AI-powered digital twins are not simply another stage of digital transformation. They represent a new organising logic for economic activity in data-rich environments. By categorising organisations according to twin maturity and ecosystem integration, Rick Aalbers, Saeed Khanagha and Chris Vialle show that long-term value increasingly accrues to those that can transform digital twins from internal optimisation tools into shared infrastructures for co-ordination, prediction and collective action.
Across industries, a fundamental shift is underway in the sources of competitive advantage. Organisations are moving away from competing on physical assets, distribution strength or financial scale, and toward their ability to build “digital twins” – real-time, AI-powered virtual replicas of customers, operations and environments that are continuously updated with live data, so decisions can be simulated and tested before they are taken in the real world.
In manufacturing, these take the form of production and supply chain twins. In healthcare, patient-level models enable personalised, predictive care, including continuous monitoring and early intervention. More broadly, the frontier of competition is defined by which organisations can generate the most accurate and actionable digital representations of reality – and even more fundamentally – who can orchestrate these across ecosystems of data, partners and institutions.
AI-powered digital twins are emerging as a new competitive core in organisations that increasingly cut across traditional industry boundaries – banks moving into mobility or housing services, technology firms entering healthcare or manufacturers selling predictive maintenance rather than machines. Yet this shift remains fragile. Ecosystems governed purely by market dynamics (such as open data-sharing initiatives where no participant is responsible for common standards) tend to fragment, while those dominated by a single actor (as when a big tech platform owner sets the rules for everyone else) may risk constraining innovation and participation. Consistent with our earlier work on innovation in nascent ecosystems, the long-term viability of such arrangements depends on continuously balancing legitimacy, trust and stakeholder interests across organisational boundaries – between the businesses that share data, the technology providers that run the underlying platforms, and the regulators that oversee them.
The central challenge is therefore structural rather than purely technological: enabling secure, sovereign data sharing while maintaining co-ordination and collaboration. Real value emerges only when digital twin infrastructures are supported by governance mechanisms capable of sustaining these balances over time.
Strategic archetypes for organisations in the ecosystem era
Translating this shift requires a clearer understanding of how organisations are positioned and how they compete from those positions.
Our Digital Twin Repositioning Matrix maps organisations according to twin maturity and ecosystem integration, revealing four distinct competitive positions and strategic trajectories.
The matrix plots organisations along two axes: Internal Twin Maturity (the sophistication of AI and data infrastructure) and External Ecosystem Integration (the depth of connectivity to external data and partners). The four archetypes define not only technological positions, but also distinct modes of competition and performance outcomes.
The first strategic archetype, the Isolated Ledger, occupies Q3, the quadrant of low maturity and low integration. While prevalent in traditional banks, similar patterns can be observed in legacy manufacturers and healthcare providers operating on fragmented systems. These organisations maintain a static, backward-looking view, relying on siloed historical data. They are cost-based competitors with limited differentiation. Their inability to personalise or predict leaves them exposed to more agile entrants. Performance is typically stable but declining, particularly as competitors leverage richer data ecosystems. Their position may remain temporarily viable in regulated or protected environments, but structurally they face obsolescence. Predictions suggest that ecosystems will increasingly be orchestraded by a small number of lead players, and organisations in this quadrant risk seeing their most profitable products and customer relationships picked off one by one by more adaptive players. The strategic priority is foundational: consolidating internal data and building a minimum viable twin to enable future participation.
ABN AMRO, the third-largest bank in the Netherlands by total assets, serves as a case in point. Maintaining decades-old mainframe infrastructure while executing a hybrid-cloud migration that retains its legacy stack as a strategic backbone the bank typifies the Isolated Ledger archetype. Though the bank has launched Gen-AI initiatives like the Rikkie chatbot, its data remains largely siloed within traditional banking systems. Their low-code applications deliver rapid development cycles, yet the integration between internal systems and external ecosystem partners remains limited. As of 2026, the bank is working toward unifying legacy systems into a single AI-enabled operating model, but the governance gap between technological deployment and organisational alignment constrains their ability to anticipate market shifts beyond traditional financial services boundaries.
In contrast, the Fragmented Innovator (Q4) represents organisations with high internal maturity but low external integration. Often large financial institutions or advanced manufacturers, these have invested heavily in AI and analytics, developing sophisticated internal models. Competitively, they operate as capability leaders but ecosystem laggards. They can achieve significant efficiency gains and optimise internal processes, yet their performance is constrained because their models capture little of what happens outside the organisation – market shifts, partner behaviour or changing customer circumstances. Their simulations are precise but incomplete, leading to a structural disadvantage in anticipating external shocks or customer needs. Under certain conditions (such as environments with highly proprietary data) this position can be temporarily advantageous. But over time, the lack of ecosystem integration limits growth and innovation. The strategic imperative is selective openness: engaging with external data sources and partners to enrich the twin without eroding core advantages.
ASML, a supplier to the semiconductor industry, exemplifies this quadrant with exceptional internal AI maturity deployed across its lithography tool lifecycle, from real-time machine monitoring and predictive maintenance to supply-chain logistics optimisation. In 2025 ASML invested in French AI specialist Mistral AI to co-develop AI-enhanced lithography solutions, demonstrating sophisticated internal capabilities. Autonomous AI agents ingest sensor feeds, simulation outputs and market data to recommend actions and optimise equipment performance. But ASML’s ecosystem remains relatively closed, focused primarily on proprietary process data and a selective network of universities, research institutes and strategic customers. While their internal simulation precision is unmatched in semiconductor manufacturing, their external data integration is constrained by the highly specialised nature of lithography technology and intellectual property considerations, limiting their ability to anticipate broader market dynamics.
