Orchestration, custom models, and strategic guidance.
Welcome back to the second half of our series on how AI is making its way into PLM. In Part 1, we kicked things off by introducing a four-level framework that shows how organizations can move up the AI maturity ladder, depending on their capabilities:
- Level 1: Tool-Native AI
- Level 2: AI Across Enterprise Systems
- Level 3: Orchestrating Work with AI
- Level 4: Building Custom AI Models
Part 1 covered Levels 1 and 2, establishing that while most organizations progress sequentially, the sequence isn’t mandatory. Part 2 explores Levels 3 and 4 and concludes the series by providing realistic adoption timelines through 2027 and offering strategic guidance for navigating this transformation.
Level 3: Orchestrating work with AI
Goal-oriented AI that plans and orchestrates workflows.
Level 3 represents a fundamental change. Instead of responding when asked questions, AI becomes goal oriented. You give it an objective, and the AI generates its own plan to achieve it, orchestrating workflows and adapting as conditions change. This can be user-initiated (“prepare this assembly for manufacturing review and route it appropriately”) or fully autonomous (“when a supplier’s delivery slips, identify impacts and coordinate the response”).
The boundary between Level 2 and Level 3 is often blurred in solution provider positioning. Many capabilities marketed as “agentic AI” are sophisticated AI assistants that synthesize information and recommend actions, even proactively. The distinction is whether AI is generating multi-step workflows to achieve goals (Level 3) or providing information and recommendations that humans use to decide next steps (Level 2). The line isn’t always clear, which is why setting realistic expectations matters.
Level 3 is where the gap between what’s technically possible and what organizations can deploy at scale becomes most apparent. You’ll see impressive demonstrations at conferences and hear about pilot projects, but production-scale deployments with measurable ROI are rare.
Consider this example: an engineer proposes a material change in a critical component. AI detects the change request, identifies all affected assemblies and downstream dependencies, checks regulatory requirements and certifications that need updating, queries suppliers for material availability and lead times, generates a risk assessment by analyzing historical failure data, and creates a coordinated review workflow, routing the change to appropriate stakeholders based on impact analysis.
Solution providers demonstrate these concepts in controlled settings. Siemens Industrial Copilot (co-developed with Microsoft for broader manufacturing applications, separate from the Teamcenter Copilot discussed in Part 1) and SAP Joule are examples, but most implementations remain pilots as of the date of the publication of this article.
The challenges are primarily organizational and legal. Governance frameworks must define AI decision authority boundaries for every workflow. When can AI act autonomously? When does it need approval? These boundaries vary by industry and company, often taking years to develop. Liability questions follow: “Who’s responsible when AI autonomously approves a material substitution that later fails?”
In regulated industries like aerospace, automotive, and medical devices, every decision must be auditable. Current AI capabilities often lack the transparency regulators need, and no specific regulatory frameworks for agentic AI exist yet. Organizations don’t grant autonomy to systems they don’t trust, and that trust develops gradually, typically requiring 18 or more months of seeing AI work correctly at Level 2. Change management and cultural acceptance often determine success or failure more than technical capability.
Level 3 requires everything from Level 2, operating reliably. Without this foundation, you can’t troubleshoot autonomous systems. You also need enterprise-grade integration for action coordination, workflow engines with rollback capabilities, robust error handling, and real-time monitoring. Equally important are governance frameworks that define autonomy boundaries, liability frameworks, and regulatory-compliant audit trails.
For 2026 and 2027, expect limited controlled production deployments among well-resourced organizations that have invested years in building the necessary governance frameworks. Most organizations will still be working on Level 2. Broader Level 3 adoption is likely beyond 2029.
Level 4: building custom AI models
Custom models tailored to proprietary needs and constraints.
Most companies will succeed at Levels 1 through 3 by leveraging the foundation models provided by solution providers. However, for certain organizations, it makes sense to train or fine-tune their own models—either to address specialized requirements or to capitalize on proprietary data that serves as their intellectual property and competitive differentiator. This is where Level 4 comes into play.
Level 4 examples span multiple AI types, including custom vision models for composite inspection, fine-tuned language models for regulatory documentation, physics-informed neural networks for specialized simulations, and AI surrogate models replacing expensive computational analyses with your proprietary test data. These custom models don’t operate in isolation.
A custom AI surrogate model accelerates generative design exploration by replacing expensive simulations (Level 1). A custom language model trained on your technical documentation enables better information synthesis across your digital thread (Level 2). Custom supply chain models enable autonomous decision-making about supplier selection and inventory management (Level 3).
For Level 4, you need the right tools, significant computational resources, expertise, and clear justification. Perhaps 10 to 15% of organizations will pursue Level 4, typically those with clear competitive needs in regulated industries. Level 4 is optional, not required, but for those with the right needs and capabilities, it will provide a genuine competitive advantage.
Realistic adoption timeline
Let’s look at where this is heading based on current trajectories, grounded in what solution providers are shipping and current industry adoption patterns.
Late 2025 (Current State): Level 1 has become widespread with early adopters proving value. Smart money went into Level 2 foundation work: digital thread establishment, data quality improvement, and integration infrastructure. Industry has learned that data quality matters more than algorithm sophistication.
