Q&A: How AI Is reshaping product lifecycle leadership and decision-making. Hint: A lot depends on data
What you’ll learn:
- Product development is moving faster than traditional PLM approaches were designed to support.
- One constant remains: AI depends on data. And operational AI relies on governed digital thread data.
- Manufacturers need to start with a strong data foundation. Data quality, consistency, and context across the product lifecycle are essential.
Editor’s note: Following his recent appointment as CEO of Aras, Leon Lauritsen joined Smart Industry to discuss how AI is reshaping product lifecycle management and the leadership priorities that come with it.
Smart Industry: As AI moves from experimentation to operational reality, how are PLM leadership priorities changing?
Leon Lauritsen: Product development is moving faster than traditional PLM approaches were designed to support. AI is no longer something organizations experiment with on the side. It’s increasingly embedded in day-to-day work. This has shifted leadership priorities from managing information to enabling decisions.
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This takes AI beyond search and natural language analytics and into daily decision support. Decisions can no longer wait. Organizations need systems that can interpret, anticipate, and respond as work happens. A pure system-of-record model is no longer sufficient—leaders are prioritizing platforms that shorten decision cycles and reduce coordination friction.
One constant remains: AI depends on data. Operational AI relies on governed digital thread data, and organizations that can expose that data to AI-driven and agentic services will have a clear advantage.
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SI: Why have implementation cost, governance, and interoperability become bigger challenges than feature innovation?
Lauritsen: Product development is inherently defined by change. Rapid, successful implementation of software functionality is essential to keeping pace.
But implementation and change management often cost multiples of the software itself, and those long timelines create real bottlenecks. This must change.
AI, paired with more flexible software paradigms, offers a path forward with true disruptive potential.
Governance and interoperability are just as critical, especially in an AI-driven environment. AI value depends on access to contextual, high-quality data, but that access must be governed carefully. Without proper classification and access controls, organizations risk exposing sensitive intellectual property or trade secrets.
Interoperability matters because AI innovation will be decentralized across startups, large technology providers, internal teams, and research organizations. No single vendor will own it all. Manufacturers need platforms that allow them to integrate new capabilities as they emerge and evolve without being locked into rigid architectures.
Operational AI relies on governed digital thread data, and organizations that can expose that data to AI-driven and agentic services will have a clear advantage.
SI: What should manufacturers focus on to avoid AI-driven complexity and long-term technical debt?
Lauritsen: The most common mistake is leading with technology instead of intent. Organizations often adopt AI tools without clearly defining which decisions they are trying to support. When that happens, AI accelerates complexity rather than outcomes.
Manufacturers need to start with a strong data foundation. Data quality, consistency, and context across the product lifecycle are essential. AI will amplify whatever it is fed. When the data is fragmented or governed inconsistently, the result is unreliable insights, conflicting decisions, and hidden risk.
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Governance by design is critical. AI systems must support human decision-making and remain explainable and traceable, learning only from appropriately classified data. Without those guardrails, organizations embed rigidity and risk into their systems, creating technical debt that becomes harder to unwind over time.
The objective is not automation for its own sake. It is intelligent support that helps people make better decisions faster, while preserving accountability and adaptability.
The most common mistake is leading with technology instead of intent. Organizations often adopt AI tools without clearly defining which decisions they are trying to support.
SI: Your leadership approach combines strong performance expectations with a more decentralized, Scandinavian management style. How does that work in practice?
Lauritsen: Performance, execution, and accountability are essential. Expectations need to be clear, decisions need to be made, and results need to be measurable. That is especially true in a private equity-backed environment.
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At the same time, I am very intentional about minimizing unnecessary hierarchy. Scandinavian leadership traditions place a strong emphasis on trust, cooperation, and delegation. People are encouraged to take ownership, think creatively, and move forward without waiting for layers of approval.
For me, effective leadership is about combining clarity with empowerment. When teams understand the goals and are trusted to act, they move faster and innovate with more confidence.
In an AI-driven environment where change is constant, that balance is not optional. It is what allows organizations to adapt and stay competitive.
About the Author
Scott Achelpohl
Head of Content
I've come to Smart Industry after stints in business-to-business journalism covering U.S. trucking and transportation for FleetOwner, a sister website and magazine of SI’s at Endeavor Business Media, and branches of the U.S. military for Navy League of the United States. I'm a graduate of the University of Kansas and the William Allen White School of Journalism with many years of media experience inside and outside B2B journalism. I'm a wordsmith by nature, and I edit Smart Industry and report and write all kinds of news and interactive media on the digital transformation of manufacturing.