記事を聴く
Product Lifecycle Management (PLM) has outgrown its origins as an engineering utility and is rapidly becoming the central nervous system of modern product enterprises. What was once a digital vault or a passive system of record is now evolving into a dynamic decision-making engine that spans the full product lifecycle.
In 2026, PLM defines not just how products are designed and built, but also how they are produced, delivered, sustained and monetized. Its influence extends beyond the engineering domain into manufacturing execution, supply chain resilience, regulatory compliance and customer experience. The future of PLM is not data management; it’s enterprise orchestration.
At HCLTech, we see this transformation unfolding clearly across industries. The organizations that lead over the next decade will be those that treat PLM not just as an engineering platform, but as an enterprise operating model. One that connects product strategy to execution with speed, intelligence and trust.
Several converging forces are driving this shift:
- Products are becoming software-defined and continuously evolving
- AI is moving from assistance to controlled autonomy
- Sustainability, regulation and geopolitics are turning product data into a strategic asset
Together, these forces are reshaping PLM for 2026 and beyond.
Trend 1: PLM evolves as the nerve center of the enterprise operating system
Many product and manufacturing companies still operate within highly fragmented IT environments, often running hundreds of disconnected applications across engineering, operations and service. This fragmentation creates latency, risk and poor decision continuity. Leading enterprises are responding by repositioning PLM as a layered Enterprise Operating System (EOS). One that unifies applications, data, infrastructure, people and process into a coherent execution framework.
This is not just technology consolidation; it is an organizational shift. At HCLTech, we often help clients dramatically simplify engineering landscapes. For example, reducing 120+ applications to fewer than 40, while preserving governance, compliance and flexibility. The outcome is a PLM foundation capable of supporting modular innovation, faster product cycles and global-local operating models at scale.
Trend 2: Closing the loop across engineering, factory and field becomes non-negotiable
Traditional product development followed a linear flow: concept, design, build and service.
That model no longer holds. Connected products, digital manufacturing and real-time service telemetry demand closed-loop engineering, where insights from the factory and the field continuously inform design, quality and lifecycle decisions. Industry analysts estimate that by 2027, the majority of manufacturers will rely on real-world product usage data to influence new product design and service models. The implication is clear: engineering decisions must be informed by how products actually perform, not just how they were intended to perform.
In 2026, the competitive differentiator is not digital thread visibility alone, but how fast feedback loops translate into action.
Trend 3: PLM-ALM convergence accelerates software-defined products
As mechanical, electronics and software domains converge, PLM can no longer remain mechanically centered. Increasingly, product value is delivered through software, data and control logic, even in traditionally hardware-led industries. This drives the convergence of PLM, ALM and systems engineering, with traceability spanning:
- Requirements and architectures
- Multi-domain BOMs
- Software versions and OTA updates
- Compliance and verification artifacts
At HCLTech, we see PLM-ALM convergence not as a point integration, but as a new operating cadence, where hardware baselines coexist with continuous software delivery, coordinated through executable systems engineering.
Trend 4: AI in PLM — From copilots to governed autonomy
AI is now embedded across engineering, but 2026 marks a turning point. The question is no longer if AI belongs in PLM, but which decisions can be delegated safely, measurably and responsibly.
The industry is moving from copilots that enable search, summarization and drafting toward agentic workflows that can propose changes, route approvals and trigger downstream actions in helper and advisor roles with human oversight. At the same time, analysts caution that many agentic initiatives will fail without strong governance and ROI clarity.
Our view at HCLTech is pragmatic: AI in PLM must be treated as a controls problem, not a novelty. Successful implementations define guardrails around:
- Explicit versus implicit knowledge
- Cost of error
- Traceability, auditability and rollback
Through our XLM.AI solution, clients are already realizing tangible outcomes, such as up to 50% productivity gains, 40% faster time-to-market and 25% lower development costs, by applying AI where it creates provable value.
Trend 5: Digital twins become operational workhorses
The digital twin conversation is evolving rapidly, moving beyond abstract “models of everything” to practical, layered implementations that deliver measurable value in daily operations.
