Generative AI has rapidly evolved from a technological curiosity to a boardroom priority, empowering the creation of original content in text, images and audio formats by learning from data and generating human-like outputs at scale. Agentic AI takes this further by enabling systems to act more like digital collaborators – capable of making decisions, initiating tasks, and executing actions on your behalf, often across complex workflows. This article explores how organizations can move beyond isolated experiments to responsibly scale GenAI and agentic capabilities across the enterprise.

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��ѻ��ý and NVIDIA’s strategic collaboration provides an innovative AI solution designed to transform the way enterprises build and scale AI factories.

This work is aimed to assist organizations, particularly those in regulated industries or with substantial on-premises infrastructure investments, deploy agentic AI into their operations. By leveraging NVIDIA AI Enterprise software, accelerated infrastructure, and the ��ѻ��ý RAISE platform, companies can expect a seamless, high-performance AI solution ready for the future.

Managing AI at scale

��ѻ��ý RAISE is our AI resource management platform, able to manage AI applications and AI agents across multiple environments within a single managed solution. This enables organizations to separate their solution from systemic risk and, leveraging NVIDIA NIM microservices, can centralize AI evaluation, AI FinOps, and model management. The business can then focus on delivering AI-augmented work, while the AI Risk Management team focuses on managing risk, costs, and technical challenges. 

This is a paradigm shift, placing the AI Factory at the center – and not only for private implementation, but as the global point for AI management.

“This new collaboration with NVIDIA marks a pivotal step forward in our commitment to bringing cutting-edge AI-powered technology solutions to our clients for accelerated value creation. By leveraging the power of the NVIDIA AI Stack, ��ѻ��ý will help clients expedite their agentic AI journey from strategy to full deployment, enabling them to solve complex business challenges and innovate at scale.” – Anne-Laure Thibaud, EVP, Head of AI & Analytics Global Practice, ��ѻ��ý

Benefits for modern enterprises

Imagine the ability to deploy agentic AI capabilities with a single click. Our partnership extends the reach of the ��ѻ��ý RAISE platform, bringing these capabilities to NVIDIA’s high-performance infrastructure. This enables companies to realize value more swiftly, and reduce total cost of ownership and deployment risk. Additionally, with the NVIDIA Enterprise AI Factory validated design, we guide organizations in building on-premises AI factories leveraging NVIDIA Blackwell and a broad ecosystem of AI partners.

Some of the other key features to support the navigation of complex, agentic AI solutions include:

• Rapid prototyping and deployment: Speeding up the deployment of AI agents through ready-to-use workflows and streamlined infrastructure, minimizing time-to-market.
• Seamless integration: Embedding AI agent functionalities into current business systems to enhance automation, operational efficiency, and data-informed decision-making.
• Scalability and governance: Deploying AI agents within strong governance models to ensure regulatory compliance, scalability, and consistent performance. ��ѻ��ý RAISE provides specialized agentic features – such as governance, live monitoring, and orchestration – to provide centralized management and measurable outcomes.

Scaling AI in private, on-premises environments

Our solution is designed to help organizations rapidly scale AI in private, on-premises environments. It supports key requirements such as data sovereignty and compliance to meet regulatory and data residency mandates. It also ensures resiliency and high availability for business continuity, security, and privacy controls for air-gapped environments. This solution delivers ultra-low latency for a diverse set of real-time use cases like manufacturing or healthcare imaging, and edge or offline use cases for remote, disconnected environments.

��ѻ��ý RAISE and Agentic Gallery: Demonstrating the future

Alongside NVIDIA, we are bringing the power of ��ѻ��ý RAISE to on-premises infrastructure. This open, interoperable, scalable, and secure solution paves the way for widespread AI adoption. To illustrate our capabilities, we are launching the Agentic Gallery, a showcase of innovative AI agents designed to address diverse business needs and drive digital transformation.

��ѻ��ý and NVIDIA have collaborated on over 200 agents, leveraging the NVIDIA AI Factory to create a robust ecosystem of AI solutions. This collaboration has led to the development of the Agentic Gallery, which is set to revolutionize the way businesses approach AI.

Is your organization ready to place the power of an AI Factory at the center of its business? Get in touch with our experts below.

Brose has more than 14,000 suppliers worldwide – and that means communication can be a challenge. Brose had already transformed its supply chain by creating a single sign-on portal that allowed suppliers to access back-end applications. Now, by adding generative AI, it is delivering even more innovation to make life easier for suppliers.

Brose is a global automotive supplier that builds mechatronic components and systems for doors, seats, electric devices, and electronics in 69 locations in 25 countries. One out of every two cars built in the world contains at least one Brose product.

Streamlining supplier communication

In 2023, the company worked with ��ѻ��ý and SAP to co-innovate a supplier integration app built on SAP’s Business Technology Platform (BTP). This proof of concept became the ��ѻ��ý Supplier Integration for Automotive (CSI4Auto) tool, and delivered a single digital gateway and central collaboration platform for the company’s 14,000 suppliers. The solution eliminated time-consuming, complicated, and resource-intensive daily processes.

CSI4Auto at Brose provides suppliers with a single sign-on to access back-end applications, with central access to any cloud or on-premises application out of the box. And supplier administrators can easily manage new user onboarding, while self-registration allows supplier employees to sign on for different legal entities. The content available to a supplier or legal entity was controlled based on what was relevant. The streamlined process enhances user autonomy and ensures a more efficient and transparent collaboration.

The optimized workflow paid big dividends. The new supplier integration application delivered an 80% reduction in manual effort, 50% faster supplier user onboarding, and a 20% decrease in support volume.

Solving the next challenge

While CSI4Auto solved an immediate business challenge, onboarding new employees on the supplier side still had some lingering hurdles. Suppliers usually receive specifications and quality standards in extensive documents. New employees would spend a lot of time manually reviewing the documents to find the right information for their role.

