The AI Hype at Pack Expo

October 8, 2025

The AI Hype at Pack Expo — And Why It’s Mostly Smoke

Walk the aisles at Pack Expo these days, and you’ll AI Robot see it everywhere: “AI-powered this,” “machine learning that,” “autonomous intelligence embedded.” It’s become the theme du jour, the umbrella under which nearly every technology—and every exhibitor—wants to live.

But here’s the painful truth: for most companies, AI is acting less like a transformative engine and more like a fancy search box.

That may sound harsh, but it’s often accurate. Many booths and solutions pitch predictive models, automated insights, or “smart decisioning,” but in practice what’s shown is usually a data lookup + simple rule logic wrapped in a slick UI. None of which is inherently bad—but it’s not the full promise people expect.

At Pack Expo, the buzz is different than in actual use. We see this in three main ways:

People treat AI proposals like they’re guaranteed to solve complex problems almost automatically, with little human oversight.
Some teams adopt AI features too confidently, assuming they’re “safe” or “obvious,” leading to errors or misinterpretations.
Others stall entirely, waiting for “real AI” to arrive instead of doing the foundational data work that should come first.

In this post, we want to examine why that disparity exists, what we should push back on, and how teams can move forward sanely—especially in spec and packaging data worlds.

The Promise vs. The Reality: Why AI So Often Under-delivers

1. Hype cycles and AI washing

We’ve seen this pattern before: a new buzzword emerges, companies rally behind it, every vendor claims to “use AI,” and pretty quickly expectations diverge from outcomes. The term AI washing describes when vendors exaggerate or misrepresent how much AI is involved in their products.

At Pack Expo, you’ll hear “our system uses machine learning” as if that’s enough. But often what they mean is: “We use rules + statistical scoring + heuristics under the hood, and we call it ML.”

That’s not inherently wrong; just don’t pretend it’s autonomous or magical.

2. Garbage in, garbage out

No AI model can fix messy, inconsistent, or incomplete data. In spec systems, packaging data, regulatory attributes—they tend to be rife with gaps, subjective fields, unstructured attachments, and change logs without lineage. AI thrives on clean, structured, annotated data. When your dataset is messy, AI will hallucinate or default to the average.

Studies in business analytics show that for many structured data problems, traditional models (logistic regression, decision trees, ensemble methods) often equal or outperform deep learning in accuracy, interpretability, and reliability.

So employing “AI” as a band-aid over data issues is a risky move.

3. Overconfidence and “blind trust”

When people see the “AI” label, they tend to assume it’s smart, accurate, or infallible. That leads to decisions made with a blind trust that the machine has it right—especially for people without domain expertise or risk awareness.

In practice, model predictions need review, context, thresholds, and guardrails. But many Expo products don’t emphasize human-in-the-loop controls—they sell full automation instead.

4. The “cool use case” trap

Many booths demo AI in contexts where it’s easy to make things look impressive: predictive dashboards, visualizations, scorecards. But once you push that into real operations—where integration, edge cases, missing data, versioning, anomalies, auditability, and error handling matter—those models often fail or require heavy tuning.

What We Observed at Pack Expo

AI robots pack expo

While we can’t name specific companies, here are patterns we saw in conversations, demos, and hall passes:

Booths offering “AI for packaging compliance” showed dashboards with colored risk scores, but when questioned, their logic was mostly rule-based with thresholds.
Smart search engines were selling “spec retrieval via AI,” but in demos they mostly did fuzzy matching, synonym lookup, and prioritized results via popularity. Nothing predictive.
Some vendors claimed automatic spec correction (e.g., adjusting attribute values). When asked about training data or error rate, they deferred: “we’ll refine after deployment.”
Attendees telling me: “We’d love to use their AI feature, but we can’t until our data is better.” That’s often true—even the best model can’t fix disconnected data pipelines.

These observations echo what analysts have been saying: that AI is overpromised and underdelivered in many industrial and manufacturing applications.

Why People Still Buy Into It (Despite Limitations)

You may wonder: with all those caveats, why do so many people adopt AI features eagerly?

Psychological appeal: AI sounds futuristic, smart, and “trend-forward.” It gives a label that signals cutting-edge status.
Vendor pressure and competitive arms race: If one exhibitor says “AI,” others feel compelled to catch up.
Marketing leverage: “We use AI” is a powerful tagline, even if what’s behind it is modest.
Incremental gains: Even limited AI (recommendations, matching, ranking) can yield productivity boosts if applied carefully.
Fear of being left behind: Companies worry that not adopting AI will make them look obsolete—so they dive deeper even with weak ROI.

But the danger is making important decisions based on weak or unverified AI outputs—especially in regulated, compliance-heavy, data-critical domains like packaging, EPR, specs, and sustainability.

What Should You Do Instead (So You Don’t Get Burned)

Here’s how to approach “AI at Expo” demos and vendor claims with healthy skepticism, and how to push your own organization toward practical use.

1. Demand transparency, not hype

Ask vendors for:

Training data sources
Accuracy metrics / error rates
How they handle anomalies, outliers, missing values
Versioning, audit trails, and “explainability” features
What happens when the model fails — fallback, human override

If they talk only about “self-learning,” “autonomous,” or “predictive” without those details, press harder.

2. Start small, pilot, validate

Don’t bet your entire spec system’s future on generative AI. Instead:

Pick a narrow use case you know (ex: material matching, tolerance validation, spec search ranking).
Run parallel manual + AI for a period. Compare results.
Hold “failure review” meetings to catch where AI went wrong.
Only scale once confidence, error bounds, and processes exist.

3. Invest in data infrastructure first

Make sure your underlying data architecture is strong:

Clean master records
Controlled vocabularies and dictionaries
Relationship mapping
Change workflows
Data governance (who owns what, approvals, version control)

If data isn’t good, AI is a band-aid, not a solution.

4. Always embed human review

Even if a model is 90% accurate, that 10% error may cost you compliance violations, waste, or regulatory fines. In domains like packaging or EPR, humans must sit in the loop for exceptions or edge cases.

5. Monitor model drift and feedback loops

Models degrade over time. If you change spec practices or update materials, retraining is required. Maintain feedback loops so users flag bad predictions, and have a process for continuous improvement.

6. Don’t outsource ownership entirely

Even if a vendor provides “AI modules,” your team must own oversight, auditing, and risk controls. Don’t be the “operator” with no visibility.

Reframing AI: It’s Not the Destination, It’s an Accelerator

AI used as acceleration by humans Here’s a better mindset:

AI is an accelerator—not a miracle tool.

Your ultimate success comes from data discipline + user workflows + governance.

AI should amplify what’s already working, not mask what’s broken.

At Pack Expo, people treat “having AI” like a checkbox. But real competitive advantage will come from people who balance AI + policy + domain context, not those who blindly trust it.

So if you’re walking the floor, engaged in demos, or judging your next vendor:

Ask questions, not pitches.
Don’t get starry-eyed at terms like “autonomy” or “predictive.”
Insist on pilot results, audit access, and fallback controls.

Because the next few years will sort out who used AI wisely—and who confused buzz with substance.

Here’s to building smarter, not just more marketed.

Here are URLs to some of the sources referenced (or similar ones that support the arguments) in this blog:

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Posted in Business Process