How virtual twins and AI companions are replacing enterprise war rooms

Enterprises remain reliant on outdated, manual decision-making methods

Even after years of digital transformation projects and billions spent on enterprise software, many companies still make crucial decisions through manual, disconnected methods. Executives gather teams in virtual or physical “war rooms,” interpreting fragmented data with Excel sheets and approximations. This is not a failure of technology, it’s a failure of integration.

Enterprise systems were built to optimise specific domains, finance, supply chain, or production, but not to communicate easily with each other. When CEOs and department heads ask questions that cross those boundaries, teams must manually extract, align, and reconcile data from multiple systems. The process is slow, error-prone, and keeps organizations stuck in reactive mode instead of making real-time, data-informed decisions.

For leaders, this signals a critical inflection point. Companies that continue to rely on patchwork data processes will be outpaced by those that operate with integrated, living models of their businesses. Time spent aggregating information is time lost to competitors who act faster. True transformation means eliminating these silos so decisions happen with speed and confidence, not guesswork.

Morgan Zimmerman, CEO of 3DExperience at Dassault Systèmes, shared how this plays out in practice. He described an electronics manufacturer managing component shortages, where survival depended on how fast decisions were made each morning. Their teams worked manually through €100 million arbitration meetings as they tried to anticipate supply disruptions before competitors did. This level of decision pressure is now normal in global industries, and the old model of managing it is no longer viable.

Unified virtual twins offer a transformative framework for integrating disconnected enterprise data

Dassault Systèmes’ new 3DExperience Generation 7 platform addresses this fragmentation. It introduces what the company calls “virtual twins”—digital versions of products, processes, and supply chains combined into a single, structured representation. This unified approach makes all relevant data, design details, supplier information, cost, production schedules, available in context and in real time. It effectively connects what traditional systems keep apart.

Virtual twins aren’t just about visualization. They serve as a living data framework where every element of a business is represented and continuously updated. When leaders want to understand, for example, how tariffs affect business performance, they can see precisely which products are impacted, where production happens, and which suppliers are at risk. This reduces the latency between market signals and executive action.

For executives, this changes how decisions are made. Instead of waiting days for analysis, insights can surface instantly from data already correlated across systems. It removes manual layers and reveals how different business elements influence one another. The benefit is not only faster action but also the ability to run predictive simulations that anticipate change rather than reacting to it.

Morgan Zimmerman, CEO of 3DExperience at Dassault Systèmes, explained that this level of unification is central to the company’s strategy. By projecting structured information from multiple sources into a consistent digital model, companies can finally overcome the decades-old problem of fragmented information. For large enterprises where timing defines advantage, unified virtual twins can transform operational speed into a competitive edge.

Conversational AI “virtual companions” democratize access to enterprise insights

Dassault Systèmes is moving toward a future where anyone in an organization can query business data directly, without technical barriers. The company’s upcoming “virtual companions”—Aura, Leo, and Marie, are AI-driven agents designed to make enterprise information accessible through simple conversation. Each assistant carries distinct expertise: Aura focuses on business analysis and strategic insights, Leo supports engineering and production, and Marie handles scientific and materials-based knowledge.

These companions provide precise, relevant responses to natural-language questions by operating on unified enterprise data. Executives, project managers, or engineers can simply ask about shipment timelines, costs, or project delays and receive answers drawn directly from connected systems such as ERP or manufacturing execution platforms. When external events, like tariff changes or supply shortages, occur, Aura can integrate market data to assess their direct financial or operational impact using the company’s own data models.

For leadership teams, this represents a significant shift in how information moves within a business. Instead of filtering queries through analysts or IT departments, insights can surface immediately across all levels of the organization. This decentralization of knowledge empowers faster decision-making and reduces reliance on intermediaries. However, successful adoption will depend on how well these AI companions handle industry-specific terminology and real-world complexity.

Morgan Zimmerman, CEO of 3DExperience at Dassault Systèmes, noted that these AI systems leverage the company’s decades of accumulated industry knowledge encoded in its software. This foundation ensures that the AI understands domain-specific language, improving precision and context in every response. Over time, their capabilities will expand as new “skills” are added, an ongoing evolution aligned with enterprise needs.

Integrating legacy systems requires a fundamental overhaul of data architecture

One of the biggest obstacles to unified data and AI-driven insights is the legacy system landscape inside large organizations. Most enterprises operate on a mix of ERP, product lifecycle management, and manufacturing execution systems that were never built to share data seamlessly. Dassault Systèmes’ model addresses this by restructuring how information is projected and maintained across digital environments, but achieving that integration requires rethinking the entire data architecture.

For it to work effectively, information from each system must be synchronized in real time and remain consistent across the enterprise. When one source updates, say, a design change or an altered supplier record, it must instantly reflect throughout the unified environment. Businesses also need robust conflict resolution processes to prevent data discrepancies that could undermine executive decisions. These architectural changes go far beyond connecting APIs; they require establishing clear data models that define how components, suppliers, and manufacturing resources relate.

