EU AI Act GPAI: 30/60/90-day GTM compliance plan

Why this matters now

General-purpose AI transparency obligations under the EU AI Act kicked in in early August, and the EU AI Office released initial guidance this month, including templates and a reporting portal. Major model providers updated their system cards and disclosures in response. The rules are real, the timelines are present tense, and fines are not hypothetical.

If you ship AI-powered features into the EU or market them to EU users, you cannot treat compliance as a legal side quest. You need a fast, practical plan that protects go-to-market velocity without creating a paperwork tar pit. I built the plan below for Heads of Product, Growth, and PMM who need to launch on time and sleep at night.

I think about this like the first 10 kilometers of a marathon: you lock your pace, avoid early spikes, and make deliberate moves. Same with compliance. Small, consistent steps now save you from a late-race blow-up.

What product and growth leaders need to solve

You likely are a deployer that integrates or fine-tunes GPAI from a third party. The GPAI provider has primary duties, but you still have concrete responsibilities:

• You must give users clear information when they interact with AI features
• You must keep technical documentation and records that show you understand model capabilities, limitations, and mitigations in your use case
• Your marketing cannot overclaim or hide material limitations
• You must manage vendor risk and ensure you can pass through the provider's transparency info to your users and regulators

The good news: most of this can be operationalized with a small set of repeatable artifacts, gated by your release process.

The minimum viable compliance stack

Here are the seven artifacts I recommend every team builds in the next 90 days. Keep them thin, practical, and versioned in your product repo.

1. AI Feature Register

• One line per user-facing AI capability
• Includes target users, purpose, model family, data sources, prompts, outputs, and risk notes

2. Model Bill of Materials (MBOM)

• Foundation model name and version, provider, finetunes, adapters, tools, retrieval sources, and infrastructure location

3. Training and Data Summary

• High-level description of training and adaptation datasets used by your application: types, sources, licensing posture, personal data triggers

4. Capability and Limitation Summary

• What the feature does well, known failure modes, bias and hallucination characteristics, latency and throughput expectations, and safe-use guidance

5. Evaluation Pack

• Tasks, metrics, thresholds, red-team scenarios, and results that show the feature behaves within expected bounds

6. Safety Mitigations and Guardrails

• Input filters, output filters, refusals, human-in-the-loop steps, rate limits, abuse detection, and incident response triggers

7. User-Facing Disclosures

• UI copy and docs that tell users they are interacting with AI, what data is used, how to opt out where relevant, and how to get help

You can ship a lot with these seven artifacts. They let you prove you know what you are deploying, show you can control it, and explain it honestly to users.

30/60/90-day plan that keeps velocity

I use a 30/60/90 structure to layer speed, depth, and governance. Treat each milestone as a go/no-go gate for new AI launches and for scale-up of existing ones.

Day 0-30: Stabilize and disclose

Goal: inventory, basic documentation, clear user-facing notices, and safe defaults.

• Create the AI Feature Register

  • Pull from product specs, experiments, and roadmap. If it touches prompts or model outputs, it goes in the register.

• Build the Model Bill of Materials

  • For each feature, capture foundation model, version, hosting mode, finetunes, RAG sources, and tool integrations.
  • Ask providers for their updated system cards and EU AI Act transparency docs. Store them alongside the MBOM.

• Draft the Capability and Limitation Summary at feature level

  • Keep it to one page per feature. Include known failure cases. Use language you would be comfortable showing a regulator and a customer.

• Publish user-facing disclosures and in-product labels

  • Add a short label near AI-generated outputs: AI-generated content. Review carefully and verify before use.
  • Add a help center article that explains the AI features, data usage, and safe-use guidance. Keep marketing and product consistent.
  • Add a first-run disclosure for generative features that need consent or make material use of user data.

• Implement baseline logging and retention

  • Log prompts, outputs, model version, and safety filter events for eval and incident review. Obfuscate or tokenize where practical.

• Ship a basic evaluation pack

  • Define 5 to 10 critical tasks per feature, an accuracy or quality threshold, and a small red-team set to stress refusal boundaries.

• Update your marketing claim review process

  • Require substantiation for performance claims. If you say faster, safer, or more accurate, maintain a file with test evidence.

• Vendor and data checklist

  • Confirm whether your provider allows or disables training on your data by default.
  • Capture data residency and transfer paths for the model and your RAG store.

• Assign owners and create a standing AI launch review

  • Product owns the register and capability summary
  • Engineering owns MBOM and logging
  • Legal and PMM co-own disclosures
  • Security owns incident response and data controls

Definition of done for Day 30

• Register and MBOM exist and cover 100 percent of live AI features
• Disclosures live in product and help center
• Evaluation pack runs for each feature and is recorded
• Substantiation process live for marketing claims

Day 31-60: Test, mitigate, and align contracts

Goal: deepen evaluations, add guardrails, and align legal artifacts so growth can scale.

