Aras expands engineering and product leadership to advance AI-native PLM
Feb 3, 2026 - Aras appointed Przemek Berendt as Senior Vice President of Engineering and AI Transformation and Vijay Raman as Vice President of Product Acceleration. Their mandate: scale product development, speed up SaaS delivery, and push an AI-first product strategy across the Aras platform.
Why this matters for product and engineering leaders
Traditional PLM wasn't built for software-defined products, multi-domain systems, and the rate of change you manage today. Aras is betting that AI-assisted workflows tied to a connected digital thread will become the operating system for modern product teams.
- Faster SaaS release cycles and more frequent value drops
- AI assistance embedded in core workflows (e.g., change impact, requirements traceability, supplier risk)
- Stronger digital thread across CAD, ALM, PLM, ERP, and MES integrations
- Heavier focus on security, data quality, and auditability for regulated environments
Who's joining and what they bring
Przemek Berendt built Volvo Cars' Krakow R&D center from the ground up, growing it to 200+ engineers working on next-gen vehicle platforms. He previously founded Talent Alpha, helped scale Luxoft from $100M to $900M in revenue, and held product and engineering roles at Procter & Gamble. He holds an MSc in Computer Science from DePaul University and completed Harvard Business School's Program for Leadership Development.
Berendt's focus is clear: help product teams collaborate better and use AI to turn insight into better products-without adding friction.
Vijay Raman brings deep experience across Generative AI, SaaS, and cloud platforms. He has led cross-functional product, engineering, and go-to-market teams; owned P&Ls exceeding $175M; launched large portfolios; and led M&A integrations at ibi and TIBCO. His priority is to scale SaaS and AI capabilities and speed product delivery to drive adoption and measurable outcomes.
Aras CEO Leon Lauritsen underscored the point: the demands on product organizations have outgrown legacy PLM approaches, and these hires are about accelerating how insight and impact reach teams.
What to do now
- Map your digital thread. List systems (CAD, ALM, PLM, ERP, MES) and the data each owns. Flag gaps, duplicate sources, and manual handoffs. If helpful, review NIST's overview of the digital thread for manufacturing for shared language and scope (NIST resource).
- Set your SaaS path. Build a 12-24 month plan: migration waves, change management, regulatory needs, and ROI modeled against upgrade cadence, admin effort, and downtime avoided.
- Get AI-ready. Inventory datasets, owners, access policies, data quality, and lineage. Define model governance, bias testing, and audit trails-especially for safety-critical use cases.
- Prioritize 3 quick wins. Examples: parts and BOM classification, ECO impact analysis, supplier risk signals, requirement-test-defect traceability. Measure with time-to-decision and rework reduction.
- Lock in KPIs. ECO cycle time, release frequency, reuse rate, defect escape rate, supplier on-time quality, and model/requirement traceability coverage.
- Upskill the team. Product, engineering, and quality need practical AI skills (prompting, data literacy, evaluation). A curated path by role can shorten the ramp-see AI courses by job.
About Aras
Aras provides PLM and digital thread solutions delivered on a low-code platform. The company connects disciplines and partners across the product lifecycle so teams can work from the same source of truth. Learn more at aras.com.
Further reading
- Practical ways AI is changing product development and operations: McKinsey analysis
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