Finding Value with AI in an Industry 5.0 Transformation: From ‘Automation for Savings’ to Human-Centric Growth

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On February 26, 2026, MIT Technology Review published an item titled “Finding value with AI and Industry 5.0 transformation”. The piece sits in the increasingly crowded intersection of industrial transformation, AI adoption, and that somewhat mischievous phrase executives love: “value realization.”

Unfortunately for reporters (and fortunately for paywalls), the full Technology Review page is not directly accessible to me due to site restrictions, so I’m relying on other verifiable sources to build a fact-checked, expanded analysis. Still, the headline and syndicated excerpt indicate a clear thesis: Industry 5.0 is pushing companies to measure AI success not only in cost savings and efficiency, but also in growth, resilience, and human-centric outcomes—including how people and machines work together. citeturn3search1turn0search0

That’s a meaningful shift. Industry 4.0 was often sold as “connect everything, automate everything, and congratulations, you now own a smart factory.” Industry 5.0, in contrast, is increasingly framed as a course correction: keep the data, AI, robotics, and digital twins—but design the system around human well-being, sustainability, and resilience, not just throughput.

Let’s unpack what that actually means, why it matters now (in 2026, not in some hazy “future of manufacturing” keynote), and how industrial leaders can build AI programs that don’t die as expensive proofs-of-concept. We’ll also talk about the uncomfortable parts: security, governance, and the fact that “human-centric” can become marketing confetti unless you operationalize it.

Industry 5.0: Not a Sequel, More Like a Patch Release

The most useful way to understand Industry 5.0 is to stop thinking of it as “Industry 4.0, but with more AI.” The European Commission (EC) has been explicit that Industry 5.0 complements and extends Industry 4.0 and puts emphasis on three pillars: human-centricity, sustainability, and resilience. citeturn0search0turn0search5

In the EC framing, the industrial system is not just judged by output and productivity. It’s also judged by whether it respects planetary boundaries and whether it keeps workers safe, skilled, and meaningfully involved. citeturn0search0

Industry 4.0’s success—and its blind spots

Industry 4.0 made a lot of things real: connected sensors (IIoT), cheap compute, cloud analytics, OT/IT convergence, cyber-physical systems, and—more recently—machine learning models that can spot defects, forecast equipment failures, and optimize energy use.

But it also developed habits that Industry 5.0 is trying to break:

  • Efficiency obsession (and an allergy to anything not immediately measurable on a spreadsheet).
  • Automation-first design that treats people as a cost center to be minimized.
  • Fragile supply chains optimized for cost, not for shocks and disruptions.
  • Security debt from connecting legacy industrial systems to modern networks without modern guardrails.

In academic and industry literature, Industry 5.0 is often described as a response to these limitations, tying industrial transformation to sustainability and resilience goals and re-centering humans in the loop. citeturn0search2

What the MIT Technology Review Item Signals (Even Through the Paywall Fog)

A syndicated excerpt (republished elsewhere) attributes a quote to Sachin Lulla, EY’s Americas Industrials and Energy Transformation Leader, arguing that to realize Industry 5.0, companies need to go beyond cost and efficiency toward growth, resilience, and human-centric outcomes—plus new ways of working where people and machines collaborate. citeturn3search1turn3search0

This is consistent with what we see across industrial AI programs: the “find a use case, automate a task, save some money” approach can work, but it’s not enough to justify long-term platform investment. Once the first few use cases are done, the organization hits a wall unless it has a broader operating model for AI.

Even more importantly, when the value story is only “reduce headcount” or “reduce downtime,” it tends to trigger internal resistance—especially from the people whose process knowledge you need for models to work. Industry 5.0’s human-centric framing is partly moral and partly practical: if you want industrial AI to scale, you need workers to trust it, use it, and help improve it.

Defining “Value” in Industrial AI: A Better Scorecard

AI value in industrial settings is notoriously easy to overstate and surprisingly hard to realize. Not because the algorithms are useless, but because the factory is a real place with real constraints: safety rules, aging equipment, network segmentation, regulatory oversight, and production schedules that don’t pause for your MLOps sprint.

