Does AI really solve the problems we have? What does it mean for innovation and intellectual property? Will AI replace patent analysts? In this webinar with IamIP, I cut through the fog of AI hype and share a practical framework for understanding where AI genuinely adds value, and where it falls short.
We Are Trapped in a Hype#
Let me be direct: we are trapped in an AI bubble. In 2025, investments into AI reached $1,000 billion. The revenue generated from AI in the same year? $120 billion. That gap is enormous, and the pattern is familiar. I saw exactly this in the year 2000 with the internet bubble. Every company needed a website and an online store, massive amounts of startups appeared, and then on March 10, 2000, the bubble burst.
The indicators today are equally clear. An MIT study on the state of business AI in 2025 found that enterprises spent roughly $30 to $40 billion on generative AI, yet 95% saw no profit and loss impact. Only 5% generated real value.
We are living in the era of the AI idiot, where ChatGPT cowboys, clueless politicians, and short-term hyped managers burn billions.
Please do not misunderstand: I think AI is absolutely awesome, and I use it every day as my personal assistant. But our job as leaders is to cut through the fog and see what truly matters.
AI Has No Brain. Use Your Own.#
There is widespread confusion about the word “intelligence” in artificial intelligence. AI is a system that gathers information, interprets it, and derives conclusions. It is not a system that simulates human thinking. It is a pattern matching engine that predicts the next word, the next pixel, or the next video frame based on statistical patterns in training data.
If you ask ChatGPT whether you can trust it, it tells you itself: it is a pattern matching engine that predicts the next word. No real understanding of the world, no self-motivation, no planning, no self-reflection. When you tell it a ball on a table gets kicked, it predicts “the ball is on the floor” not because it understands physics, but because statistically that is the most likely next sentence.
Can You Build AI Systems You Can Trust?#
Out of the box, ChatGPT gives roughly 50% accurate answers. With a RAG (Retrieval Augmented Generation) system using vector databases and your own documents, accuracy improves to 60-70%. To reach 95% accuracy, close to human expert level, you need a master agent with specialized sub-agents, each with their own RAG systems, databases, and extensive prompt engineering.
We built such a system at Zühlke with an insurance company. Their support staff needed help navigating complex contracts. The system boosted productivity massively, but it required significant engineering effort, extensive testing by human experts, and humans remain in the loop. You cannot just use AI out of the box; it requires engineering.
Protecting Your Data#
If you use a SaaS LLM service, your data goes to them. They may promise not to use it, but if you have genuine concerns about data security, the only option is to host your own LLMs on-premises or run them locally. Tools like LM Studio and Ollama make it possible to run local LLMs on your own machine. The trade-off: smaller context windows and reduced capabilities compared to cloud-hosted models.
A Framework for AI in Your Organization#
I developed a framework with two dimensions. On the horizontal axis: does the work stay the same (just powered by AI) or is the work itself transformed? On the vertical axis: do humans hand over the work to AI, or do humans and AI work together?
This creates four quadrants:
Automate and Orchestrate (work stays same, AI takes over): Today, AI automatically scans global patent databases, removes duplicates, and pre-classifies results. In the future, AI agents could handle filings, renewals, and compliance across jurisdictions, with humans focusing only on exceptions and critical approvals.
AI-First (work transforms, AI takes over): Today, AI co-pilots support inventors with prior art searches and IP system updates. In the future, AI systems could automatically detect market trends, draft patents, and prioritize R&D projects with minimal human input.
Co-Create on the Frontier (work transforms, humans + AI together): Today, IP experts use AI to draft patent claims and summarize prior art. In the future, AI co-pilots could use internal and global legal data to suggest arguments, predict counterparty responses, and simulate outcomes of legal cases.
Augment and Scale (work stays same, humans + AI together): Today, AI helps with strategic decisions through simulation of technology trends and innovation opportunities. In the future, AI agents could continuously scan patent, research, and market trends, working alongside legal, R&D, and business teams to shape IP strategies.
The Human Advantage#
AI has clear strengths: pattern recognition in large datasets, fast calculation, logic, and automation. Humans have different strengths: empathy, intuition, contextual understanding, cultural sensitivity, and the ability to recognize irony and linguistic nuance. A recent study found that current AI systems reflect the culture of western English-speaking societies, meaning cultural sensitivity is simply not present.
Gary Kasparov, who famously lost to a chess AI, put it perfectly:
“The future is not man versus machine. It is man with machine versus man without.”
Our job as leaders is to create environments where humans and AI work together, each contributing their strengths.
Key Takeaways#
- We are in an AI bubble. Investments far exceed returns. Be strategic, not swept up in the hype.
- AI is a pattern matching engine, not a thinking machine. It has no brain. Use your own.
- Accuracy requires engineering. Out-of-the-box AI is roughly 50% accurate. Reaching expert-level accuracy (95%) takes specialized agents, RAG systems, and significant effort.
- Protect your data. If it must stay private, host your own models. SaaS means your data leaves your control.
- Use the four-quadrant framework to decide where AI adds value: automate, go AI-first, co-create, or augment.
- The future is humans with AI. Create environments where both work together, amplifying human creativity with machine intelligence.