Language was another obstacle. Working in 25 countries means documents need to be maintained in multiple languages, requires a significant effort. And it was more material that employees needed to wade through before they could find the right information.

Introducing AI-supported innovation

Brose needed to provide relevant information easily, while reducing the administrative burden. The answer: the Supplier Chatbot.

Working with ��ѻ��ý, Brose harnessed the power of generative AI to create a chatbot specifically to serve its supplier community. The chatbot is trained on the supplier documents and is ready to answer questions. The advantages include the following:

• Quick answers: Employees can ask specific questions and receive precise information immediately, skipping the tedious document searches.
• Always available in any language: The AI enables continuous support for suppliers worldwide in any language, without concern for time zones – even without previously translated documents.
• Role-based answers: The chatbot provides tailored information based on the role of the person making the inquiry.

Added to CSI4Auto, the chatbot is an intelligent, user-friendly solution for supplier portals, and it increases the efficiency of collaboration across the supply chain.

��ѻ��ý and Brose brought the Supplier Chatbot from idea to reality within a few weeks, because:

• The modular CSI4Auto architecture enables the seamless integration of new innovations
• AI services in SAP BTP support rapid market introduction
• The co-innovation model combines the expertise of ��ѻ��ý, Brose, and SAP to allow joint pilots to be designed, implemented, and tested quickly.

Enhancing the supply chain

Supply chain transformation is challenging. Streamlining supplier communications adds efficiency and great collaboration. Using CSI4Auto and the Supplier GPT, companies can optimize processes and future-proof the organization to ensure the supply chain continues to operate smoothly. Improved workflows help everyone.

AI technologies can solve some of the most complex problems facing supply chains. By embracing innovation, companies can reshape workflow operations for the better.

��ѻ��ý champions co-innovation to foster sustainable and shared solutions that lead to a competitive advantage. Digital platforms are indispensable, and processes must constantly adapt. We want to elevate digital collaboration between companies and suppliers to achieve better business outcomes.

To find out more about how we made this solution possible, reach out to me on .

��ѻ��ý’s innovative AI agent tool is helping organizations in the public sector and beyond to reduce the cost and time of modernizing their legacy applications

For years, and across industries, rapid developments in digitalization have been creating challenges for companies around adapting to technological change. Particularly challenging are “legacy issues” such as outdated applications or obsolete software that need transferring to current technologies to stay maintainable.

This has unfortunately resulted in us talking about legacy modernization for so long that the first modernizations are already due to be modernized again.

A legacy modernization often involves rewriting the existing application code almost completely, as the original solution was likely based on a different technology or programming language. A software development team could still do this work manually, but it would involve considerable effort.

This is where Gen AI-augmented software engineering comes into play. It allows the development team to automate repetitive tasks by outsourcing them to generative AI. But while providing developers with simple, recurring code fragments is an exciting way to increase productivity and reduce costs, it only marginally reduces the effort involved in a legacy modernization. As a result, these projects remain manual, time-consuming and costly.

Figure 1: ��ѻ��ý research: Turbocharging software with AI
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How �䲹�貵�𳾾��Ծ�’s AI agents are transforming legacy modernization

At ��ѻ��ý, we have developed an innovative approach that takes advantage of agentic AI coding agents to significantly reduce the time needed to modernize legacy applications.

Our AI agent tool – a sophisticated multi-agent system – is purpose-built to make legacy systems future-safe. We have designed it to support software teams in migrating custom-built applications from outdated technology stacks to modern platforms.

At the heart of the solution is the orchestration of a collaborative team of AI agents. This allows development teams to automate a large portion of the modernization process (see figure 2), resulting in a far more efficient, scalable approach to modernizing and migrating software.

Figure 2: Development focuses on defining what needs to be done and leaves much of the processing to the AI agents

Let’s call an AI agent to do the job

Unlike traditional chatbots that simply return responses, AI agents take ownership of tasks and actively drive them forward. They operate autonomously, optimizing based on new information or past mistakes. But they can also interact with large language models, other agents, or non-AI tools such as compilers.

In �䲹�貵�𳾾��Ծ�’s AI agent tool, multiple agents collaborate to modernize a legacy application and transition it to a new technology stack. A human orchestrator defines the overall migration process, providing a structured set of instructions to guide the agents.

The instructions transfer �䲹�貵�𳾾��Ծ�’s deep expertise to the agent, both in understanding the legacy system and in designing the target software architecture. They also determine the specific role each AI agent is assigned in the migration.

So that the transition runs smoothly, the roles of these AI agents mirror those in a human development team migrating a legacy application (see figure 3). A software developer agent analyzes the existing source code and rewrites it using the target technology. A testing or quality assurance (QA) agent then validates the code against predefined test cases. If any tests fail, the QA agent provides detailed error messages and returns the code to the developer for revision.

Once the code has passed all the tests, a DevOps agent takes over to build the complete application and checks it for runtime issues. In this way, every function of the original application is faithfully reimplemented in the new technology stack.

Figure 3: Get the job done – the power of agentic AI agents

An applicable approach across sectors

At ��ѻ��ý, we are already using this approach with many clients in the global public sector and beyond.

A German organization, for example, was looking for a solution to modernize its approximately 40 outdated applications. The client could not develop those applications any further but also recognized the need to integrate new features and switch to a modern technology platform.

Migrating all those legacy applications manually would have been very time-consuming and costly. Thanks to our AI agent tool, though, a large part of this previously manual migration could be automated. The amount of development effort needed dropped correspondingly, and the project costs fell significantly – freeing up the client to concentrate on developing innovative features.

Would you like to try �䲹�貵�𳾾��Ծ�’s AI agent tool for yourself?

By automating the process, our tool makes it faster and more cost-effective to switch legacy applications over to new and future-safe technologies.