For C-level executives, this is not a simple technical challenge, it’s a structural transformation that impacts governance, compliance, and cost control. An integrated architecture becomes the foundation for trustworthy, real-time intelligence. Without it, AI-driven insights risk becoming another layer of approximation rather than genuine understanding.

Morgan Zimmerman, CEO of 3DExperience at Dassault Systèmes, emphasized that the strength of the company’s platform lies in its capacity to represent the true complexity of its customers’ operations. This capability, he said, forms the “baseline projection system” necessary to scale AI within the enterprise. For leaders, the takeaway is clear: sustainable digital transformation will depend on restructuring data from the core, not patching it on top of existing systems.

Intellectual property protection and data rights are critical concerns in AI adoption

As AI becomes integrated into corporate ecosystems, the question of who owns the data, and how that data is used, has become central to enterprise strategy. Many manufacturers now work with global networks of suppliers and partners, which introduces complexity in data-sharing agreements. When AI systems train on joint datasets, ownership of derivative knowledge and model outputs must be clarified. Without this control, companies risk exposing competitive knowledge or violating regulatory obligations.

Dassault Systèmes has introduced IP lifecycle management to address these risks. This framework tracks when and how shared data can be accessed, learned from, and reproduced through AI models. It sets permissions defining which datasets are allowed for training and monitors the lineage of models built on external inputs. This ensures transparency over data provenance while protecting the intellectual property that underpins innovation. In highly regulated industries such as aerospace, healthcare, or defense, this control can determine whether data collaboration is even possible.

Executives need to view IP governance not as a compliance requirement but as a strategic asset. Robust IP protection creates the trust necessary for external collaboration and data exchange, both of which are essential for AI to scale. Enterprises that implement clear governance on data usage will have a decisive advantage in innovation speed and ecosystem partnerships.

Morgan Zimmerman, CEO of 3DExperience at Dassault Systèmes, stated that IP protection today goes beyond securing access, it means securing the right not to train or learn from data without explicit consent. This shift signifies a new era of responsible AI use, where protecting partner data integrity becomes a requirement for business continuity as much as for compliance.

The shift from manual war rooms to AI-driven, real-time analysis signals a new operational model

Dassault Systèmes envisions replacing manual reconciliation and approximation-based decision workflows with automated, AI-driven environments. By combining unified virtual twins with conversational AI companions, organizations can analyze performance, risk, and opportunity within seconds instead of days. This convergence turns enterprise data into a continuous, real-time decision system, where insights are always current, interconnected, and actionable.

For executives, this shift means operating with clarity and speed across the entire business. Decisions no longer depend on teams compiling static reports; they flow directly from live data models that reflect ongoing operations. When questions arise on costs, production efficiency, or geopolitical impacts, answers emerge instantly from unified datasets, not fragmented databases. This real-time responsiveness can redefine competitiveness for global manufacturers and complex supply ecosystems.

Implementation will not be simple. Success depends on addressing integration complexity, maintaining rigorous data governance, and managing behavioral change across organizations. C-suite leaders must ensure that technical innovation is matched with organizational flexibility and data discipline. When these conditions align, the impact extends beyond operational efficiency, it transforms how companies perceive and act on information.

Morgan Zimmerman, CEO of 3DExperience at Dassault Systèmes, positioned this as a fundamental rethinking of enterprise operations, moving from departmental silos toward what he described as “a single point of understanding of the data landscape.” The company plans to launch its virtual companions in mid-2026 as a fully cloud-based service. Whether this model becomes the standard for enterprise intelligence will depend on how effectively these systems deliver measurable value once deployed at real-world scale.

Key executive takeaways

• Outdated decision systems slow enterprise responsiveness: Legacy data silos and manual processes force teams to make critical decisions through slow, fragmented workflows. Leaders should prioritise unified data strategies to accelerate analysis and reduce dependency on manual cross-referencing.
• Unified virtual twins drive faster, informed decisions: By connecting product, process, and supply chain data into a single digital model, businesses can make decisions in real time. Executives should implement virtual twin frameworks to improve forecasting and agility in volatile markets.
• Conversational AI expands enterprise data access: Dassault’s AI companions, Aura, Leo, and Marie, enable natural-language interaction with enterprise data. Leadership should explore conversational AI to make instant insights accessible to non-technical teams and improve cross-functional productivity.
• Modern data architecture is essential for reliability: Integrating legacy systems into unified data environments demands restructured architectures with consistent data models. Executives should treat data integration as a strategic transformation, ensuring real-time accuracy and long-term scalability.
• Data governance defines trust in AI-driven collaboration: As AI increasingly learns from shared supplier and partner data, IP protection becomes critical. Leaders must establish governance frameworks that clearly define data usage rights to secure external collaboration and regulatory compliance.
• Real-time AI analysis redefines enterprise operations: The shift from manual reporting to AI-driven insight enables faster, evidence-based action. C-suite leaders should align teams, governance, and technology investments to fully leverage continuous, real-time decision-making systems.