AI and Automation in Hospitality Training Course

Participants can assess common predictive analytics applications and interpret their performance measures.

• Supervised learning predicts cancellation, upgrade, churn and ancillary purchase propensity
• Unsupervised clustering segments guests using behavioural, value and preference variables
• Recommendation engines rank relevant offers using constraints and response signals
• Forecast accuracy compared through MAE, RMSE, bias and forecast value-add
• Training, validation and test sets prevent misleading model performance claims
• Concept drift monitored after seasonality, events, distribution shifts and policy changes

Participants can design controlled generative AI workflows for guest enquiries and staff support.

• Large language model capabilities separated from factual knowledge and judgement
• Retrieval-augmented generation grounds concierge responses in approved property content
• Prompt patterns specify role, context, constraints, examples and output format
• Knowledge-base articles structured for retrieval, version control and escalation
• Multilingual response quality checked for tone, accuracy, cultural fit and safety
• Agent handoff rules preserve conversation history, consent status and accountability

Participants can specify automation designs for priority operational workflows and exceptions.

• Process mining exposes bottlenecks in reservations, housekeeping, maintenance and billing
• Robotic process automation handles deterministic, rules-based back-office transactions
• Workflow orchestration routes exceptions using service-level rules and ownership
• Housekeeping optimisation combines departure forecasts, room status and staffing constraints
• Predictive maintenance prioritises equipment alerts by failure probability and guest disruption
• Queueing models size front-desk capacity from arrival patterns and service times

Participants can assess AI-supported commercial decisions while maintaining pricing and brand controls.

• Revenue-management decisions connect demand forecasts, inventory controls and distribution costs
• Dynamic pricing guardrails protect rate integrity, fairness and brand positioning
• Length-of-stay controls evaluated through displacement analysis and contribution margin
• Ancillary offer triggers timed using journey stage, availability and propensity scores
• Channel performance compared using net revenue, cancellation risk and acquisition cost
• Overbooking policies tested against no-show forecasts, walk costs and service recovery

Participants can define AI-supported guest-service journeys with appropriate escalation and recovery controls.

• Conversational AI designs distinguish transactional requests from emotionally sensitive interactions
• Intent taxonomy covers pre-arrival, in-stay, post-stay and disruption enquiries
• Sentiment analysis flags dissatisfaction while preserving context and escalation pathways
• Personalisation rules use declared preferences, consent, recency and frequency limits
• Service recovery playbooks assign authority, compensation thresholds and follow-up actions
• Accessibility testing checks chatbot language, screen-reader compatibility and alternative channels

Participants can plan workforce changes, accountability and adoption measures for automated processes.

• Task automation analysis separates elimination, augmentation, redeployment and new controls
• Skills matrix identifies data literacy, prompt use, supervision and escalation needs
• Standard operating procedures document automated steps, exception paths and approvals
• RACI matrices clarify accountability among operations, technology, vendors and managers
• Adoption metrics track usage, override rates, rework and employee sentiment
• Change communications address role impacts, training commitments and feedback channels

Participants can establish governance requirements for safe, lawful and accountable hospitality AI use.

• NIST AI RMF functions organise governance, mapping, measurement and management
• ISO/IEC 42001 controls inform AI management-system roles and documented evidence
• Data minimisation limits collection to defined service and operational purposes
• Privacy impact assessments identify lawful basis, retention, access and transfer risks
• Bias testing compares error rates across relevant guest and employee groups
• Vendor due diligence examines model provenance, subcontractors, security and exit provisions

Participants can build a measurable business case and monitoring structure for AI initiatives.

• Baseline measures establish current cost, time, quality and guest-experience performance
• Benefits cases separate hard savings, avoided costs, revenue and risk reduction
• ROI calculations include implementation, integration, training, maintenance and oversight costs
• Balanced scorecard links financial, guest, process and workforce measures
• A/B testing measures conversion effects while controlling offer and channel differences
• Dashboard thresholds trigger review when service, fairness or accuracy deteriorates

Participants can produce a sequenced implementation plan for a prioritised hospitality AI use case.

• Use-case portfolio scores feasibility, value, risk, data readiness and ownership
• CRISP-DM stages organise business understanding, data preparation, modelling and deployment
• Pilot charters define hypotheses, scope, success measures, safeguards and decision gates
• Integration architecture maps PMS, CRS, CRM, POS and workforce systems
• Operating model assigns product ownership, monitoring cadence and incident response
• Ninety-day action plans sequence approvals, procurement, implementation and review