• Expand evaluations and red-teaming

  • Add bias, toxicity, and hallucination tests tailored to your domain. For B2B workflows, include false positive and false negative costs.
  • Add latency and cost targets per feature. Evaluate retry and tool-use chains for regression risk.

• Strengthen safety mitigations

  • Add input classifiers, better refusal policies, and safe-complete templates.
  • Add human-in-the-loop where stakes are material. For example, require approval for outbound emails or public content.

• Document and publish safety approach

  • Create a Safety Mitigations and Guardrails doc per feature and a one-page program overview you can share with customers.

• Update contracts and policies

  • Update your DPA to reflect AI data use and subprocessor model providers.
  • Amend vendor contracts to require provider transparency artifacts, model versioning notices, and breach notification aligned to your SLAs.
  • Align your privacy policy with user-facing disclosures.

• Add content provenance and labeling where feasible

  • Watermark or add metadata for generated assets when they leave your system, especially for marketing outputs.

• Create a marketing and sales enablement pack

  • One slide that explains how the feature works, when it fails, and the safety measures in place.
  • FAQ for common customer questions on data usage, training, and control options.

• Prepare regulatory contact pack

  • Keep a folder with all seven artifacts, updated system cards from providers, and a short cover note about your use cases. This is your grab-and-go pack for audits or enterprise security reviews.

Definition of done for Day 60

• Expanded evals with bias, toxicity, and hallucination checks
• Safety mitigations documented and implemented for high-risk touchpoints
• Contracts and policy updates executed or in-flight with clear timelines
• Sales and marketing enablement live and consistent with disclosures

Day 61-90: Prove control at scale

Goal: demonstrate operational maturity while preserving shipping speed.

• Automate your evaluation pipeline

  • Nightly or pre-release evals per feature. Fail the build if thresholds regress. Keep dashboards visible to product and leadership.

• Implement versioning and rollback controls

  • Track model version, prompt version, and safety config in release notes. Maintain a one-click rollback path.

• Run a tabletop incident response drill

  • Simulate a harmful output incident and a data leak scenario. Identify paging, comms, and remediation steps.

• Implement periodic transparency updates

  • Quarterly refresh of the Capability and Limitation Summary. Annotate changes in model versions and mitigations.

• Conduct a DPIA for material features

  • Where personal data is involved in automated decisions or profiling, run a data protection impact assessment and record mitigations.

• Review build vs partner decisions

  • Compare total cost and compliance posture of your current provider against alternatives that have stronger transparency materials.

• Establish your AI release gate

  • No AI feature ships without the seven artifacts and green status on eval thresholds and disclosures. Small teams can keep this in a 30-minute weekly review.

Definition of done for Day 90

• Automated evals and rollback in place
• Incident response tested
• Quarterly transparency update process live
• Release gate enforced and documented

What counts as good-enough transparency for deployers

The EU AI Act focuses much of the GPAI transparency burden on providers, but deployers still need to reflect that transparency to users. Here is what good-enough looks like for SaaS and marketplaces.

• Clear interaction disclosure in product

  • Simple label and a short explainer available from the UI

• Honest capability and limitation summary

  • Describe typical accuracy ranges for your use case, known blind spots, and expected supervision

• Training and data use description for your feature

  • State whether you or your provider use customer data to improve models and provide opt-out where appropriate

• Evaluation evidence on file

  • Keep a concise summary of test methodology, datasets, and thresholds for customers and regulators

• Safety measures explained

  • List filters, refusals, and human review steps. Include user controls like export or deletion pathways

Sample disclosure copy you can adapt

• You are interacting with an AI assistant that generates content based on your inputs. Outputs may be inaccurate or incomplete. Review carefully before use. We do not use your inputs to train foundation models unless you explicitly opt in. Learn more in our help center.

Keep it readable. Legalese loses users and invites scrutiny.

Build vs partner: how to choose models without slowing GTM

You do not need to own the model to own the outcome. In fact, for most teams, the fastest path to compliant value is to choose a provider that is already leaning into the EU AI Act.

Use this quick filter when selecting or renewing a provider:

• Transparency maturity

  • Current system card updated since August 2025
  • Published training data summary at model family level
  • Clear redress channel and incident policy

• Deployment controls

  • Data isolation options, logging controls, and no-training-by-default guarantees
  • Version pinning and stable APIs

• Proven evals for your use case

  • Structured output reliability, tool-use accuracy, and latency SLOs

• Commercial alignment

  • Clear terms on IP, indemnity for generated content in your use case, and export controls for EU data

• Roadmap fit

  • Strong support for function calling, RAG, and low-latency interaction if your product needs it

When to build

• You have data privacy constraints that require self-hosting
• You need deterministic behavior that current GPAI cannot deliver
• You can staff safety, evals, and ops for a model lifecycle

When to partner

• Your differentiation is in workflow design, UX, and data context
• You want to move quickly and your provider can meet transparency needs today

This is like tennis footwork: pick the stance that lets you get the next ball back with control. Overcommitting to a custom model too early can lock your feet when you need to pivot.

