The 4-Stage Integration Gap Holding Food Manufacturers Back in 2026

Key takeaways:

• Most food manufacturers operate in a hybrid state of partially automated processes, and understanding where your operation sits on the integration spectrum is the first step toward smarter 2026 technology investments.
• Nearly 70% of food companies cite cost as their top barrier to digital transformation, but the companies seeing the strongest returns focus on decisions first and tools second.
• Integration maturity isn’t about buying more technology. It’s about connecting what you already have so the right people can make better decisions faster.

Most food and beverage companies today operate in a hybrid state of partially automated processes, according to our 2025 State of Food Manufacturing: Digital Transformation report. They’ve invested in systems. They’re collecting data. And yet something isn’t clicking.

You’ve probably seen it in your own operation. The enterprise resource planning (ERP) system says one thing. The production floor says another. Somewhere in between, a supervisor reconciles the two in a spreadsheet that only they understand.

That gap between what your systems know and what your teams can act on? That’s an integration maturity problem. And as you build your 2026 technology roadmap, it’s worth understanding exactly where those gaps are before you spend another dollar on new tools.

The good news is you don’t need to rip everything out and start over. You just need a clear picture of where you are, what’s working, and which connections would make the biggest difference this year.

What stage of integration maturity are you in? 

Integration maturity describes how well your systems, data, and people work together across your operation. It’s not a score you pass or fail, but more of a map that shows where information flows smoothly and where it gets stuck.

The concept breaks down into four stages. Every food manufacturer sits somewhere on this spectrum, and most sit in more than one place at a time depending on the function.

Stage 1: Manual and siloed 

Data lives in isolated systems, paper logs, or individual spreadsheets. Teams make decisions based on what they can see in their own area. Information crosses departments through phone calls, emails, or shift-change conversations. This may work when operations are small, but it doesn’t scale well, and it often creates blind spots when things move fast.

Stage 2: Partially connected

Some systems talk to each other. You might have an ERP handling procurement and finance while a separate manufacturing execution system (MES) tracks production. There are dashboards, maybe some real-time monitoring on a few lines. The challenge here is that connections between systems are often manual or batch-updated, so the data your team sees at 8 a.m. may not reflect what happened at 3 a.m.

Stage 3: Actively integrated 

Core systems share data in real time or near-real time. Quality, production, inventory, and compliance data flow into shared views. Teams across departments can access the same information without waiting for someone to pull a report. Decisions happen faster because the context is already there. This is where most food manufacturers are trying to get, and it’s where the return on technology investments starts to compound.

Stage 4: Predictive and adaptive

Data doesn’t just flow. It informs what’s likely to happen next. Predictive maintenance flags equipment issues before they cause downtime. Demand planning adjusts production schedules based on real-time signals. Quality systems learn from historical patterns to reduce variability. Fewer companies have reached this stage across their full operation, but many are applying it in targeted areas.

One important thing about these stages: they aren’t a ladder you climb in order. A single facility might have Stage 4 predictive maintenance on one line and Stage 1 paper logs in its sanitation tracking. That’s normal. But looking at your operations this way can help you pinpoint gaps and decide which ones matter most for your 2026 priorities.

Costs and complexity often stand in the way

If you feel like your operation is somewhere between “we have some good systems” and “nothing talks to each other the way it should,” you’re in the majority.

Our digital transformation report found that most surveyed companies allocate 26% to 50% of their equipment and systems budgets to digital and automation projects. Automated warehouse management systems, real-time production monitoring, and robotics delivered the strongest returns in 2025. And respondents predict that artificial intelligence (AI) and machine learning will have the greatest impact on food manufacturing over the next few years.

That’s real progress. And yet, our survey found that budget constraints are the single biggest barrier to deeper technology adoption. That tracks with findings from the Institute of Food Technologists’ (IFT) benchmark survey, where nearly 70% of respondents cited cost as their top challenge. Legacy system integration came in second at 53%, followed by employee reskilling (31%) and interdepartmental buy-in (28%).

In other words, it’s not that food manufacturers don’t want to integrate. It’s that the path forward is expensive, complicated, and often unclear.

This is where the maturity model earns its keep. Instead of asking “should we invest in AI?” or “do we need a new ERP?”, the better question is: “Where are our biggest integration gaps, and which ones create the most drag on decisions that affect yield, quality, uptime, or compliance?”

Decisions matter more than dashboards

One of the most common patterns in food manufacturing technology adoption looks like this: a company invests in a system that provides great visibility into operations. The dashboard looks impressive. The data is there. But nobody’s using it to make different decisions.

As we explored in our recent article on what most food manufacturers get wrong about digital transformation, the companies that see real ROI from their technology investments are the ones that design around decisions, not data. They ask questions like: When this metric moves out of range, who acts? What’s the standard response? How fast should it happen?

Eric Ibarra, Chief Technology Officer at Waites, reinforced this point in our article, noting that AI isn’t a substitute for “the human experience that is crucial to the manufacturing industry. While AI can assist in reviewing repetitive data or managing mundane tasks, it won’t replace the expertise that comes as a result of domain expertise and hands-on interactions.”

Integration maturity, then, isn’t really about how many systems are connected. It’s about whether those connections help the right people make better decisions at the right time.

