The advent of digital twins has revolutionised the industrial sector, enabling real-time simulations, predictive maintenance, and data-driven decision-making. These virtual replicas of physical assets, processes or systems are extensively used across industries such as manufacturing, energy, aerospace, and construction. However, as this technology matures and gains traction, licensing and monetising digital twins becomes a nuanced challenge involving legal, technical, and commercial considerations.
Unlike traditional software, digital twins function in a complex environment of interconnected systems, dynamic data feeds, and continuous updates. Structuring a licensing deal for this technology in industrial contexts, therefore, requires careful attention to intellectual property (IP), usage rights, data governance, compliance, interoperability, and risk management. Companies investing in or deploying digital twins must formulate licensing strategies that maximise value while safeguarding critical interests.
The Complexity of Intellectual Property in Digital Twins
At the heart of any licensing discussion lies IP. In the case of digital twins, IP can exist in various forms: the underlying simulation software, 3D models, data processing algorithms, user interfaces, analytics engines, and even the data ingested and generated during operation—all of which may come from different providers. Depending on the level of complexity, multiple vendors and parties may have partial ownership or usage rights.
A well-structured licence must explicitly define who owns what. For instance, if a digital twin is based on a proprietary engine developed by a third-party software company but customised for a specific industrial process, the base engine might be licensed, while the customisations may belong to the industrial client or a system integrator. Without clarifying these elements, disputes may arise around who can modify, repurpose, or commercialise components of the digital twin.
Moreover, creators must consider whether a digital twin is a one-off product or part of a platform that will be deployed across multiple installations or clients. IP protection mechanisms should account for scalability and future adaptation, especially when features are added or modified post-deployment.
Data Rights and Governance
Digital twins thrive on live and historical data—sensor streams, operational logs, environmental inputs, and user interaction data. Licensing a digital twin, therefore, involves not only software IP but also the data ecosystem in which it operates. One of the most challenging aspects is determining who owns and controls the data that flows in and out of the system.
For example, in a scenario where an original equipment manufacturer (OEM) provides a digital twin to their client along with data collection tools, but the digital twin is hosted on a third-party cloud service, multiple parties are suddenly involved in the lifecycle of the data. The client may have rightful ownership of the raw operational data, while the OEM may want access to improve their products over time. The cloud provider may also have terms that influence how long or in what way data is stored and shared.
Licensing contracts need to comprehensively define:
– Who owns the rights to raw and processed data
– What levels of access each party has
– How data privacy and security are ensured
– Whether data can be used for training machine learning models
– Retention periods and obligations post-termination of the deal
These provisions are especially critical in highly regulated industries like healthcare, energy, and defence, where strict data compliance laws exist. Approaches like data anonymisation, federated learning, and sovereign cloud deployments can help navigate these restrictions while still yielding business value.
Types of Licensing Models in Industrial Contexts
Digital twins can be monetised and licensed through several models, each tailored to different business strategies and usage patterns. Choosing the right model depends on whether the objective is to sell a standalone solution, embed a capability within a broader service, or create a long-term collaboration with value sharing. Common models include:
1. Perpetual Licensing with Maintenance Agreements
Here, the client pays once for the digital twin software, often with an annual fee for updates and support. This model appeals to enterprises that want long-term, local ownership and control. However, it may limit the vendor’s ability to evolve or monetise future enhancements unless specifically negotiated.
2. Subscription-Based Licensing
As with Software-as-a-Service (SaaS), the digital twin is licensed on a recurring basis—monthly, annually, or otherwise. This approach provides flexibility to scale, update, and terminate with agility. Vendors are incentivised to enhance performance and features, while clients are relieved of large upfront costs. Subscription models also open doors to usage-based pricing, which is gaining popularity in industrial IoT contexts.
3. Outcome-Based Licensing
In outcome-based models, licensing is tied to measurable business results, such as cost reductions, yield improvements, or downtime avoidance. This model requires deeply integrated partnerships with shared risk and reward, often necessitating joint dashboards and trust frameworks. It suits mature implementations that allow for accurate benchmarking and ground truth validation.
4. OEM or Platform Licensing
Digital twin capabilities may be embedded within a broader industrial platform or service. In such cases, the digital twin may not be separately licensed to the end-user but bundled into OEM agreements. Licensing structures here will need to manage volume-based fees, white labelling rights, and license-back clauses.
5. Joint Ventures and Co-Creation Licences
Increasingly, digital twins are co-developed by multiple stakeholders including clients, academic institutions, and systems integrators. Licensing in such cases needs to address joint ownership, exclusivity terms, and revenue-sharing mechanisms. It’s vital to establish up front how derivative works are handled and who holds re-licensing rights.
