act requires technical documentation for high-risk systems. Most teams cannot produce a coherent record of how their AI systems were designed, tested, or monitored.
AI governance sits between engineering, legal, and compliance. Without a structured process, it falls through the gap — until a regulator, customer, or incident forces the issue.
The GreenPilot AI Governance Readiness assessment covers every dimension that matters for enterprise AI deployments under EU regulation — including the cost and carbon angle that no other governance framework addresses.
We map each AI system in scope against the EU AI Act risk tiers: unacceptable, high-risk, limited-risk, and minimal-risk. For high-risk systems, we identify the applicable obligations: technical documentation, conformity assessment, human oversight, and registration requirements.
AI systems that process personal data require a legal basis, data minimisation, and often a DPIA. We review your AI data flows against GDPR requirements and flag gaps your DPO needs to address.
LLM API costs and cloud compute for AI workloads are growing without visibility. We assess your AI spend attribution, token governance, infrastructure sizing, and carbon impact — and identify where a smaller model or different architecture would reduce cost and emissions.
AI systems influencing decisions need defined human oversight mechanisms. We review whether your deployments have documented escalation paths, override controls, audit trails, and monitoring processes that satisfy EU AI Act and internal governance requirements.
Beyond current deployments, we assess whether your organisation has the foundations to scale AI responsibly: data readiness, team capability, vendor governance, and a structured process for evaluating new AI use cases before they are built.
Every assessment produces a single structured report your CTO, General Counsel, DPO, and sustainability lead can all use. No raw data dumps. No dashboard to interpret. A document with findings, risk ratings, and a prioritised action list.
Overall readiness score, top 3 risks, and recommended immediate actions. Written for board and C-suite.
Each AI system classified by risk tier. Applicable obligations listed. Prohibited use-case check result.
Personal data flows in AI pipelines. Legal basis gaps. DPIA triggers. Flagged for your DPO.
LLM API spend by use case. Token governance gaps. CO₂e estimate for AI workloads. Right-sizing recommendations.
Escalation path gaps. Override control status. Logging and monitoring assessment.
Every finding ranked: Critical / High / Medium / Low. Owner suggested. Effort estimated. Timeline recommended.
AI governance is not an engineering problem alone. The readiness assessment is designed to produce output that is useful to every stakeholder who has a stake in responsible AI deployment.
You need a defensible governance position before the next AI deployment. The report gives you a risk classification, a documented decision trail, and a prioritised action list your team can execute.
Technical documentation · Risk classificationGDPR applies to AI systems processing personal data. The EU AI Act adds new obligations. The report surfaces the gaps your legal team needs to address — structured, not raw data.
GDPR observations · AI Act obligationsAI workloads have a carbon footprint. Cloud and AI emissions may become relevant to sustainability reporting, supplier questionnaires, or value-chain data requests — especially for companies working with larger CSRD-reporting customers. The report includes a CO₂e estimate for your AI usage, labelled by confidence level.
AI carbon estimate · CSRD-relevant dataLLM API costs are growing without visibility. The report includes an AI cost attribution baseline and identifies where spend can be reduced without reducing capability.
Cost attribution · Right-sizing opportunitiesTell us which AI systems you have in scope, your cloud provider, and your primary concern — compliance, cost, carbon, or all three. We will follow up to discuss fit and scope. No commitment required.