What’s more, as every migration path is different, we customize our tool to the modernization context each time.  The enablement team will support you with analyzing your specific migration paths and conducting pilots.

Finally, in case you are wondering, the question of sovereignty does not play a role here. That is because the AI agents run both in your public cloud environment and “air-gapped” on-premise.

Agentic AI marks a paradigm shift from reactive AI systems to autonomous, goal-driven digital entities capable of cognitive reasoning, strategic planning, dynamic execution, learning, and continuous adaptation with a complex real-world environment. This article presents a technical exploration of Agentic AI, clarifying definitions, dissecting its layered architecture, analyzing emerging design patterns, and outlining security risks and governance challenges. The objective is strategically equipping the enterprise leaders to adopt and scale agent-based systems in production environments.

1. Disambiguating Terminology: AI, GenAI, AI Agents, and Agentic AI

�䲹�貵�𳾾��Ծ�’s and top technology trends for 2025 highlight Agentic AI as a leading trend. So, let’s explore and understand various terms clearly.

1.1 Artificial Intelligence (AI)

AI encompasses computational techniques like symbolic logic, supervised and unsupervised learning, and reinforcement learning. These methods excel in defined domains with fixed inputs and goals. While powerful for pattern recognition and decision-making, traditional AI lacks autonomy, memory, and reasoning, limiting its ability to operate adaptively or drive independent action.

1.2 Generative AI (GenAI)

Generative AI refers to deep learning models—primarily large language and diffusion models—trained to model input data’s statistical distribution, such as text, images, or code, and generate coherent, human-like outputs. These foundation models (e.g., GPT-4, Claude, Gemini) are pretrained on vast datasets using self-supervised learning and excel at producing syntactically and semantically rich content across domains.

However, they remain fundamentally reactive—responding only to user prompts without sustained intent—and stateless, with no memory of prior interactions. Crucially, they are goal-agnostic, lacking intrinsic objectives or long-term planning capability. As such, while generative, they are not autonomous and require orchestration to participate in complex workflows or agentic systems.

1.3 AI Agents

An agent is an intelligent software system designed to perceive its environment, reason about it, make decisions, and take actions to achieve specific objectives autonomously.

AI agents combine decision-making logic with the ability to act within an environment. Importantly, AI agents may or may not use LLMs. Many traditional agents operate with symbolic reasoning, optimization logic, or reinforcement learning strategies without natural language understanding. Their intelligence is task-specific and logic-driven, rather than language-native.

Additionally, LLM-powered assistants (e.g., ChatGPT, Claude, Gemini) fall under the broader category of AI agents when they are deployed in interactive contexts, such as customer support, helpdesk automation, or productivity augmentation, where they receive inputs, reason, and respond. However, in their base form, these systems are reactive, mostly stateless, and lack planning or memory, which makes them AI agents, but not agentic. They become Agentic AI only when orchestrated with memory, tool use, goal decomposition, and autonomy mechanisms.

1.4 Agentic AI

Agentic AI is a distinct class where LLMs serve as cognitive engines within multi-modal agents that possess:

• Autonomy: Operate with minimal human guidance
• Tool-use: Call APIs, search engines, databases, and run scripts
• Persistent memory: Learn and refine across interactions
• Planning and self-reflection: Decompose goals, revise strategies
• Role fluidity: Operate solo or collaborate in multi-agent systems

Agentic AI always involves LLMs at its core, because:

• The agent needs to understand goals expressed in natural language.
• It must reason across ambiguous, unstructured contexts.
• Planning, decomposing, and reflecting on tasks requires language-native cognition.

Let’s understand with a few examples: In customer support, an AI agent routes tickets by intent, while Agentic AI autonomously resolves issues using knowledge, memory, and confidence thresholds. In DevOps, agents raise alerts; agentic AI investigates, remediates, tests, and deploys fixes with minimal human input.

Agentic AI = AI-First Platform Layer where language models, memory systems, tool integration, and orchestration converge to form the runtime foundation of intelligent, autonomous system behavior.

AI agents are NOT Agentic AI. An AI agent is task-specific, while Agentic AI is goal-oriented. Think of an AI agent as a fresher—talented and energetic, but waiting for instructions. You give them a ticket or task, and they’ll work within defined parameters. Agentic AI, by contrast, is your top-tier consultant or leader. You describe the business objective, and they’ll map the territory, delegate, iterate, execute, and keep you updated as they navigate toward the goal.

2. Reference Architecture: Agentic AI Stack

2.1 Cognitive Layer (Planning  and Reasoning)

• Foundation Models (LLMs): Core reasoning engine (OpenAI GPT-4, Anthropic Claude 3, Meta Llama 3).
• Augmented Planning Modules: Chain-of-Thought (CoT), Tree of Thought (ToT), ReAct, Graph-of-Thought (GoT).
• Meta-cognition: Self-critique, reflection loops (Reflexion, AutoGPT Self-eval).

2.2 Memory Layer (Statefulness)

To retain and recall information. This is either information from previous runs or the previous steps it took in the current run (i.e., the reasoning behind their actions, tools they called, the information they retrieved, etc.). Memory can either be either session-based short-term or persistent long-term memory.

• Episodic Memory: Conversation/thread-local memory for context continuation.
• Semantic Memory: Long-term storage of facts, embeddings, and vector search
• Procedural Memory: Task-level state transitions, agent logs, failure/success traces.

2.3 Tool Invocation Layer

 Agents can take action to accomplish tasks and invoke tools as part of the actions. These can be built-in tools and functions such as browsing the web, conducting complex mathematical calculations, and generating or running executable code responding to a user’s query. Agents can access more advanced tools via external API calls and a dedicated Tools interface. These are complemented by augmented LLMs, which offer the tool invocation from code generated by the model via function calling, a specialized form of tool use.

