Hidden Cost of Digital Twins: Beyond Its Benefits

Key Takeaways

• Digital twins mirror physical systems in real time, enabling predictive maintenance, efficiency, and safety gains.

• Case studies from various industries reveal clear, data-driven benefits of digital twins, such as less downtime, greater energy efficiency, more efficient workflows, and quicker time-to-market.

• Legacy systems often lack compatibility with digital twin platforms, requiring expensive upgrades and maintenance.

• As tasks move from manual execution to data-driven oversight, employees must adapt to new, more analytical responsibilities.

• Long-term success in integrating digital twins depends on balancing innovation with empathy, supporting both systems and the people behind them.

No one can predict their future when technology races forward, relentlessly. A friend of mine, working alongside a pump technician at a refinery, shared a story that didn’t sit right with me.

A digital twin (a virtual mirror of the plant’s machinery) flagged a pressure spike in a pump. His coworker, sharp and seasoned, jumped into action, resolving the issue and averting a catastrophic failure that could’ve cost millions of dollars an hour in lost production. Colleagues nodded in quiet approval, a rare moment of pride in the grind.

Two weeks later, that same coworker was called into HR and told he was terminated. The digital twin, the very system he’d helped succeed, now automated his diagnostics. A hero one day, obsolete the next.

Yes, technology drives production to new heights. But when transformation comes at the cost of eliminating human roles entirely, can we still call it remarkable innovation?

What Is Digital Twin Technology?

Digital twins are virtual representations of real-world systems, processes, or assets that are continuously updated with live data. I’ve seen different types of digital twin models, which enable organizations to simulate and optimize everything from individual machines to entire factory floors or infrastructure networks.

The National Institute of Standards and Technology (NIST) explains that these digital replicas integrate IoT sensors, AI, and advanced simulation models to create dynamic, predictive systems that evolve alongside their physical counterparts. From aerospace and automotive engineering to smart cities and healthcare, digital twins are becoming foundational to the next generation of operational intelligence.

The projected jump of digital twins’ global market from $17 billion in 2024 to over $259 billion by 2032 makes it one of the fastest-growing segments in industrial technology.

The Double-Edged Promise

Having personally witnessed the emergence of new technologies in the workplace, I’ve seen how organizations that adopt digital twins gain significant advantages.

This naturally leads to a key question: why are so many businesses embracing this technology now? The answer lies in its compelling promise:

• Predictive Maintenance: NIST notes that digital twins can reduce maintenance costs by 30% by forecasting failures with high precision, minimizing unplanned downtime.

• Cost Efficiency: The Manufacturing Leadership Council (MLC) highlights that digital twins significantly enhance operational efficiency by enabling real-time monitoring and predictive analytics, allowing manufacturers to optimize processes and reduce costly disruptions.

• Safety Gains: Digital twins also improve safety around the workplace, reducing the need for manual inspections in hazardous environments and thereby lowering risks to workers. 

• Innovation Edge: Simulation-driven design accelerates product development, cutting research and development costs. As Anand Rao, a professor of applied data science & AI at Carnegie Mellon University, notes:

"They could do forward projections, look at interventions, introduce a specific change to see the impact; they could [ask] whether doing X leads to reaching their sustainability goals, that’s something industries are just beginning to use digital twins to do."

Gains on the Surface

Several companies are already ahead of the curve and are now reaping the benefits of deploying digital twins on the work floor.

At its electronics factory in Erlangen, Germany, a major tech company implemented an end-to-end digital twin to model, simulate, and optimize every stage of production. The system enhances the planning phase, predicts disruptions, and streamlines material flows. 

This has led to a 40% reduction in material circulation, a 10% boost in AGV performance, and up to 70% savings in ventilation energy. The digital tech system is also able to shorten the process of optimization from months to days, supporting both sustainability goals and faster time-to-market.

An American aircraft engine supplier is also leveraging digital twins to track each engine's lifecycle from assembly through retirement. Each engine is assigned a digital “thumbprint,” a living model that evolves in real time based on telemetry, usage patterns, and environmental conditions. These models allow engineers and operators to simulate stress scenarios, predict failures, and tailor maintenance schedules with surgical precision, and as a result:

• Unnecessary repairs are avoided, as the digital twin distinguishes between routine wear and early signs of critical degradation

• Engine lifespan and reliability increase, since parts are only replaced when data supports it, rather than on fixed intervals.

• Technicians can plan and execute service interventions with minimal physical inspection, minimizing on-site service time and enhancing safety in hazardous environments.

• Sustainability improves, as fewer parts are wasted and engines remain in service longer, reducing resource consumption and carbon output.

Aside from these sectors, I’ve also heard from colleagues in the field about logistics firms making real progress. One of them mentioned an international shipping company that’s using digital twins to simulate entire cargo routes, accounting for weather, fuel efficiency, and customs data. The result: faster delivery times, fewer fuel stops, and lower carbon emissions.

The Hidden Cost of the Digital Twins

But despite their advantages, integrating digital twins with legacy systems isn’t simply plug-and-play. Outdated control systems, disconnected factory software, and inconsistent data flow all make integration difficult. Without reliable, well-structured data, a digital twin quickly becomes ineffective.

• Ongoing recalibration needs: Even after deployment, digital twins aren't static. Small variations, like changes in tooling, shifts in material quality, or natural wear and tear on machines, can cause the virtual model to drift out of sync with reality. To stay accurate, these systems require continuous updates and recalibrations, often needing the expertise of expensive simulation engineers.

