June 2023

Executive Summary

Digital twins have been gaining market attention with increased adoption, particularly in the last seven to eight years since 2015. Digital twin use cases are diverse and penetrate virtually every corner of the digital world to create virtual replicas of objects, processes, persons, places, and their lifecycles (view our Digital Twin Landscape)

A study of online content investigated the activity, dynamics and maturity of the digital twin market. This study harvested and analyzed a corpus of 4950 targeted web content documents with natural language processing (NLP) to evaluate digital twin market activity between 2015 and 2022. Although the analysis only captures digital twin activity reported online, we believe it provides visibility for overall market activity and momentum, measured using a Online Activity Index (OAI). The OAI describes a market in terms of three phases:

  • Speculative to reflect the early stages of market development when it is defining itself.
  • Transitional when speculation has peaked, and market sentiment recalibrates towards a long-term position, and;
  • Clarity when the market and its future development (or decline) are well understood.

Between 2015 and 2022, the OAI for digital twins has consistently increased in the speculative phase and, based on historical precedent, will likely transition between 2025 and 2027 toward sustainable market expansion. Under this scenario and with historical cloud revenue growth as a precedent, global digital twin expenditures are forecast to increase from USD 8.4 to 77.4 billion between 2020 and 2030, with a 32 percent cumulative annual growth rate (CAGR). Although the digital twin OAI increased consistently between 2015 and 2022, there is the potential for future disruptions during the speculative phase. If this were to occur, a protract speculative period that peaks in 2027 and a transition period until 2029 is likely, in which case the forecasted global digital twin market would reach USD 69.7 billion in 2030. Alternatively, if a more optimistic market outlook had the transition period halved, the market is forecast to reach USD 124 billion by 2030.

Over 300 companies and organizations spanning thirty-eight industry categories were identified in online content, with semantically similar impressions to the 'digital twin' phrase. Microsoft, Nvidia, Siemens, Bentley Systems, and AWS had the most impressions (i.e., were the most 'vocal'). But since the digital twin market is highly fragmented, the 50 most active companies online only accounted for 56 percent of total impressions.

The three industry categories with the most online activity for digital twins included industrial infrastructure, enterprise software and services, and simulation and modeling. Industrial infrastructure use cases include machine management, condition monitoring, predictive maintenance and operations, and process automation and optimization. Enterprise software and service use cases commonly integrate software ecosystems across disparate environments to enable digital twin capabilities. Industrial infrastructure and enterprise software opportunities are significant but constrained by complex and siloed ecosystems, operations, organizational structures, and business cases.

Digital twins naturally fit and eventually supersede many specialized simulation and modeling solutions. As a result, pure-play simulation and modeling providers must fortify their differentiation, particularly as digital twins become embedded product features that circumvent the need for specialized simulation capabilities.

As the digital twin market matures, it will create tremendous opportunities for digital innovation in most industries. However, to succeed, digital twins must avoid potential missteps in critical areas, including technology over-reach, ecosystem siloing, insufficient standardization, use cases that lack business cases, POCs that cannot scale, and inadequate stakeholder engagement.


Digital twins are in the limelight as the world is increasingly digitized and grapples with the convergence of the digital and physical worlds and broader metaverse concepts. The notion of a digital twin was first coined by Michael Grieves at the University of Michigan in 2002 and later adopted by NASA in 2010, to describe a virtual replica of a physical object and its lifecycle using real-time data, AI, and simulation models. Since then, digital twin market activity has accelerated with seemingly endless use cases for virtual replicas of objects, processes, persons, places, and their lifecycles. Although digital twins are nascent, they will likely play important roles in organizations' digital transformation strategies and must be clearly understood.

