The Comprehensive Guide to Product Lifecycle Management

Much of our daily work consists of gathering information, searching through resources, rewriting the same ideas in different formats, or summarizing discussions. This kind of grunt work naturally makes people look for ways to save time, reduce repetition, and reach a useful outcome with less friction. And agentic AI presents itself as a powerful shortcut, capable of turning a rough thought into a polished email, a meeting into a concise summary, or a manual workflow into an automated step.

For many teams, that is precisely the trap: that AI chatbots and copilots are capable of the same kind of conceptual work people do instinctively when taking a vague goal, breaking it into meaningful parts, and moving it toward a useful outcome. And they, eventually, become lazy in the wrong way.

AI adoption starts with realistic expectations

The public promise around AI adoption suggests something close to a general-purpose thinking partner. A system that can understand goals, interpret context, and work through a problem much like an experienced colleague would. But that couldn’t be further from the truth.

AI is a predictor, not an understanding system

At its core, AI predicts likely continuations. It does not understand your business model, your operating constraints, or the relationships between the moving parts inside your organization in the way a person does. It does not know why one exception matters more than another, why a process exists in its current form, or what tradeoffs sit behind a decision unless those things are made explicit.

As a result, AI can be fast, but still shallow. It may generate something that looks complete while missing the mechanics that actually matter to the business.

AI can sound professional and still be wrong

One of the reasons AI is so persuasive is that it presents information with confidence. It writes clearly, and structures arguments well, but fluent output should not be mistaken for expertise.

An experienced professional in any domain will typically outperform AI where nuance, context, and consequence matter. They can spot what is missing, question what does not fit, and recognize when an answer is technically plausible but practically wrong. AI, by contrast, can hallucinate, or flatten important distinctions.

More context does not automatically make AI better

The model does not understand information the way a person does. It does not inherently know which detail is strategically important, which relationship between inputs actually drives the outcome, or which exception should outweigh the broader pattern. When too much context is added without clear structure, prioritization, or framing, important information gets diluted by secondary or weak signals, and the output reflects surface-level correlations.

This is why strategic selection still remains a human responsibility.

The human role in AI strategy and implementation

A properly structured and governed AI strategy is what turns agentic AI from a shortcut into a system. But that structure does not come from the model itself. It comes from the person using it. The one that determines what information matters, what can be ignored, what the actual goal is, and which constraints should shape the output. AI does not make those decisions well on its own, at least not reliably in its current state.

For this reason, every human-agent exchange should be guided by five essential elements:

1. A scope with boundaries

AI performs best when the task is defined clearly enough that the system knows what it is being asked to do and what sits outside its role. Without boundaries, AI tends to default to broad, generic responses that may look complete but lack operational relevance.

A system asked to support a service workflow, for example, needs to know whether it is drafting a response, classifying an issue, extracting information, or recommending a next step.

2. A defined process

AI is most effective when it supports a process rather than replacing one that has never been properly defined. If the workflow itself is unclear, inconsistent, or heavily dependent on undocumented workarounds, AI will reflect that ambiguity, not resolve it.

This is often where organizations overestimate the technology. They assume the model can compensate for process gaps, while in practice it usually amplifies them.

3. A definition of quality

If no one defines what “good” looks like, AI cannot reliably produce it. Quality has to be made explicit. That means deciding what level of accuracy is acceptable, what kinds of mistakes matter, what should trigger review, and where the cost of error is too high for guesswork.

In customer communication, “good” may mean clarity and consistency. In regulated documentation, it may mean traceability and compliance. In an internal support workflow, it may mean speed with a human checking exceptions. AI cannot infer those standards reliably on its own.

4. Curated context

Context is valuable only when it is relevant, structured, and timed correctly. That means selecting the inputs that genuinely influence the task, excluding what does not, and making the relationships between inputs as legible as possible.

In a manufacturing setting, that might mean prioritizing machine status, order constraints, and maintenance windows over general historical information, while in a commercial process, grounding the system in account history, product rules, and current workflow stage.

5. Controlled rollout

AI should not move from a promising output to full autonomy in a single step. Before a system is trusted in real operations, teams need to test it in practice, compare its output against human judgment, and define where review remains necessary.

Let AI run beside people first, supporting the workflow, exposing patterns, proving its reliability, and monitoring performance over time. Teams need to see where the system performs well, where it fails, what kinds of errors recur, and when changes in context or model behavior require adjustment. That is how trust is built.

A practical framework for AI implementation in business

Many AI implementation efforts stall because organizations move to agents before they are ready. At CLEVR, we often see this happen when the process is not yet clearly defined and trust has not yet been built into the workflow. That is why we have developed a six-step methodology for organizations preparing for the agentic future.

