Working with on-board AI: adapting your methods to optimize your day-to-day catering operations

Understanding the role of AI in your business tool

The integration of artificial intelligence into foodservice management tools is no longer a distant prospect: it is already making its presence felt at the heart of business software, ERPs and procurement platforms. Its aim is not to supplant human expertise, but to enrich it, refine decision-making and automate the most time-consuming tasks.

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However, adopting a tool incorporating AI is not simply a matter of using new software. It involves adjusting your work habits, evolving your methods and, above all, learning to coexist intelligently with these systems. For while AI is capable of calculating, anticipating and recommending, it remains a decision-making tool, requiring human intervention to express its full potential.

The challenge is therefore twofold: take advantage of AI's capabilities while preserving the finesse of human analysis.

Concretely, how can teams in charge of purchasing, inventory and production adapt their day-to-day work? What are the best practices to ensure that AI becomes a true ally, not just another technological constraint?

Adjusting your organization to rely on AI on a daily basis

With embedded AI, certain tasks are optimized, but require a new approach to work organization.

1. For a purchasing and supply manager: supervise rather than manage manually

Before AI, a buyer would spend hours consolidating requirements from different sites, comparing supplier prices and manually triggering orders. Today, these tasks can be automated, but they require human validation to ensure their relevance.

In this sense, adopt a supervisory rather than an execution posture:

• Verify and adjust recommendations rather than create orders from scratch
• Fix rules and trigger thresholds to automate certain strategic commands
• Take advantage of intelligent alerts to avoid risks of out-of-stock or overstock

Example: The AI in your production cycle detects that a key product (e.g. green salad for lunch menus) is experiencing a sharp rise in consumption on Fridays. Rather than placing a fixed order every week, the buyer validates an adjusted dynamic order.

2. For a facility manager: steering with intelligent indicators

AI-enabled software doesn't just automate ordering, it also offers a holistic view of profitability and cost optimization.

Exploit AI dashboards to fine-tune your management:

• Consult automatic alerts on cost variances to quickly adjust your margins
• Use purchasing and consumption trends to plan your menus more effectively
• Exploit optimization recommendations to adjust your order quantities or suppliers

Example: Your order-taking AI detects a 10% increase in the cost of animal proteins over one month. The manager can then adjust his offer by favoring plant-based alternatives or renegotiating his volumes with his suppliers.

3. For a chef: adapting to AI recommendations to optimize production

In the kitchen, AI can be used to adjust production volumes in line with sales forecasts and avoid waste.

The AI in the menu production program is in this case used as an assistant to optimize inventory and preparation management:

• Consult production suggestions based on forecasted sales
• Adapt portions and recipes according to remaining stocks and AI alerts
• Integrate field data (customer preferences, feedback on certain dishes) to refine AI recommendations

Example: The AI embedded in the production software detects that there are 30 kg of vegetables left in stock with a short BBD. It recommends adjusting the week's recipes to integrate these products as a priority, thus avoiding unnecessary losses.

Developing a collaborative approach between humans and AI

The integration of AI into business tools is based on a simple but essential idea:it's a co-pilot, not an autopilot. While it can analyze millions of pieces of data in a matter of seconds and detect trends invisible to the human eye, it remains a decision-support tool. It's up to the user to validate, refine and, sometimes, contradict its recommendations.

The mistake would be to adopt a passive posture, accepting the AI's suggestions without questioning them. The opposite would be just as counter-productive: ignoring its recommendations would be tantamount to depriving your company of a powerful optimization lever.

The right reflex? Consider AI as an expert assistant that learns from its interactions. The more you interact with it, the more relevant its recommendations become.

Adapt your business approach to co-construct with AI

1. Correct and refine your suggestions

If an order forecast seems unsuitable, take the time to identify why:

• An exceptional event has altered sales?
• A consumer trend is changing?
• The software underestimated an external factor (weather, local competition)?
• ... ?

In most AI software embedded in a software layer, human corrections are taken into account and used to improve future forecasts. The more users adjust the tool, the better it becomes.

Example: A restaurant manager identifies a sudden rise in sales of a specific dish, not anticipated by the AI. Rather than ignoring this fluctuation, he reports it to the tool, which will adjust its forecasting models for the following weeks.

2. Sharing observations with the team

AI is a great tool for centralizing data, but it doesn't yet have human intuition. Field observations remain paramount:

• A chef notices that certain products are underused? He can adjust the AI to avoid ordering too many units.
• A procurement manager spots a quality problem with a supplier? He can influence future orders.

Example: In a restaurant chain, several establishments note a sudden drop in consumption of a key ingredient. By sharing this feedback via the software, the order-taking AI automatically adjusts supply volumes across the entire network.

3. Regularly compare AI forecasts and field reality

AI is powerful, but it needs to be challenged regularly to guarantee its relevance. Comparing generated forecasts with actual results helps identify discrepancies and refine the tool.

Good reflex: plan a monthly analysis point where the team compares AI recommendations with actual performance to detect any necessary adjustments.

AI is much more than an automation tool: it's a major performance lever within a multi-site catering chain that, properly used, enables work to be carried out more efficiently, with more precision and fewer errors.

By adjusting its working methods, interacting with AI and adapting its use over time, every user - from facility manager to chef - can derive tangible benefit from these technologies now embedded natively in almost all production management software.

Far from being a constraint, artificial intelligence is becoming a true ally, capable of transforming internal processes and bringing more agile, responsive management.

???? Discover how Adoria helps professionals integrate AI into their day-to-day management with its AI-assisted order advice module and move to a predictive and intelligent approach to your supplies.