Will Agentic AI Eat the Industrial World? The End of Engineering as We Know It

 

The Big Question

The fundamental question isn’t what if Agentic AI could make better engineering decisions than humans? It’s when will Agentic AI make better decisions than humans.

This shift is already happening. The rapid evolution of large language models (LLMs) and agentic AI means that AI is moving beyond an assistive role and toward independent decision-making and is a fundamental transformation of the industry.

Why This Matters

For decades, Germany’s industrial strength has been built on engineering expertise. Precision manufacturing, rigorous processes, and deep technical know-how have been at the core of global industrial competitiveness. Agentic AI, however, is changing the rules.

We’re no longer talking about AI as just another IT tool. We’re looking at a fundamental shift in who—or what—makes engineering decisions.

From CoPilot to Agentic AI

Agentic AI is becoming the engineer.

We’re transitioning from CoPilot AI—which assists human engineers—to Agentic AI, which independently executes tasks and decisions. This shift is crucial because it moves AI beyond being a passive assistant and turns it into an active problem-solver that autonomously handles critical aspects of industrial workflows.

• CoPilot AI: Helps humans by providing suggestions, detecting errors, and optimizing processes collaboratively.
• Agentic AI: Works independently, executing tasks with minimal human intervention and owning decision-making processes.

Agentic AI systems will be built to achieve specific outcomes, such as optimizing workflows, processing vast amounts of sensor data, or making supply chain decisions in real-time.

Automation Leveraging Agentic AI: Tasks and Roles at Risk

AI is not just replacing software tools—it’s replacing entire roles and decision-making processes. The key question industrial leaders should ask is which roles will be automated? Today, the roles most likely to be automated satisfy these criteria

• Routine and repetitive tasks (e.g., quality control, data entry, scheduling)
• Decision-making based on structured data analysis (e.g., demand forecasting, preventive maintenance decisions)
• Tasks with clear objectives and outcomes (e.g., procurement optimization, logistics management)
• Roles not requiring emotional intelligence or nuanced human judgment (e.g., factory-floor inspections, order processing)

While AI won’t replace every engineering function, the roles it can automate will be deeply transformed.

Agentic AI Potential Across the Design-to-Operate (D2O) Process

AI’s impact on industrial engineering will vary across the Design-to-Operate (D2O) lifecycle. Some phases will remain human-led, while others will see rapid automation.

D2O Phase Automation Potential

Design Low—AI will assist, but human creativity remains key

Plan High—Structured, data-driven tasks are highly automatable

Procure High—Decision-making and optimization can be AI-driven

Make Medium—AI will automate repetitive factory tasks but require oversight

Deliver Medium—AI can optimize logistics; human intervention needed for exceptions

Operate High—AI will drive predictive maintenance and monitoring

Winners and Losers in the AI Industrial World

Who Wins?

✅ AI-native startups—New entrants that build AI-first industrial solutions

✅ Software-first industrial companies—Those that integrate AI deeply into their core operations

✅ Industries and markets that scale AI quickly—Companies that move fast in AI adoption will outcompete slower incumbents

Who Loses?

❌ Companies treating AI as just another IT tool—They will fail to grasp its transformational power

❌ Slow, incremental innovators—Gradual change won’t be enough in a world where AI moves exponentially

❌ Engineers who don’t upskill into AI roles—Traditional engineering jobs will evolve, and those who resist learning AI will be left behind

The 3 Critical Questions for the Future of Engineering

Engineering isn’t disappearing—it’s evolving. Success in the Agentic AI-driven industrial world depends on answering three critical questions:

1. Which tasks and roles will be replaced by AI agents?
2. What should the agentic AI infrastructure look like?
3. What do you need to believe for industrial data to become usable and useful?

Final Thought

We are at the tipping point of the next industrial revolution—one where AI engineers machines, products, and processes with increasing autonomy. Engineering excellence will no longer be defined solely by human ingenuity but by how well humans and Agentic AI work together.

More here 👇

https://www.slideshare.net/slideshow/will-ai-eat-the-industrial-world-the-end-of-engineering-as-we-know-it/277360253

This article was first puslished on LinkedIn on April 1, 2025