Industry 5.0 encourages organizations to expand their AI value scorecard beyond traditional KPIs. Here’s a pragmatic framework that maps well to the EC’s pillars. citeturn0search0

1) Growth value (not just “savings” value)

Cost reduction is the easiest story to sell—until you run out of costs to cut. Growth value includes:

  • New service revenue (e.g., predictive maintenance sold as a service, outcome-based contracts).
  • Higher product quality that enables premium pricing or reduces warranty claims.
  • Faster time-to-market using simulation, digital twins, or accelerated design loops.

Digital twins—virtual representations used for simulation and optimization—have become a major building block in this story, especially when combined with AI for forecasting and decision support. citeturn3search2

2) Resilience value (the KPI you only love after a disaster)

Resilience is not just “backup suppliers.” In an AI-enabled Industry 5.0 context, it can mean:

  • Faster changeovers and reconfiguration of production lines.
  • Better demand sensing and inventory positioning.
  • Early warning systems for equipment, quality drift, or supply disruptions.

The EC’s Industry 5.0 messaging explicitly highlights resilience and supply chain robustness as outcomes of a human-centric, sustainable transformation. citeturn0search0turn0search5

3) Human-centric value (safety, augmentation, retention)

Industry 5.0 doesn’t mean “less automation.” It means more deliberate automation. Human-centric value shows up as:

  • Reduced safety incidents via better sensing, warnings, and safer human-robot collaboration.
  • Augmented operators with decision support, guided workflows, and on-the-job learning.
  • Higher retention because jobs feel less like wrestling with variability and more like supervising a system.

The EC highlights optimizing human-machine interactions as a way to empower workers rather than replace them. citeturn0search0

4) Sustainability value (beyond carbon dashboards)

Industrial sustainability can be a compliance exercise—or a genuine transformation lever. AI can support sustainability by:

  • Energy optimization (especially with dynamic pricing and peak shaving).
  • Material efficiency and scrap reduction.
  • Predictive quality to reduce rework and waste.

Industry 5.0’s sustainability pillar is tightly linked to circular processes and resource efficiency, including redesigning value chains and production methods. citeturn0search0turn0search4

The “AI + Factory” Reality Check: Why Value Gets Stuck

Every industrial AI program eventually meets the same three monsters. They are not glamorous, but they are undefeated in the wild.

Monster #1: Data exists, but it’s not usable

Factories generate mountains of data—PLC signals, historian logs, MES events, SCADA alarms, maintenance notes. But the data is often:

  • Missing context (units, calibration, operating mode).
  • Not synchronized (time drift across systems).
  • Locked in vendor silos.
  • Not labeled (and labeling is labor-intensive).

This is why Industry 5.0 conversations increasingly turn into data governance conversations—who owns what, who can share what, and how trust is maintained across the data-service-knowledge stack. Recent research surveys argue that trustworthy industrial intelligence requires integrated governance across these layers. citeturn3academia13

Monster #2: Models work in a notebook, then die in production

Industrial AI doesn’t fail because gradient descent is broken. It fails because deployment is hard:

  • OT environments are segmented for safety and reliability.
  • Edge constraints limit compute and connectivity.
  • Change management is slow (for good reasons).
  • Models drift as equipment ages, recipes change, and suppliers vary.

Scaling requires MLOps discipline, monitoring, and retraining strategies that don’t interrupt production. Industry 5.0 effectively raises the bar: your AI must be not only accurate, but dependable, explainable enough for operators, and safe enough for regulated environments.

Monster #3: Trust, safety, and security are bolted on too late

The more you connect industrial systems, the more you expand attack surfaces. At the same time, AI systems introduce new failure modes: opaque decisions, data leakage, and brittle behavior under unusual conditions.

For organizations trying to operationalize “trustworthy AI,” NIST’s AI Risk Management Framework (AI RMF 1.0) is a widely referenced voluntary framework, released on January 26, 2023, intended to help manage risks to individuals, organizations, and society from AI systems. citeturn1search0turn1search1

In industrial settings, the key point is not “comply with a framework.” The point is to build repeatable practices: risk assessment, model evaluation, incident response, and clear ownership.

