Building a Digital Twin of an Organization

What is a digital twin?

A digital twin is an end-to-end virtual representation of a physical object, system, or business process, continuously enriched with data so that it reflects what is happening. For enterprise leaders, one of the most valuable forms is the process digital twin: a dynamic model of how an organization actually operates.

Digital twin technology generally falls into two categories:

Physical digital twins represent objects, products, or infrastructure. These are the twins most often associated with jet engines, factories, production lines, buildings, or cities. They help teams understand how complex physical systems behave and how changes may affect performance.

Process digital twins, also called Digital Twins of an Organization (DTO), represent how a business operates. They show how processes, people, systems,, rules, data, and controls work together across the enterprise.

In large enterprises, the two categories can overlap. A manufacturer may have a digital twin of a production facility and a process digital twin of the workflows that support procurement, logistics, quality control, finance, and customer delivery. Both can matter, but they answer different business questions.

This page focuses on that second category: the digital twin of an organization.

The ARIS Perspective

The ARIS perspective starts with the business process.

Most digital twin conversations begin with sensors and IoT. ARIS starts with the repository of models, SOPs, and other documentation that reflect how your business operates. That process knowledge creates the designed view of the organization: how work should happen, which systems are involved, who owns each step, what rules apply, and where controls are required.

Process mining then enriches that designed view with execution data. It shows how work actually moves through systems such as SAP, Salesforce, and ServiceNow, where the process follows the intended path, and where it deviates.

That model-first philosophy is what makes the ARIS approach to a Digital Twin of an Organization governable, auditable, and long-lived. The twin is not just a visualization of activity. It connects process design, execution evidence, and governance so teams can understand how the business works and improve it with confidence.

This is why digital twin technology is becoming so important for business transformation. McKinsey reports that 70% of C-suite technology executives at large enterprises are already exploring and investing in digital twins. The process digital twin market is also projected to grow from $2.56 billion in 2025 to $9.95 billion by 2030, reflecting a 31% CAGR.

For ARIS, the value of a digital twin is not simply that it mirrors the business. It gives the organization a governed way to understand how work is designed, how it actually runs, and where process intelligence can drive better outcomes.

What is a Digital Twin of an Organization?

A Digital Twin of an Organization (DTO) is a governed, virtual model of how a business operates across processes, people, systems, data, rules, and controls. It connects the designed view of the business with evidence of how work actually happens in near-time, giving teams a clearer way to understand, improve, and govern operations.

A DTO is not simply a process map, an org chart, or a dashboard. Those tools each show parts of the business, but they do not show how the business operates as a connected system. A process map shows how work is intended to flow. An org chart shows reporting lines. A dashboard shows performance metrics. A DTO brings these views together and connects them to actual process execution.

It shows how processes move across departments, which systems support them, where people make decisions, what rules apply, which controls are required, and where performance or compliance issues may appear. It helps leaders move from assumptions about operations to a fact-based understanding of how work gets done.

Gartner has noted that DTOs will become critical as digital business systems increasingly rely on the continuous integration of human and machine intelligence. That matters because modern organizations are not run by people and systems alone. They are run by the interactions between people, systems, data, rules, and decisions.

In the ARIS context, a Digital Twin of an Organization has three connected layers: the process design layer, the execution layer, and the simulation and governance layer.

The process design layer

The process design layer defines how work is intended to happen. It includes process models, workflows, roles, responsibilities, systems, policies, risks, rules, controls, and documentation.

In ARIS, this layer is built through process design and modeling capabilities that help organizations turn process knowledge into a governed repository. That may include BPMN models, EPC frameworks, SOPs, policy documentation, interviews, and other internal materials that describe the business’s operations.

This gives the organization a trusted reference point. Before teams can understand whether a process is performing well, they need to know what the process was designed to do.

The execution layer

The execution layer provides insights into how work actually happens.

Process mining connects to source systems such as SAP, Salesforce, and more to extract event log data and reconstruct the paths, variations, delays, and exceptions that occur as work moves through the business.

This keeps the DTO grounded in operational evidence. It shows where the intended process is followed, where it drifts, and where hidden inefficiencies, risks, or improvement opportunities appear.

The governance and control layer

This layer turns the DTO into a long-lived business asset.

It helps teams compare execution against the intended process, identify deviations, manage changes, support auditability, and maintain control as operations evolve. This is especially important for regulated industries, operational resilience, and AI governance, where organizations need to understand not only what happened, but whether it happened in line with approved processes and controls.

This is where ARIS’s model-led perspective matters. Many approaches build the digital twin up, using process mining first and the model as an output. ARIS builds the digital twin model down, starting with the governed process understanding the organization already has, then uses process mining insight to validate, enrich, and improve it.

That distinction matters because a model-led DTO is governable and shows what the process is supposed to do, how it actually runs, and where the business needs to take action. This allows organizations to control what the process is supposed to do, not just observe what it does, which is a non-negotiable for regulated industries and for AI governance under the EU AI Act.

Real-World Digital Twin Examples and Use Cases

Digital twin use cases show how organizations use virtual models to improve planning, operations, customer experience, and compliance. Physical digital twins often improve assets and production environments. Process digital twins improve how work moves through the enterprise, helping teams reduce delays, identify deviations, and govern change.

These examples show why digital twin technology has expanded beyond physical systems. A factory twin helps improve production planning. A process digital twin helps improve business operations. Both matter, but they serve different transformation goals.

