Digital Twins Can Cut Costs by 15%. So Why Aren’t More Companies Using Them? DTaaS May Be the Missing Layer
by 
Digital twins can cut product development time by up to 50%, reduce CapEx and OpEx by up to 15%, and help companies discover problems before they become expensive physical mistakes. Yet Siemens says many organizations still lack the expertise to deploy them. In 2026, that gap is creating a new opportunity: Digital Twin as a Service.
Digital Twins Have an Adoption Paradox
The business case for digital twins is getting difficult to ignore. McKinsey research shows that 70% of C-suite technology executives at large enterprises are already exploring or investing in digital twins. Companies successfully using Digital Twin Technology have reported development-time reductions of 20% to 50%, up to 25% fewer quality issues, and 5% to 10% higher aftermarket revenue in some product categories. [Source: McKinsey]
The technology can also reduce the time required to deploy new AI capabilities by up to 60% and lower capital and operating expenditure by up to 15%. [Source: McKinsey]
Then there is what is happening in factories right now.
At CES 2026, Siemens revealed that PepsiCo’s early deployment of its Digital Twin Composer delivered a 20% increase in throughput, identified up to 90% of potential issues before physical modifications, achieved nearly 100% design validation, and supported 10% to 15% CapEx reductions. [Source: Siemens, CES 2026]
These are not hypothetical benefits.
So why hasn’t every industrial company scaled Digital Twin Technology?
Because there is a massive difference between understanding a digital twin’s value and possessing the engineering capability to build, connect, deploy, and continuously operate one. That gap may create one of industrial technology’s most important emerging categories: Digital Twin as a Service, or DTaaS.
Siemens Thinks 2026 Is the Inflection Point
Leoluca Scurria, product manager for Simcenter Executable Digital Twin at Siemens Digital Industries Software, made an unusually specific prediction for 2026.
When discussing the next big industrial “as-a-service” opportunity, he identified Digital Twin as a Service as the main trend Siemens expects.
The reason is more important than the prediction.
Many organizations have valuable digital-twin opportunities but do not have the knowledge required to integrate them internally. Specialist providers can therefore build, deploy, and support the twins on their behalf. [Source: Siemens Simcenter, 2026]
That exposes the real bottleneck.
It is no longer:
Can Digital Twin Technology create value?
Increasingly, it is:
Can we operationalize it without assembling an entire digital-twin engineering organization?
DTaaS attempts to solve exactly that problem.
What Is Digital Twin as a Service?
Digital Twin as a Service is a model for consuming digital-twin capabilities without having to build and manage every technical layer internally. A sophisticated digital twin may require:
Simulation models
- IoT and sensor data
- IT/OT integration
- Cloud or edge computing
- Data pipelines
- AI models
- Model validation
- Cybersecurity
- Deployment infrastructure
- Continuous maintenance
- Domain expertise
Traditional approach:
Your company owns that complexity.
DTaaS approach:
A provider abstracts more of that complexity and delivers the twin as an operational service.
This is not simply a theoretical definition.
Academic researchers developing DTaaS architectures describe platforms that manage reusable models and data assets, storage, compute provisioning, communications, and monitoring so users can operate at the digital-twin level rather than managing the underlying infrastructure themselves. [Source: Talasila et al., Digital Twin as a Service]
That is why the SaaS comparison is useful. SaaS did not eliminate software complexity. It moved much of that complexity behind a service boundary. Digital Twin as a Service could do something similar for industrial simulation.
The Economics Explain Why DTaaS Matters
Consider product development. McKinsey estimates that around $30 trillion in corporate revenue depends on products that have not yet reached the market. [Source: McKinsey]
That makes development speed enormously valuable. Companies using digital twins have already reported:
20% to 50% shorter development cycles
25% fewer production quality issues
5% to 10% additional aftermarket revenue
Some aerospace and defense organizations have cut advanced-product development time by 30% to 40% using digital-twin approaches. [Source: McKinsey]
But those gains usually require significant engineering maturity. This creates a classic technology adoption problem. High potential value + high implementation complexity = service opportunity. DTaaS attacks the second half of that equation.
PepsiCo Shows What the Business Case Can Look Like
One of the strongest 2026 examples comes from PepsiCo. The company is working with Siemens and NVIDIA to create high-fidelity digital twins of manufacturing and warehouse environments.
Instead of physically modifying a facility and discovering problems afterward, teams can recreate machines, conveyors, pallet routes, operator movement, and material flows virtually. AI agents can then simulate alternative configurations before anything changes in the physical factory.
