Over the past decade, restaurant reservation technology has largely been defined by incremental improvements to digital booking interfaces and marketplace reach. Phone calls, while still a major source of reservations for many full-service and casual dining restaurants, have remained stubbornly manual, labor-intensive, and prone to missed opportunities during peak periods. In my view, that dynamic is beginning to change, as a new wave of voice AI integrations suggests a meaningful shift in how reservations are captured and managed within existing reservation platforms.
In recent weeks, three separate announcements involving OpenTable point to growing momentum around automated, voice-based reservation handling. Maple, Loman AI, and SoundHound AI have each unveiled integrations that automate or extend reservation interactions using voice technology, though they approach the problem from different entry points. Taken together, these announcements indicate that voice-driven reservations are becoming more tightly connected to the core reservation infrastructure used by restaurants.
Maple’s integration with OpenTable is focused on automating inbound restaurant phone calls related to reservations. According to the company, its conversational AI answers calls and assists with reservation handling by connecting directly with OpenTable. For operators, this addresses a long-standing operational challenge. Phone reservations often peak during the same windows when staff are busiest serving guests, leading to missed calls or inconsistent handling. By automating call answering and reservation interactions, Maple positions voice AI as a way to reduce front-of-house workload while maintaining reservation availability beyond staffed hours.
Loman AI’s OpenTable integration is positioned as a full voice-based reservation management solution. The company says its AI phone agent can book, modify, confirm, and cancel reservations using natural language conversations, with updates reflected in OpenTable. This places Loman among a growing group of hospitality-focused voice AI providers targeting reservation and guest communication workflows. The key distinction is the direct integration with OpenTable’s reservation logic, which removes the need for restaurants to reconcile separate systems or manually re-enter bookings captured by AI.
SoundHound AI’s OpenTable integration extends voice-based reservations beyond the restaurant itself. Through this integration, users can search for restaurants and make reservations using in-vehicle voice assistants, with availability drawn from OpenTable’s network. From my perspective, this represents an expansion of the reservation funnel, capturing guest intent earlier in the decision-making process. It also highlights how reservations are increasingly intersecting with discovery, navigation, and mobility platforms.
OpenTable’s role across these announcements is notable. Rather than offering a single, proprietary voice AI solution, OpenTable supports integrations with multiple voice AI providers, enabling restaurants to adopt automation while continuing to use the same reservation system of record. OpenTable has publicly positioned voice AI integrations as a way for restaurants to take reservations around the clock, and existing documentation shows support for several third-party voice AI partners.
That momentum is not limited to OpenTable’s ecosystem. Yelp, for example, has also expanded aggressively into AI-powered call handling with the launch of Yelp Host and Yelp Receptionist as part of its Fall 2025 product release. Yelp Host is designed specifically for restaurants, functioning as an AI phone agent that can answer calls, manage reservations, update wait times, capture special requests, and send guests text links to join waitlists, browse menus, or place pickup and delivery orders. The product integrates directly with Yelp Guest Manager for existing customers and is available as a standalone subscription starting at $149 per month, or $99 per month for Guest Manager users. Yelp has said additional capabilities, including automatic waitlist enrollment, will be rolled out shortly.
Yelp Receptionist extends similar voice-driven functionality beyond restaurants to a wider range of local service businesses, handling calls 24/7, answering common questions, collecting lead information, providing quotes, and scheduling appointments. It will launch initially for a limited group of eligible businesses at $99 per month, with broader availability planned later this year. Both tools are pre-trained on Yelp’s business data and can operate either as a full replacement for manual call handling or as overflow support during peak periods. Together with existing AI features such as Yelp Assistant, Menu Vision, and conversational voice search, the launches underscore Yelp’s continued evolution from a discovery and review platform into a more operationally embedded technology provider.
These developments are unfolding amid intensifying competition in the broader reservation and guest management landscape. Restaurant technology platforms are increasingly competing to become the system through which guest data, bookings, and engagement flow. Reservation capabilities are no longer standalone tools; they are closely linked to marketing, guest communications, and operational decision-making. Voice AI adds another layer to that competition, particularly as restaurants seek ways to offset labor constraints without compromising service accessibility.
From an operator standpoint, early adoption of voice AI for reservations is likely to be driven by practical considerations such as reducing missed calls and extending reservation coverage during peak or off-hours. Over time, however, the strategic value may lie in the data generated by these interactions. Voice-based reservation conversations can capture timing preferences, party size patterns, and intent signals that, when integrated into a reservation platform, may support staffing decisions, promotional targeting, and demand forecasting.
My view is that voice AI for reservations is moving along a familiar path in restaurant technology adoption. Initial deployments focus on efficiency and cost containment, but broader value emerges as automation becomes embedded in existing platforms and workflows. The providers most likely to gain traction will be those that integrate cleanly with systems restaurants already trust, rather than forcing parallel processes or fragmented data flows.
For restaurants, the question is no longer whether voice AI will intersect with reservation management, but how and through which partners. As Maple, Loman AI, and SoundHound demonstrate, reservation interactions are expanding across channels and contexts. Restaurants evaluating these technologies should consider not only immediate labor benefits, but also how much visibility, control, and data ownership they retain as voice becomes an increasingly common gateway to the table.
