Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

With a strong academic foundation in Mechanical Engineering and Business Administration from RWTH Aachen, and a PhD in mechanical engineering with a focus on digital transformation, I bring a unique blend of technical and strategic expertise. 

I previously led a department at the Fraunhofer Institute for Production Technology, overseeing digital transformation initiatives with a team of over 20 people. Currently, I serve as a Director in a global IT technology group, where I drive large-scale digital transformation programs across various domains, including digital continuity, PLM, business processes, and target operating models.  

Q2. What is the projected growth trajectory of the AI-powered simulation and digital twins market through 2030, and which industries are expected to drive this expansion?

Market Growth Projections

Multiple industry reports predict rapid expansion:

• A 360iResearch study estimates that the AI-powered digital twins market will grow from $26 billion in 2024 to $142 billion by 2030, at a CAGR of ~32.5%
• Grand View Research forecasts the broader digital twin market to rise from about $25 billion in 2024 to $156 billion by 2030, at a CAGR of 34.2 %
• Revaia (referencing Fortune Business Insights) projects the digital twin simulation market will soar from $15 billion in 2023 to $173 billion by 2030, with adoption growing at 36 % annually
• A KBV Research report estimates that digital twins will reach $195 billion by 2030, growing at a CAGR of 41.3%

AI-enabled digital twins are expected to grow from ~$25B today to anywhere between $140–195 billion by 2030, at approximately 32–41 % CAGR.

Key Industry Drivers

Manufacturing & Industrial Automation

• Largest adoption segment (~32–41 % share)
• Enables real-time asset optimization, predictive maintenance, smart factory operations, and cost efficiency

Automotive & Transportation

• Heavy use in virtual car design, testing, and supply chain simulation
• Formula 1 utilizes thousands of digital twins per race, while OEMs simulate vehicles and logistics

Energy & Utilities

• Supports grid management, renewable integration, and predictive maintenance in asset-heavy environments

Aerospace & Defense

• Government programs are integrating digital twins for mission-critical systems
• Used for aircraft lifecycle simulation, battlefield systems, and secure AI workflows

Healthcare & Life Sciences

• Medical simulations, device testing, organ modeling, and personalized treatment using human digital twins

Smart Cities & Infrastructure

• Urban planning, building efficiency, and environmental impact modeling using city-scale twins

Emerging Tech Ecosystem

Industrial Metaverse / Spatial Computing

Platforms like Nvidia Omniverse are central to simulating factory layouts, training robots, and collaborating virtually across the automotive industry.

AI & IoT Integration

Real-time sensor data combined with machine learning makes digital twins adaptive, predictive, and self-optimizing.

Q3. What are the emerging "target pictures" for intelligent factories, and how are they reshaping long-term capital allocation and technology sourcing strategies?

Emerging target pictures for intelligent factories focus on real-time, AI-driven, modular, and sustainable operations. These include closed-loop digital twins, autonomous systems, edge-to-cloud data fabrics, and reconfigurable production lines.

They are reshaping capital allocation by shifting investment from heavy physical assets to digital infrastructure, AI platforms, and simulation tools. Payback cycles are becoming increasingly shorter, and budgets are now prioritizing software, data services, and AI maintenance.

In technology sourcing, companies favor modular, AI-native platforms, co-innovation partnerships, and secure, standards-compliant vendors—moving away from siloed hardware toward integrated, flexible ecosystems.

Q4. Which frontier technologies are gaining boardroom attention, and what are their maturity timelines?

Boardrooms are focusing on frontier technologies that promise strategic impact:

GenAI copilots

GenAI copilots for engineers are gaining traction for automating design and simulation tasks. 

Maturity: early adoption (2024–2026)

Real-time digital twin feedback loops

They enable continuous optimization. 

Maturity: scaling (2025–2027)

Industrial foundation models

It offers deep AI insights across factory systems. 

Maturity: experimental (2025–2028)

Autonomous production scheduling

Uses AI for real-time decision-making. 

Maturity: emerging (2025–2027)

Composable manufacturing platforms

It supports plug-and-play flexibility.

Maturity: mid-stage (2024–2026)

AI-driven sustainability twins

Optimizes energy and emissions. 

Maturity: growing fast (2024–2026)

These technologies are reshaping digital investment priorities and operating models.

Q5. How are firms leveraging ecosystem partnerships to derisk digital transformation roadmaps? Give some examples

Firms are leveraging ecosystem partnerships to reduce risk, accelerate deployment, and access specialized capabilities:

• With hyperscalers, manufacturers co-develop cloud-native digital twin platforms, ensuring scalability and cybersecurity (e.g., a global automotive firm using a hyperscaler's industrial cloud to unify plant data)
• With SaaS integrators, companies streamline MES/PLM/ERP integration, reducing legacy complexity and time-to-value (e.g., a chemical producer aligning its global factories via a modular SaaS suite)
• With deeptech startups, firms tap into AI, computer vision, or edge computing innovations, often via pilot sandboxes (e.g., a consumer goods firm testing AI defect detection with a startup in two plants before scaling)

These partnerships enable phased rollouts, share innovation risk, and accelerate ROI while building internal capabilities in parallel.

Q6. Which regional players in Europe, the U.S., or Asia are emerging as strong contenders in domain-specific transformation, for example, semiconductor fab digitization or aerospace MRO digitalization?

Several regional players are driving domain-specific digital transformation across key industries:

Europe

A leading semiconductor manufacturer is digitizing fabs with AI-based process optimization and digital twins. An aerospace OEM is advancing MRO digitalization using predictive maintenance and immersive tech across service sites.

U.S

A major chipmaker is building next-gen digital fabs with proprietary AI tools and cloud infrastructure. An aerospace and defense firm is deploying IoT and AI across MRO operations to cut turnaround times.

Asia

A top foundry is integrating machine learning for yield and defect prediction in advanced nodes. A major airline is piloting AI-driven MRO workflows and digital training for technical crews.

These efforts reflect a growing trend of specialized digital strategies tailored to industry needs and regional strengths.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

How are you aligning your digital transformation roadmap with measurable business outcomes—such as margin improvement, asset efficiency, or time-to-market—and what leading indicators are you tracking to ensure progress?