WORKGROUP 8 OUTPUT
The essential framework path: what it is, the minimum records to establish, the five governance pillars and the questions to ask every AI vendor.
Framework at a glance
AI GOVERNANCE FRAMEWORKStart with the five entry points and pillar summaries on this page, then apply the framework proportionately to your organisation’s size, risk and context. Within each pillar, capabilities define what the organisation needs to be able to do and the outcomes effective governance should achieve.
Before the pillars
Hospitality organisations are rapidly embedding AI within their operations, but adoption is moving faster than the industry’s ability to govern it consistently. The framework provides a common, practical reference for organisations developing their own AI governance frameworks.
Read the full section →AI governance is the system of roles, decision rights, processes, controls and evidence through which an organisation determines which AI systems may act in its name, for what purpose and under whose authority.
Read the full section →Governance functions describe what governance does. Governance principles are what good governance must stand for while doing it.
Read the full section →AI governance literacy is the knowledge and understanding people need to recognise, question, use, oversee and escalate AI appropriately. It is not about teaching people how AI models work technically.
Read the full section →The following eight domains define the core governance knowledge expected of people who approve, operate, use or oversee AI in a hospitality organisation.
A governance-literate person does not need to be able to explain how an AI model works technically. They should be able to answer the governance questions that matter:
The objective is not technical expertise. It is the ability to recognise AI, understand its governance boundaries, make informed decisions, identify concerns and escalate appropriately.
DOMAIN OF KNOWLEDGE | WHAT PEOPLE NEED TO UNDERSTAND | WHAT THIS ENTAILS | HOSPITALITY EXAMPLES |
|---|---|---|---|
1. WHERE AI IS ALREADY PRESENT | AI is not limited to LLMs or generative AI. AI may enter the organisation without a deliberate decision to “buy AI”. The concept of shadow AI | Recognise AI embedded in existing software, introduced through vendors, brought in by employees, added through software updates, or contained in everyday consumer tools. Understand that shadow AI creates governance risk and why visibility is the starting point for governance. | ChatGPT used by staff to respond to emails; AI features added to Microsoft 365; dynamic pricing in revenue management; AI in CRM or PMS platforms; a marketing agency using AI to process guest information. |
2. WHAT AN AI SYSTEM IS INTENDED AND NOT INTENDED TO DO | Every AI system should have an approved purpose and defined operating boundaries. | Understand the system's intended purpose, approved users, permitted data, decisions it may influence and activities it is prohibited from performing. Recognise when use has expanded beyond the approved purpose. Understand that an AI system may have limitations, uncertainty or reliability constraints that affect whether it is suitable for a particular purpose or context. | A guest chatbot may answer questions about check-in times and restaurant hours, but should not approve refunds, waive cancellation fees or change reservation records. A marketing AI may draft content but not publish unreviewed legal or privacy claims. |
3. WHAT DATA IT USES AND WHO CAN ACCESS IT | AI governance requires visibility of what data enters a system, where it comes from, where it goes and who is responsible for it. | Understand data sources, data ownership, data lineage, access, vendor processing and traceability. Recognise the roles of the data steward, AI system owner, technical owner and vendor/procurement owner. | Guest reservation data moves from the PMS to the CRM, then to an AI personalisation tool. Employees should understand what information is used, who owns it, who can access it and whether a vendor can process it. |
4. THE DIFFERENCE BETWEEN AI OUTPUT AND AUTHORITATIVE SYSTEM STATE | AI-generated information is not automatically the organisation's authoritative system state or official business record. | Understand the distinction between model output - generated, predicted, inferred or recommended information - and the authoritative system state maintained by the organisation's designated system of record. Know when AI output must be verified. Understand that AI outputs may be inaccurate, incomplete, misleading or generated with unwarranted confidence, and should be interpreted in the context in which the system is being used. | A chatbot says a guest has a reservation, but the PMS shows none. The PMS prevails. An AI pricing system recommends a 15% rate increase, but the approved rate in the PMS/revenue system is the authoritative state. |
