Michael Lovelace - VP of Strategic Growth
October 2, 2026
One of the most revealing conversations at FS/TEC was not whether AI will reshape the restaurant industry. It was how long operators expect that transformation to take.
Across many of our conversations, QSR leaders viewed meaningful AI value as being one to three years away for early adopters and potentially five years away from broader industry adoption. This was not a formal industry forecast, but it reflected a common sentiment among the operators, technology providers and industry leaders we met.
That outlook is understandable. Restaurants operate across complex technology environments that may include separate systems for point of sale, inventory, labor, loyalty, digital ordering, delivery and finance. These platforms generate substantial amounts of data, but the information is often fragmented and difficult to translate into a coordinated operational response.
Organizations also have legitimate concerns about integration, data quality, governance, implementation costs and whether their teams will trust and act on AI-generated recommendations.
But operators do not need to wait for fully autonomous restaurants or an enterprise-wide AI transformation to begin creating measurable value.
Operational Intelligence connects data and signals across restaurant platforms, adds business context and uses AI to help operators answer four questions:
This does not require restaurants to replace their existing platforms. Instead, an Operational Intelligence layer can combine information from POS, inventory, loyalty and other operating systems with external signals such as weather, local events and community activity.
CoServeIQ uses those signals to deliver insights and recommendations around three areas that directly affect restaurant performance: production, waste and shrinkage, and revenue and margin.
Restaurant production requires a constant balance.
Underproduction can result in unavailable products, longer wait times, lost revenue and dissatisfied guests. Overproduction can increase waste, reduce product quality and push food costs beyond acceptable levels.
Traditional forecasts are often based primarily on historical sales. Operational Intelligence can add current context by considering factors such as:
Instead of relying only on what happened during a comparable shift last week or last year, operators can receive recommendations based on what is happening now and what is expected to happen next.
The objective is straightforward: have the right products available when guests want them without producing more than the restaurant can reasonably sell.
Waste management is closely connected to production, but it requires a more detailed set of decisions:
Operational Intelligence can compare production, sales, inventory and waste data to identify patterns that may be difficult to recognize within separate systems.
For example, the platform might identify that a particular product is consistently overprepared during a specific daypart, that a promotion is producing uneven demand across locations or that weather conditions are changing the expected product mix.
The value is not simply another waste report at the end of the week. It is a timely recommendation that allows the operator to adjust before the waste occurs.
Over time, the organization can also identify recurring sources of shrinkage, compare performance across locations and distinguish isolated incidents from broader operational patterns.
Higher sales do not always produce better restaurant economics. Product mix, promotions, labor, waste and execution all affect the margin ultimately delivered by each location.
Operational Intelligence can help operators identify opportunities such as:
It can also help close the gap between corporate promotions and local execution.
A promotion may be strategically sound at the corporate level but create challenges if a location does not have the inventory, capacity or operating conditions to execute it effectively. Connecting promotional plans with local labor, inventory, waste and external signals gives operators the context needed to prepare and gives corporate teams visibility into what is actually happening in the field.
The goal is not simply to recommend selling more. It is to help each restaurant make decisions that improve revenue quality and protect margin.
Most QSR organizations already have dashboards for sales, inventory, labor and loyalty. The challenge is that operators must still move between those systems, interpret the information and determine what action to take.
CoServeIQ is designed to connect those signals and convert them into operational recommendations that can be delivered to the appropriate person at the appropriate time.
AI adoption across the restaurant industry will continue to mature. More advanced capabilities including autonomous agents and coordinated actions across multiple platforms may take time to become mainstream.
But the first measurable value does not need to be one, three or five years away.
CRG is delivering operational insights and recommendations from data already being generated across restaurant platforms today, while continuing to expand CoServeIQ’s integrations and capabilities.
The best place to start is with one meaningful operational decision:
Dashboards help operators understand what happened. Operational Intelligence helps them determine what to do next.
Partners in your digital transformation.
.png){kind=small}
