For the past year, the hospitality industry has been under pressure to define its AI strategy.
Operators, suppliers, investors, and boards have all been asking some version of the same question: What are we doing with AI?
In the early stages, that urgency created a crowded market of tools promising transformation. Some were genuinely useful. Others were little more than generic AI wrappers searching for a real use case.
That phase is starting to mature.
The next chapter of AI in hospitality will not be defined by hype. It will be defined by specificity, operational value, and the ability to solve real business problems faster than legacy systems ever could.
That is where F&B Insights offers a compelling example.
In a recent conversation with EMERGING, Aashish Dalal of F&B Insights outlined how the company is using AI to solve one of the most persistent inefficiencies in on-premise food and beverage: turning fragmented data into actionable recommendations.
The lesson for the broader hospitality market is clear. AI’s strongest use case is not simply helping teams access more information. It is helping them decide what to do next.
The problem is not a lack of data
Hospitality and beverage businesses are not short on data.
There’s POS data. Depletion data. Menu data. Activation data. Field execution data. Customer behavior data. Competitive intelligence. The problem is that most of this information lives in different systems, owned by different teams, and rarely compounds into a true learning system.
Aashish summarized the issue directly: “Data abundance is not the problem.”
In the on-premise alcohol market, the inefficiency is especially clear. Alcohol producers spend more than $20 billion each year on on-premise trade marketing, including menu placements, activations, and field execution. Yet many of those decisions are still made on instinct.
The data exists, but it’s often siloed across multiple systems. Without a connected framework, teams are left interpreting reports manually, rebuilding the same arguments every quarter, and making high-value decisions without a reliable feedback loop.
That creates a structural gap.
If the data does not turn into action, it does not create advantage.
Why dashboards are no longer enough
For years, the dominant software model in hospitality has been dashboard-driven.
A user logs in, pulls a report, reviews the information, interprets the data, and decides what to do next. That approach can be useful, but it still places a heavy burden on the operator, sales representative, or manager to translate information into action.
F&B Insights is approaching the problem differently.
Rather than asking users to search for insights, the platform is designed to push prescriptive recommendations directly to them.
As Aashish explained, “This is not a dashboard that they’re logging into. This is not a report. It’s an actual list that we’ll send to them that they can act on literally tomorrow.”
That distinction matters.
The future of hospitality AI is not just better reporting. It’s decision support that’s embedded into the workflow. The most valuable tools will not simply tell users what happened, they’ll help them understand what to prioritize, why it matters, and how to act on it.
For beverage brands, that might mean identifying which accounts to target in a new market. For field sales teams, it might mean receiving a prioritized list of venues to visit in a specific territory. For operators, the same principle can apply across menu optimization, inventory, staffing, marketing, and guest engagement.
The power of AI is not only in analysis, it’s in reducing the distance between insight and execution.
Scout and the shift toward prescriptive AI
One of the clearest examples of this shift is Scout, the AI agent developed by F&B Insights.
Scout delivers account intelligence directly to field sales representatives. Instead of requiring reps to interpret static dashboards, Scout provides a prioritized list of accounts to contact, the reasoning behind each recommendation, and practical guidance on how to approach each venue.
In a market like Chicago, for example, a sales representative could use Scout to understand which accounts to prioritize across a specific territory, who to speak with, and what message is most likely to support a successful menu placement or activation.
This is where AI starts to function less like a tool and more like an operating layer.
It helps teams move from broad information to specific action. It supports route planning, account prioritization, competitive intelligence, and tactical execution. Most importantly, it gives sales teams a clearer understanding of where their time is most likely to produce value.
That has major implications for hospitality and food and beverage businesses.
In an environment where labor, time, and attention are increasingly constrained, the ability to focus teams on the highest-value actions becomes a meaningful competitive advantage.
AI should start with the problem
One of the strongest themes from the conversation was the importance of building AI around real operational problems.
Aashish described a common issue in the technology market: solutions looking for problems.
That is where many AI tools fall short. They begin with the technology, then try to force it into a business use case. The better approach is the opposite.
“We’ve got to start with the problem and figure out how we can layer on technology,” Aashish said.
For hospitality operators, this is the right filter.
AI should not be adopted because it sounds innovative. It should be adopted because it improves a specific workflow, unlocks measurable efficiency, increases revenue visibility, reduces manual effort, or helps teams make better decisions.
That could mean using AI to:
- prioritize sales opportunities
- reduce menu and inventory inefficiencies
- improve marketing attribution
- automate repetitive administrative work
- personalize outreach
- identify stronger account-level opportunities
- improve forecasting and field execution
The common thread is not AI for its own sake, it’s AI attached to a clear business outcome.
This is where F&B Insights’ approach becomes especially relevant. The company is not simply applying AI broadly to the beverage market. It is using AI to address a specific structural inefficiency: fragmented data preventing smarter trade marketing decisions.
That is the kind of focused application that will define the next wave of hospitality technology.
AI as an amplifier of human ability
The conversation also reflected a broader EMERGING belief: the best technology should amplify human capability.
There’s understandable anxiety around AI and automation. Some job functions will change. Some repetitive tasks will become automated. But the more interesting opportunity is not replacing skilled people, it’s giving them better leverage.
Aashish described how AI is already changing the way his own team operates. F&B Insights uses AI not only in its product, but across internal workflows, including go-to-market strategy, outbound communication, and knowledge management.
As he put it, “Every workflow has an AI layer on it.”
That allows a lean team to operate with the leverage of a much larger organization.
For hospitality businesses, this is a powerful model. AI can reduce time spent on repetitive, tactical work and allow teams to focus on higher-value reasoning, strategy, relationship-building, and execution.
The goal is not to remove the human element from hospitality. The goal is to make human teams faster, sharper, and more effective.
This matters because hospitality remains a deeply human industry. Relationships, trust, service, creativity, and judgment still matter. AI is most valuable when it supports those strengths rather than trying to replace them.
The EMERGING takeaway
The AI hype cycle is giving way to a more practical phase.
The next generation of hospitality AI will not be measured by how futuristic it sounds. It will be measured by whether it helps teams act with greater speed, precision, and confidence.
F&B Insights is a strong example of this shift. By turning fragmented menu, market, and trade marketing data into prescriptive recommendations, the company is showing what AI can look like when it is built around a real industry problem.
For operators, the lesson is to start with the workflow. For suppliers, it’s to focus on where better intelligence can drive stronger execution. For investors, it is to look for companies that are embedding AI into core business processes rather than layering it on as a feature.
The future of AI in hospitality is not another dashboard, it’s intelligence that shows up where work actually happens.
And the companies that solve that problem first will have a structural advantage over those still waiting for the perfect AI strategy to arrive.