In 2026, we’re witnessing the strongest traction across three areas:
- Asset twins are enabling predictive maintenance and improving uptime and ROI
- Process twins are being used for optimizing operation by simulation, scenario analysis and variation management
- Product twins are supporting configuration accuracy, evolution across the lifecycle, quality assurance and real-world usage analytics
For instance, one automotive manufacturer we work with produces over 300,000 vehicles annually, however, only five are identical. This extraordinary level of variability makes traditional optimization models ineffective. Digital twins provide the tools to manage that complexity at the unit level, empowering engineering teams to deliver consistent outcomes across highly customized production lines.
Trend 6: Hyper-personalization turns variant management into a core capability
The industry has moved beyond mass customization. In 2026, mass personalization at industrial scale becomes the baseline expectation. Customers demand localized configurations, software features and service outcomes, while engineering still needs reuse, certification-grade traceability and cost control. This makes variant intelligence a first-class PLM capability. Rules, constraints, requirements and verification artifacts must scale alongside geometry. AI-assisted simulation and validation are becoming essential as variant counts explode. At HCLTech, we help clients design modular architectures that enable diversity without sacrificing speed or governance.
Trend 7: Making PLM the engine of sustainable innovation
Sustainability is no longer a downstream reporting activity. Regulatory pressure, customer expectations and frameworks such as the EU Digital Product Passport are pushing sustainability into the design phase. In 2026, PLM platforms must structure and govern data related to:
- Material composition and provenance
- Repairability and circularity
- Carbon, water and energy impact across the lifecycle
Industry research suggests that embedding sustainability early in design can reduce lifecycle emissions by more than half. This is not a compliance exercise, it is a competitive one. PLM becomes the system that makes sustainability executable.
Trend 8: Designing for disruption, certifying for continuity
Supply chain volatility is no longer an operational issue alone, it is a product definition challenge.
Trade uncertainty, export controls, regionalization and security requirements mean products must be designed for multiple sourcing, rapid requalification and regulatory adaptability. In 2026, resilience engineering becomes embedded within PLM workflows, enabling organizations to answer critical questions quickly: ‘What can we build, certify and support under scenario X?’ That capability separates resilient enterprises from reactive ones.
Trend 9: The workforce shift: Engineering the future workforce with human–AI collaboration
The talent challenge facing manufacturers is not just labor availability, it is the ability to operate hybrid teams where AI handles routine cognition and humans focus on judgment, ethics and innovation. PLM user experience and workflow design matter more than ever. Winning organizations will redesign engineering work itself, through role-based experiences, guided processes, embedded knowledge capture and enforceable standards. AI will not replace engineers. However, engineers who effectively collaborate with AI will outperform those who don’t.
Trend 10: Securing the digital thread becomes mission-critical
As PLM systems become increasingly integrated with manufacturing execution, supplier collaboration and service operations, they also become a high-value target for cyber threats. The stakes are high. Product data such as intellectual property, configurations and operational insights represent the crown jewels of any engineering-driven enterprise. In 2026, securing this digital thread requires a robust and proactive security posture. This includes enforcing Zero Trust access controls to safeguard engineering data, maintaining strict segmentation between IT, OT and IOT environments, implementing immutable backups with rehearsed recovery protocols and ensuring digital provenance to support trustworthy automation and traceability. Without these foundational safeguards, the integrity of the digital thread cannot be guaranteed, making it impossible to scale AI or autonomous operations with confidence.
The 2026 PLM executive agenda
Across industries, we see five actions that distinguish PLM leaders from adopters:
- Design the product data foundation for autonomy, not just visibility
- Operationalize PLM-ALM-MBSE traceability for software-defined products
- Close the loop between engineering, manufacturing and service
- Treat sustainability as a product definition constraint
- Prove AI value through governed, outcome-driven pilots
In 2026, PLM is no longer about managing complexity; it is about orchestrating advantage. Deep engineering expertise, platform innovation, and AI-driven acceleration are enabling enterprises to turn PLM into a strategic growth engine. As the point where strategy meets execution, the decisions made within PLM will define who leads the next era of manufacturing.