Digital twins emanate from simulation (e.g., computer-aided design and computer-aided manufacturing CAD/CAM) and performance monitoring and management systems, which have existed since the earliest days of computing. These systems typically support targeted and bespoke applications and saw advancements in the 1980s and 90's with desktop computing and, more recently, with the proliferation of cloud computing, IoT, and artificial intelligence (AI). By comparison, digital twins leverage similar capabilities but differ in the following areas:

  • Rather than being limited to bespoke applications, digital twins enable a broad range of use cases, including objects, processes, persons, and places, with large and dynamic data sets from disparate sources (e.g., drone imagery and real-time IoT instrumentation) with standardized schemas.
  • Instead of analyzing an object, process, person, or place for a snapshot in time or for a particular scenario, digital twins create a digital replica that depends on real-time or periodic data updates to mimic its lifecycle.

Even with this distinction, the definition of a digital twin is open to manipulation, with some companies exaggerating the capabilities of legacy solutions as though they are digital twins. Unfortunately, this exaggeration is a common characteristic of a nascent market but falls by the wayside with market maturity.

This report uses targeted web crawlers, natural language processing (NLP), and data analytics to analyze online content and investigate the vibrancy and maturity of the digital twin market, the players that are getting noticed, and the technologies and verticals they promote.


The research study consisted of two phases. The first was a detailed analysis of online content to identify critical patterns and prioritize subsequent primary and secondary research. Since online content includes some bias and exaggeration (which was removed where possible for the study) and excludes undisclosed and confidential activities, it does not provide a complete view of the digital twin market. Still, it is sufficient for evaluating key companies and trends, including market activity, momentum, drivers, inhibitors, opportunities, and future directions.

The online content analysis used several proprietary Python-based data science tools, which included the following components:

  • Web Search APIs that use Google and DuckDuckGo web content indexes. The APIs collected 40,000 targeted search results. The study used a sample of 4950.
  • Natural Language Processing (NLP) to filter and prioritize content that best represents the 'digital twin' topic, identify the named entities for expanded web searches, and measure the 'digital twin voice' of industry players and market categories.
  • Curation to identify and classify legitimate named entities in the content.

An Online Activity Index (OAI) was developed to track historical and forecast future online activity based on search results, reported keyword search trends, and historical benchmarking. The Index estimates the sentiment and activity relating to emerging technologies like digital twins as they mature. A positive gradient indicates improving sentiment with increased market activity and vice versa. The Index also tracks and predicts the phases of maturation, which reflect periods of early speculation, transition, and market clarity.

The 'online digital twin activity' for companies/organizations were estimated by counting the corpus documents where the companies/organizations have close semantic similarity to the 'digital twin' phrase in the content. These companies/organizations were also classified into industry categories to define the digital twin landscape. Thirty-eight industry categories were identified (see Appendix). The prevalence of each industry category was estimated by aggregating the digital twin impressions of each company into their respective categories.

Keywords were identified for adjacent technologies, including network connectivity, the Internet of Things (IoT), artificial intelligence (AI), cloud computing, metaverse, robotics, and computer-aided design (CAD). Document content with a semantic similarity between these keywords and the 'digital twin' phrase was identified to estimate the technology overlap.

Robust Activity and Outlook for Digital Twins

The Online Activity Index (OAI) for digital twins predicts that it is seven years into its speculation phase, which is expected to peak in 2025 and transition between 2025 and 2027, after which market clarity is anticipated. Between 2015 and 2023, digital twins had a 0.51 OAI gradient, compared with 0.79 for 5G over the same period and 0.39 for Augmented Reality over a comparable period between 2009 and 2017.

The digital twin OAI gradient has been relatively stable since 2015 (i.e., a 0.99 linear coefficient of determination R2), which indicates a steady increase in market sentiment and activity throughout the period. By contrast, the augmented reality OAI oscillated between 2009 and 2013 with trial-and-error experimentation to ultimately derive sustainable market opportunities after 2019.

