By Dustin Stone, RTN editor – 12/14/2025
In recent months, Grill has quietly but deliberately reshaped how it positions its platform in the increasingly competitive restaurant technology market. While the company hasn’t announced a single headline-grabbing product launch, a series of disclosures via earnings calls, investor filings and industry tracking reports indicate a clear strategic shift: Toast is moving beyond transaction processing and point-of-sale features toward AI-driven operational intelligence focused on profitability, labor efficiency and menu performance.
This development reflects the broader pressures facing restaurateurs. Persistent labor shortages, rising food costs and volatile demand patterns have made it more difficult for operators to rely on historical reports or static dashboards. As a result, foodservice technology providers are being asked to provide not only data, but also actionable insights. Toast’s recent focus on AI-driven forecasting, margin analysis and real-time performance monitoring indicates that the company sees this as the next battleground for platform differentiation.
In its latest earnings discussions, Toast executives highlighted increased investments in layers of intelligence that add to core point-of-sale and payments data. Rather than positioning AI as a standalone feature, the company presented it as an integrated capability designed to help operators understand which menu items drive true profitability, anticipate labor needs based on demand signals, and identify operational issues before they impact margins or customer experience. These capabilities build on Toast’s existing access to high-frequency transactional data across tens of thousands of restaurants.
One area of focus is the visibility of menus and margins. Restaurants have long struggled to understand the difference between high-volume and high-profit products, especially when ingredient costs fluctuate. Toast’s extensive analytics are intended to help operators evaluate menu performance in near real-time, taking into account food costs, pricing and sales mix. This puts Toast in more direct competition with in-house analytics specialists, as well as point-of-sale competitors that are working to integrate cost intelligence into their platforms.
Workforce forecasting is another essential part of Toast’s strategy. With high salaries and limited staffing flexibility, operators increasingly need tools that can recommend staffing levels based on expected demand rather than fixed schedules. Toast said it uses machine learning models to surface workforce insights related to historical sales trends, time of day and day of week trends, with the goal of helping restaurants more closely align staffing with actual traffic.
The company also highlighted improved real-time reporting and alerts that flag unusual performance changes, such as sudden drops in sales, labor percentages falling outside normal limits, or menu items underperforming compared to expectations. While these tools may seem progressive on their own, together they represent a broader shift toward proactive management rather than reactive reporting.
Toast’s approach reflects a broader shift in the restaurant technology landscape. Competitors such as Square And BY have expanded their AI and analytics capabilities, often positioning them as part of unified operating systems rather than add-ons. In this environment, competitive advantage increasingly lies in the ability to transform operational data into timely recommendations without adding complexity to restaurant teams.
What sets Toast’s recent message apart is its focus on integrating intelligence directly into the workflows operators already use. By keeping AI-powered insights within the same platform that handles orders, payments and workforce management, Toast aims to reduce friction and increase adoption. This strategy also reinforces the company’s broader value proposition as a system of record for restaurant operations, rather than just a point-of-sale provider.
At the same time, Toast’s gradual rollout reflects a measured approach. Rather than making sweeping claims about generative AI or autonomous decision-making, the company focused on practical use cases related to cost control, labor efficiency, and menu optimization. This restraint may resonate with operators who are wary of experimental technologies but are receptive to tools that offer clear operational benefits.
For restaurateurs, the implications are significant. As platforms like Toast expand their layers of intelligence, expectations that technology can support everyday decision-making will continue to grow. Operators evaluating technology stacks can increasingly prioritize systems that provide predictive insights and early warnings, rather than relying on separate reporting tools or manual analysis.
Toast’s recent initiatives also highlight a broader reality for the industry: AI is no longer a future concept in restaurant technology, but an emerging core capability. Whether it’s forecasting, alerting, or margin analytics, intelligence is becoming an essential feature of modern restaurant platforms. The competitive question is not who will adopt AI, but who will deploy it in a way that is reliable, actionable, and closely aligned with how restaurants actually operate.
As 2026 approaches, Toast’s evolving strategy suggests that the next phase of competition will be defined less by hardware or payment rates than by platforms that can help operators manage complexity with more confidence. For an industry where margins are thin and conditions change quickly, this change may be more consequential than any single feature release.