2.4 Orchestration Layer

• Agent Frameworks: LangGraph (DAG-based orchestration), Microsoft AutoGen (multi-agent interaction), CrewAI (role-based delegation).
• Planner/Executor Architecture: Isolates planning logic (goal decomposition) from executor agents (tool binding + result validation).
• Multi-agent Collaboration: Messaging protocols, turn-taking, role negotiation (based on BDI model variants).

2.5 Control, Policy & Governance

• Guardrails: Prompt validators (Guardrails AI), semantic filters, intent firewalls.
• Human-in-the-Loop (HITL): Review checkpoints, escalation triggers.
• Observability: Telemetry for prompt drift, tool call frequency, memory divergence.
• ABOM (Agentic Bill of Materials): Registry of agent goals, dependencies, memory sources, tool access scopes.

3. Agentic Patterns in Practice

(Source-OWASP)

As Agentic AI matures, a set of modular, reusable patterns is emerging—serving as architectural primitives that shape scalable system design, foster consistent engineering practices, and provide a shared vocabulary for governance and threat modeling. These patterns embody distinct roles, coordination models, and cognitive strategies within agent-based ecosystems.

• Reflective Agent : Agents that iteratively evaluate and critique their own outputs to enhance performance. Example: AI code generators that review and debug their own outputs, like Codex with self evaluation.
• Task-Oriented Agent :Agents designed to handle specific tasks with clear objectives. Example: Automated customer service agents for appointment scheduling or returns processing.
• Self-Learning and Adaptive Agents: Agents adapt through continuous learning from interactions and feedback. Example: Copilots, which adapt to user interactions over time, learning from feedback and adjusting responses to better align with user preferences and evolving needs.
• RAG-Based Agent: This pattern involves the use of Retrieval Augmented Generation (RAG), where AI agents utilize external knowledge sources dynamically to enhance their decision-making and responses. Example: Agents performing real-time web browsing for research assistance.
• Planning Agent: Agents autonomously devise and execute multi-step plans to achieve complex objectives. Example: Task management systems organizing and prioritizing tasks based on user goals.
• Context- Aware  Agent:  Agents dynamically adjust their behavior and decision-making based on the context in which they operate. Example: Smart home systems adjusting settings based on user preferences and environmental conditions. 
• Coordinating Agent :Agents facilitate collaboration and coordination and tracking, ensuring efficient execution. Example: a coordinating agent assigns subtasks to specialized agents, such as in AI powered DevOps workflows where one agent plans deployments, another monitors performance, and a third handles rollbacks based on system feedback.
• Hierarchical Agents :Agents are organized in a hierarchy, managing multi-step workflows or distributed control systems. Example: AI systems for project management where higher-level agents oversee task delegation.
• Distributed Agent Ecosystem: Agents interact within a decentralized ecosystem, often in applications like IoT or marketplaces. Example: Autonomous IoT agents managing smart home devices or a marketplace with buyer and seller agents.
• Human-in-the-Loop Collaboration: Agents operate semi-autonomously with human oversight. Example: AI-assisted medical diagnosis tools that provide recommendations but allow doctors to make final decisions.

4. Security and Risk Framework

Agentic AI introduces new and very real attack vectors like (non-exhaustive):

• Memory poisoning – Agents can be tricked into storing false information that later influences decision
• Tool misuse – Agents with tool or API access can be manipulated into causing harm
•  Privilege confusion – Known as the “Confused Deputy,” agents with broader privileges can be exploited to perform unauthorized actions
• Cascading hallucinations – One incorrect AI output triggers a chain of poor decisions, especially in multi-agent systems
• Over-trusting agents – Particularly in co-pilot setups, users may blindly follow AI suggestions

 5. Strategic Considerations for the enterprise leaders

5.1 Platformization

• Treat Agentic AI as a platform capability, not an app feature.
• Abstract orchestration, memory, and tool interfaces for reusability.

5.2 Trust Engineering

• Invest in AI observability pipelines.
• Maintain lineage of agent decisions, tool calls, and memory changes

5.3 Capability Scoping

• Clearly delineate which business functions are:
• LLM-augmented (copilot)
• Agent-driven (semi-autonomous)
• Fully autonomous (hands-off)

5.4 Pre-empting and managing threat

• Embed threat modelling into your software development lifecycle—from the start, not after deployment
• Move beyond traditional frameworks—explore AI-specific models like the MAESTRO framework designed for Agentic AI
• Apply Zero Trust principles to AI agents—never assume safety by default
• Implement Human-in-the-Loop (HITL) controls—critical decisions should require human validation
• Restrict and monitor agent access—limit what AI agents can see and do, and audit everything

5.5 Governance

• Collaborate with Risk, Legal, and Compliance to define acceptable autonomy boundaries.
• Track each agent’s capabilities, dependencies, and failure modes like software components.
• Identify business processes that may benefit from “agentification” and identify the digital personas associated with the business processes.
• Identify the risks associated with each persona and develop policies to mitigate those. 

6. Conclusion: Building the Autonomous Enterprise

Agentic AI is not just another layer of intelligence—it is a new class of digital actor that challenges the very foundations of how software participates in enterprise ecosystems. It redefines software from passive responder to active orchestrator. From copilots to co-creators, from assistants to autonomous strategists, Agentic AI marks the shift from execution to cognition, and from automation to orchestration.

For enterprise leaders, the takeaway is clear: Agentification is not a feature—it’s a redefinition of enterprise intelligence. Just as cloud-native transformed infrastructure and DevOps reshaped software delivery, Agentic AI will reshape enterprise architecture itself.

And here’s the architectural truth: Agentic AI cannot scale without platformization.

To operationalize Agentic AI across business domains, enterprises must build AI-native platforms—modular, composable, and designed for autonomous execution.

The future won’t be led by those who merely implement AI. It will be defined by those who platformize it—secure it—scale it.