Integration maturity is a market advantage

It’s tempting to think of integration as a plant floor concern. But the companies closing their integration gaps fastest are gaining advantages that show up well beyond the production line.

When production, quality, and supply chain data connect in real time, you can respond faster to retailer demands, adjust SKU mix based on actual sell-through, and get new products to shelf weeks sooner. As Kerry’s innovation team recently discussed, the ability to turn consumer trends into scalable products quickly is a competitive differentiator. That speed depends heavily on how well R&D, operations, quality, and commercial data flow together.

In a market where consumer preferences shift fast, pricing pressure is intensifying, and retailers expect shorter lead times, the manufacturers who can sense demand signals and translate them into production decisions without a two-week lag hold a structural edge. That edge starts with integration.

Simple questions that reveal integration gaps

You don’t need a consultant or a six-month audit to get a useful picture of where you stand. Start with these questions, organized by function.

• Production and quality: Can your quality team access real-time production data without leaving their system? When a quality hold happens, how long does it take to trace the issue back to the source? If the answer is “hours” or “it depends on who’s on shift,” there’s a gap worth examining.
• Maintenance and uptime: Does your maintenance team know which equipment is most likely to fail this week? Or are they still running on calendar-based schedules and reacting when things break? As Rob Ratterman, co-founder and CEO of Waites, noted, predictive maintenance “is immediate, simple with the right vendor, and sets the stage for every other advanced manufacturing solution.”
• Supply chain and inventory: Can your procurement team see real-time inventory levels across all locations? Or are they placing orders based on yesterday’s numbers and a best guess? How quickly can you adjust production plans when a supplier delivers late or a customer changes an order?
• Compliance and traceability: Could you complete a mock recall in under four hours? Do your traceability records connect raw materials to finished goods in a single system, or does someone need to pull from three different databases and a binder?
• Cross-site coordination: If one plant solves a recurring downtime issue, how long does it take for other facilities to learn about it? Is there a mechanism for sharing those improvements, or does each site figure things out independently?

The answers to these questions will give you a clear view of where integration gaps create friction, and where closing those gaps would have the biggest impact on your 2026 performance.

You can move to the next stage without overhauling everything

Not every integration gap requires a new system. Some of the highest-impact improvements come from connecting what you already have.

1. Connect your ERP to your plant floor. If your ERP and MES aren’t sharing data in real time, you’re making planning decisions based on delayed information. Even a basic integration between these two systems can improve scheduling accuracy, reduce inventory carrying costs, and speed up order-to-shipment timelines.
2. Standardize definitions across sites. One of the most underrated integration challenges is when two plants measure Overall Equipment Effectiveness (OEE) differently. Before you can benchmark or share best practices across facilities, you need agreement on what the numbers mean. This is a people and process problem rather than a technology one.
3. Build decision routines around your data. For every dashboard or alert you create, define who owns the response, what the expected action is, and how fast it should happen. This is the step that separates companies that get ROI from their technology investments and those that just have expensive screens on the wall. 
4. Start with the problems that cost you the most. The IFT survey found that food manufacturers’ top motivators for digital transformation are improving production efficiency, enabling data-driven decision-making, and reducing costs. Those goals should drive your integration priorities. Instead of asking “what technology should we buy next?”, ask “what recurring problem would go away if the right people had the right information at the right time?”

If you’re finalizing technology budgets or roadmaps for the year ahead, run them through the integration maturity lens before you approve.

Getting the strongest returns this year will require spending more deliberately, closing the gaps that matter, and making sure every new connection translates into a better decision on the plant floor.

FAQ for food manufacturing leaders

Q: What’s the difference between digital transformation and integration maturity?

A: Digital transformation is the broad effort to adopt new technologies across your operation. Integration maturity is more specific. It describes how well those technologies (and your people and processes) work together. You can invest heavily in digital transformation and still have low integration maturity if your systems don’t share data or your teams can’t act on the information they receive.

Q: Do I need to replace my legacy systems to improve integration maturity?

A: Not necessarily. Many food manufacturers improve integration by adding middleware, application programming interfaces (APIs), or data platforms that connect existing systems rather than replacing them. The goal is to close the gaps that create the most friction. Sometimes that requires new technology. Often it requires better connections between what you already have.

Q: How do I know which integration gaps to prioritize?

A: Focus on the decisions that have the biggest impact on your bottom line. Where does your team spend the most time gathering information manually? Where do delays in data create downstream problems? Where do you see the same issue recurring across shifts or sites? Those patterns point to the integration gaps worth addressing first.

Q: What role does AI play in integration maturity?

A: AI is most valuable when it sits on top of a solid data foundation. If your systems aren’t connected and your data isn’t clean, AI tools won’t have much to work with. For food manufacturers at earlier stages of integration maturity, the priority should be getting core systems to share reliable data. AI becomes a powerful layer once that foundation is in place.

Q: How long does it take to see results from integration improvements?

A: It depends on the scope. Targeted integration projects, like connecting your ERP to plant floor systems, standardizing data definitions across sites, or building decision routines around existing dashboards, can show measurable improvements in weeks or months. The companies that see the fastest ROI tend to start small, prove value quickly, and scale from there.