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A recap of GBQ's webinar on moving beyond the monthly close and building real-time visibility into profitability.
Most restaurant finance teams know the feeling. Sales dip on a random Tuesday, and by the time the monthly close explains why, the moment to act has already passed. That gap between what happened and when you find out about it was the starting point for GBQ Partners' recent webinar, "Beyond the Monthly Close: How to Leverage AI and Data Analytics to Understand and Anticipate Business Performance."
GBQ Tax & Advisory Partner Ryan Kilpatrick opened the session, and Geoff Marsh, managing partner at AMEND Consulting, led the discussion on where AI and analytics actually stand today for restaurant and multi-unit operators, separate from the hype. As a restaurant CPA firm working alongside operators coast to coast, GBQ is no stranger to helping clients turn scattered data into decisions they can act on.
Here are a few of the highlights.
Click here to access the on-demand webinar, "Beyond The Monthly Close: How To Leverage AI & Data Analytics To Understand & Anticipate Business Performance"
Why Monthly Close Was Never Built For Forward-Looking Decisions
Marsh pointed out that monthly close reporting was designed to close the books accurately, not to give operators a real-time read on performance. For many multi-unit restaurant groups, that means finance teams spend an outsized share of their week manually pulling together profitability reports in Excel, often because sales data is scattered across multiple point-of-sale systems, delivery platforms, and store-level spreadsheets.
The result, according to data Marsh shared, is that data integration challenges are close to universal among restaurant organizations. Without a standardized way to pull that information together, every reporting cycle starts from scratch.
The AI Reality Check: Amplify, Don't Replace
One of the more grounded parts of the discussion addressed a question a lot of executives are asking right now: how much can AI actually do for a finance or operations team today?
Marsh referenced widely reported findings, including MIT research showing that most enterprise generative AI pilot programs stall without delivering meaningful financial results, along with Gartner data indicating that roughly half of generative AI projects get abandoned after the proof-of-concept stage, often due to data readiness gaps or unclear business value. His point wasn't that AI is overhyped. It's that AI works best as a tool that amplifies the people already running your business, not a replacement for their judgment.
That framing led into what Marsh called the "human sandwich," a simple model for how AI should fit into a restaurant's operations: a person defines what the process should accomplish, AI handles the repetitive research and pattern recognition in the middle, and a person makes the final call. Marsh walked through how that plays out in a customer service scenario, and the same structure applies to areas like accounts payable review and expense management.
Five Structural Problems Behind Restaurant Reporting Headaches
Marsh outlined five recurring issues that make profitability reporting harder than it should be for restaurant operators, including inconsistent point-of-sale systems across locations, the added complexity of e-commerce and third-party delivery revenue, and the impact of frequently changing menus and promotions on data consistency.
He also made a point worth sitting with: a lot of what looks like a technology problem is actually a people problem. How a general manager codes labor hours or enters a shift isn't carelessness. It reflects how that manager runs their location, and it's one of the biggest reasons performance data doesn't roll up cleanly across regions.
A Practical Path Forward, Without Giving It All Away
Rather than pointing operators toward an off-the-shelf dashboard, Marsh laid out a phased approach built around standardizing data collection, creating a unified data model, and building governance around how information is defined and used across the organization, what he referred to as a data dictionary and KPI source map.
He also shared a realistic timeline for what this typically takes, along with a rough cost range for the underlying technology stack that surprised more than a few attendees on the call. Marsh's larger point was that budget usually isn't the barrier. Organizational alignment is.
The webinar closed with a look at role-specific reporting and why coaching store and regional managers matters just as much as the technology behind the dashboard, along with Marsh's four key takeaways for any operator trying to move from manual reporting to a real analytics capability.
Watch the Full Session On-Demand
This recap only scratches the surface. The full webinar includes Marsh's complete breakdown of the five-step roadmap, the technology stack most restaurant groups are using in 2026, and live audience polling that shows exactly where organizations like yours likely stand today. Watch the on-demand recording to get the full picture.
How GBQ's Restaurant Accountants & Business Technology Solutions Team Can Help
Watching the webinar is a strong first step. Turning it into a plan is where most organizations get stuck. GBQ's Business Technology Solutions team works with the firm’s restaurant practice to map existing systems, build the data governance structure Marsh described, and design AI tools that fit how your team actually operates, not a generic off-the-shelf template.
That includes support through GBQ's AI Clarity offering, built specifically to help business leaders cut through AI noise and identify where automation will actually move the needle for their organization.
If you're evaluating how to bring more real-time visibility into your restaurant's performance, our restaurant industry team is a good place to start that conversation.
Frequently Asked Questions
What is the difference between AI and traditional business analytics?
Traditional analytics reports on what already happened. Generative AI can help interpret patterns, summarize information, and suggest recommendations, but it still requires a person to define the goal upfront and review the output before acting on it.
Why do most restaurant organizations struggle with real-time profitability reporting?
Multiple point-of-sale systems, delivery platforms, and manual data entry across locations create inconsistencies that are difficult to reconcile without a centralized, standardized data structure.
How long does it typically take to build a centralized reporting system?
Based on the experience shared during the webinar, an initial rollout, covering data standardization, KPI definitions, and a first model version, typically takes around six months, with additional data sources layered in afterward.
Where can I learn more about AI strategy for my business?
GBQ's Business Technology Solutions team, including its AI Clarity offering, works with business owners and finance leaders to build practical, right-sized AI and data strategies. Contact GBQ to start a conversation.