Harnessing Financial Operations for Smarter Cloud Transitions

Technology is transforming every organization and business, driving them to surpass economic development. Many enterprises are continuously adopting and shifting workloads to the public cloud, expecting numerous benefits such as flexibility, scalability, agility, and cost savings. However, with the myriad of options available for cloud adoption, there is also a risk of uncontrolled expenditure. It is quite common for enterprises to express that they are not receiving the benefits they anticipated from the shift from data centers to the cloud. The following section of this article delves into those key challenges in detail.

When an organization decides to move to the cloud, it starts with migration planning. Often, gaps in migration planning, inadequate assessments, lack of cloud-ready staff, complex designs, failed migrations, rework, and app or tool dependencies extend migration timelines beyond expectations. Businesses pay for their existing on-premises infrastructure while incurring new expenses for cloud migration, leading to a migration bubble. Additionally, migrating only a portion of the infrastructure while leaving other components on-premises prevents businesses from enjoying the full benefits.

Our practical experience shows that merely migrating workloads from on-premises or co-located data centers to the cloud is not enough. Regardless of the chosen hyperscaler, issues arise when clients overlook cloud best practices, leading to challenges in cloud governance and cost management. It is evident that many enterprises are still approaching cloud adoption with a data center mentality and are hesitant to embrace essential cloud features like autoscaling, on-demand provisioning, and self-service, which have the potential to drive significant innovation.

The shift from data centers to the cloud has also disrupted traditional procurement processes by empowering developers with greater purchasing authority. It enables engineers to spend company funds with just a click of a button or a line of code, bypassing the lengthy conventional procurement procedures including purchase requisitions, calling tenders, vendor scouting, and purchase orders.

Due to these challenges, monthly bills from hyperscalers can spiral out of control, extending the payback period for investments and negating the benefits of cloud transition. Therefore, it is crucial to develop a comprehensive migration strategy with operational governance controls to avoid potential pitfalls and adhere to cost optimization goals, commonly referred to as FinOps. This approach helps free up budgetary funds and accelerates the shift to the cloud. Enterprises must ensure their personnel are cloud-ready and have strong procedures to analyze expenditures and identify key cost drivers. Assessing available cloud resources is also advisable for optimization.

The primary goal of every organization is to lower technological costs, and the cloud is no exception. As companies continue to invest more in the public cloud, recurring cloud run costs will increase. This trend underscores the growing importance of FinOps as a recognized financial management discipline.

The history of systems engineering is rooted in the need to manage and integrate complex projects with significant components or ‘systems’, especially during times of rapid technological advancement. So, it should come as no surprise that the concept grew from the large-scale military endeavors required during the Second World War. The need to ensure everything worked together efficiently gave birth to the systematic planning and coordination methods that remain at the heart of modern systems engineering concepts.

Whilst the underpinning principles of systems engineering have remained unchanged, how they are applied in practice is constantly evolving, as large-scale industrial projects push the boundaries of complexity and scale. This remains especially relevant in aerospace and defence (A&D). Today’s A&D systems are more intricate and connected than ever before. Consider autonomous robotics surveillance systems; electronic warfare operations; or the current generation of long-haul passenger aircraft — all of which involve layers of complexity beyond the conventional industry programs for which systems engineering was originally designed.

Smart systems engineering shows promise in production

The rise of intelligent digital technologies has also dictated how systems engineering is practiced.

It has driven the evolution of common methodologies into Model Based Systems Engineering (MBSE) – in which, advanced tools allow engineers to create virtual twins of complex systems. These improve design and testing whilst smoothing the integration of new technologies such as artificial intelligence (AI) and autonomy, ensuring their introduction is safe and efficient, and predicting their effects on the overall system. The emergence of MBSE now offers companies a way to design smarter, collaborate better, and innovate faster, creating virtual twins and limiting the need to build a physical prototype until everything has been simulated and tested digitally first.

Why MBSE for A&D manufacturing?

MBSE has already been transformational for the design and development of novel A&D systems, but ��ѻ��ý and Dassault Systèmes believe that it has the potential to achieve much more. This is why we are working together to explore the application of MBSE further along the A&D product lifecycle, into manufacturing and production.

MBSE is highly relevant here because it is very effective at streamlining processes, improving quality, and managing complexity – some of the biggest challenges for large scale manufacturing teams in the A&D industry. By applying the same digital tools used in the development of a new system to its manufacture, A&D companies can simulate the required production process, including assembly lines, resource allocation, and workflow. This gives them the visibility to optimize the production schedule, minimize bottlenecks, and improve efficiency all before physical production begins.

MBSE’s ability to foster more effective collaboration between the many moving parts of a large production operation means it is an effective way to remove internal silos that can slow down and complicate large projects. MBSE makes this possible by bridging the gap that exists between design and production and the various teams within. It offers a single source of truth, using digital tools that integrate both processes. This has become essential because in this environment, engineers and production teams are often separated, and when they do work together, they rarely speak the same language. Both create dangerous gaps in the system lifecycle that can result in delays, waste, and cost. MBSE gives both groups a way to connect through a common view of real time data about both of their worlds, and helps them avoid issues such as mismatched specifications or unclear instructions. This is particularly important in the delivery of large A&D projects such as 6^th generation fighters or high earth orbit satellites, which often involve intricate assemblies of hundreds of thousands of components, all reliant on each other and all feeding into a considerable overall system. Here MBSE can help production teams ensure that every part fits together correctly by defining precise relationships between components and systems at the earliest opportunity, reducing human errors during assembly.

��ѻ��ý and Dassault Systèmes join forces

At ��ѻ��ý and Dassault Systèmes, our teams have combined their respective experience of MBSE to offer a disruptive capability specifically designed for A&D production. Our collective experience spans every aspect of systems engineering, digital transformation, and production processes throughout the lifecycle of aerospace and defence systems, giving us a unique perspective on how the theory of MBSE can be applied in practice for tangible benefits.

