How Human-Centric AI Is Changing Decision-Making in Manufacturing and IoT

Industrial IoT has reached a moment of maturity.

Across manufacturing, energy, food and beverage, biofuels, and other complex industrial sectors, organizations have done the hard work. Sensors are in place. Connectivity is established. Data flows continuously from the plant floor into historians, cloud platforms, and analytics tools.

These investments worked.

Teams now have unprecedented visibility into operations. Performance can be monitored in real time. Trends are tracked across shifts. Alerts signal when conditions move outside expected ranges.

Yet many organizations are discovering something unexpected: despite all this data, day-to-day operational decisions are still difficult. Confidence is uneven. The same issues surface repeatedly. And the ROI promised by connectivity alone often feels just out of reach.

The problem is not data availability or infrastructure gaps. It is the gap between data and decisions — between seeing what is happening and knowing when to act, why it matters, and what action will actually help.

That gap defines the next phase of industrial IoT.

The Data Collection Era Delivered. What’s Next?

Most manufacturing organizations have moved beyond the “prove IoT works” phase. Dashboards provide visibility. Historians capture data. Analytics platforms generate reports.

These systems accomplished exactly what they were designed to do: they made operations visible and data accessible.

What remains is a more fundamental challenge. Now that data is flowing, how do teams consistently turn it into better decisions, faster, without adding complexity or risk?

This is not primarily a technology problem. It is a translation problem.

Reports are generated. Alerts fire. Metrics update. But when an operator needs to decide whether to adjust a setpoint, investigate a trend, or wait for the next shift, the data often does not provide a clear answer.

That gap forces experienced personnel to rely on instinct in situations where insight could help. It also makes it harder for newer team members to develop the pattern recognition that seasoned operators build over years.

Beyond the Visibility Plateau

Traditional industrial IoT platforms and analytics tools have delivered real value by improving monitoring and transparency. Dashboards, alerts, and reports help teams see what has happened and identify when predefined thresholds are crossed.

These capabilities are foundational. The question is whether they are sufficient.

Visibility alone rarely answers the questions operators and engineers face in real time:

• Is this change normal variability or early process drift?
• Why did performance differ across shifts, batches, or operating conditions?
• Which action matters now, and which can wait?
• Is this signal meaningful, or simply noise?

In environments with continuous, biological, or highly variable processes, teams still rely heavily on experience and intuition to fill these gaps. That reliance is not a failure of existing technology. It is a signal that the next layer of capability — decision support that works on top of existing infrastructure — has not kept pace with data collection.

This is the visibility plateau: transparency without sufficient context to act confidently.

From Analytics to Decision Intelligence

Decision intelligence represents an evolution in how industrial IoT data is used. It does not replace existing systems. It builds on them.

Rather than focusing solely on point metrics and thresholds, decision intelligence observes process behavior over time using data already flowing through historians, SCADA systems, MES platforms, and analytics tools.

It learns how stable, high-performing operations behave under real conditions and surfaces meaningful deviations as they emerge — without requiring new sensors, replacing platforms, or disrupting workflows.

The distinction matters because industrial processes are dynamic systems, not static snapshots. A single temperature reading means little without understanding load, operating mode, material variability, and recent history.

In practice, decision intelligence helps teams:

• Distinguish normal variability from meaningful instability
• Detect early signs of drift before alarms or quality losses occur
• Understand why performance changes, not just that it did
• Prioritize actions based on impact, timing, and risk

These capabilities are especially valuable in processes where cause and effect are delayed, nonlinear, or influenced by human actions — exactly where dashboards and threshold-based alerts tend to fall short.

What We’ve Seen Work in Practice

Across industries, a consistent pattern emerges: AI delivers the most value when it integrates with existing infrastructure and starts by understanding the process, not by attempting to control it or replace established systems.

Teams gain confidence when AI:

• Works with existing data sources
• Learns how the process behaves when it is operating well
• Flags deviation early, before it becomes obvious or costly
• Explains changes in language operators and engineers understand
• Connects operational behavior to business outcomes
• Operates transparently, so insights can be validated against experience

Ken Garfinkle, CIO at Broan-NuTone and a Golgix customer, described the shift this way:

“Golgix helped us operationalize AI rapidly, monetize untapped data, and equip our teams with predictive intelligence to drive productivity up. We’ve expanded scope twice.”

From the operations side, Ellie Antova, General Manager at Pellet Technologies and a Golgix customer, shared a complementary perspective:

“Our operations reports now show production, team performance, and AI-driven recommendations. I can log in on my phone and see what’s happening instead of relying on gut feel. We also uncovered unexpected value — inventory management, raw material optimization, even training differences between shifts.”

Together, these experiences highlight a broader shift: from reacting after performance suffers to making informed decisions earlier, using infrastructure already in place.

Why Human-Centric AI Matters

As AI adoption accelerates across industrial IoT, trust becomes the limiting factor.

Human-centric AI is designed to support decision-making rather than replace it. Instead of issuing opaque recommendations, it provides context, explanation, and traceability so teams understand what is happening and why.

Manufacturing environments are physical systems operated by people who carry institutional knowledge and accountability. AI that ignores this reality will be worked around, not worked with.

Human-centric AI:

• Respects operator expertise
• Makes insights interpretable and defensible
• Encourages adoption rather than resistance
• Enables learning, not just automation
• Integrates with existing systems

This approach turns AI into a partner that strengthens human judgment instead of undermining it.

Process Context Over Point Metrics

One of the most persistent challenges in industrial analytics is treating data points in isolation.

A pressure alarm may fire, but is it due to equipment wear, upstream changes, ambient conditions, or a normal operating transition? Dashboards surface the alarm. They do not provide the context.

Decision intelligence focuses on process context, recognizing patterns across time and conditions using existing data. This allows teams to determine whether a deviation is localized or systemic, temporary or structural.

Context reduces noise and increases confidence — not by adding more data, but by making sense of what already exists.

Complementing Automation, Not Rushing It

Automation and advanced control have their place, but they are not always the first or safest step.

Regulatory requirements, safety considerations, and operational risk often demand a measured approach. Many organizations want to understand and stabilize processes before allowing systems to act autonomously.

Human-centric decision intelligence delivers value immediately by improving decision quality today, while supporting automation strategies when organizations are ready.

What This Means for the Future of Industrial IoT

As industrial IoT matures, differentiation will come from how well platforms support human decision-making, not from how much infrastructure they require.

The most effective solutions will:

• Build on existing investments
• Reduce cognitive load
• Highlight signal over noise
• Work alongside people
• Deliver value without demanding blind trust

The next phase of industrial IoT is not about collecting more data. It is about helping people make better decisions with the data they already have.

Closing Thoughts

Industrial IoT has built a strong foundation of connectivity and visibility. The opportunity now lies in elevating how that foundation is used.

By pairing existing infrastructure with human-centric AI that understands process behavior and respects how people work, manufacturers can move from seeing operations to truly understanding them — and improving them sustainably.

The sensors are in place. The data is flowing.
The next step is turning insight into confident action.

Jessica Morrison is Vice President of Strategic Partnerships & Growth at Golgix, an industrial AI company focused on human-centric decision intelligence for complex manufacturing processes. Golgix works across industries to help teams detect process drift early, understand variability, and make better decisions using existing operational data — without replacing infrastructure, displacing people, or forcing premature automation.