The digital twin concept is far-reaching, with tremendous experimentation and disruptive innovation potential. But a relatively steady digital twin OAI indicates the digital twin solutions adopted since 2015 have not endured missteps and noticeable negative sentiment periods. We believe that this reflects a rather conservative approach by the industry towards digital twin solutions and, in some cases, the augmentation of established solutions with digital twin capabilities. Suppose the market were to accelerate disruptive digital twin innovations and endure negative sentiment periods. In that case, we believe it would result in broader digital twin adoption, but the market speculation phase would likely be protracted by 24 months to peak in 2027 and transition until 2029. However, we believe this is a less likely outcome for digital twins and that more speculative and risky solutions will fall under the guise of the emerging notion of the metaverse.

The digital twin OAI is forecasted from 2023 to 2030 using the augmented reality OAI since 2019 as a benchmark but with a modified gradient to better reflect the historical digital twin OAI gradient since 2015. The forecast also includes a shallower transition phase that accounts for the historical digital twin OAI stability.

A forecast profile was developed for global digital twin technology expenditures. This profile used the digital twin OAI to identify market inflection points and a Gompertz approximation for historical cloud revenues as a reference for growth rates. For the base-case OAI, global digital twin expenditures are forecast to increase from USD 8.4 to 77.4 billion between 2020 and 2030, with a 32 percent cumulative annual growth rate (CAGR). However, if the digital twin market were to experience disrupted innovation with a protracted speculation period that peaks in 2027, the forecasted digital twin global market would reach USD 69.7 billion by 2030. An optimistic OAI with halved transition period would have the market reach USD 124 billion by 2030.

Market Activity Index Digital Twins

Digital Twin Market Voices: The Players

The document corpus for the study identified over 300 companies and organizations with semantically similar impressions to the 'digital twin' phrase (digital twin impressions). The most 'vocal' companies and organizations included Microsoft, Nvidia, Siemens, Bentley Systems, AWS, Matterport, NASA, GE, Ansys, IEEE, and ESRI. However, since the market is highly fragmented, the 50 most 'vocal' companies only accounted for 56 percent of impressions.

Company Digital Twin Impressions

Microsoft Azure launched its digital twin solution in 2018 as an extension of its cloud based IoT platform and integrated with Azure's services. Since then, many players have partnered with Microsoft to capitalize on its current leadership position with digital twins and Azure cloud services. However, competition is coming from other cloud providers, including AWS, which launched its IoT Twin Maker in 2021, and GCP, which currently offers targeted solutions such as its Google Supply Chain Twin.

Nvidia promotes its Omniverse Platform for data center applications, including AI and digital twins. Nvidia has also launched several digital twin solutions, including nVidia Earth 2, for high resolution weather system simulations and a digital twin of DSN's (Deutschland) rail network comprising 5700 stations and 33,000 km tracks.

Siemens has a range of digital twin initiatives that capitalize on its consulting and software services for the process and manufacturing industries, smart infrastructure for buildings and energy systems, rail networks, medical technology, and digital health services. In addition, Siemens is applying digital twin solutions within its facilities with proof of concept (POC) and minimum viable product (MVP) solutions that can be adapted and enhanced before being rolled out to its clients. Given the breadth of Siemens' digital twin solutions, we believe it is under-represented by the digital twin impressions in the document corpus used for this study.

Bentley Systems is an infrastructure engineering software company with a long heritage in computer aided design (CAD) and simulation, with annual revenues of USD 1 billion in 2022. Bentley has been aggressively advancing its digital twin capabilities since it launched its iTwin product in 2017, which it plans to integrate across all its software products. Its iTwin solution already supports a range of use cases, including cell phone tower and civil infrastructure surveys and large scale construction projects, such as the Ezhou Huahu Airport in China with 25 million modeling components and the Diablo Dam on the Skagit River with 82 million survey points.

Matterport contrasts Siemens by being over represented with digital twin impressions in the document corpus used for the study. Matterport has leveraged its high-performance 3D video capabilities to establish itself as a leader in digital twinning for real estate. It is aggressively digitizing commercial buildings and residential properties for which it offers subscription services. As of February 2023, Matterport had 9.2 million spaces (28 billion square feet) under management, with 701 thousand subscribers. Its annual revenues were USD 165 million in 2023, corresponding to an average monthly revenue per subscriber of USD19.61. Matterport became a public company in 2021 and expects to achieve profitability in 2025.

