AI + Quantum in Manufacturing: Bold Predictions, Reality Checks and Real-Life Examples

There’s a long-standing fear - or joke - among plant managers:

Now picture this imaginary AI + Quantum (AIQ) manufacturing crew of the future!

It’s a beautiful Thursday morning in the year 2035 and the AIQ manufacturing crew is coming together for an offsite team meeting to brainstorm new approaches. AI and Quantum Computing have significantly advanced, however, AI supremacy AGI (Artificial General Intelligence) has not been achieved yet. Therefore, John, the human plant manager is still around. Unlike popular predictions, John is not a data scientist and he can’t code. Instead, as the human in the loop with his many years of deep, hands-on domain experience he steers the AI-powered factory with a calm hand - supported by his five AIQ wingmen, the AI agents.

The team is enjoying an energizing cup of tokens coffee whilst LLM granny Olivia is indulging in memories of how it all started with GenAI. To be fair, she has advanced her capabilities and is proudly showing off some cool tokenization tricks.

Suddenly, the quantum watchdog Milo barks loudly. With his superposition skills he can be anywhere in the AI-powered factory at the same time, ensuring smart, secure and sustainable manufacturing is optimized. His barking is not a good sign and indeed, he has caught Mossius, the bad guy of the AIQ family who has infiltrated a digital twin with deepfake malware.

Immediately, Seco, the OT Security avenger, comes up with a disruptive action plan: He proposes to John to quickly destroy 14 infected industrial control systems before further damage is done. John approves his idea and Seco executes.

In the meantime, Adam, the production AI whizz, has restored the autonomous lot size one production with the help of Caesar, the manufacturing AI robot, whereas Emia, the AI sustainability elf, is busy tackling energy management to optimize energy costs, energy efficiency and carbon footprint of the factory.

Perfect OEE (Overall Equipment Effectiveness: Availability x Performance x Quality) is restored  and Milo happily nibbles on his token bone which John gave him as a treat for his attentiveness.

Prominent AI + Quantum Predictions in the Context of Manufacturing

Very much in contrast to the well-known and waning Gartner AI hype cycle, prominent AI leaders predict that this was just the beginning of AI. They work on unprecedented advancements that challenge our perceptions and reshape our world, especially in the manufacturing sector.

Nvidia’s Jensen Huang for example highlighted the journey from early perception AI towards GenAI, agentic AI and physical AI in his recent GTC keynote. Gartner confirms this development:

“Agentic AI and then physical AI are the next steps/phases after generative AI. AI agents will have the ability to act autonomously with minimal human supervision, adapting and executing goals in complex environments. They will have a strong business impact across a range of industries and environments.”

_{Source: GTC March 2025 Keynote with NVIDIA CEO Jensen Huang}

According to Meta’s Yann LeCun, the current paradigm of GenAI and large language models (LLMs) may soon be obsolete because they lack an understanding of the physical world. He predicted a “new paradigm of AI architectures” within 5 years and a “decade of robotics” during his Davos session as reported by TechCrunch.

Driven by such developments, the global market size of AI in manufacturing is estimated to grow seven fold between 2023 to 2028, reaching USD 21 billion by 2028, according to Markets and Markets.

Clearly, physical AI in combination with robotics will play a key role in manufacturing. But there is more to come:

Quantum advantage is here! This claim has been made repeatedly by several large tech companies, lately also by D-Wave. Taking the recent breakthrough developments into account, quantum computing is at the brink of beating classical benchmarks for selected use cases. The combination of quantum computing with AI at scale will change the world. 

“The unparalleled potential of this emerging technology lies in its ability to solve problems that are currently unsolvable by even the most advanced supercomputers” states Guy Diedrich, SVP and Global Innovation Officer at Cisco in his Forbes article What’s Next After AI: The Future Of Quantum. “The breakthrough will come this year with quantum machine learning” predicts Dr. Enrique Solano, Co-CEO at Kipu Quantum.

AI + Quantum Reality Check @ Hannover Messe 2025

_{© Deutsche Messe}

Unsurprisingly, AI was the key topic at Hannover Messe, the world’s largest manufacturing fair where the term Industry 4.0 was coined in 2011. On the occasion of the recent fair in April 2025, Bitkom, the German digital association, conducted a representative survey of the German manufacturing sector with the following key findings:

• 42% of German industrial companies are already using AI for production processes
• Physical AI is not here (yet): Only one in five companies (19%) are using AI in robotics, with 46% being in the planning or discussion stage
• Energy Management is the top emerging use case: Although Energy Management AI applications are currently being used by only 7% of companies, around two thirds of companies are planning or discussing the implementation (64%).

Manufacturing is on a journey, elevated by AI to new performance and resilience levels. AI agents have arrived. Previously GenAI was only used for generation of text or images to be used as AI assistants, now it is leveraged for the shopfloor with industrial copilots. Other typical AI applications include predictive analytics, simulation, especially digital twins, optimization and automation which are highlighted in IDC’s recent report A Strategic Approach for AI Implementation in Discrete Manufacturing.

Most importantly, it’s a shift in mindset:
“When every engineering and manufacturing process is defined, there is no need anymore for example for classic change management. Interconnected AI agents will take over this role autonomously in a digital twin. It’s like autonomous driving.” explains Harald Lukosz from Bosch Rexroth AG.

Regarding physical AI, humanoid robots and cobots were showing off their capabilities and Boston Dynamics’ robot dogs were walking the halls like in previous years. However, there is still some way to go for physical AI with one of the key success factors being industrial-grade ruggedness. 

