An AI use-case register is not bureaucracy. It is the operating map that shows which AI tools are being used, what workflow they affect, what data they touch, who owns the outcome, and what approval or monitoring is required before the system can act. For organisations moving from pilots to AI-assisted workflows, it is now one of the simplest ways to keep momentum without losing control.
The practical problem is familiar: teams start with chat, summarisation, drafting, or analysis. Then a workflow owner asks whether the assistant can update a record, send a response, triage a case, generate an evidence pack, or trigger the next step. At that point, the risk profile changes. The issue is no longer "which model should we use?" It is "which business process are we changing, and can we prove the controls around it?"
Source: The European Commission published draft high-risk AI classification guidelines on 19 May 2026 to help providers, deployers, and authorities assess whether systems should be classified as high-risk under Article 6 of the AI Act. The same month, it opened consultation on transparency obligations that apply from 2 August 2026 for systems that interact with people or generate synthetic content. See Draft Commission guidelines on the classification of high-risk AI systems and Consultation on the draft guidelines on transparency obligations under the AI Act.
What an AI use-case register actually records
A useful register is a live delivery artefact, not a compliance spreadsheet that nobody updates. It should record enough to let an operations leader, risk owner, technology lead, or executive sponsor understand the work at a glance.
At minimum, each use case should capture:
| Field | What it answers |
|---|---|
| Workflow and business owner | Where does this sit, and who owns the result? |
| User group | Which team, customer, citizen, or partner is affected? |
| AI role | Is the system drafting, recommending, deciding, acting, or monitoring? |
| Data touched | Does it use personal, confidential, regulated, or operationally sensitive data? |
| System access | Can it read, write, trigger, call tools, or change records? |
| Human oversight | Who reviews outputs, and when is approval mandatory? |
| Risk tier | Is this low-risk assistance, controlled decision support, or higher-risk automation? |
| Evidence required | What logs, tests, approvals, and decision records must be retained? |
The point is to make the real operating model visible. A chatbot that drafts an internal summary is not the same as an agent that opens a ticket, updates a CRM, or sends a customer response. They need different ownership, access, testing, and oversight.
Why the register has become urgent in 2026
Three things have changed.
First, regulatory guidance is becoming more concrete. The EU AI Act draft high-risk guidance sets out two main routes for high-risk classification: AI used as a safety component or product covered by Annex I legislation, and AI systems falling into Annex III use cases. The transparency guidance also shows that disclosure, marking, and labelling obligations can extend beyond high-risk systems.
Source: The European Commission says the high-risk guidance includes practical examples and follows the structure of Article 6 of the AI Act. It also says the transparency rules will require providers to inform users when they are interacting with an AI system and to implement machine-readable marks for generative AI outputs, while deployers must inform people in specific situations such as deepfakes and AI-generated public-interest publications. See the Commission's high-risk classification draft guidelines and transparency obligations consultation.
Second, agentic AI is pulling systems closer to real operational access. The Australian Signals Directorate's Australian Cyber Security Centre and its Five Eyes partners warn that agentic AI can act across tools, data sources, memory, and planning workflows, and that organisations should avoid broad or unrestricted access, especially to sensitive data or critical systems.
Source: The joint Five Eyes guidance was first published on 1 May 2026 by ASD's ACSC with CISA, NSA, Canada's Cyber Centre, New Zealand's NCSC, and the UK's NCSC. It recommends adopting agentic AI with security in mind, assessing use, never granting broad or unrestricted access, and using agentic AI only for low-risk and non-sensitive tasks. See Careful adoption of agentic AI services.
Third, adoption is outrunning readiness. Dun & Bradstreet reported in May 2026 that 97 percent of surveyed organisations have active AI initiatives, but only 5 percent say their data is adequately ready. Nasuni reported that 90 percent face barriers to scaling AI, with data security, integration roadblocks, and data trust among the leading blockers.
Sources: See Dun & Bradstreet's AI Momentum Survey release and Nasuni's State of Enterprise File Data Annual Report 2026 announcement.
The buyer problem: nobody can approve what nobody can see
Most stalled AI rollouts are not blocked by a lack of enthusiasm. They are blocked because the approval question arrives late.
The workflow team has a promising demo. Legal asks what data is being processed. Cyber asks what systems the agent can reach. Operations asks who owns exceptions. The executive sponsor asks how value will be measured. Nobody has one current view, so the project slows down or gets pushed back into experimentation.