It is the same framework we use in our advisory process to assess AI maturity, identify structural gaps, and define the right next step on the path toward more advanced agentic systems:

1. Understand the work

Look at how the process actually runs in practice, including manual workarounds, exceptions, and informal steps that never appear on an org chart.

2. Find the real opportunities

Identify where AI can create meaningful leverage without introducing unnecessary risk (e.g. processes that include repeated effort, predictable bottlenecks, valuable time being lost on manual work, etc.)

3. Design deliberately

Decide what the AI is supposed to do, what information it needs, and what should remain within human scope.

4. Define what “good” means

Determine what acceptable performance looks like, what kinds of mistakes matter, which outputs require review, and where the cost of error is too high for approximation.

5. Run beside people first

That makes it possible to compare outputs, identify failure patterns, and learn where human review still adds the most value.

6. Monitor and improve

The strongest AI systems improve because they are observed, adjusted, and scaled from proven use cases rather than assumed to be finished from day one.

Make your AI strategy the real leverage

AI can create a unique competitive advantage, but only when it is applied with structure, context, and a clear definition of success. That is especially true for organizations looking beyond prompts and toward agents, automation, and more autonomous systems. With 30+ years of experience in digital transformation, and a strong portfolio in AI agents, AI solutions, and automation, CLEVR helps organizations take those first steps with clarity.

From understanding where AI can create value, to designing the right use case, and building a roadmap toward more advanced agentic systems, we help turn early ambition into practical progress. One step, one use case, and one trusted outcome at a time.

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Every year, Siemens Realize LIVE brings together manufacturers, engineers and technology leaders to explore where the industry is heading and how that future is taking shape in practice. Product updates, roadmap announcements, and technology highlights across sessions, customer stories, and partner discussions have shown this year that manufacturing is no longer about optimizing individual technologies. It is about connecting systems, intelligence and people into one coherent whole. This shift is particularly visible in how AI in manufacturing is evolving from isolated use cases into connected, operational systems.

This shift builds on a long standing ambition within the industry to create connected environments where data flows seamlessly across PLM, ERP, MES and supplier networks, improving efficiency, visibility and decision making. The introduction of AI further expands this landscape, not only increasing the potential for automation and insight, but also raising critical questions around how these capabilities can be translated into measurable and scalable operational value.

Here are the 4+1 ideas that defined Realize LIVE 2026 and what they mean for the future of manufacturing.

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1. AI is becoming operational, not experimental

For years, AI has been positioned as a powerful tool, supporting individuals through copilots, predictions and insights. At Realize LIVE 2026, it became clear that this phase is evolving toward execution at scale.

AI use cases in manufacturing are moving from assistance to orchestration within real workflows. Instead of responding to prompts in isolation, it is being embedded into processes where it can support decisions, coordinate systems and automate multi step activities across the lifecycle.

The introduction of Intelligence Center X signals this transition explicitly. By connecting enterprise data, lifecycle context and workflows in a governed environment, organizations can deploy AI agents alongside people as part of a hybrid workforce, moving from isolated pilots to production level execution with traceability and control.

The question is no longer whether AI works. It is how organizations embed it into connected systems and structured workflows, with the governance required to deliver consistent, measurable and scalable value.

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2. Connected systems matter more than isolated innovation

Despite continued investment in digital transformation, many manufacturers still face challenges when it comes to scaling innovation across the enterprise. The underlying issue is not a lack of technology, but a lack of connectivity, particularly across PLM, ERP and MES integration layers that are essential to enable scalable AI in manufacturing.

Across organizations, systems remain fragmented, data is distributed across silos, and AI initiatives are often introduced as isolated pilots. While these efforts deliver local improvements, they rarely translate into measurable impact across the full product lifecycle.

In manufacturing, design, engineering, production and service are inherently interdependent. As a result, optimizing individual components in isolation does not drive systemic improvement. Connecting their existing systems into a cohesive digital thread does.

This is exactly what our CEO, Tim Claes, emphasized in his keynote, framing this challenge and opportunity through what he defined as the Holy Trinity of Manufacturing:

• PLM as the backbone for product data and the digital thread
• Smart factory as the layer connecting IT and OT, enabling visibility and control across operations
• Low code and AI as the orchestration layer that connects systems, workflows and decision making

Individually, each of these domains delivers value. However, the real impact emerges when they operate as part of a connected system. This is increasingly becoming the approach that manufacturers adopt to remain competitive and relevant in a rapidly evolving market.