The growth and PMM checklist: no-stall launch

• Messaging hygiene

  • Avoid absolute claims like zero hallucinations or perfect accuracy
  • Anchor claims in tested outcomes for named use cases

• Disclosure consistency

  • Align website copy, help center articles, and in-product labels

• Content ops for AI outputs

  • Label generated content that leaves your platform
  • Maintain an approval workflow for public assets

• Sales and CS enablement

  • One-pager on how the AI feature works, limitations, and customer controls
  • Talk tracks for data use, opt-outs, and incident response

• AEO and discoverability

  • Ensure your AI features are described in a structured, retrieval-friendly way so answer engines can cite you correctly
  • Upcite.ai helps you understand how ChatGPT and other AI models are viewing your products and applications and makes sure you appear in answers to prompts like 'Best products for…' or 'Top applications for…'. Use that visibility work to validate that your disclosures and messaging are consistent across answer engines.

Practical examples to copy

Example 1: AI email draft assistant in a CRM

• Feature register entry: Draft outbound email from notes and CRM fields
• MBOM: Hosted foundation model, version pinned; RAG to account notes; tool use to fetch contacts
• Capability and limitation summary: Good on summarizing notes, weak on nuanced tone; may include outdated facts if notes are stale; expected latency 1.8 seconds
• Evaluation pack: 20 tasks across industries; target 85 percent human-acceptable drafts; red-team prompts for privacy leakage and sensitive topics
• Safety mitigations: No free text about health or legal topics; enforce tone templates; human approval required before send
• Disclosures: In-product label; first-run modal explaining data use and review requirement

Example 2: Marketplace listing optimizer

• Feature register entry: Rewrite product titles and descriptions
• MBOM: Foundation model, lightweight finetune on your catalog style guide; metadata watermark on exported text
• Capability and limitation summary: Strong on clarity, weak on domain jargon; occasional brand name overreach flagged by filter
• Evaluation pack: 50 listings; target uplift in readability score and CTR proxy; hallucination detection for unauthorized claims
• Safety mitigations: Brand dictionary, compliance keyword filter, human review for regulated categories
• Disclosures: Label on generated suggestions; help article with examples of safe edits

Operational RACI that fits in a sprint

• Product: owns register, capability summaries, release gate
• Engineering: owns MBOM, logging, evaluation automation, rollback
• Legal and Privacy: own disclosures, DPA, DPIA, marketing claim review
• Security: owns incident response, abuse monitoring
• PMM: owns messaging hygiene, enablement, content labeling policies
• Data Science or AI Platform: owns evaluation design, red-teaming, safety policies

Keep the routine light. A 30-minute weekly AI review with these owners is enough to unblock launches.

Risks, fines, and how to avoid stall-outs

You do not need to rewrite your stack to meet GPAI era expectations. You need to show that you know what is in your product, you evaluate it, and you tell users the truth. Most fines stem from misleading claims, missing disclosures, or ignoring known risks.

Avoid stall-outs by:

• Bundling compliance with your normal PRD and release process
• Reusing provider transparency docs, not rewriting them
• Shipping small disclosures now and iterating
• Automating evaluations to limit manual cycles

What to do this week

• Stand up the AI Feature Register and MBOM
• Add AI-generated content labels in product
• Write one-page capability and limitation summaries for your top 3 AI features
• Run a 10-sample evaluation and document results
• Align marketing claims with a short substantiation file
• Request updated transparency docs from your model provider

If all you do is the list above, you will be meaningfully safer by Friday and your roadmap will keep moving.

How Upcite.ai helps

Visibility is part of compliance and growth. If answer engines misunderstand your product, users will too. Upcite.ai helps you understand how ChatGPT and other AI models are viewing your products and applications and makes sure you appear in answers to prompts like 'Best products for…' or 'Top applications for…'. It also highlights inconsistencies in your public descriptions and disclosures that can create risk. I use it as an early warning system for both AEO and compliance hygiene.

Final thought and next step

Compliance is not a detour. It is your pacing strategy for the second half of the race. Start with the seven artifacts, lock in the 30/60/90 cadence, and tie the release gate to real evaluations. If you want a fast diagnostic, I am happy to share a copy of the templates and run a 45-minute working session with your product, growth, and PMM leads. Or bring in Upcite.ai to map how answer engines see your AI features and fix the gaps before your next launch.