From Hype to Habits: Where AI Is Really Embedded Today
On stage, Graham Donoghue and Steve Schwab cut through the hype around AI for vacation rentals. Despite the noise about AI guest messaging tools and conversational booking, both leaders say the real wins are internal. Revenue and vacation rental pricing AI, reporting, reconciliation, SOP generation, owner communications and upsell scripting are already embedded in daily workflows. Schwab noted that brands within the Casago/Vacasa group are finally getting business-intelligence depth that used to be reserved for giant operators, allowing teams to see beyond headline KPIs and into full P&L nuance. Donoghue’s Forge Holiday Group has similarly focused on back-office efficiency instead of shiny consumer features. The direction of travel is clear: by the time guest-facing AI is mature, most mid-sized operators will already be running short term rental automation behind the scenes to power vacation rental revenue management, cleaner ops, and more consistent guest experiences.
Data: The Bottleneck Behind Every ‘Smart’ Pricing or Guest Tool
Both CEOs came back to the same obstacle: dirty, fragmented data. Donoghue’s team spent months preparing 500 million pricing records before any vacation rental pricing AI could learn from them, underscoring how PMS exports, OTA feeds and in-house spreadsheets rarely line up cleanly. Schwab warned against “vibe-coding” AI features without API access or structured data, describing teams that reach 80% prototype and then stall. In practice, this means that AI for vacation rentals lives or dies on data hygiene: consistent property attributes, reliable calendars, photo metadata, and clear taxonomies across systems. When information is fuzzy, models simply hallucinate, whether that is an owner statement or a guest message. For mid-sized operators, the panel’s advice was unglamorous but sharp: invest one solid quarter in cleaning and consolidating data before layering on any AI guest messaging tools or advanced vacation rental revenue management engines.
Proving ROI and Rethinking Staffing Around Automation
The panelists framed AI investments as operational bets that must show up in measurable ROI, not experiments for their own sake. While they did not publish specific numbers, they repeatedly tied success to classic hospitality metrics: occupancy, ADR, RevPAR, response times and margin. Internally focused AI for vacation rentals is already enabling leaner teams to manage more units by automating reporting, documentation, and routine owner and guest communications. That shift naturally changes staffing models: fewer people on repetitive front-desk tasks, more emphasis on oversight roles that review AI outputs, and capacity to route maintenance and housekeeping more intelligently. Schwab highlighted how smaller brands in his group now access enterprise-level intelligence without enterprise headcount. The underlying message for operators is that AI should either lift revenue through better pricing and conversion, or reduce cost-to-serve through short term rental automation; anything else is a distraction.
Adoption, Risk and What Smaller Operators Should Actually Do First
Donoghue was candid that simply handing staff new tools failed; adoption only followed once AI use became non-negotiable and wrapped in clear guardrails. Forge runs a tiered rights system and a defined roster of approved tools, specifying which data can go where and how outputs are reviewed. That discipline matters as guest-facing AI grows, bringing risk around misleading guest messages, perceived pricing unfairness and regulatory scrutiny. For smaller hosts and boutique managers, the panel’s implied playbook is pragmatic: do not try to out-build platforms like Guesty or the big OTAs. Start with vendor tools that plug into your PMS, and prioritise internal use cases with fast ROI: reporting, listing copy, SOPs, owner updates. Leave experimental trip-planning chatbots for later. Above all, avoid overbuying shiny AI features; focus on a few tightly scoped workflows where you can track before-and-after impact on revenue, responsiveness and team productivity.