Industry 5.0 Use Cases Where “Value” Looks Different

Let’s make this tangible. Below are AI use case clusters where Industry 5.0 shifts the target metric from “faster/cheaper” to “better outcomes for people, resilience, and sustainability.”

1) Predictive maintenance → Predictive reliability

Classic Industry 4.0 pitch: predict failures to cut downtime. Industry 5.0 upgrade: predict failures and redesign work so maintenance teams are safer and less reactive.

  • Human-centric twist: reduce night callouts; improve planning; cut hazardous emergency interventions.
  • Resilience twist: maintain production stability during supply shocks by extending asset life intelligently.
  • Sustainability twist: reduce waste from catastrophic failures and unnecessary part swaps.

2) Computer vision quality → “right-first-time” manufacturing

Vision systems can spot defects faster than humans in many contexts, but the best deployments treat AI as a second set of eyes that helps operators catch issues early and understand root causes.

  • Human-centric: fewer repetitive inspection tasks; more diagnostic work.
  • Growth: higher yield and better customer satisfaction.
  • Sustainability: less scrap and rework.

3) Scheduling optimization → shock-aware planning

Traditional scheduling optimizes for utilization. Industry 5.0 scheduling includes uncertainty: supplier variability, workforce availability, energy constraints, and cyber incidents.

This is where “resilience value” becomes measurable: time-to-recover, not just OEE.

4) Human-robot collaboration (cobots) → safer and more inclusive work

Human-robot collaboration is frequently cited as a key element in human-centric manufacturing programs supported by EU research initiatives. citeturn0search5

The Industry 5.0 promise isn’t “robots everywhere.” It’s “robots where they make work safer and more ergonomic,” including:

  • Material handling in constrained spaces
  • Assistance for repetitive strain tasks
  • Precision placement where fatigue causes errors

And yes, it requires training. Some surveys suggest that training and workforce readiness lag behind adoption ambitions, which should surprise exactly no one who has ever watched a cobot get introduced on a line without involving the people who run the line. citeturn3search2

A Practical Roadmap: How to Capture AI Value in an Industry 5.0 Program

Industrial leaders often ask for a “roadmap,” which is executive-speak for “please reduce this chaos into a list I can put in a slide deck.” Here’s one that’s actually useful.

Step 1: Start with a value hypothesis, not a model

Write down the value hypothesis in plain language:

  • What outcome improves?
  • Who benefits (operators, maintenance, planners, customers)?
  • How do we measure it?
  • What risks could negate the value (safety, false alarms, cyber exposure)?

This aligns with the Industry 5.0 emphasis on measuring outcomes beyond dollars saved—human-centric and resilience outcomes included. citeturn3search1turn0search0

Step 2: Pick “platform + lighthouse” together

A lighthouse project without a platform is a demo. A platform without a lighthouse is a science fair.

You need both:

  • Lighthouse: one use case with real operational impact in 90–180 days.
  • Platform: data pipelines, model deployment, monitoring, identity and access controls, and documentation patterns you can reuse.

Step 3: Build human-centric design into the workflow

Human-centric doesn’t mean “ask for feedback once.” It means:

  • Operators are co-designers of alerts and interfaces.
  • Models provide actionable explanations (not necessarily full interpretability, but enough for decisions).
  • Training is part of deployment, not an afterthought.

EU initiatives around human-centric manufacturing explicitly emphasize worker empowerment, skills, and human-robot collaboration in open, smart environments. citeturn0search5turn0search0

Step 4: Treat AI governance like safety engineering

If you’re deploying AI into industrial processes, governance should feel closer to safety engineering than to “model approval committees.” Use a risk-based approach and define:

  • Model change control (who can update, when, and how tested).
  • Incident reporting and rollback procedures.
  • Performance thresholds and alarms.
  • Security and privacy controls, especially if data crosses enterprise boundaries.

