AI READINESS · Built on the CoSAI Shared Responsibility Framework
AI adoption is blocked.
Not because companies don't want AI, because nobody knows who is responsible for it.
Register AI assets, map obligations, collect evidence, and export proof for auditors - powered by the CoSAI Shared Responsibility Framework.
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Who this conversation is for
Software vendor
The situation. You sell an AI-enabled product. Your enterprise deals are stalling at procurement because your prospects cannot get internal approval.
The blocker. Their legal and compliance teams cannot answer: who inside our organization is accountable if this AI system causes a problem?
Partner inquiryEnterprise buyer
The situation. Your vendors are ready to deploy AI into your organization. Your board and legal team are asking questions nobody has answers to.
The blocker. You don't know which regulations apply to you, who internally owns AI decisions, or what documentation an auditor would expect.
Start free assessmentConsultant or advisor
The situation. Your clients are being asked about AI governance and coming to you first. You need a structured methodology, not a custom spreadsheet.
The blocker. Without a recognized framework, your recommendations lack the auditability your clients need to satisfy their own boards and regulators.
Consultant signupThe problem
When an AI system causes harm, fails an audit, or violates a regulation, the first question is always the same: "who is responsible?"
For traditional software, organizations have established procurement processes, vendor contracts, and compliance frameworks that answer this question. For AI systems, most organizations have nothing. The accountability gap is real, growing, and already costing companies contracts, audits, and incidents they cannot explain.
The gap exists at every level of the AI system — from the business decisions that authorize AI use, to the data that feeds it, the applications built on it, the infrastructure that runs it, and the vendors who supply it. Each level has a different accountable party. Most organizations have not identified any of them.
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What's in Pro
The five accountability layers, who owns what
Based on the CoSAI Shared Responsibility Framework. Accountability flows downward from business strategy. Each layer is governed by the layer above it.
| Layer | Name | Accountable party | Scope of accountability |
|---|---|---|---|
| L1 | Business | C-Suite / Governance | Strategy, policy, regulatory accountability |
| L2 | Information | Data Owners | Data management, lineage, classification, privacy |
| L3 | Application | Dev Teams | AI assets, lifecycle, integration, controls |
| L4 | Platform | Platform Providers | Infrastructure, APIs, monitoring, operations |
| L5 | Supply Chain | Vendors / Model Providers | Foundation models, training, provenance, risk |
The CoSAI five-layer model
Accountability flows from business strategy downward. Each layer is constrained by the policy set in the layers above it.
Five questions to start the internal conversation
If your organization cannot answer these questions today, you have an accountability gap. The goal is not to have perfect answers — it is to know which questions need owners.
- L1If our AI system caused harm to a customer, who internally would be held accountable?
- L2Do we know what data is being used to train or run our AI systems, and who owns it?
- L3Which of our AI applications have been formally risk-assessed and by whom?
- L4Which vendors provide infrastructure for our AI systems, and what are they responsible for?
- L1–L5Do we know which regulations apply to our planned AI use cases — and can we prove compliance?
AI Readiness — The platform that maps AI accountability across all five layers, identifies which regulations apply to your use case, and produces documentation your auditors, customers, and board will accept.
Built on the CoSAI Shared Responsibility Framework · EU AI Act · ISO 42001 · NIST AI RMF