We also recognise that MBSE is not a magical solution to address every manufacturing challenge. But we can see it is already proving powerful for supporting the identification of high-level solutions and the subsequent articulation of detailed designs for A&D systems. We believe that MBSE has the power to enhance A&D manufacturing by improving efficiency, quality, and agility, ensuring that the complex systems we are designing today for the future of aerospace and defence can be built accurately and delivered on time. 

In our first blog on the topic of Model Based Systems Engineering (MBSE) we looked at the bigger picture – where systems engineering came from, how its evolution into MBSE has become an important opportunity for Aerospace and Defense (A&D) innovators, and why it should also be integrated into their production environments.

In this blog we are going to delve deeper into how MBSE can help A&D companies solve some of their most pressing production challenges – outlining the nine that our customers tell us they experience most often.   

1. Bridging the Gap Between Design and Manufacturing

MBSE provides engineers a way to create a single digital repository of all the information related to a project. This acts as the single source of truth and is used to integrate design and manufacturing teams – giving everyone visibility and access to data from every system and process involved. This ensures that manufacturing teams have access to accurate, up-to-date information about the product. It helps avoid issues like mismatched specifications or unclear instructions, which can lead to production delays or errors. And it provides a common language for both engineers and production teams to use, bringing together two very different worlds that have traditionally struggled to understand each other.

MBSE also enables manufacturing and production teams to approach challenges with a System of Systems (SoS) perspective. This gives them a view of the wider environment in which individual production systems take place that recognizes how they all connect to create complex integrations, working together to achieve a higher-level capability of that no single system could achieve alone. As A&D programs become larger, more complex and more intricate, this is a way to make sure teams are aware of global production challenges that could be missed if individual products or processes are simply viewed in isolation.

2. Enhancing Production Planning

MBSE allows A&D manufacturers to simulate the production process in a virtual environment before physical manufacturing begins. By creating a comprehensive 2D digital simulation of assembly lines, resource allocation, and workflow, manufacturers can identify potential inefficiencies, bottlenecks, or conflicts in the production process early on. By leveraging MBSE’s predictive capabilities, production teams can test different scenarios, adjusting schedules, workforce distribution, and equipment usage to optimize efficiency. This means that manufacturers can make data-driven decisions about how to best allocate resources, whether it’s ensuring that critical components arrive just in time or that personnel with the right expertise are positioned where they are most needed.

3. Supporting Complex Assembly

The scope of modern A&D systems is becoming vast. They often involve intricate assemblies with thousands of components. each with precise tolerances, dependencies, and functional relationships. They also require a blend of multiple types of technologies including software, advanced materials, electronics, and sensors. Small mistakes can result in much bigger problems downstream. A single misalignment, incorrect specification, or missing part can cause costly delays, rework, or even mission-critical failures. MBSE provides a structured, model-first approach to managing this complexity by defining precise relationships between components, systems, and subsystems – integrating all subsystems from the outset. This ensures that every part is correctly positioned, oriented, and integrated within the larger system or SoS. Engineers and production teams can use these digital models to validate component interactions, identify potential fit or alignment issues before production begins, and simulate the step-by-step assembly process.

Furthermore, MBSE enables seamless communication across teams involved in different stages of the assembly process. This includes creating a a single source of data that connects design intent to the physical assembly process. By providing a single source of “truth” in this way, all stakeholders—designers, engineers, technicians, and suppliers—are always aligned with the latest specifications and assembly instructions. This is particularly valuable in large-scale A&D programs, where different teams may be working on different sections of an aircraft, spacecraft, or Defense system, often across multiple facilities or even countries.

4. Quality Assurance and Testing

MBSE integrates quality assurance and testing into the digital engineering process to help teams prepare for manufacturing, ensuring defects are identified before production begins. By simulating and validating processes within a virtual environment, manufacturers can detect potential weaknesses, optimize performance, and reduce costly rework.

MBSE also standardizes testing protocols, providing a unified reference for evaluating compliance and streamlining quality control across production sites. This is particularly important in A&D where the scale and complexity of systems means teams are often spread across multiple sites and countries – all with different infrastructure. And it simplifies the regulatory compliance process by maintaining a comprehensive digital record of all testing and validation, ensuring adherence to industry standards while expediting certification.

5. Facilitating change management

In A&D production, changes to requirements or designs are inevitable due to evolving customer needs, regulatory updates, supply chain constraints, or technological advancements. Managing these changes efficiently is crucial to maintaining production schedules, ensuring quality, and minimizing cost overruns. MBSE provides a structured, digital approach to change management by integrating real-time updates into a unified digital simulation that is already used as the single source of truth  by the production team.

Rather than relying on fragmented documentation and manual updates, MBSE ensures that any design or process modification is instantly reflected across all related components, systems, and workflows. This automatic propagation of changes reduces the risk of inconsistencies, miscommunication, and outdated information reaching the factory floor. And because engineers, production teams, and suppliers all work from the same updated model, maintaining alignment and avoiding costly errors caused by working with obsolete specifications.

MBSE also improves impact analysis by enabling manufacturers to simulate and assess the consequences of proposed changes before implementation. By analyzing how modifications affect system performance, assembly sequences, or supply chain logistics, manufacturers can make data-driven decisions that balance efficiency, cost, and feasibility. This predictive capability helps prevent disruptions and ensures that changes enhance rather than hinder production.

6. Supply Chain Integration

Large-scale industrial manufacturing in the A&D sector relies on intricate, multi-tiered supply chains, with components sourced from numerous suppliers across different regions. Ensuring that each supplier delivers parts on time, to the correct specifications, and in sync with production schedules is critical for maintaining efficiency and avoiding costly delays. MBSE enhances supply chain integration by providing a standard system modelling approach and creating a common communication framework. This not only aligns suppliers with manufacturing requirements but provides them an easy way to engage up and down the supply chain to ensure seamless collaboration and coordination across all stakeholders.