NIST’s AI RMF positions itself as voluntary guidance for managing AI risks across sectors and use cases, which can help structure governance conversations. citeturn1search1

Step 5: Expand from use cases to capabilities

The mature endpoint is not “we have 47 AI models.” It’s:

  • We have a repeatable pipeline from problem to deployment.
  • We can prove value and manage risk.
  • We can onboard plants faster than we build slides.

The Security Angle: Industry 5.0 Can’t Ignore Cyber Reality

Industry 5.0 discussions sometimes sound pleasantly optimistic: humans and machines collaborating, sustainability everywhere, resilient supply chains humming like a well-tuned Kubernetes cluster. But the industrial world is also a target-rich environment.

As you connect OT systems and deploy AI, you have to account for:

  • Model manipulation (data poisoning, adversarial inputs)
  • Data leakage (sensitive process data, IP, supplier information)
  • Inference-time attacks (especially with vision systems)
  • Expanded remote access and identity risks

Research continues to point out gaps between governance frameworks and real-world AI security needs, particularly as AI systems are integrated into critical infrastructure contexts. citeturn1academia13

My reporter’s translation: if your AI program doesn’t have a security partner from day one, you’ll either ship something risky—or ship nothing at all once security reviews finally show up.

Expert Perspectives: What the Ecosystem is Signaling

Industry 5.0 is not owned by any single vendor, standards body, or consultancy. It’s an ecosystem storyline—sometimes coherent, sometimes opportunistic.

The EU perspective: societal outcomes matter

The European Commission’s Industry 5.0 concept is explicit about a broader purpose for industry: prosperity that respects planetary boundaries and worker well-being, with resilience as a first-class goal. citeturn0search0

The enterprise transformation perspective: value is shifting to growth

From the EY angle—reinforced by the quote attributed to Sachin Lulla—the message is that industrial leaders must reframe AI value toward growth and new opportunities, not just savings. citeturn3search1turn3search0

This aligns with broader survey work published by EY on enterprise AI adoption, suggesting increasing investment levels and a push toward scalable foundations (data infrastructure, workforce capabilities) rather than isolated experiments. citeturn3search3

The researcher perspective: trustworthy AI is not optional

Academic reviews increasingly emphasize that Industry 5.0 adoption should incorporate trustworthy AI principles early, highlighting gaps and challenges moving from Industry 4.0 to Industry 5.0. citeturn3academia14

What to Watch in 2026: The Industry 5.0 “Second Act”

If Industry 4.0’s first act was connectivity and automation, Industry 5.0’s second act is likely to be about governance, interoperability, and measurable outcomes. Here are a few trends that look particularly consequential:

  • Cross-enterprise data sharing with constraints: more need for federated or policy-controlled architectures, especially in supply networks. citeturn3academia13
  • Digital twins moving from pilots to operational decision-making: not just visualization, but closed-loop optimization tied to real KPIs. citeturn3search2
  • Human-robot collaboration scaling with better UX and safety models: less “robot cage theater,” more integrated shop-floor design. citeturn0search5
  • AI risk management becoming standard practice: with frameworks like NIST AI RMF used as a common language between engineering, legal, security, and operations. citeturn1search0turn1search1

Conclusion: Industry 5.0’s Real Question Is Who Benefits

Industry 5.0 can sound like a rebrand. And yes, there will be rebranding. There will be slide decks. There will be at least one factory tour where someone points at a dashboard and whispers, “AI.”

But the underlying shift is real: the industrial world is recognizing that AI transformation must be judged by more than operational efficiency. It must also produce resilience in a volatile world, sustainability in a resource-constrained world, and human-centric outcomes in a labor market where skills, safety, and dignity are not optional.

The MIT Technology Review item—“Finding value with AI and Industry 5.0 transformation”—is a timely reminder that value is not a property of the model. It’s a property of the system: people, processes, governance, and technology working together.

And if you want the mildly funny tech reporter takeaway: Industry 5.0 is what happens when factories realize that “move fast and break things” is a lot less charming when “things” includes conveyor belts, safety interlocks, and occasionally, the laws of physics.

Sources

Bas Dorland, Technology Journalist & Founder of dorland.org