As artificial intelligence becomes more prevalent across industrial value chains, a new question is coming to the forefront: can organizations trust AI at scale? As AI moves from experimentation into operational use, transparency, traceability, and accountability are becoming essential requirements—especially in regulated industries. Looking ahead to 2026, industrial AI is entering a more demanding phase of maturity. In this context, Leon Lauritsen, CEO of Aras, explains why trust in AI depends on strong data, governance, and context—and why PLM is emerging as a critical foundation for scalable, decision-ready industrial AI.
Artificial intelligence is evolving from an experimental technology to an operational necessity. Algorithms are becoming more capable, use cases are expanding, and AI is increasingly embedded in industrial operations – from process optimization and predictive maintenance to assisted engineering and decision support.
As AI becomes operational, organizations must be able to trust, explain, and stand behind the decisions it helps inform. Demonstrating reliability, traceability, and accountability is becoming just as important as accuracy, particularly in industrial environments where safety, compliance, and responsibility matter.
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From Performance to Proof: A Paradigm Shift for AI
For years, the primary question around enterprise data was whether the result was correct. Today, an additional question is becoming unavoidable: why is this result correct?
Many AI systems deliver strong outputs, but how those results are produced is not always clear. In critical industrial sectors – manufacturing, mobility, medical technology or defense – this lack of transparency limits how far AI can be trusted. As AI plays a greater role in operational and engineering decisions, expectations around security, regulatory compliance, and legal responsibility increase.
In 2026, explainability, verifiability, and auditability will be central to scaling AI responsibly in industrial environments.
European Regulation: Traceability Becomes a Condition for Market Access
This transformation is also being accelerated by the changes in the regulatory landscape. The EU AI Act and initiatives such as the EU Digital Product Passport are raising expectations around transparency, traceability, and accountability in the use of AI.
Organizations need to understand how AI-driven insights are produced, what data they are based on, and who is accountable for the decisions they support. In this environment, AI systems that cannot be traced or explained will struggle to deliver sustained business value – and compliance is becoming a structural requirement that shapes how industrial digital architectures are desined and operated.
PLM as the Foundation of Trusted AI
As these requirements converge, the question is no longer simply which AI tools to deploy, but on what foundation they should run. Trust is not created by algorithms alone – it depends on the quality, context, and governance of the data and processes that feed them. This is where product lifecycle management (PLM) takes on a new role.
Beyond managing product development, PLM provides a connected, authoritative foundation for product data, decisions, validations, tests, and changes across the lifecycle. That continuity provides the context needed to explain and audit AI-supported decisions – an essential capability in regulated, high-stakes environments.
AI only creates value when it is grounded in clear intent and a strong data foundation. Too often, organizations lead with technology instead of clearly defining the decisions AI is meant to support. When that happens, AI accelerates complexity rather than outcomes.
When data is siloed or governed inconsistently, AI amplifies unreliable insights, conflicting decisions, and hidden risks. Governance by design ensures that AI supports human decision making, remains explainable and traceable, and only learns from appropriately classified data.
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Adaptive Intelligence in Support of Human Decision-Making
As AI becomes operational, these demands place new expectations on PLM itself. PLM has primarily served as a system of record. In an AI-driven environment, it needs to become a system of guidance.
This is where adaptive intelligence comes into play. Rather than simply storing information, PLM platforms must help organizations identify signal in the noise, illustrate potential impacts before issues cascade, and align teams in real time. By continuously analyzing relationships across data, processes, and decisions, adaptive intelligence supports earlier insight, clearer trade-offs, and faster coordination as work unfolds.
This does not replace human decision-making – it strengthens it. Decisions remain in the hands of experts, but they are informed by contextualized, reliable, and explainable initelligence. The result is shorter decision cycles, lower coordination friction, and PLM solutions that can evolve at the pace of the busines.
An Enabler of Innovation
In 2026, the direction is clear. AI is moving into daily operations and organizations must integrate it thoughtfully into daily business processes. Operational AI requires platforms that can interpret, anticipate, and respond as work happens.
Organizations that invest in governed, traceable data architectures and decision-ready platforms will be better positioned to operationalize AI with confidence. Transparency will not hold innovation back—it will enable it.
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