This is partly down to MBSE’s ability to integrate supplier data directly into the design and production workflow. By linking supplier-provided digital models with the overall system architecture, manufacturers can conduct virtual fit and performance tests before parts arrive at the assembly line so integration issues become less likely and all components work together as intended.

MBSE also supports supply chain resilience by enabling real-time monitoring and predictive analytics. Manufacturers can track the impact of supply chain disruptions—such as material shortages, shipping delays, or regulatory changes—on production schedules and system performance. By simulating different sourcing scenarios within a virtual twin of all manufacturing operations, companies can identify alternative suppliers or adjust production timelines in advance, mitigating risks before they escalate.

7. Faster production ramp up and scalability

Adhering to delivery schedules for mission-critical capabilities is paramount for A&D programs. Manufacturers are increasingly turning to MBSE to significantly reduce the timeline from initial concept to delivering a functional product to customers. MBSE facilitates more efficient and accurate design iterations, enabling earlier entry into production. This approach allows companies to scale up production rates more rapidly and with greater assurance. ​

8. Compliance and Traceability

In most large A&D projects, every part of the manufacturing process must meet strict regulations and standards. MBSE provides a detailed, traceable digital record of how designs and processes comply with these requirements, making audits and certifications easier. This is invaluable for regulatory compliance, certification, and quality assurance, particularly in highly regulated industries such as A&D. It also improves collaboration between teams by providing clear visibility into the evolution of product designs and manufacturing processes.

9. Cost Control and Risk Reduction

MBSE contributes to significant cost savings throughout the product lifecycle. By catching design flaws early and reducing rework, companies can avoid expensive changes later in development. This methodology also streamlines compliance with industry regulations, helping manufacturers avoid costly penalties and production halts.

And by identifying these potential production challenges early in the planning phase, MBSE helps manufacturers anticipate and address material constraints, process inefficiencies, and integration issues before they become costly roadblocks. Effectively, by simulating different scenarios and evaluating the impact of various constraints, MBSE allows teams to make informed decisions that optimize efficiency and resource allocation. This proactive approach ensures that production processes remain on schedule, reducing the likelihood of unexpected delays or last-minute redesigns that themselves can lead to short- and long-term financial consequences.

Many of these challenges are already material for aerospace and defense companies. They will only become more onerous as products and systems continue to increase in complexity. Act now and start to develop your MBSE capability for production teams before they do.

At a time when techceleration is reshaping business, how can organizations adapt and scale IT models to utilize technologies effectively?

The rapid evolution of technology is transforming industries at an unprecedented pace. Generative AI, agentic AI, and the emerging potential of artificial general intelligence (AGI) are not just generating attention, they are fundamentally reshaping how businesses create value, innovate, and operate.

These technological advances are accelerating service delivery, client interactions, and business models, making IT integral to every value stream and function within companies. This shift is driving the phenomenon we call techceleration, where software increasingly fuels customer and consumer value across all industries (softwarization). Simultaneously, ubiquitous technologies make it essential for everyone in any business function to engage with them (pervasive tech).

Welcome to the world of techceleration

Techceleration is more than a trend, it’s a strategic shift in how organizations deliver value, innovate, and maintain competitiveness. Technology has moved beyond being just a set of tools; it’s now integral to reshaping business operations, collaboration, and growth.

Three major forces shape this shift:

• Software as a central value driver throughout the customer lifecycle.
• Technologies like AI, IoT, and blockchain impacting all business functions, not just IT.
• Continuous transformation replacing one-time change efforts, with a focus on speed, adaptability, and purpose-driven innovation.

Moreover, technology’s role in sustainability is growing. The adoption of sustainable technology products can reduce environmental impact and enabling smarter energy use while enhancing performance. Yet technology also poses challenges for sustainability goals such as increased compute demands from generative AI. 

To bridge the gap between technological progress and organizational readiness, it’s crucial to select the right technologies while fostering operating models and cultures that fully harness their potential. 

Technology is not just for engineers it’s a business imperative. Our report, “” predicts that by 2030, 29% of company revenue will stem from software and digital solutions a 400% increase from 2022.

Keeping pace with sustainable technology trends helps organizations align innovation with environmental goals.

Softwarization as strategy

As the global economy shifts towards a software-driven paradigm, businesses must embrace software as a core strategic asset. The transformation of operating models towards software-driven frameworks is essential to remain competitive. According to our “The Art of Software” report, 65% of organizations recognize that software will drive future disruption and strategic advantages.

In the automotive industry, for instance, 90% of companies are generating new revenue through software-defined products, and 61% have enhanced customer experiences through software.

These innovations are increasingly being designed with sustainable technology principles in mind, ensuring that digital transformation also supports environmental stewardship.

Pervasive tech integration

Technology has become an indispensable enabler across all business functions a phenomenon we call pervasive tech. In a world shaped by rapid change and digitalization, operational processes are increasingly automated and standardized. This makes it vital for every business function to integrate technology deeply, from competencies to culture.

AI agents reduce manual workloads significantly, while workflow automation enhances efficiency. Consequently, it is essential for employees at every level to adapt to pervasive technologies, ensuring seamless integration into daily tasks and long-term strategies.

Nine + one priorities for tech leaders  

Navigating the forces of techceleration, softwarization, and pervasive tech requires focusing on strategic priorities that foster sustainable growth and competitive advantage. Based on insights from client engagements, global SMEs, and the ��ѻ��ý Research Institute, we have identified nine core priorities for tech-savvy CxOs, plus one crucial focus on sustainability: 

Exploring the future of tech 

Over the coming months, we will take a deep dive into each of these areas – techceleration, softwarization, and pervasive tech – to explore how organizations can adapt, scale, and thrive. Stay tuned for our views on opportunities and challenges as well as practical recommendations. 