Ensuring Interoperability and Standardisation
Industrial operations often involve a patchwork of legacy systems, vendor-specific protocols, and incompatible data formats. For a digital twin to function effectively across this heterogeneous landscape, interoperability is crucial. Licensing arrangements should therefore account for compliance with open standards, API accessibility, and the ability to integrate with other systems.
Vendors may be tempted to deploy digital twins in a closed ecosystem to lock in clients, but such approaches often stall adoption due to integration roadblocks or the fear of vendor dependence. Instead, encouraging interoperability can serve as a competitive advantage, especially in industries moving toward Industry 4.0 and smart manufacturing. Licensing should clearly articulate the extent of interoperability support and penalties or remedies in cases of non-compliance.
Customisation, Upgrades and Third-Party Access
Few industrial clients adopt off-the-shelf digital twins without any form of customisation. Whether it’s incorporating unique operational parameters, simulating custom-built machinery, or adhering to internal compliance norms, most implementations require some level of adaptation. Therefore, licence agreements should define what customisation is allowed, who bears the cost, and how ownership over modified versions is structured.
Moreover, digital twin software often undergoes iterative updates, especially when machine learning models are involved. The licence needs to address:
– How upgrades are delivered and deployed
– Whether previous versions are supported
– How changes impact customisations
– What role the client plays in testing and validation
Additionally, many clients may want to give access to third-party consultants, auditors, or partners who interact with or build atop the digital twin. The licensing terms should anticipate such scenarios, adding provisions for sublicensing, third-party access limitations, and acceptable usage conditions.
Compliance and Export Control
In sectors such as aviation, defence or energy, export control and localisation laws may prohibit the cross-border deployment or use of certain technologies. If a digital twin encapsulates performance simulation of a defence asset or a proprietary fuel process, sharing such data or algorithmic descriptions across jurisdictions may be tightly regulated.
Licensing deals must hence consider compliance with the jurisdictions in which they are deployed. Legal teams should ensure that export control obligations are fulfilled, and sensitive modelling data is not inadvertently shared outside permitted geographies. The use of cloud storage can compound these risks further, necessitating regional data residency assurances and perhaps the exclusion of particularly sensitive features from broader versions of the twin.
Termination, Exit Strategies and Transfer of Rights
No software arrangement is future-proof, and parties must prepare for disengagement or change in strategy. Perhaps the industrial process being modelled is decommissioned, or the client wishes to switch suppliers. Whatever the reason, licensing terms should define what happens upon termination.
Will the client retain usage rights for the last version of the digital twin? Can the vendor revoke access? Is there a data handover mechanism in place? Who is obligated to delete client-specific configurations? For jointly-developed twins, are there provisions to transfer or partition rights?
Additionally, in case of mergers, acquisitions, or bankruptcies, licensing agreements should define change-of-control clauses. This is critical when digital twins play a central role in operational intelligence and continuity.
Security, Liability and Risk Allocation
Security failure in a digital twin can have cascading effects—from data theft to incorrect operational decisions based on flawed simulations. Particularly for twins deployed in critical infrastructure, stringent cybersecurity provisions must be built into the agreement.
Licensing clauses should include:
– Minimum security standards
– Audit rights and incident reporting timelines
– Liability for breaches, data corruption or unauthorised access
– Insurance and indemnity coverage
– Service-level agreements (SLAs) for uptime, data freshness and response times
In many cases, insurers and regulators will require evidence that these terms exist and are actively monitored. This formalisation serves not just legal interests but also helps build trust in the digital twin ecosystem.
Contracting Best Practices
To navigate these complexities, industrial firms and technology providers should adopt a few best practices:
– Engage cross-functional teams including legal, engineering, security and operations early in the negotiation process
– Identify dependencies clearly, such as third-party libraries, hardware specifications, and cloud hosts
– Use modular contracting to differentiate core licences from ancillary services like training, support, and customisation
– Apply robust mechanisms for measuring usage, especially for outcome-based or usage-based models
– Keep flexible provisions for roadmap alignment, co-development rights and feedback loops
Pilot programs can also help validate assumptions before full-scale contracts are signed. Meanwhile, industry consortia and standards bodies are beginning to generate templates and frameworks that can reduce uncertainty and streamline contracting.
The Future of Digital Twin Licensing
The future of licensing in the digital twin space will likely be shaped by broader shifts in digital transformation, artificial intelligence, and collaborative ecosystems. We may see more modular, dynamic licences where features are unlocked based on operational needs, or where blockchain and smart contracts enable real-time metering and revenue sharing.
As digital twins increasingly become foundational to digital factories, autonomous fleets, and intelligent cities, licensing will evolve from a basic operational contract to a strategic asset management function. Getting it right today lays the groundwork for scalability, resilience, and innovation tomorrow.