For business leaders, the highest-value digital twin use cases often begin with a specific operational question: why is order-to-cash slower than expected, where are customer journeys breaking down, which process variants create compliance risk, or where can AI safely support work?

Are you ready for digital twinning?

Ask your team these five questions before starting a digital twin program:

1. Do you have detailed records of what happened and when in your core systems—such as ERP, CRM, or service management platforms? Without this data, you won’t be able to see how your processes actually work in practice.
2. Have you defined and documented at least one end-to-end process in BPMN or an equivalent notation? The process model is the skeleton of the twin. Without it, you have data but no reference point.
3. Do you have a clear business question you want the twin to answer? For example: why is our order-to-cash cycle 30% longer than planned? Or, where is our AI automation opportunity? The more specific, the faster the value.
4. Is there a process owner who will act on what the twin reveals? Digital twins produce insights, not decisions. You need a human owner to close the loop.
5. Do you have management support to invest in at least a four- to six-week first project?

That last question matters. Deloitte’s 2025 process mining survey found that management support is the number one barrier to adoption, cited by 41% of organizations, up from 26%. Early, measurable outcomes create the internal evidence—and confidence—needed to expand digital twinning from a first use case to a broader process intelligence capability.

If you answered yes to most of these questions, you are ready to start. If not, ARIS Fast Track services are designed to get you to yes in weeks.

How process intelligence builds your digital twin: Four steps

To create a digital twin of an organization, start with the process foundation, connect it to execution data, analyze the gap between design and reality, then govern the twin as operations change. This four-step method turns a static process view into a useful process digital twin.

Step 1: Design the twin foundation

Before data can strengthen a digital twin, the organization needs to define what the process is designed to do.

This starts with process modeling. BPMN 2.0 models and other frameworks help describe the process based on SOPs, documentation, interviews, and existing operational knowledge. The repository stores these assets and displays them graphically so teams can understand roles, systems, rules, controls, and dependencies in context.

This is the designed view of the business.

Step 2: Mine the execution reality

Once the process foundation is defined, process mining connects to source systems such as SAP, Salesforce, and ServiceNow. It extracts event log data that reveals how processes actually execute across systems, teams, and departments.

This is where the DTO becomes grounded in fact.

The gap between Steps 1 and 2 reveals where value is hiding. Bottlenecks, deviations, rework, compliance risks, process variants, and automation opportunities become visible because teams can compare what was intended with what actually happened.

Step 3: Simulate what-if scenarios

With a digital twin informed by process data updated with 24 hours, teams can evaluate BPMN what-if scenarios before changing live operations.

Each scenario can use actual process volumes, cycle times, and variation patterns from the execution reality uncovered in Step 2. That gives teams a stronger basis for understanding how a proposed process change may affect cycle time, throughput, cost-per-case, SLA exposure, and backlog risk.

This is what makes the digital twin useful for process improvement. It connects the designed BPMN model with recent execution data so teams can test change options against how work actually runs before updating the governed process model.

Govern and control

The ARIS digital twin is a long-lived, versioned asset. As processes evolve, the twin is updated so the governed process model continues to reflect how the business is designated to operate.

Conformance checking compares actual executed processes against the approved model, alerting teams when execution drifts from the designated process. That gives process owners a clearer way to identify deviations, control gaps, and compliance risks before they become larger operational issues.

That is what makes the ARIS DTO audit-ready and relevant for AI governance. It creates a governed record of process design, execution evidence, changes, and deviations, which helps organizations show not only how work was performed, but whether it followed the right processes, controls, and oversight.

This is what makes the ARIS approach different. Other process intelligence platforms may provide a snapshot of a process in motion. ARIS gives organizations a governed digital twin that connects what the process was designed to do, how it actually runs, and whether it remains aligned with business rules, controls, and transformation goals.

All in one integrated suite.

Digital twin and AI: The readiness layer

A digital twin of an organization gives AI the process context it needs to create measurable value. It shows what a process is supposed to do, how work actually moves, which controls apply, and where human oversight is required.

That context is becoming the difference between AI experimentation and AI return. According to PwC’s 2026 Global CEO Survey, 56% of CEOs report no financial benefit from AI investments, and only 12% say AI has delivered both cost savings and revenue gains. McKinsey research also shows that more than 80% of companies using AI globally have seen no significant bottom-line impact.

The problem is not the technology alone. It is the absence of process context.

AI agents cannot operate safely or effectively inside complex enterprises if they do not understand the process they are acting within. They need to know the rules, dependencies, decision points, controls, exceptions, and real-world process variations that shape how work gets done. A DTO provides that context by connecting process models, execution data, conformance insights, and governance history.

This also matters for compliance. The EU AI Act becomes fully applicable in August 2026 and raises expectations for explainability, audit trails, and human oversight. For organizations deploying AI into business processes, that evidence cannot be assembled after the fact. It needs to be generated as processes run, change, and improve.

PEX Network reports that 40% of organizations already use AI agents for business transformation, while 59% plan to invest in the next 12 months. Yet only 43% have an AI governance policy. That gap makes the DTO more than a planning tool. It becomes the operational foundation for AI governance.

ARIS gives AI agents the process knowledge, control structures, and conformance rails they need to operate safely and improve continuously.

The companies that are getting the real AI ROI are not the ones with the most models. They are the ones that understood their processes first.