�䲹�貵�𳾾��Ծ�’s Value Realization Office makes integrating AI solutions, enhancing efficiency, and unlocking value across operations and processes possible in just a few clicks.

The Connected Enterprise is a vision for most organizations – a seamless flow of actions and communication built on the shared goal of ultimately driving value. The value proposition is clear: better prioritization of activities, quicker decision making, and a hyper-efficient organization supercharged by the right technology.

The main aspects of a Connected Enterprise include transparency facilitated by data integration of the utmost quality, with information flowing seamlessly through systems and being interpreted by human beings. An example of a truly Connected Enterprise is one that enables actions taken on one system not just to be reflected on other systems but also to trigger actions on those systems via automation or bots.

Introducing the VRO

The Value Realization Office (VRO) is the engine that keeps the Connected Enterprise running. It is a future-ready AI-enabled solution that brings to light transformation happening across the Connected Enterprise and catalyzes value being generated. It connects business operations in a virtuous cycle of prioritization, focused execution and value generation.

The VRO is instrumental in all steps of the innovation cycle; from brainstorming, and design, to deployment, and finally to the management of performance (pre-, during, and post go-live). Key pillars of the VRO include:

• Defining strategic intent
• Enabling senior stakeholder sponsorship
• Deconstructing strategic intent into business priorities
• Facilitating the orchestration of the development and its adoption
• Proactively measuring the generation of committed business outcomes.

With a value-first mindset as the key design principle, the VRO enables a forward-looking value-based approach to transformation rather than a metric or KPI-based traditional approach, which is retrospective. Value can be “unlocked” throughout various aspects of the enterprise from people and processes to technology and data, all of which the VRO integrates.

From experience, we observed five iconic changes that delivered an infinite value flow to our clients – including our One Operating Model, One Team mindset, Digital Channel Switch, Gen AI adoption, and proven Connected Enterprise approach of tomorrow (see diagram below).

By shedding light on the results – good and bad – of business decisions, the VRO is integral to good business management. It not only keeps the Connected Enterprise running but also permeates all areas of the organization, encapsulating value to become a must-have in any corporate environment. The VRO is truly the conductor of the connected value-generating orchestra.

The VRO: Enabling the Connected Enterprise

The value-first mindset enables the VRO to focus on primary areas that the enterprise should improve. Organizations often wish to improve standalone metrics. However, by setting up the VRO we invite organizations to think more broadly and strategically, often aiming to improve the bottom line directly. Once set up, a curated value taxonomy including strategic Value Unlocks at each level is sourced using our best-in-class Digital Global Enterprise Model (DGEM).

We run an extensive analysis of the business, relying on �䲹�貵�𳾾��Ծ�’s assets and industry experience to pick relevant Value Unlocks that will generate the biggest impacts on the bottom line. The Value Unlocks are then distilled into various initiatives that are pursued across various facets of the Connected Enterprise. Performance management is dictated and monitored by the VRO, ensuring there is value at each step, maintaining the overall vision of a Connected Enterprise.

The VRO has proven its worth in various industries such as CPRD, infrastructure, manufacturing, media and entertainment, and energy services. Feedback from clients reaffirmed that the VRO was the centerpiece to the Connected Enterprise, ensuring that the target operating model (TOM) was an embodiment of the business strategy, concentrating on Value Unlocks at each facet of the TOM. This enables us to create an “infinite value flow” for our clients with value realization as the start and goal (see diagram above).

By identifying strategic objectives around revenue growth, cost reduction, and cash optimization, the VRO has the potential to deliver significant value across things such as bad debt and cost recovery, employee retention, productivity, and revenue uplifts, to name a few. Notable improvements could also be achieved in order fulfilment optimization, DPO and DSO, duplicate payment elimination and fraud detection.

Knowledge needs to be kept within any Connected Enterprise and leveraged to upskill people. The VRO sets up the right environment for digital knowledge management, including automated workflows, seamlessly linked with our clients’ learning hubs. Data and system integration are also key to a robust and operational Connected Enterprise. The VRO is supported by a host of tools across functionalities, such as:

• Knowledge library and learning – Business Optix, SAP Enable Now
• Process excellence – Digital Global Enterprise Model
• Training and people analytics – Edligo
• Process discovery – digital twin tools such as Celonis and Signavio
• Technology optimization – ClearSight
• Value driven dashboards – built on PowerBI, Tableau, and advanced analytics tools.

The VRO: Looking ahead

We are only scratching the surface here in exploring how the VRO enables the Connected Enterprise. It has the potential to become an AI enabled digital consultant, feeding on past innovations, case studies, transformations and transitions, and analyzing and defining portfolio initiatives to build a truly comprehensive, forward-looking, value-driven Connected Enterprise. The Connected Enterprise of the future, combined with the VRO of the future, has the power to reimagine how we structure and design organizations, eliminating siloes between traditional functions.

As an example of how the VRO orchestrates the Connected Enterprise from end to end, Value Unlocks are designed by R&D and engineering teams and executed throughout the supply chain processes by integrated supply chain operations which impact delivery and have cascading effects on cash conversion cycles and carbon emissions monitored by F&A teams. This also impacts customer and supplier experiences evaluated and bolstered by CX teams – all of which can directly impact the bottom line.

Thus, Connected Enterprises could be overhauled to be structured by value delivery flows rather than by function. These structures would seamlessly fit into the VRO’s design principles and framework to empower bold decisions with a broader purview. It might even be possible to have a “generative VRO” analyzing the past, changing the present, and predicting the future to automatically feed the transformation pipeline.

This is a prime example of how our offerings and portfolios bring synergy opportunities to collaborate, strategize, and work as one global integrated force to help our clients make the meaningful connections they want.