For a start, robotics company Dexterity has launched Mech, which it calls the first industrial super-humanoid robot designed to transform logistics and manufacturing according to a report by IoT World Today. Mech uses physical AI — a system of hundreds of AI models running on an onboard AI supercomputer — to understand and handle a range of complex tasks from stacking random box arrangements to coordinating its two arms in tight spaces and delicately gripping fragile packages using an in-built sense of touch. 

Three additional, personal observations, complement the reality check at Hannover Messe:

1. It’s all about ecosystems

As always, it’s about teamwork and not superior, individual AI agents. Digital, interconnected and cross-functional AI + Quantum ecosystems consisting of software, intelligent machines and infrastructure enable companies to collaborate across platforms, securely share data, and drive innovation collectively. Just imagine the benefits of an AI powered ecosystem between an industrial automation, a software and a digital infrastructure company.

2. Quantum Computing is the new kid on the block, hand in hand with AI

Clearly, the ChatGPT moment of Quantum Computing is not here yet. However, early adopters in the manufacturing industry are already experimenting with optimization and simulation use cases, for example for new corrosion-resistant materials in the aerospace industry. As such, Quantum Computing can be seen as an enabler hand in hand with AI.

This poses attractive business opportunities for innovative start-ups such as Kipu Quantum leveraging their novel algorithms using counterdiabatic protocols. The company provides its expertise in Higher-Order Unconstrained Binary Optimization (HUBO) Mapping, a powerful framework for translating intricate optimization problems like job scheduling into quantum-solvable formats.

3. Significant dichotomy between AI leaders and AI oblivions

Most of the above statements clearly apply to AI leaders, meaning manufacturing organizations who fully embrace AI. These are typically large, global companies which even have dedicated AI roles, but also many promising, disruptive start-ups. However, on the other hand, there is a significant, invisible share of organizations who have not progressed with their digital transformation and are oblivious to AI, the AI oblivions. In many cases, these are Small and Medium-Sized Enterprises (SMEs) who lack the expertise and needed investments to take on the AI journey.

Real-life Customer Examples of AI in Manufacturing with Splunk/Cisco

Our customer Zeppelin, a global leader in sales and services for construction machinery, power systems, rental equipment and plant engineering, highlights in the blog From Zero to LLM-Hero: Plan, Architect and Operationalize your AI Assistant in Splunk how to connect Splunk data with LLMs to interact with them. 

The goal was to create an AI assistant that would allow employees to query any pricing information about used machinery. A question with the AI assistant can contain a user prompt such as: “How have Caterpillar MH3022 prices in Germany changed in the last 6 months?” 

_{© Zeppelin}

Virtually controlled production a first in Audi’s body shop: Our customer Audi set the pace for a next-level smart factory by building an AI-ready network and enabling a new paradigm in software-defined manufacturing. Last month, Audi’s plant, which is in Germany’s Baden-Württemberg region, announced the initial rollout of the first virtual programmable logic controller (vPLC). This is part of a broader strategy to bring IT into the OT domain, and it’s driven by Audi’s new Edge Cloud 4 Production (EC4P) solution.

Developed with support from key partners like Cisco, EC4P enables the next level of virtualized, smart automation. So far, it is managing automation cells for the car-body assembly line at the Böllinger Höfe factory. But moving forward, it promises highly efficient control of even the most extensive arrays of robots and systems, while reducing hardware, lessening energy needs, and increasing security.

Our customer Bosch Rexroth AG, one of the world’s leading automation companies of drive and control technologies for industrial and mobile solutions and factory automation, successfully completed an award-winning energy management observability project with Splunk to reduce energy costs, increase energy efficiency and improve the carbon footprint of their model factory in Ulm/Germany, implemented by the Splunk partner Consist.

The project team enabled optimizations at scale by tackling three key levers involving AI/ML models with prediction, forecasting and alerting elements: Pricing (peak management), availability (standby) and timing (operations scheduling).

Deep integrations with Bosch Rexroth AG’s Factory Orchestration Platform (FOP) allowed for ongoing optimizations with the goal of continuous improvement based on the flywheel effect.

As a result, the following potential savings were achieved:

The Future of AI + Quantum 

Tokenize everything? Will Milo, the quantum optimization watchdog, become a reality? And can John, the human plant manager keep his role alongside his five AIQ wingmen by relying on vibe coding? Whoever the key stakeholders will be, they need to work together in digital, interconnected and cross-functional ecosystems. But most importantly and beyond the hype, the basics need to be there:

“Much like AI, quantum technology depends on a robust and scalable network to unlock its full potential. Without foundational digital infrastructure, neither AI nor quantum can rise from theoretical potential to practical application” highlights Guy Diedrich, SVP and Global Innovation Officer at Cisco.

Naturally, the future is hard to predict - even with AI. 

Read On

• Splunk AI
• 5 Big Myths of AI and Machine Learning Debunked
• Splunk Machine Learning Toolkit
• Monitoring Gen AI apps running on NVIDIA GPUs with Splunk Observability Cloud
• An Introductory Use Case Guide: Splunk Artificial Intelligence for Observability
• Splunk Artificial Intelligence and Machine Learning - Free AI and ML webinar
• Ally or Adversary: The Paradox of AI in Cyber Defense
• Splunk CISO Report
• Leveraging Data for Actionable Insights in Manufacturing
• The AI Genie is Out of the Bottle. Now What?
• Cisco Expands Partnership with NVIDIA to Accelerate AI Adoption in the Enterprise
• Embracing the AI Era: Cisco Secure AI Factory with NVIDIA
• Cisco Unveils AI Defense to Secure the AI Transformation of Enterprises
• Power Your GenAI Ambitions with New Cisco AI-Ready Data Center Infrastructure