Why digital twin growth still depends on a fragmented market

Digital twin software is growing fast. IoT Analytics estimates that the standalone market reached USD $1.3 billion in 2025 and forecasts 27% compound annual growth to USD $4.2 billion by 2030. The research firm places that category inside a USD $175 billion enabling-software ecosystem and says standalone products represent just 0.7% of spending across it. For executives, the gap between these categories matters as much as the growth rate.

IoT Analytics is a market-research company whose business benefits from demand for its market analysis and classification work. Its analysis treats digital twins as a combination of established software disciplines and knowledge of physical assets. Rapid growth in products sold specifically as digital twin software can therefore coexist with capabilities spread across many suppliers. That distinction changes how executives should read the headline market forecast.

The larger software ecosystem shapes the opportunity

The forecast measures demand for software classified specifically as digital twin software. The larger enabling-software estimate includes adjacent products such as computer-aided design, product lifecycle management and simulation software. A company can therefore buy technology for a digital twin deployment through established software categories as well as products classified as standalone digital twin software. The two market figures measure different scopes.

That difference matters for corporate strategy. A software company can participate in digital twin deployments through engineering, simulation, industrial or operational technology products. A buyer selecting a standalone digital twin product can still depend on technologies supplied through those adjacent categories. Market share in the standalone segment therefore gives one view of competitive position within a broader software environment.

IoT Analytics has also changed how it classifies this environment. In 2020, it introduced a three-dimensional framework based on hierarchical level, lifecycle phase and use. Its latest work instead examines how the market is built and sold, including raw data sources, software that structures and models assets, simulation environments and applications through which users interact with digital representations. This framework is IoT Analytics’ way of mapping supplier roles and dependencies.

The resulting model treats many products as complementary parts of a deployment. Suppliers in different layers may support the same customer while selling products from established software categories. Executives evaluating acquisitions, positioning or competitors therefore need to distinguish standalone digital twin revenue from the wider software capabilities that can support a twin.

Fragmentation follows the shape of the technology

IoT Analytics identifies more than 1,500 vendors in its latest model, spread across four workflow contexts and five technology layers. It says suppliers tend to be strong in only part of that structure and that no vendor holds a strong position across the full digital twin technology stack. Those conclusions depend on IoT Analytics’ definitions and market model. Even so, they give its fragmentation thesis a basis beyond the vendor count alone.

The technical categories in that model include functions such as modelling, connectivity, analytics, asset data, simulation and sector-specific applications. They address different parts of a digital representation and its use. A deployment drawing on several of them can therefore involve complementary suppliers across multiple product categories. This helps explain why a large enabling-software ecosystem can coexist with a much smaller standalone category.

Domain knowledge adds another dimension. A digital representation used for a power grid addresses a different physical environment from one used for a commercial building or an industrial production line. Executives assessing a supplier can examine both its software coverage and its experience with the relevant physical system. Integrations and delivery partners become material when one supplier covers only part of the required environment.

IoT Analytics Chief Executive Officer Knud Lasse Lueth summarizes the firm’s position: “Our latest research on digital twin and simulation software shows that the $1.3 billion standalone digital twin market sits within a much larger $175 billion enabling-software ecosystem. Across 4 workflow contexts, 5 technology layers, and more than 1,500 vendors, no supplier owns the full stack. This fragmentation makes domain expertise, interoperability, and partnerships central to successful digital twin deployments.”

Lueth’s statement reflects the commercial research firm’s own market framework and should be read in that context. Its strategic implication concerns the current structure described by that framework. Suppliers can expand their portfolios and take responsibility for larger parts of deployments while other layers remain distributed among partners and specialists. The practical question is how much of a specific deployment a supplier can support and where dependencies remain.

Consolidation broadens portfolios

Acquisitions provide a practical test. IoT Analytics cites deals including Synopsys-Ansys and Siemens-Altair as examples that broaden product portfolios and can close specific capability gaps. The research firm finds that these combinations have yet to produce a single unified product spanning the full digital twin stack from source data through user-facing software. That assessment reflects IoT Analytics’ own framework for the transactions.

Corporate ownership can still change competitive positions. Combining modelling, engineering or simulation capabilities can bring more of a customer’s workflow under one owner and reduce some external dependencies. Other requirements may continue to involve data sources, connectivity, sector-specific applications or specialist knowledge. Executives should therefore assess portfolio expansion and coverage of a complete deployment separately.

This distinction affects acquisition strategy. Buying an adjacent capability can expand the workflows a software group can serve and change its role in a customer deployment. Management teams can evaluate such a transaction by identifying which dependency moves inside the company and which remain with other providers. This frames consolidation around specific capabilities and the control each one provides.

Buyers need to test interoperability and accountability

A multi-supplier deployment makes interoperability an operational issue. Here, interoperability means that components can exchange the information required for the intended workflow. Buyers evaluating modelling tools, industrial connectivity, analytics, asset data, simulation environments and sector-specific applications need to understand how those components work together. The supplier map matters because responsibility can cross company boundaries.

Technical connection is only part of that assessment. Executives should also establish who maintains data exchange as products change and who handles failures that cross product boundaries. Those questions determine how responsibility is divided across software providers, systems integrators, industry specialists and the buyer’s own teams. They give buyers a more concrete test of a proposed platform than the breadth of its product list.

Domain expertise belongs in the same evaluation. Models and operating assumptions must fit the physical environment represented by the twin, while partnerships can extend a supplier beyond the layers it covers itself. Buyers can examine the proposed division of work: who structures asset data, who connects operational sources, who supplies and validates models, who integrates applications and who resolves cross-product failures. Clear ownership makes a distributed deployment easier to govern.

For vendors, the same structure makes partnership strategy part of the offering. A focused supplier may need relationships with other software providers, systems integrators or industry specialists to participate in a broader deployment. Buyers can assess how those relationships operate in the specific system they plan to build. In the market model described by IoT Analytics, the ability to connect specialized products and domain knowledge is a substantive part of competition.

Key takeaways for decision-makers

• Read the market forecast in context: Standalone digital twin software is forecast to grow from $1.3 billion in 2025 to $4.2 billion by 2030, but it sits within a $175 billion enabling-software ecosystem. Executives should assess opportunities across adjacent engineering, simulation and industrial software, not standalone products alone.
• Plan for a fragmented technology stack: IoT Analytics identifies more than 1,500 vendors and no supplier with a strong position across the full stack. Buyers should prioritize domain expertise, interoperability and clearly defined partner roles when selecting suppliers.
• Judge consolidation by capability coverage: Acquisitions such as Synopsys-Ansys and Siemens-Altair broaden portfolios but have not created a single product spanning the full digital twin stack. Leaders should identify which dependencies an acquisition brings in-house and which still require external providers.
• Make interoperability and accountability buying criteria: Multi-supplier deployments require more than technical integration. Buyers should establish who maintains data exchange, validates models and resolves failures across product boundaries before committing to a digital twin architecture.