Digital twins are widely used to model complex systems, helping us understand their behavior and predict the impact of change. For example, cities use digital twins to simulate traffic flows or energy usage, allowing planners to test decisions before implementing them in the real world.
Regulation, in a different way, also models a complex system: society. It does not define fixed processes, but describes situations, allowed behavior, and their consequences. It defines what actors may or must do, and how that leads to rights, obligations, and outcomes. In that sense, regulation provides a model of how society is intended to function.
Unfortunately, this model is still largely managed as text. Laws and policies are written and reinterpreted across organizations, leaving the underlying logic implicit and fragmented.
At the same time, the interaction between regulation and society is highly complex. Regulations span multiple policy domains, are created by different authorities, and are rarely written from a single, coherent perspective. They interact in ways that are difficult to fully understand. Society itself is complex and does not always behave in accordance with the rules. As regulations grow and automation increases, we need a structured way to understand how rules interact and to predict the impact of change.
A Digital Regulatory Twin offers that: a way to turn regulation from fragmented text into a coherent, usable representation of this underlying model.
What is a Digital Regulatory Twin?
A Digital Regulatory Twin is a structured, machine-readable representation of regulation that makes the logic of laws, policies, and guidance explicit and connects it to how an organization operates.
It goes beyond simply translating legal text into rules. Regulation is not applied directly but requires interpretation. Legal provisions need to be understood, ambiguities resolved, and choices made about how they apply in specific contexts.
A Digital Regulatory Twin captures this by making both the rules and their interpretation explicit and traceable.
To do this, regulation is interpreted using formal legal grammars that break down legal text into clear, consistent elements. Approaches such as the FLINT legal grammar and the Legal Analysis Scheme (Wetsanalyse) help structure obligations, conditions, and definitions in a way that can be reused across contexts.
Instead of multiple teams maintaining their own interpretations in policies, systems, and documentation, this creates a shared, consistent representation of how regulation is understood and applied.
This representation can be analyzed, maintained, and used directly in processes, applications, and decision-making, while evolving as regulation and its interpretation change.
How does a Digital Regulatory Twin work in practice?
It helps to look at a Digital Regulatory Twin from the perspective of a company operating across multiple markets.
Take a company like P&G. The starting point is understanding its business context.
This includes products, processes, plants, and the jurisdictions in which products are produced, sold, used, and disposed of. That is far from simple. Product categories, ingredients, packaging, and claims all matter, as do the activities across the value chain: from procurement and formulation to production, testing, market access, reporting, sales, and distribution.
Each jurisdiction brings its own set of regulations.
Based on this context, the relevant regulations are identified and interpreted using a formal legal grammar. The resulting structured representation captures key elements such as actors, actions, and objects and connects them directly to the company’s business design.
Once this foundation is in place, the Digital Regulatory Twin can be used in practice.
For example, if P&G wants to introduce a new product with a specific formulation, produced in a plant in Brazil and sold in Argentina, the model can determine which regulations apply and how they impact the new product.
It can provide precise guidance on whether the formulation is allowed, which requirements must be met, whether permits or registrations are needed, what information must be provided, and whether the intended product claims are compliant.
This is the essence of a Digital Regulatory Twin. It creates a consistent and reusable foundation that connects regulation to real-world operations, enabling companies to understand requirements, assess impact, and make informed decisions before taking action.
Where does the real value come from?
The value of a Digital Regulatory Twin does not sit in one place. It emerges across the entire lifecycle of regulation but looks different depending on your perspective.
For policymakers and regulators, it helps make the logic of regulation visible. Instead of reviewing text in isolation, they can see how rules interact. This makes it easier to identify inconsistencies early, before they become problems, and to design regulation that is clearer and more coherent.
For organizations, the value lies in implementation and change. Regulation does not need to be reinterpreted for every process or system. Instead, a shared interpretation can be linked to business processes, controls, and data. This reduces duplication, aligns implementation with regulatory intent, and makes it easier to assess the impact of regulatory or business changes as they evolve.
For systems and AI, the value is in execution. The same structured representation of regulation can be used directly in applications and decision-making, enabling consistent, traceable, and explainable outcomes across systems.
Across all these perspectives, one theme stands out: reuse. Regulation is interpreted once and applied consistently across the organization.
Why do organizations still struggle with regulatory complexity?
Most organizations already have policies, controls, and systems in place. The challenge lies in how these have been built over time.
Regulation does not follow the structure of an organization. It is created across different domains and authorities and is not written from the perspective of a single company or process. Organizations must interpret and combine these overlapping rules to understand what applies in their specific context.
At the same time, regulation cuts across legal, business, IT, and operations, while organizations are structured into many teams. There may be a limited number of legal teams, but there are typically many business teams (across divisions, product lines, plants, and channels) and many IT teams supporting different systems and initiatives.
Each of these teams approaches regulation from its own perspective. Legal teams interpret rules. Business teams translate them into processes and controls. IT teams implement them in systems. Operations teams apply them in day-to-day activities.
Without a shared model, the same regulation is interpreted multiple times across these teams. Each interpretation is tailored to a specific context or use case and rarely reused.
Over time, this creates a patchwork. Systems behave differently. Changes take longer to implement. It becomes difficult to maintain a consistent view of what the rules actually require, and compliance becomes harder to manage.
A Digital Regulatory Twin provides a shared foundation that connects these perspectives. By capturing regulatory logic once and linking it to the organization’s context, it reduces fragmentation and creates a consistent basis for interpretation, implementation, and change.
What makes Digital Regulatory Twin feasible today?
The idea of structuring regulation is not new. What has changed is the ability to do it at scale.
In the past, the effort required to interpret and model regulation in a structured way was simply too high. It depended heavily on manual work, and there were few standards or tools to support consistency and reuse across organizations or domains.
Today, that is changing on several fronts.
Advances in AI are significantly increasing the productivity of this work. AI can support the interpretation of legal text, help structure definitions, and assist in connecting regulatory logic to operational models. It does not replace human expertise, but it makes the process faster, more consistent, and easier to scale.
At the same time, collaboration platforms are emerging that allow different teams including—legal, business, IT, and operations,—to work together on a shared model, each using their own domain language. These platforms support governance, versioning, and change management. This makes it possible to manage regulatory interpretation as a continuous, coordinated process rather than a series of isolated efforts.
In parallel, there is growing momentum around open standards and rules as code. Governments and industry initiatives are exploring ways to publish regulation in machine-readable formats, making it easier to share, align, and reuse structured interpretations.
Taken together, these developments are lowering the barrier to adoption. What was previously possible only in limited, isolated cases can now be applied more broadly. This makes it feasible to manage regulation as a system, rather than as fragmented text.
Final thought
Given the complexity of regulation, and the growing importance of consistent compliance and enforcement, a natural question emerges:
Can we still afford to operate without a Digital Regulatory Twin?