2026: Level 1 becomes near universal by year end, table stakes rather than competitive advantage. Level 2 will continue to emerge, and this is where differentiation will happen. Digital thread maturity will separate leaders from followers. The skills gap will become even more apparent. Companies that tried jumping to Level 3 or 4 without a solid foundation will be course-correcting.
2027: Level 2 will be common among efficient organizations, and this is where competitive advantage will live. Organizations with a mature digital thread and strong data governance will clearly separate themselves. Data quality will remain the top challenge; executing well on this work is what will matter. Level 3 will remain a niche; production deployments will primarily exist in industries that have already invested heavily in connected manufacturing systems and can benefit from high-volume, repetitive decisions, such as automotive and semiconductor manufacturing. Organizational and governance barriers will prove higher than current enthusiasm suggests. Broader Level 3 adoption is likely beyond 2029. Level 4 will be utilized by under 15% of companies, those with specialized competitive needs.
By 2027, three distinct groups will emerge: Early adopters (likely under 20%): Level 2 complete and operating smoothly, Level 3 operational in controlled pilots, some pursuing Level 4. Fast followers (perhaps 30%): Level 1 complete and Level 2 fully deployed or underway. The rest (the majority): Still completing Level 1 and 2 fundamentals, struggling with data quality, and learning expensive lessons.
Strategic Guidance
This guidance from CIMdata is structured along three core pillars: the Foundation (Data & Time), Technology (Openness & Governance), and Action (Skills & Leadership).
- The Foundation: Data Integration and Time Investment
The digital thread is your foundation for Level 2 and beyond, connecting PLM, ERP, MES, quality, supply chain, service, and other key data-centric systems with consistent data models and real-time synchronization. As noted in Part 1, establishing this typically takes 18 to 24 months, often extending to 24 to 36 months. There is no shortcut.
2. Technology: Openness and Governance
Platform openness increasingly matters. Open platforms with robust APIs enable Level 2 and 3 success, as well as Level 4 capabilities. Closed platforms limit you to whatever AI capabilities that solution providers choose to offer.
3. Actionable Guidance
- Skills Development:
Skill requirements and implementation challenges evolve significantly across the four levels.
| Skills | Development Time / Challenge | |
| Level 1 | Prompt literacy (knowing how to ask AI effective questions), output validation, and realistic expectations about capabilities. | Develops quickly through hands-on use over 3 to 6 months. |
| Level 2 | Data governance, cross-system integration expertise, and organizational change management. | Requires focused, sustained effort, typically taking 12 to 18 months. |
| Level 3 | Governance frameworks for AI autonomy and advanced change management to build organizational acceptance of autonomous AI decision-making. | Built on cultural maturity and years of Level 2 operational experience. |
| Level 4 | Advanced expertise in AI/ML modeling, data science capabilities, and machine learning operations (MLOps) infrastructure. | Requires specialized talent acquisition or multi-year internal development. Most organizations partner rather than build |
B. By Role:
| The Strategic Mandate | CIMdata Insider Tip | |
| For Practitioners | Start Level 1 immediately and advocate strongly for the Level 2 foundation investment. | Do not skip the data quality work. |
| For Managers | Create realistic timelines (accounting for a 45% typical overrun) and invest in the Level 2 data foundation first. | Prioritize organizational change management; cultural resistance is the dominant barrier. |
| For Executives | Recognize that Level 2 is where competitive advantage lives for 2026 through 2027. Focus funding on integration and governance. | Algorithm choice matters far less than execution discipline on integration and governance. |
Here’s what the next few years actually look like.
Level 1 is expected to be near-universal by the end of 2026; it’s just table stakes.
Level 2 is where competitive advantage lives through 2026 and 2027.
Level 3 remains a niche, concentrated in regulated industries, with broader adoption more likely after 2029. Currently, most solution provider Level 3 roadmaps are aspirational.
Across all these levels, we’re talking about what CIMdata refers to as “Augmented Intelligence.” That is, AI-enabled capabilities that enhance human decision-making rather than attempt to replace it. Whether AI is helping you find information faster (Level 1), synthesizing data across systems (Level 2), or executing workflows within boundaries you’ve defined (Level 3), the goal is the same: making people more effective, not making them obsolete.
Most organizations will progress sequentially because each level builds capabilities for the next. What determines success is understanding and meeting the prerequisites for your target level.
The data foundation work matters more than which AI algorithm you choose. Level 2 is where competitive success gets defined for the next 3-5 years. And remember, plan for 45% overruns on every timeline. This isn’t pessimism, just reality.
AI may not bring a sudden revolution, but its true power lies in becoming an unseen force that transforms how we work—much as email did decades ago. Organizations that commit now to building a robust data foundation and mastering each step will be the ones leading the way, setting new standards for excellence and innovation. Instead of learning hard lessons after the fact, they will be shaping the future, empowered by AI that amplifies their strengths and unlocks new possibilities.
The journey starts today—those who embrace it boldly will not just adapt, but inspire, thriving in a world where technology and human ingenuity drive progress together.