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3. Sustainability is becoming part of everyday engineering

Another strong signal from Realize LIVE was the shift in how organizations approach sustainability in manufacturing and compliance.

Regulation is tightening, particularly in Europe, where frameworks such as CSRD, REACH and emerging requirements like the Digital Product Passport are forcing organizations to provide detailed visibility into materials, sourcing and environmental impact across the entire value chain. Adding to it the pressure specific industries face such as aerospace and defense to improve their margin, and the stricter compliance requirements and growing expectations from OEMs and partners, sustainability can no longer be a separate reporting activity, but a factor that directly influences cost, risk and competitiveness.

During his keynote, Gerrit Kiefer, our Head of Solutions and Customer Success Management in Germany, demonstrated how this transition is already taking place in practice. Together with tec4U, he showcased how compliance and sustainability can be embedded directly into engineering workflows, enabling organizations to:

• Integrate regulatory requirements directly into PLM and design environments
• Ensure full traceability of materials, components and suppliers across the product lifecycle
• Reduce manual effort in compliance reporting through automated data capture and validation
• Identify risks and compliance gaps earlier in the design phase, where they can still be addressed efficiently
• Align cost, sustainability and engineering decisions by making all relevant data available in one connected workflow
• Accelerate time to compliance while maintaining control and auditability across processes

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4. The rise of the orchestration layer

With the introduction of Intelligence Center X, Siemens clearly articulated a strategic focus on establishing an orchestration layer that connects enterprise data, workflows and AI agents into a cohesive and scalable system. An orchestration layer acts as the architectural component that enables data orchestration, workflow coordination and AI execution across systems.

The implication for manufacturers is significant. Most of them have already invested heavily in core systems such as PLM, ERP and MES. So instead of building new systems, they can activate and connect what already exists.

At CLEVR, we have consistently been advocating that large scale rip and replace strategies are no longer sustainable, nor necessary. Over time, engineering logic, domain knowledge and process intelligence have become deeply embedded within existing systems. Replacing these foundations would not only introduce risk, but also discard valuable intellectual capital.

Instead, manufacturers can introduce an orchestration layer on top of their current landscape, enabling them to connect workflows, embed intelligence and extend capabilities without disrupting what already works.

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4+1. Technology is ready. Organizations are not

If there is one thing we can safely extract from Realize LIVE 2026, it is that that the challenge is no longer technological. Manufacturers today have access to advanced PLM platforms, connected factory systems, low code environments and increasingly powerful AI capabilities. The building blocks for transformation are already in place.

Yet many AI adoption initiatives in manufacturing continue to stall. At CLEVR, we see this pattern across organizations every day. Workflows not fully understood or mapped, decision logic remaining implicit, unstructured data, or connected in a way that enables reliable, cross-system execution.

Embedding intelligence into workflows requires more than deployment. It requires clarity on how decisions are made, where responsibility sits, and how humans and systems interact.

This means the next frontier to address is organizational in nature. Manufacturers need to design workflows that are clearly defined, decisions that are explicitly structured, and governance models that can be translated into executable logic. Only then can AI agents operate reliably, make autonomous decisions within defined boundaries, and scale across the enterprise with consistency and control.

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5 actions manufacturers can take today

First, the focus needs to shift from adding more tools to connecting existing ones. Most organizations already have the core systems in place. The real opportunity lies in linking them into a coherent digital thread across design, engineering, production and service.

Second, AI initiatives need to move beyond experimentation. Instead of isolated pilots, the emphasis should be on embedding intelligence into real workflows where it can deliver measurable impact. This requires clear governance, defined decision boundaries and a strong orchestration layer.

Third, transformation strategies need to become more pragmatic. Large scale, multi year replacement programs are increasingly difficult to justify. A leave and layer approach allows organizations to start with what they have, extend it intelligently and deliver value incrementally.

Fourth, sustainability and compliance should no longer sit on the sidelines. By integrating these requirements directly into engineering and product development processes, manufacturers can turn them into a competitive advantage rather than a constraint.

Finally, organizations need to rethink how people and technology work together. As AI becomes embedded into operations, new roles, responsibilities and ways of working will emerge. Designing this deliberately is critical to making transformation succeed.

Think big with a clear vision for your organization, start small with one workflow that delivers immediate value, and scale fast once the approach proves effective.

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Faster design cycles, automated engineering workflows, and reduced manual effort across processes. These advancements build on a long-standing promise of digitalization in manufacturing: connecting data, systems, and processes to improve efficiency and decision making. As we now enter the era of AI in manufacturing, that promise is being exponentially amplified.