• High upfront and hidden costs: Launching a digital twin system isn't cheap. The expenses quickly add up from sensors, software integration, and cloud infrastructure. Even pilot projects can reach six-figure costs. Retrofitting older equipment to produce usable, reliable data often increases the investment further.

• Cybersecurity vulnerabilities: Connecting physical systems to a digital ecosystem expands the attack surface. Every new sensor or software link becomes a potential point of cyber intrusion. For industries used to isolated, offline systems, this shift brings substantial security and insurance concerns.

• Talent scarcity: Running a digital twin demands a hybrid skill set, which means professionals who understand both the physical processes of manufacturing and the digital tools powering simulations are vital in the workforce. This type of cross-disciplinary talent is rare and expensive, forcing companies to invest heavily in recruitment or upskilling.

These challenges don't diminish the potential of digital twins. But they do require careful planning and commitment. 

Beneath the Wins: Workforce Disruption

Beyond the technical challenges, there are also workforce considerations. Automation may not eliminate entire roles, but it often redistributes key responsibilities, requiring employees to adapt to evolving job functions and expectations. 

These role shifts are apparent and are currently happening in various sectors:

Companies embracing this advanced technology to enhance their workforce are pointing toward a redefinition of roles and responsibilities. But behind this transformation lie the real human costs, which are often oversimplified when described as mere reassignments:

• Skill Gaps and Reskilling Pressure: Employees are expected to rapidly acquire new digital and analytical skills. Those unable to transition may face marginalization or job loss.

• Technostress and Burnout: The constant need to adapt to evolving systems and oversight roles can create stress, particularly among less tech-savvy workers.

• Ethical and Psychological Concerns: Increased monitoring through Digital Twin platforms can blur the line between efficiency and surveillance, impacting autonomy and trust.

• Widening Inequity: Digital Twin systems could create high-tech jobs that mainly benefit workers who are already skilled, making the digital divide within and between organizations even worse.

• Cultural Displacement: Long-standing practices rooted in craftsmanship and experiential intuition may be undervalued or lost, leading to identity shifts within the workforce.

These impacts aren't abstract. In manufacturing towns, displaced workers often struggle to find comparable employment. In healthcare, staff may feel overwhelmed when AI systems dictate the workload at present. The shift in power, from human judgment to machine prediction, creates new tensions.

Navigating the Trade-Offs: Efficiency vs. Humanity

The advantages are undeniable, putting executives and engineering leads in a difficult position. As digital twins offer tighter budgets, fewer injuries, and higher precision, workplace tensions emerge. This leads to leadership challenges: how to ensure people remain empowered in increasingly automated environments.

While digital twins excel in optimizing machine operations, supply chains, and maintenance cycles, they also reshape roles that were once deeply human. Just like the pump technician who once relied on instinct and years of hands-on experience, he now finds himself jobless because he has been replaced by a data-fed machine.

In offices, similar scenarios can also occur for planners and analysts. They may face similar displacement, with predictive models taking over what were once intuition-driven strategies.

This efficiency shift isn’t inherently dehumanizing, but it demands intentional leadership. Organizations must resist the impulse to chase automation for its own sake. Instead, the goal should be augmentation: using digital twins to elevate human judgment, not replace it. 

What Responsible Integration Looks Like

Integrating digital twins responsibly involves more than installing software or hardware. It requires organizational alignment, ethical foresight, and human-centered policy. 

Several ways a company can make the transition successful include:

• Retraining Programs: Manufacturers can offer in-house digital literacy training for frontline workers, providing credentials in system monitoring and data interpretation.

• Hybrid Job Roles: Instead of eliminating roles, companies can create hybrid jobs, such as "machine learning technician" or "simulation analyst," that combine domain knowledge with tech oversight.

• Participatory Design: Achieving successful implementations should include input from the workers affected, ensuring tools meet real operational needs rather than just managerial dashboards.

• Transparent Metrics: When employee performance is monitored via digital systems, clear communication about what’s being tracked and why helps preserve trust.

Digital twins show us what’s possible when technology and data converge, so the benefits must not only mirror our machines but also, increasingly, our values. 

And when they reflect on what we choose to replace, enhance, or discard, such as the story from the refinery technician, the narrative progresses to a larger trend where technological advancement brings both operational gains and emotional strain. The promise is real. So is the cost. Managing this transition with empathy and foresight is key to long-term success.

FAQs

How much do digital twins cost?

Costs vary widely but a basic digital twin pilot project can range from a hundred thousand to several million dollars, factoring in sensors, cloud infrastructure, data integration, and simulation software. Retrofitting legacy systems often increases costs significantly.

Is a digital twin worth it?

Yes, if implemented strategically. Companies that successfully deploy digital twins often report reduced downtime, lower maintenance costs, improved safety, and faster product development.

What industries benefit most from digital twins?

Industries with complex, high-value assets or processes, such as manufacturing, energy, aerospace, and healthcare, see the most benefit. Digital twins help these sectors optimize performance, predict failures, and reduce risk in real-time.

Do digital twins eliminate jobs?

Not directly, but they do redefine job roles. Routine, manual tasks may be automated, but new roles emerge in data analysis, oversight, and system management. The key is helping workers transition and upskill to match evolving needs.

What are the biggest risks of using digital twins?

The main risks of digital twins include integration challenges, cybersecurity threats, high upfront costs, and potential data inaccuracies, which can be managed with proper planning, skilled teams, and regular system updates.

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