An AI use-case register fixes that by giving the organisation a shared object to review. It lets low-risk use cases move quickly, while higher-risk workflows get the gates they need. It also makes it easier to spot duplication, tool sprawl, unmanaged data handling, and unclear ownership before they become audit findings.
For Rettare's target buyers, this matters because AI adoption usually lives inside messy real work: intake, triage, routing, documentation, evidence preparation, service workflows, and exception handling. If the use case is not registered and owned, the rollout depends on memory, informal permission, and optimistic assumptions.
A practical risk triage model
The register should not treat every use case as dangerous. That is how governance becomes a brake. Use it to sort work into tiers.
Tier 1: Assistive use
The AI helps a person draft, summarise, search, or compare information. It has no write access and does not make decisions on behalf of the organisation. The main controls are user guidance, data handling rules, and output review.
Tier 2: Controlled workflow support
The AI prepares recommendations, evidence packs, triage suggestions, or draft actions inside a defined workflow. A person approves the final action. The controls should include workflow owner sign-off, data scope, prompt or instruction review, testing, logging, and clear escalation rules.
Tier 3: Agentic or tool-enabled execution
The AI can call tools, update records, trigger downstream steps, or interact with operational systems. The controls should include least privilege, tool allow-lists, environment separation, human approval for material writes, monitoring, rollback paths, and incident response.
Tier 4: High-impact or regulated use
The system affects access to services, employment, credit, safety, legal rights, critical infrastructure, or other high-impact outcomes. The organisation needs deeper legal, risk, privacy, security, and executive review before production use.
This tiering does not replace formal legal advice. It gives delivery teams a practical way to decide what evidence and approval path is needed.
Source: NIST describes the AI Risk Management Framework as a resource to help organisations manage risks to individuals, organisations, and society, and the Five Eyes agentic AI guidance emphasises ongoing visibility, assurance, strict access controls, and alignment with the organisation's existing security model. See NIST AI Risk Management Framework and Careful adoption of agentic AI services.
What good looks like before tool access is granted
Before an AI agent gets tool access, the register should show a complete decision trail:
- The workflow is defined in plain English.
- The business owner and technical owner are named.
- The data classes are recorded.
- The agent's permitted actions are explicitly listed.
- The approval points are clear.
- The test set or evaluation method is documented.
- The fallback path is known.
- The logs needed to reconstruct an incident are available.
- The decision to approve, reject, or limit the rollout is recorded.
This is where Agent Ops becomes practical. Agent Ops is not a label for "using agents". It is the operating discipline around approvals, guardrails, logging, fallbacks, and ownership. A use-case register gives that discipline somewhere to live.
The register also helps organisations avoid a common mistake: over-investing in platform selection before they understand the workflows. If the first ten use cases are mostly drafting and evidence preparation with human approval, the controls may be lightweight. If the next three involve system writes, sensitive data, or automated escalation, the operating model must be stronger before the tooling matters.
How to start in two weeks
Start with the work that already exists. Ask where AI is already being used or seriously discussed, then register those workflows first.
In week one, run a short inventory across operations, service delivery, digital, and support teams. Capture the use case, owner, data touched, tool used, current status, and whether the system can act or only assist.
In week two, apply a first-pass risk tier and decide the approval path. Some items can continue with clearer user guidance. Some should pause until data handling is fixed. Some should become scoped implementation candidates because the workflow is valuable and controllable.
The output should be simple: a register, a decision log, and a small backlog of approved next actions. That is enough to turn scattered AI interest into governed delivery.
The practical Rettare view
AI adoption does not need to become a slow committee process. It does need a visible operating model. A use-case register is the lightest practical starting point because it connects strategy, governance, workflow design, and implementation in one place.
For teams moving from AI pilots to production workflows, the question is not "do we need governance?" The better question is: "what is the minimum evidence we need so the right work can move faster?"
References
- European Commission, Draft Commission guidelines on the classification of high-risk AI systems, 19 May 2026.
- European Commission, Consultation on the draft guidelines on transparency obligations under the AI Act, 8 May 2026.
- Australian Cyber Security Centre and Five Eyes partners, Careful adoption of agentic AI services, 1 May 2026.
- NIST, AI Risk Management Framework.
- Dun & Bradstreet, AI adoption surges, data readiness limits ROI, 4 May 2026.
- Nasuni, Nasuni Research Finds 97% of Enterprises Are Adopting AI Agents, Yet Most Projects Fail to Meet Objectives, 18 May 2026.