What Most Food Manufacturers Get Wrong About Digital Transformation

Key takeaways:

• Digital transformation in food manufacturing requires building repeatable decision routines that improve yield, uptime, quality, and service.
• Many programs stall because they create alerts faster than the organization can respond, especially across shifts and sites.
• The most scalable approach starts by defining the decisions that matter, then designing the data, workflows, and ownership needed to execute them consistently.

Food manufacturers know the benefits of investing in new systems, connected equipment, more analytics, and more automation. But purchasing the technology is just the beginning of digital transformation. 

The real work starts after the tools go live, when the company has to turn new information into consistent actions on the plant floor and across the network. And safety, compliance, variability, and shift realities make that consistency challenging.

Investments like ERP upgrades, MES for production, sensors, dashboards, and other tools often prove their worth over time, but they don’t automatically fix performance upon install.

Instead, real value comes from how people operate around the technology:

• Who makes the call when the line goes bad?
• What’s the standard fix for repeat downtime?
• How do we clear quality holds faster without more risk?
• How do we share good ideas across sites?

Too many initiatives succeed technically but fail to boost the business because the decision infrastructure wasn’t designed as carefully as the software.

The 5 most common mistakes 

1. Confusing data visibility with decision advantage

Improving visibility on factors like line performance, downtime, quality events, and energy trends seems like an easy win, but visibility only becomes an advantage when it changes behavior.

If a particular metric moves out of range, do you have a plan for who is expected to do what and by when? Are these details part of regular discussions? If not, dashboards will likely become background noise instead of a driver of better outcomes.

So instead of starting with what results you want to see, think about what decisions you want to improve.

2. Running pilots that prove value but don’t create a scale path

Pilots tend to have dedicated attention, a good starter line, and extra troubleshooting capacity. Scaling, on the other hand, requires that pilot project to work on night shift, in peak season, and in plants with different equipment and habits.

A pilot is more likely to scale when it has:

• A clear workflow, including what changes in daily work
• A short, role-by-role adoption plan
• Standard definitions, so sites aren’t comparing different numbers
• Support and ownership once the pilot team moves on

3. Following vendor roadmaps instead of business-led decisions

Vendors are valuable partners, but product roadmaps naturally lead with features like modules, integrations, releases, dashboards, and new capabilities.

Food manufacturers tend to scale faster when the roadmap centers around decisions that create value, such as:

• Adjusting filler settings to reduce giveaway while protecting quality
• Tightening changeover decisions to reduce variability
• Prioritizing maintenance work based on failure risk and production impact
• Speeding up hold/release decisions with better context and traceability

This is also where smart manufacturing investments can become more than automation. With the right framework, predictive maintenance can create a bridge to broader operational intelligence, as Rob Ratterman, co-founder and CEO, Waites notes:

“Predictive maintenance is immediate, simple with the right vendor, and sets the stage for every other advanced manufacturing solution. By keeping your machines running, building confidence with easy wins, and letting data guide you into bigger, more transformative improvements, you’re setting your facility up for success both next year and every year down the line.”

4. Underestimating the human side of smart tools

Even the best system can fall flat if people don’t trust it, understand it, or know how to use it in the moments that matter.

This carries more weight as AI makes its way into more of our daily work. Eric Ibarra, Chief Technology Officer at Waites, points out 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.”

So rather than assuming a particular tool will make the right call on its own, build systems that amplify domain expertise. And plan for skill-building around interpretation and action, not just software training.

5. Measuring activity instead of the real progress signals

It’s tempting to report on simple numbers like lines connected or users trained, but leaders often learn more by measuring decision performance, such as:

• How fast issues move from detection to action
• How consistently teams follow the response playbook
• How often the same issue repeats and why
• Whether decisions are made using the system or outside it

These measurements are far more meaningful because they’re tied to how decisions change outcomes.

A transformation readiness checklist

Technology quickly loses its value if it doesn’t inform decisions, prompt strategic actions, and promote continuous improvement. 

Here’s a quick self-assessment designed to drive digital transformation success.

Questions to ask before approving significant investments

• What decision(s) will this improve, and what’s the baseline today?
• Who owns the outcome, and who owns the system?
• What is the response routine when the system flags an issue? (Where is it discussed? Who takes action? What’s the time expectation?)
• What will we standardize so this can scale to other lines or sites?
• What are the top two adoption risks, and how will we address them?
• How will we measure success in business terms?

Questions to ask before committing R&D or plant resources

• What plant time does this require and when (e.g., downtime windows, sanitation cycles, peak season)?
• What will change for operators, supervisors, quality, and maintenance on a normal day?
• What’s the minimum viable workflow we will support at go-live?
• How will we handle exceptions (like when the tool is wrong, data is missing, or the process breaks)?
• Who supports the plant after the pilot team steps back?
• How will we capture learnings so the next site goes faster?

FAQ for food manufacturing leaders

Q: How do we keep dashboards from becoming an interesting tool that goes unused?

A: Tie each key dashboard to a decision with an owner, a meeting or cadence where it’s reviewed, and a clear action path when something is out of range.

Q: Should digital transformation be led by operations or IT?

A: In practice, it works best as a joint effort where operations leads the outcomes and daily routines, while IT ensures reliability, security, and scalability. Operations leaders are often responsible for driving these initiatives, with collaboration across functions being a recurring need. 

Q: Where is a practical place to start?

A: Many teams start where the decision loop is straightforward and value is visible. This is often reliability, downtime reduction, or a constrained asset. Predictive maintenance is one example leaders often cite as an approachable entry point.

Q: How should we think about AI in food plants?

A: AI can be useful for pattern detection and repetitive analysis, but it tends to work best when it supports the judgment of experienced operators, maintenance teams, and quality leaders.