5. WHO IS ACCOUNTABLE FOR APPROVAL, OPERATION AND INCIDENTS | Accountability remains with people and the organisation; it cannot be transferred to the AI or vendor. | Understand accountability across the lifecycle: approval, operation, oversight, escalation and incident response. Recognise the different roles of the executive sponsor, AI system owner, business owner, technical owner and data steward. | An executive approves deployment of a guest chatbot; the AI system owner oversees it; Guest Experience owns operational outcomes; IT manages integrations; the data steward governs guest data; an identified owner coordinates an incident. |
6. WHEN A HUMAN MUST INTERVENE OR TAKE OWNERSHIP | AI may assist, recommend, predict or generate, but it cannot accept accountability or organisational authority. | Recognise when human review, approval, intervention or escalation is required. Understand human-in-the-loop, human-on-the-loop and human-out-of-the-loop oversight. Recognise situations where AI exceeds its purpose, uses unauthorised data, produces harmful or unexpected outcomes, or conflicts with authoritative records. Understand that human oversight should also consider fairness, bias, transparency and the potential impact of AI-assisted decisions on people. | A chatbot recommends a refund but a human approves it. AI may screen CVs, but humans make employment decisions. A revenue AI recommends pricing, but management decides. A chatbot conflicts with the PMS and staff verify the authoritative record. A recruitment AI consistently ranks candidates from one demographic group disproportionately lower than candidates from other groups. A human must not simply accept the recommendation; the outcome should be questioned, investigated and escalated where appropriate. |
7. HOW TO REPORT SHADOW AI, ERRORS, HARMFUL OUTCOMES OR SUSPECTED MISUSE | Governance depends on people surfacing concerns. If AI use or problems are not reported, they cannot be governed. | Recognise shadow AI, incorrect outputs, harmful outcomes, data concerns, suspected misuse and vendor changes. Know what to report, where to report it and what information will support investigation and remediation. | An employee uses a personal ChatGPT account for guest communications; a chatbot gives incorrect booking information; an employee uploads guest information to an unapproved tool; a vendor introduces a new AI feature without notification. |
8. HOW GUEST, EMPLOYEE, VENDOR AND JURISDICTIONAL CONTEXT CHANGES RISK | AI risk is determined not only by the technology but by how, where and on whom it is used. | Understand how guest impact, employee impact, data sensitivity, vendor involvement, jurisdiction, property characteristics and deployment scale affect the level of governance, oversight and accountability required. Understand that risk can change after deployment when the system, data, vendor, users, purpose, scale or operating environment changes, and that governance may therefore require reassessment. | A chatbot answering restaurant hours at one boutique hotel may present relatively limited risk. The same technology handling reservations and loyalty information for a global hotel group across multiple jurisdictions creates significantly greater governance requirements. |
The operating baseline
Organisational size does not remove the need for basic AI governance. Regardless of size, structure or maturity, every hospitality operator should establish a small set of foundational governance records that provide visibility, accountability and control over its use of AI. At minimum, the organisation should have:
These are the foundational records, not a complete governance system. They provide the minimum foundation from which more sophisticated governance can develop as the organisation's use of AI, risk and maturity increase. What differs between a single property and a global group is the depth, formality and sophistication of these arrangements, not the need for them.
Eight foundational records, plus an agent-specific supplement. The Implementation Workbook provides these eight records and a supplementary Agent Controls worksheet for organisations using AI agents under Pillar 5. Agent Controls is not a ninth foundational record.
The heart of the framework
Within each pillar, capabilities define what the organisation needs to be able to do and the outcome effective governance should achieve.
An organisation cannot govern AI effectively without knowing what AI systems it has, understanding the risks associated with their use, keeping those risks under review, and assigning clear accountability and decision rights. This pillar establishes the foundations for identifying, assessing and overseeing AI systems and ensuring that appropriate authority and accountability are assigned throughout their lifecycle.
Risk is a property of the use, not the tool. Classify each AI use case against evidence and record the rationale. Material uses include all Risk Tier 2 and Risk Tier 3 uses and any use that could meaningfully affect rights, access, price, employment, safety or privacy. See Materiality for the full definition, including changes and incidents.