NASA has operated complex simulators from the earliest years of space exploration and has been one of the earliest digital twin adopters since 2010. However, some pundits suggest that NASA has been engaged in digital twinning for a lot longer and dates back to 1970 when simulators assisted in the recovery of astronauts from the damaged Apollo 13 platform after an oxygen explosion. Today NASA's digital twin solutions include space technology, earth science, and aviation systems.

Since its founding in 2015, General Electric Digital (GE Digital) has been an early pioneer of digital twins, particularly for aerospace, the energy sector, and healthcare. For example, it has promoted its digital twins for commercial aircraft engines since 2017. GE Digital also maintains an extensive digital twin library to harmonize and accelerate customer adoption with key use cases that integrate AI, such as condition monitoring, predictive maintenance, asset performance management (APM), and optimization.

Ansys is a leading engineering simulation software and services company that supports academia and the high tech, aerospace and defense, automotive, energy, industrial equipment, materials and chemicals, consumer products, healthcare, and construction industries. Its 2022 revenues were USD 2.08 billion, an increase of 8.8 percent relative to 2021. Ansys launched its Digital Twin Builder solution in 2018 to complement its other simulation services, enable digital asset lifecycle management, and incorporate vital capabilities such as predictive maintenance. Ansys has extensive partnerships with players across the ecosystem, including Autodesk, AMD, Amazon AWS, Intel, Microsoft Azure, and Siemens.

ESRI is a Geographical Information System (GIS) software company with a dominant market share for its ArcGIS software platform. Most recently, ESRI launched its ArcGIS Reality software in February 2023.

Schneider Electric is an industrial infrastructure company specializing in automation and energy management systems. In 2022, Schneider launched its EcoStruxure Machine Expert Twin digital twin software for machine lifecycle management.

The Digital Twin Consortium is the Object Management Group (OMG) program to spearhead thought leadership and drive interoperability and standardization for digital twin solutions. In addition, the Consortium accelerates digital twin adoption by supporting industry, academic, and government-led cooperation.

The diversity of players illustrates the pervasiveness of digital twins in the global economy.

Digital Twins have Diverse Industry Engagement

Companies spanning thirty-eight industry categories were identified with digital twin impressions in the document corpus (see Appendix for industry category definitions). The three most prevalent categories included Industrial Infrastructure, Enterprise Software and Services, and Simulation and Modeling.

Segment Digital Twin Impressions

Industrial Infrastructure

Industrial infrastructure digital twin providers include ABB, Alstrom, Bobst, Bosch, CGTech, Emerson, Fanuc, Flogistix, Flowserve, Fujitsu, GE, Hexagon, Hitachi, Honeywell, Kongsberg, Rockwell, Schneider, Siemens, Toshiba, and Yokogawa. These companies provide numerous digital twin solutions for machine management, condition monitoring, predictive maintenance and operations, and process automation and optimization. The potential upside benefits of industrial infrastructure digital twins are tremendous but commonly hindered by complex operational environments, status quo inertia, workflow complexities, and siloed data integration challenges. To overcome these challenges, industrial infrastructure providers endure relatively long sales cycles and systems integration demands. Therefore, targeted use cases with clear upside benefits are favored, preferably with blueprints and best practices from other success stories.