In a connected manufacturing ecosystem, where data flows across PLM, ERP, MES, and supplier networks, AI has the potential to use this data to automate not just tasks, but decisions. Optimizing costs, reducing carbon footprint in manufacturing, and adding context to operational data are only some of the ways AI can unlock a new level of insight across the Siemens Teamcenter ecosystem, in an industry that has long struggled with fragmented data and limited visibility.

This raises an important question for manufacturers. Where does enablement software sit across this spectrum, and how ready are organizations to move toward this future?

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AI copilots in product cost management software  

Siemens is already taking active steps toward this agentic future by introducing copilots across its ecosystem, with Product Cost Management being one of the first areas where this intelligence directly impacts decision making.

Although still in a pilot phase, these copilots already demonstrate how AI can streamline cost engineering tasks by enabling features like automated part matching, instant generation of BOM (Bill of Materials) and BOP (Bill of Processes) structures, and accelerated “what if” scenario simulations.

Instead of spending time building and maintaining models, teams can, from now on, focus on evaluating scenarios, comparing alternatives, and making decisions. Complex analyses that previously required significant manual effort can now be executed faster and with greater consistency.

For organizations, this means:

• Faster evaluation of design and sourcing options

• Reduced dependency on manual data preparation  

• More consistent and reliable cost models  

• The ability to explore more scenarios in less time

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Building AI-ready manufacturing data for product cost management and sustainability

For manufacturing organizations, foundation is critical. In order to safely operationalize AI, companies first need to operationalize their data. Unstructured data leads to fragmented workflows, and fragmented workflows are one of the main reasons AI initiatives fail to move beyond pilot stages. This is where Siemens is taking another important step. By advancing how enterprise data is stored and structured, it strengthens the underlying foundation required to make those capabilities effective at scale.

1. Cloud for scalable AI-driven product cost management

As AI evolves toward more autonomous and agent-driven workflows, organizations need platforms that are scalable, connected, and continuously updated. Cloud environments provide that foundation and that's why the ability to transition enterprise environments to the cloud is a key element.

With the latest enhancements in Teamcenter Product Cost Management, existing users can bring their value plugins and customized cost breakdowns into the cloud environment without losing the functionality they depend on. This allows organizations to achieve greater scalability, accelerate innovation cycles, and reduce operational overhead, while building a reliable foundation for advanced analytics and AI driven decision support at scale.

2. User experience for faster cost and sustainability decisions

A modernized user interface reduces friction in daily workflows and improves access to relevant data. Instead of navigating complex systems, users can now more easily find, interpret, and act on the information they need. Improved navigation, reorganized layouts, and more intuitive access to core functionalities are some examples of such enhancements, while improved search capabilities and clearer data visualization allow teams to focus more efficiently on evaluating cost drivers, comparing scenarios, and supporting decisions.  

3. APIs and data readiness for manufacturing intelligence

Finally, Siemens has taken important steps to further optimize how systems interpret data, generate insights, ensuring that enterprise knowledge is not only available, but also structured, connected, and accessible in a way that enables advanced analytics and scalable AI-driven applications.

Enhanced calculation capabilities and REST API extensions enable automation, integration with external systems, and more advanced reporting and analytics.

Improvements in data models, KPI flexibility, and manufacturing cost visibility provide a more granular and accurate view of cost drivers and profitability.

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How CLEVR enables product cost management software and AI in manufacturing

Technology alone does not create intelligence. Organizations still need to define where decisions are made, which data should inform them, and how insights can be embedded into daily operations. This starts with structuring data in a consistent and scalable way, and ensuring that product, cost, and operational information is standardized across systems.  

From there, workflows can be automated to embed business rules, engineering logic, and financial models directly into processes, a foundation AI models can effectively build on, learning from reliable data and delivering insights that are accurate, actionable, and aligned with how the organization operates.

At CLEVR, we combine deep industry expertise with a team of specialists in advanced software solutions to help manufacturers translate these capabilities into measurable outcomes. With over 30 years of experience delivering tailored technology solutions across manufacturing, marine, aerospace, and defense, we understand the realities of complex, legacy environments and how to evolve them.  

By tailoring leading platforms such as Siemens and Mendix to each organization’s context, we work alongside our customers to embed domain knowledge, business rules, and engineering logic into scalable workflows, and augment them with AI where it delivers the most value. In practice this means that we activate structured cost and sustainability management, supported by the governance and orchestration required in an increasingly complex and data driven future.  

As Product Cost Management continues to evolve and AI becomes embedded across the digital landscape, manufacturers have an opportunity to rethink how cost, sustainability, and profitability insights are used, and with the right partner, transform them into powerful decision engines.