TIER | USE PROFILE | MINIMUM GOVERNANCE | HOSPITALITY EXAMPLES |
|---|---|---|---|
0 - Assistive | Internal drafting or analysis; approved data; no external action; meaningful human review; low consequence. | Register; named owner; acceptable-use and data controls; incident reporting. | • Marketing staff using ChatGPT to draft social media post ideas before review • A manager using AI to summarise internal meeting notes • Housekeeping using AI to draft an internal shift-handover summary |
1 - Controlled | Low-to-moderate consequence; bounded guest/staff assistance; reversible outcomes; reliable supervision. | Documented assessment; testing; monitoring; role-based oversight; vendor review; escalation. | • A guest-service chatbot answering routine questions like check-in times or spa hours • AI drafting first-pass responses to guest reviews, reviewed before posting |
2 - Material impact | Material guest/employee, financial, access, eligibility, pricing or service impact; or significant personal data. | Formal approval by the designated accountable authority; cross-functional challenge; stronger testing/logging; human intervention; transparency/contestability; periodic review. | • AI-generated dynamic pricing recommendations feeding into room rates • AI-assisted interview scheduling in HR • AI staff rostering and scheduling • AI influencing loyalty status changes or guest eligibility decisions |
3 - High consequence | High potential consequence, significant autonomy, difficult-to-reverse outcomes, significant safety, security, privacy, biometric or highly sensitive data or other material impact. | Default: do not deploy until necessity, appropriate legal/ethical review, robust controls, executive approval, continuous monitoring, suspension capability and independent assurance are demonstrated, with effective suspension capability. | • Facial recognition used for guest or staff identification and access • Emotional recognition (visual and voice) • An autonomous agent authorised to issue refunds or compensation without human sign-off • AI-driven access control tied to safety and security systems across a portfolio • An AI tool screening and ranking job candidates |
A hotel can buy or outsource its AI technology, but it remains accountable for how that AI is used and for its impact on guests, employees and the business.
ACCOUNTABLE PERSON / ROLE | DEFINITION | ACCOUNTABLE FOR |
|---|---|---|
Executive sponsor | Senior person with authority to support, approve or reject significant AI decisions. | Risk appetite, resources and approval of Tier 3 AI uses. |
AI governance lead | Person or function accountable for the organisation's overall AI governance arrangements and their consistent application. | The AI register; consistent tier assignment; ensuring every AI system has its roles assigned; tracking that reviews and controls take place; reporting AI governance status to the executive sponsor. |
AI system owner | Person accountable for the AI system throughout its organisational lifecycle. | Approved purpose, risk, performance, lifecycle and overall accountability for the AI system. |
Business / process owner | Person accountable for the business process in which the AI is used. | Workflow fit, guest/employee impact, adoption and fallback arrangements. |
Technical owner | Person responsible for the technical implementation and operation of the AI system. | Architecture, identity, integration, testing, security and technical change control. |
Data steward | Person responsible for the appropriate management of data used by the AI system. | Data source, purpose, quality, access, retention and correction. |
Security / privacy / legal | Specialist functions providing review and advice within their respective areas. | Specialist review, assurance and advice within their remit; does not replace system ownership. |
Vendor / procurement owner | Person responsible for managing the organisation’s relationship with the AI vendor or provider. | Vendor due diligence, contractual controls, service levels, change notification and exit arrangements. |
Property accountable manager | Person with operational authority for the property’s use of AI. | Local operating conditions, staff readiness, escalation and authority to suspend use where necessary. |
In smaller organisations the AI governance lead may be an existing role. Where that person also owns an AI system, the review of that system should be carried out by someone else. The effectiveness of the organisation's AI governance should be reviewed periodically, through internal review, internal audit or an external party proportionate to the organisation's size, and reported to the executive sponsor.
Roles are responsibilities, not required headcount. Retain a documented second check where operator and approver are the same person, appropriately independent review of higher-risk uses, and a named person able to suspend local use. See the small-property example in the full guide.
AI can affect people directly or indirectly, including through decisions, communications, recommendations and workplace processes. Organisations should ensure that AI is used transparently, fairly and responsibly where it affects guests, employees or other individuals, with appropriate human oversight and meaningful opportunities to question, correct or challenge AI-influenced outcomes.
AI systems depend on data and connected technologies, creating governance responsibilities for how data is used, how systems are protected, how performance and controls are tested, and how governance can be demonstrated. This pillar establishes the safeguards needed to ensure that AI operates securely, uses data appropriately, remains within its approved boundaries, and can be appropriately challenged, interrupted or corrected, with important governance decisions, actions and outcomes evidenced in a manner proportionate to risk.
Hospitality organisations increasingly rely on vendors and technology providers to deliver AI capabilities embedded within the systems used to operate their businesses. AI may therefore enter a hotel through procurement, existing software, vendor updates, integrations or third-party services, without the organisation directly developing or controlling the underlying AI. This creates governance dependencies and risks relating to vendor transparency, data use, security, system performance, contractual accountability, changes to AI functionality and the organisation's ability to intervene when something goes wrong.