Enterprise Software and Services

Companies like Databricks, edgeTI, Mendix, Microsoft, Neo4j, Oracle, Salesforce, SAP, ServiceNow, Tibco, and Waylay enable digital twin capabilities with integrated enterprise software ecosystems that span disparate data and information silos. For example:

  • Databricks provides integrated data and AI solutions to simplify digital twin implementations.
  • EdgeTI uses its real-time operations platforms to create digital twins of organizations. These digital twins combine dynamic operational data with contextual data to characterize business model operationalization.
  • Mendix offers a low code development environment for digital twin creation.
  • Neo4j provides its graph data platform to manage complex interconnected systems for digital twins.
  • Oracle has extended the reach of its enterprise IoT cloud service with digital twinning capabilities.
  • Players like Waylay have implemented digital twin solutions in Salesforce's AppExchange.
  • ServiceNow has integrated digital twinning into its workflow and business process optimization solutions, and;
  • Tibco provides an integrated platform environment for enabling digital twin solutions.

Enterprise software and service companies are well positioned to capitalize on digital twin service opportunities but constrained by the complexity of the environments and ecosystems where their potential digital twin opportunities reside. For the most part, we believe that enterprise software companies should not aggressively pioneer first mover initiatives with digital twins. Instead, they should focus the next 24 months on ensuring their platforms are digital twin-ready, emphasizing AI and data consolidation and management.

Simulation and Modeling

Digital twins originated from and have provided a disruptive evolutionary path for simulation and modeling solutions. Companies evolving their solutions with digital twin capabilities include ABB (Robot Studio), Akselos, Altair, Ansys, Archibus, Autodesk, Cadenas, Cadmatic, Comsol, Corys, Emulate3D, IoTify, MapleSoft, Mathworks, Simio, and Sketchfab. For example,

  • ABB is enabling digital twin capabilities across a broad range of solutions, including simulation and modeling for robotics.
  • Akselos is an engineering simulation company that has recently raised venture capital to expand its digital twin capabilities in the energy sector.
  • In 2022, Altair Engineering announced its 'One Total Twin platform' to reposition its digital twin offering as a high-performance integrated platform to support entire product lifecycles.
  • Since acquiring SpaceIQ in 2020 with Serraview, Archibus and its partners have enabled digital twin solutions to support workspace management and building information modeling (BIM) systems.
  • In 2021, Autodesk announced its 'Tandem' cloud-based digital twin and, in 2022, laid out development plans for Tandem to initially focus on Revit (Revise Instantly) building information management (BIM) solutions.
  • In 2023, Cadmatic announced a partnership with Contact Software to develop product lifecycle management (PLM) solutions for shipbuilding.
  • Many digital twin solutions use Multiphysics software platform for various use cases, including batteries in electric vehicles, electric motors, and construction concrete.
  • Cory's total immersion simulators enable various digital twin applications, particularly in the transportation and energy sectors.
  • Emulate3D is a Rockwell company that provides digital twins for virtual equipment and plant commissioning.
  • IoTify provides IoT simulators and end-to-end digital twin-testing solutions.
  • MapleSoft has partnered with companies like Oracle and AWS for digital twin solutions that leverage its simulation software.
  • In 2022, Mathworks announced its 2022b release of Matlab and Simulink that incorporated a variety of digital twin capabilities, including an upgrade to Simscape Battery product for battery pack digital twinning.
  • Simio simulation software has enabled various digital twin use cases, such as oil pipeline terminals, robotics, education, and public service infrastructure.
  • In 2022, Sketchfab (acquired by Epic Games in 2021) announced a partnership with Matterport to integrate its 3D immersive and interactive object library for users to augment Matterport's digital assets.

Although digital twin technology naturally evolves from simulation and modeling, players in the space must anticipate disruptive market forces that will ensue over the next 24-36 months. In particular, the rate and nature of digital twin adoption will vary significantly across industries and markets. As this occurs, simulation and modeling will evolve from specialized and bespoke solutions to digital twin features within the products, services, and systems they support. Therefore, simulation and modeling companies must anticipate this transition and:

  • Recognize the broader business process and human resource implications of their prioritized solutions and product roadmaps.
  • Create best-of-breed solutions, maintain superior know-how, and carefully target markets where simulation and modeling capabilities are differentiated.
  • Embrace standardization and carefully map the evolution from proprietary solutions to maintain favorable value chain boundaries and;
  • Anticipate the broader ecosystems and diverse data sources that digital twins encompass. This will demand alliances and partnership strategies and require simulation and modeling companies to pay careful attention to the ecosystem components they are willing to relinquish versus those they need to control for self-reinforcing market benefits.

