Drew Shields, Solutions Director at iTradeNetwork

This year’s IFMA Presidents Conference made something clear within the first few sessions. Every link in the foodservice value chain is dealing with the same challenge. There’s plenty of data, but it’s scattered across fragmented systems, formats, and workflows. Leaders aren’t struggling with a lack of available data, but they are struggling with data they can’t fully trust. 

Across many of the conversations we had with operators, distributors, and manufacturers, the focus was on building the clarity and consistency needed to use the data they already have, rather than Future-state AI. In other words, AI can only be operationalized when it has a solid foundation. That theme aligns directly with our work inside the Cerena Solution Suite experience, which centers on unified data, smart workflows, and connected decision-making.

What Attendees Talked About Most

Throughout the event, teams consistently described the need for:

• Cleaner, more consistent item data
• Stronger alignment across suppliers, distributors, and operators
• Practical automation that fits inside existing workflows
• Early wins that build confidence instead of overwhelming teams
• Tools that reduce manual work rather than add to it

These themes shaped every discussion, regardless of company size. They also reflect what we hear across our customer base as organizations look for dependable ways to strengthen accuracy and minimize rework.

How Data Becomes Actionable

During the “Data to AI” session, the room filled quickly and the questions pointed in a similar direction. Organizations want to reduce waste, protect service levels, and act faster, but inconsistent data slows everything down. I shared a simple view that resonated with many attendees. Data doesn’t create value until something influences it. AI can strengthen forecasting, improve pricing discipline, and support daily execution, but only when the inputs are aligned and reliable.

A second misconception came up often. Many leaders assume AI requires a full transformation on day one. In reality, the most successful paths start small. Contained automation inside workflows teams already use generates earlier wins and reduces project risk. It also ensures people remain in control of decisions while automation handles repetitive tasks. This is the same approach we take across the iTradeNetwork platform, where embedded intelligence supports workflow adoption rather than disrupts it.

Manufacturers Focused on Margin Protection

For manufacturers, the conversation centered heavily on margin pressure. When foodservice operates on two cents of margin for every dollar of revenue, small inconsistencies have a meaningful impact. Attendees repeatedly asked how to turn what they already have in their systems into something dependable.

The answer pointed back to shared master data. When every team works from the same item identities, contract structures, and pricing details, operations stabilize. That consistency removes rework, speeds planning, and creates the conditions needed for automation to take on more value. This is why master data accuracy continues to be a core focus for iTradeNetwork customers, particularly those managing large product catalogs across multiple trading partners.

Preparing for 2026

As companies look toward 2026, several takeaways stood out.

• Leaders want AI supported by clean, unified data
• They expect tools to work within their existing processes
• They’re prioritizing connected partners over siloed systems
• They’re focused on speed to value, not ambitious rebuilds

The shift from data chaos to data confidence is already underway. These priorities mirror what we hear across the customers and partners we support, particularly as they look for simpler ways to create dependable, connected data across their operations. The organizations that invest in clarity, consistency, and collaboration will be the ones that move fastest.