The third archetype, the Siloed Connector in Q2, describes organisations with high external integration but low internal maturity. These are well-connected, common in platform-dependent sectors (such as retail, travel and insurance distribution, where customer access runs through third-party platforms) and increasingly visible in healthcare. But they lack the internal capabilities to translate data into insight. Their competitive position is inherently fragile. While they benefit from access to diverse data streams, they struggle to capture value, often becoming dependent on stronger ecosystem players. Performance is volatile, with risks of data overload, compliance failures and inability to explain AI-driven decisions. This position may be viable in early-stage ecosystems where connectivity is the primary requirement, but it becomes unsustainable as competition shifts toward intelligence and orchestration. The strategic priority is to build internal analytical and AI capabilities to convert access into advantage.
Philips, a technology conglomerate, illustrates the archetype. Over the past decade the company has undergone a profound transformation into a focused health technology company, overhauling its extensive external ecosystem partnerships while continuing to face challenges in internal analytical consolidation. The company’s partnership with Salesforce created an open digital-health ecosystem, connecting Philips medical devices, wearables and sensor-enabled applications into unified platforms. In 2025 Philips showcased AI-driven diagnostics and patient-monitoring solutions relying on cloud-based data management and automation. But their Future Health Index reports acknowledge persistent trust gaps in healthcare AI adoption, with regulatory complexity under the EU AI Act creating compliance challenges. Philps exemplifies the core vulnerability of the Siloed Connector: the company excels at building internal ecosystem connections – partnerships, platforms, and integrated service networks – but has not yet achieved the internal analytical maturity needed to consolidate these diverse data streams into decisive, proprietary clinical insights. This leaves the company structurally dependent on stronger platform players who can orchestrate value capture across the ecosystem, as typical for Q1.
The strategic target across industries is what we call the Orchestrated Platform, at Q1, combining advanced AI-driven twins with strong ecosystem integration. These act as value orchestrators: in finance, anticipating life events and offering proactive solutions; in healthcare, enabling continuous monitoring and intervention; and in manufacturing, supporting self-optimising production and supply chains. Rather than competing within markets, they shape them through superior customer outcomes, network effects and ecosystem-wide value capture. Success, however, depends on maintaining trust and effective governance. Their strategic challenge is to scale while balancing control and openness through hybrid orchestration models. Siemens AG, once best known as a traditional engineering and manufacturing powerhouse, which has transformed into a leading technology company focused on digital industries, intelligent infrastructure, mobility and healthcare, serves as a case in point.
It does not merely participate in digital ecosystems but actively shapes them by embedding its proroprietary capabilties – digital twins, generative AI and the internet of things orchestration – across partners’ and customers’ operations creating structural dependencies that position the firm as the central value-orchestrating node rather than a participant at the platforms edge. Siemens’ Digital Twin Composer creates photorealistic, real-time environments merging 2D and 3D twin data with live operational feeds. The platform supports Industry 5.0 use cases including AI-driven design optimisation, predictive maintenance and virtual commissioning. Siemens’ acquisition of Dotmatics recently extended their digital twin strategy into life sciences. Their Industrial Copilot generative AI tool and virtual PLCs already steer production at Audi facilities in Germany. Exemplary of a Q21 player, Siemens plots as an ecosystem orchestrator shaping manufacturing markets rather than merely competing within them.
The dynamic journey from Isolated Ledger to Orchestrated Platform
Moving across the quadrants of our matrix is not a linear progression. It is a dynamic, iterative journey marked by cycles of expansion, tension and reconfiguration. Our dozen semi-structured interviews with executive stakeholders, system architects and regulatory officers around the world, coupled with broader industry comparative desk research, indicate that transitions are consistently constrained by a governance gap in which technological capabilities outpace organisational and regulatory alignment.
In practice, repositioning toward the Orchestrated Platform is rarely linear. As organisations add new capabilities, they run into growing tensions over who controls data and decisions, and have to renegotiate how control and collaboration are shared with their partners. Each step up in AI capability raises new governance questions, and organisations must repeatedly choose between two ways of coordinating the ecosystem: taking the lead themselves or building consensus among partners.
AI-Powered digital twins as the new competitive core
AI-powered digital twins are emerging as a new foundation of competitive advantage. The four-quadrant framework demonstrates that success depends not simply on technological sophistication, but on the ability to combine advanced digital twin capabilities with ecosystem integration and governance. Organisations may follow different developmental paths, yet long-term value increasingly accrues to those that can transform digital twins from internal optimisation tools into shared infrastructures for co-ordination, prediction and collective action.
This shift redefines the nature of competition itself. Advantage is no longer derived primarily from scale, assets or standalone capabilities, but from the ability to create accurate, real-time representations of complex systems and orchestrate them across networks of stakeholders. The unit of competition therefore moves beyond the individual organisation toward the ecosystem, while static resources are supplemented by dynamic, AI-enabled simulations.
Organisations that successfully integrate data, intelligence and ecosystem co-ordination are positioned not merely to participate in emerging networks, but to shape them. Our ongoing work reveals that technological capability alone is insufficient. As digital twins become foundational infrastructure, governance becomes a strategic concern rather than a supporting function. Sustainable advantage therefore depends on hybrid orchestration: governance arrangements that combine co-ordination and standard-setting with openness, collaboration and adaptability.
AI-powered digital twins are not simply another stage of digital transformation. They represent a new organising logic for economic activity in data-rich environments. Organisations that develop both the capabilities and governance mechanisms required to orchestrate these ecosystems will shape the future competitive landscape. Those that do not may find themselves increasingly dependent on platforms and infrastructures designed by others.
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