Digital Twins and their Companion Technologies

Digital twins create rich technology ecosystems that include network connectivity, IoT instrumentation, AI/ML intelligence, and in many cases, cloud computing for extensibility. The availability and maturity of these and other companion technologies are crucial for digital twin solutions to function effectively. Impressions were identified where phrases relating to these companion technologies appeared with semantic similarity to the 'digital twin' phrase in the document corpus. In particular, AI/ML had impressions in 16.5 percent, IoT in 11.5, cloud computing in 8.3, and networking and connectivity in 8.1 percent of documents. By comparison, the metaverse, CAD, and robotics had impressions in 5.1, 1.5, and 0.9 percent of documents, respectively.

Where to From Here

The digital twin market is robust and well-positioned for continued prosperity but is likely to see some future attrition, particularly amongst simulation and modeling solution providers. Its OAI has maintained a relatively linear gradient since 2015, indicating that there haven't been any substantial missteps or market failures to negatively impact the overall market sentiment towards digital twins. We believe that this illustrates a level of conservatism in the market and that more disruptive approaches toward digital twins are positioned under the broader notion of the metaverse. However, as the digital twin market continues to mature, there is still the potential for future missteps, particularly in the following areas:

  1. Technology over-reach that pays insufficient attention to established workflows and processes, human resources and their incentives, and status quo inertia.
  2. Ecosystem silos and insufficient standardization constrain data integration efforts for digital twins to function as intended.
  3. Use cases adopted even when they lack sufficient business case justification.
  4. Proof of concepts that cannot scale because of technical and market constraints and;
  5. Inadequate digital twin stakeholder engagement.

As digital twins continue to mature with fortified roles in broader digital transformation initiatives, their market dynamics, companion technology demands, and priorities will change. As a result, digital twin players, particularly pioneers and early adopters, must continually distinguish reality from hype, challenge their strategies and create contingencies to respond to industry changes. Players who achieve this will likely benefit from a burgeoning digital twin market for the foreseeable future.


The online content analysis gleaned the following industry categories for digital twins.

Agriculture: Companies like John Deere that provide technology solutions to the agricultural industry

Architectural, Engineering, and Construction: Companies like Bentley Systems, Buildots, Procore, and Siteaware.

Automotive: Automotive OEMs including BMW, Ford, Hyundai, Toyota etc., and suppliers such as APR, Hankook, Hyperbat, RFPro, and Cepton.

Aviation and Aerospace: Companies like Airbus, Boeing, Dassault, Maxar, SpaceX, and Thales.

Blockchain: Companies like CasperLabs, Ethereum, and Fetch.ai.

Cloud Services and Technology: Companies like Amazon/AWS, Meta, Microsoft/Azure, Rackspace, VMWare, and Uber.

Communications Services and Technology: Companies like Cisco, Ericsson, Korea Telecom, Nokia, NTT, Singtel, and Verizon.

Compute Platforms: Companies like ASUS, Dell, HP, and Lenovo.

Consumer Durables and Electronics: Companies like Apple, Ikea, LG, Panasonic, Samsung, and Whirlpool.

Data and Analytics: Companies like Absolutdata, Faircom, Heex, Kinetica, Palantir, and Synctwin.

Data centers and Neutral Hosts: Companies like Equinix, QTS, and Wiwynn.

Digital Twin Platforms: Companies like Ada Mode, CargoValue, Cosmo Tech, Deepcake, Matterport, Mevea, Prevu3D, Scaleout, Spinview Thynkli, vHive, and Willow.

Energy and Sustainability: Companies and organizations like ADNOC, Aker, Aramco, BP, Chevron, FutureOn, Exxon, Nuscale, Saipem, Shell, Twaice, and Utilidata.