The early results are significant:
20% higher throughput
10% to 15% lower CapEx
Up to 90% of potential issues identified before physical changes
Nearly 100% design validation
[Source: Siemens, January 2026]
At a Gatorade manufacturing facility, the reported 20% throughput improvement was achieved within three months. [Source: Siemens Digital Logistics]. Some facility-design and optimization work that previously required months can now be completed in days. [Source: Siemens]
This is where Digital Twin Technology becomes easier to understand. The value is not the virtual factory. The value is avoiding expensive mistakes in the real factory.
The Executable Digital Twin Changes the Equation Again
DTaaS becomes even more interesting when combined with the Executable Digital Twin, or xDT. Traditional simulation models often remain inside engineering departments.
An Executable Digital Twin takes validated engineering intelligence and packages it into a self-contained model capable of operating alongside the physical asset. Sensor data enters. The xDT evaluates the asset’s behavior. Engineering insight comes out. That creates applications such as:
- Predictive maintenance
- Virtual sensing
- Remaining useful-life estimation
- Performance optimization
- Anomaly detection
- Operational decision support
Siemens’ 2026 Simcenter xDT Gateway can centrally deploy, manage, update, scale, and retire multiple Executable Digital Twin instances across operational environments. [Source: Siemens Simcenter, April 2026]
That infrastructure is strategically important. Digital twins are moving from bespoke engineering projects toward manageable software assets. Once something becomes deployable, repeatable, monitorable, and centrally managed, offering it as a service becomes much more practical.
The Industrial Infrastructure Has Finally Caught Up
Why now?
Because several technology curves are converging. Factories have spent years adding sensors. Industrial equipment is increasingly connected. Edge computers can execute sophisticated models closer to machinery. Cloud platforms provide elastic compute. IT and OT systems are becoming more integrated. AI can analyze simulation and operational data at much greater scale.
Scurria argues that the data foundation created by industrial digitalization since 2020 is one reason xDT adoption can accelerate in 2026. [Source: Siemens Simcenter]
The wider industrial ecosystem is moving in the same direction.
In January 2026, Siemens and NVIDIA expanded their collaboration around an Industrial AI Operating System, combining digital twins, simulation, real-world engineering data, accelerated computing, and industrial AI. [Source: Siemens]
In February, Dassault Systèmes and NVIDIA announced a shared industrial AI architecture combining virtual twins with AI infrastructure and science-grounded world models. [Source: NVIDIA]
This is bigger than one vendor. Digital twins are becoming infrastructure for industrial AI.
DTaaS Could Solve the Skills Problem
Here is where Digital Twin as a Service could become particularly valuable. Building a useful twin requires more than hiring a data scientist. A team may need mechanical engineers, simulation experts, controls engineers, data engineers, IoT specialists, cloud architects, AI engineers, cybersecurity specialists, and people who deeply understand the physical asset.
Those capabilities are difficult to assemble. Even harder to maintain.
And expensive to duplicate across every organization that wants Digital Twin Technology. DTaaS creates another option:
Rent the capability instead of building the entire capability stack.
That could make sophisticated twins accessible to mid-sized manufacturers, infrastructure operators, equipment suppliers, energy companies, and other organizations that cannot justify creating large specialist teams.
Build or Buy? A Simple DTaaS Decision Framework
Build internally when: Your digital twin creates unique strategic IP. You already have strong simulation and IT/OT capabilities. You need maximum model control. You expect large-scale, long-term internal deployment. Consider Digital Twin as a Service when:
- You have a high-value physical asset or process.
- Downtime or inefficient operation is expensive.
- You have usable operational data.
- You lack specialist digital-twin expertise.
- You want faster time to value.
- You need a pilot before making a major platform investment.
Avoid both when:
- Your physical process is inexpensive to experiment on.
- The required data does not exist.
- There is no measurable decision the twin would improve.
- That final point matters.
A digital twin without a business decision attached to it can quickly become an expensive visualization project.
The Biggest Opportunity May Not Be Selling Digital Twins
The more disruptive opportunity could be what DTaaS does to industrial business models. Imagine a compressor manufacturer. The traditional model is:
Sell compressor → Sell parts → Sell maintenance
Now add an Executable Digital Twin. The manufacturer can continuously estimate equipment health, predict maintenance, optimize operation, and recommend interventions. The business model becomes:
Equipment + monitoring + prediction + optimization + guaranteed performance
Instead of selling machinery, manufacturers can increasingly sell uptime, efficiency, capacity, or output. Siemens explicitly connects xDT technology with this broader equipment-as-a-service trend. That could turn Digital Twin as a Service from an IT purchasing model into a recurring industrial revenue model.
The 2026 Digital Twin Shift in One Sentence
For years, companies asked:
“Can we build a digital twin?”
The better question became:
“Can the digital twin create measurable ROI?”
Now the question is changing again:
“If the ROI exists, why should we build the entire stack ourselves?”
That is the opportunity behind DTaaS.