Organisations should therefore govern AI throughout its lifecycle, including how AI is procured and provided by third parties, how it is deployed and operated within approved boundaries, how changes and incidents are managed, and how systems can be suspended, replaced or retired. Reliance on a vendor does not transfer the organisation's accountability for how AI is used or for its impact on guests, employees and the business.
AI agents can increasingly act in roles that were previously performed by people, including searching, evaluating options, making or recommending decisions, communicating, negotiating, booking, purchasing and taking other actions on behalf of an individual or organisation. This changes the nature of AI governance because an AI system may no longer simply produce an output for a person to consider; it may exercise delegated authority and act within a business or transactional environment.
Hospitality organisations therefore need to govern both AI agents acting on their behalf and AI agents acting on behalf of guests, partners, vendors or other external parties. An organisation's own agents may interact directly with people, systems, vendors and other AI agents, while external agents may interact with the organisation's booking, pricing, service or other operational systems as a substitute for human decision-making.
Organisations should establish clear governance over agent identity, delegated authority, permissions, boundaries, actions and accountability, including how authority is granted, limited, monitored and withdrawn. Where agents interact with other agents or automated systems, the organisation should ensure that identity and authority remain attributable and that an agent cannot acquire greater authority merely through delegation or interaction with another agent. AI-to-AI interaction should therefore remain within defined governance boundaries, with appropriate mechanisms to detect unexpected behaviour, escalate decisions, require human intervention where necessary, and interrupt or stop agent activity that exceeds its authorised role.
The fundamental governance question is not simply what an AI agent can do, but what it has been authorised to do, on whose behalf, within what boundaries, and with what consequences if it acts incorrectly.
Before procurement or renewal
Informative companion to the vendor governance areas in Pillar 4. A property-usable question set; record the answers as part of the AI Vendor Due Diligence Record.
ASK THE VENDOR | WHAT TO ESTABLISH |
|---|---|
What AI or models power this system, and how are they updated? | Model and provider chain, material changes and how changes are communicated. |
What data do you collect and what is it used for? | Data categories, purpose, inputs, outputs, telemetry and secondary uses. |
Do you use our data to train or improve your models? | Whether organisational data trains or improves models, and whether it is isolated from other customers. |
Are your recommendations informed by other customers’ data? | Whether pricing or other recommendations draw on non-public data from other customers, including competitors, and how it is segregated. |
Where is our data stored and processed, and does it cross borders? | Locations, jurisdictions, subprocessors and transfer safeguards. |
How long do you retain our data, and can it be deleted? How do you carry out applicable deletion requests and restrictions on data use or sharing across connected systems, subprocessors and shared-property databases? | Retention for inputs, outputs, logs and backups; deletion process and evidence; how applicable deletion requests and restrictions on data use or sharing are carried through connected systems, subprocessors and shared-property databases; any exceptions or limitations and how fulfilment is confirmed. |
Who can access our data? Where AI agents access our systems, does each agent have a distinct identity, limited permissions and an exportable record of its actions, and can its access be revoked independently? | Vendor personnel, subprocessors, support arrangements and access controls; distinct agent identities, permissions limited to the approved role, exportable action records sufficient for investigation, and independent revocation of agent access. Action records remain subject to appropriate privacy and retention controls. |
What security controls and assurance evidence support this service? | Authentication and access controls, including multi-factor authentication (MFA) for human accounts; relevant security testing and independent assessments; any certifications and their scope, validity and applicability to the service being purchased. Certifications should support, not replace, assessment of the proposed use. |
What can the AI do without human approval? | Action boundaries, permissions, human review, override and suspension capability. |
How can we disable or suspend the AI feature, who can do so, and what happens to the underlying service? | The available shutdown or suspension control; who has authority and access to use it, including property-level access where relevant; whether vendor intervention is required; the effect on the underlying service; and the fallback arrangements. |
How will we know if something changes or goes wrong? | Material-change and incident notification, relevant records and escalation. |
What happens if your service, underlying model provider or cloud provider becomes unavailable? | Which hotel functions stop or degrade; available manual or alternative processes; outage communication, recovery arrangements and how interrupted transactions are reconciled. |
What happens when the relationship ends? | Data return or deletion, records, continuity and exit assistance. |
Who owns the outputs this system generates for us, and do you indemnify us against third-party claims arising from them? | Ownership or licence of outputs, any vendor claims over outputs, and the scope, limits and exclusions of indemnity and liability for third-party claims. |