Enterprise Software and Services: Companies like Databricks, edgeTI, Mendix, Microsoft, Neo4j, Oracle, Salesforce, SAP, ServiceNow, Tibco, and Waylay.

Gaming and Entertainment: Companies like Disney, FIA Motorsports, Epic Games, and UEFA.

Geographical Information Systems and Location Based Services: Companies and organizations like ESRI, GeoSim, Loukung, Navvis, Trimble, and YoGeo.

Healthcare: Companies like Abbott, Astrazeneca, Exactcure, FEops, GSK, Siemens Healthineers, Insilico, Medtronic, NHS, Pfizer, Roche, Sanofi, Standigm, and Virtonomy.

Hospitality and Tourism: Companies like Hilton and Marriott.

Immersive Reality: Companies like FractureReality, FusionVR, Hadean, Hevolus, Kinemagic, Microcloud Hologram, Microsoft Hololens, Mytaverse, Oculus, Taqtile, VictoryXR, VisionLib, and WiMi.

Industrial Infrastructure: Companies like ABB, Alstrom, Bobst, Bosch, CGTech, Emerson, Fanuc, Flogistix, Flowserve, Fujitsu, GE, Hexagon, Hitachi, Honeywell, Kongsberg, Rockwell, Schneider, Siemens, Toshiba, and Yokogawa.

Industrial Software and Services: Companies like Aegis Software, ePlan, Metroscope, PTC and Softserve.

Insurance and Finance: Companies like Aviva, BNP Paribas, and HSBC.

Logistics and Supply Chains: Companies like Arendai, DHL, Digimarc, Dijitalis, Llamasoft, Maersk, Makersite, Mosca, and Unipart.

Manufacturing: Companies and organizations like Aizon, Amgen, Basf, Bayer, Celsa, Cesmii, Foxconn, Henkel, Pepsico, Sandvik, Stratasys, and Siemens Tecnomatix.

Maritime: Companies like Docktech, Furuno, Keppel, KSOE, Marin, and Navantia.

Marketing and Advertising: Companies like Flyy, and Ultraleap.

Military and Defense: The armed forces and companies like Babcock, DSTG, Lockheed, and Northrop.

Mining and Geo-Technology: Companies like Seequent (acquired by Bentley Systems in November 2021).

Professional and Managed Services: Companies like Arup, Atos, Aveva, Axians, BearingPoint, Capgemini, Deloitte, DHI, Fugro, IBM, KPMG, Lanner, Mosimtec, PwC, Ramboll, RisingmMax, ServiceMax, TCS, and Wipro.

Real Estate: Companies like Cupix, Facilio, JLL, Lendlease, Matterport, Resonai, Thoughtwire, and Zillow.

Research and Development:Companies and organizations like CERN, DARPA, Fraunhofer, ICHEC and OpenAI.

Retail: Companies like Bigthinx, Browzwear, Kraft Heinz, Kroger, Mcdonald's, and Unilever.

Semiconductors and Subsystems: Companies like Nvidia, NXP, Qualcomm, and Synopsys.

Simulation Modeling and Design: Companies like ABB Robot Studio, Akselos, Altair, Ansys, Archibus, Autodesk, Cadenas, Cadmatic, Comsol, Corys, Cosmo Tech, Emulate3D, IoTify, MapleSim, Mathworks, Rescale, Simio, Sketchfab, and SolidWorks.

Smart Cities: Companies like Cityzenith, Nedap, and Open Space.

Software Platforms: Companies and organizations like Aera, Akka, Apache, Jupyter, and Keras.

Standards and Advocacy: Organizations like the Digital Twin Consortium, IEC, IEEE, IET, ITU, OMG, and United Nations.

Transportation: Companies and Organizations like CSX, Cummins, Scania, and Shift2Rail.

Universities: like Harvard, MIT, Purdue, Swinburne, UCLA, and Witchita.