Final Takeaway
Digital twins are approaching an important transition. The technology is proven enough to deliver measurable outcomes. McKinsey reports development-time improvements reaching 50%, quality improvements reaching 25%, and CapEx and OpEx reductions reaching 15%.
PepsiCo is reporting 20% higher throughput and 10% to 15% CapEx reduction from early digital-twin deployments. Siemens is building infrastructure for centrally deploying and managing Executable Digital Twins. Industrial AI platforms are increasingly treating digital twins as their connection to the physical world. Yet expertise remains a bottleneck. That combination creates the conditions for Digital Twin as a Service.
The winners may not be the companies that build the most visually impressive virtual factories. They will be the companies that make Digital Twin Technology repeatable, deployable, measurable, and accessible without requiring every customer to become a digital-twin expert.
SaaS transformed enterprise software by separating software value from the burden of operating the software stack. DTaaS could bring the same idea to industrial intelligence. And if Siemens’ 2026 prediction proves correct, the next phase of the digital-twin market will not be defined by who can build the best twin. It will be defined by who can make digital twins easy enough for everyone else to use.
Digital Twin as a Service (DTaaS) is a delivery model where organizations access digital twin capabilities through a managed service instead of building and maintaining the entire technology stack internally. A DTaaS provider can handle simulation models, data integration, cloud or edge infrastructure, deployment, monitoring, and ongoing maintenance.
DTaaS connects data from physical assets, sensors, IoT systems, enterprise software, and engineering models to a managed digital twin. The provider handles much of the underlying infrastructure while the customer uses the twin to monitor performance, simulate scenarios, predict failures, optimize operations, and support business decisions.
A digital twin is a virtual representation of a physical asset, system, or process connected to real-world data. Digital Twin as a Service is the business and technology model used to deliver, operate, and maintain that capability for customers without requiring them to manage every technical component internally.
An Executable Digital Twin, or xDT, is a self-contained digital twin model that packages engineering and simulation intelligence into deployable software. It can run alongside a physical asset, process operational data, estimate conditions that may be difficult to measure directly, and support applications such as predictive maintenance and performance optimization.
An Executable Digital Twin describes the technology used to package and deploy engineering intelligence, while Digital Twin as a Service describes how digital twin capabilities can be delivered and consumed. Executable Digital Twins can therefore become an important technical foundation for DTaaS offerings.
DTaaS is gaining attention because companies increasingly recognize the value of Digital Twin Technology but may lack the simulation, IoT, data engineering, IT/OT, cloud, and domain expertise required to deploy it at scale. Siemens has identified Digital Twin as a Service as an important 2026 trend for addressing this implementation gap.
Digital twins can help organizations shorten product development, identify design problems earlier, reduce downtime, optimize asset performance, improve quality, and lower operating costs. McKinsey reports that companies using digital twins have achieved 20% to 50% reductions in product development time, while some applications can reduce capital and operating expenditure by up to 15%.
DTaaS is particularly relevant to industries operating expensive or complex physical assets, including manufacturing, automotive, aerospace, energy, utilities, transportation, logistics, buildings, infrastructure, and industrial equipment. The strongest use cases typically exist where downtime, inefficient operation, physical testing, or equipment failure carries significant costs.
Software as a Service (SaaS) delivers software applications through managed cloud infrastructure. Digital Twin as a Service (DTaaS) applies a similar service model to digital twins, combining software with simulation models, physical asset data, connectivity, compute infrastructure, and engineering expertise.
The required data depends on the use case but can include sensor readings, IoT data, engineering specifications, CAD models, simulation data, maintenance records, historical operating data, environmental conditions, and enterprise system data. More data is not automatically better. The data must support the specific decisions the digital twin is designed to improve.
Common challenges include integrating fragmented data, connecting IT and operational technology, maintaining accurate models, accessing simulation expertise, ensuring cybersecurity, managing infrastructure, proving ROI, and scaling successful pilots across multiple assets or facilities. DTaaS aims to reduce some of this implementation burden.
DTaaS ROI should be tied to measurable business outcomes such as reduced downtime, lower maintenance costs, increased throughput, lower energy consumption, faster product development, fewer physical prototypes, reduced CapEx, improved quality, or longer asset life. Companies should establish baseline metrics before deployment and compare them against post-implementation performance.
DTaaS is likely to become an important part of industrial AI rather than replace all internally developed digital twins. Digital twins provide AI systems with structured representations of physical assets and environments, while AI can make those twins more predictive and autonomous. Together, they can support simulation, optimization, predictive maintenance, and AI-driven industrial decision-making.
Companies should first identify a measurable operational problem and then evaluate data availability, integration requirements, model accuracy, cybersecurity, deployment architecture, interoperability, scalability, vendor expertise, ownership of generated data and models, and expected ROI. A small, measurable pilot is often more useful than beginning with an enterprise-wide digital twin initiative.
