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It was a wonderful evening with many conversations, familiar faces, and new connections.

What I always enjoy about the Zug Pharma Group is the mix of people: long-standing members, new members, pharma professionals, life sciences experts, entrepreneurs, consultants, service providers — all coming together to exchange ideas and stay connected.

These events show how strong the life sciences community in Switzerland is. They also show how much value comes from meeting in person. A short conversation over a drink can lead to a new idea, a business opportunity, a useful introduction, a fresh perspective, or a stronger relationship.

Every month, a different sponsor supports the Thursday Apéro by covering the drinks. I think this is a great tradition. It keeps the event open, welcoming, and easy to attend.

On Thursday, October 1st, KVALITO Consulting Group will sponsor the Zug Pharma Group Thursday Apéro. We are very happy to support this community. KVALITO has been working with life sciences and healthcare companies for many years. For us, this is a good opportunity to meet people, listen, exchange ideas, and contribute to the network that makes Zug such an important pharma and biotech hub.

I hope many of you will join us on October 1st. Come for a drink. Come for a conversation. Come to meet old and new members.

We are looking forward to seeing you there. Stay tuned.

The goal was simple: to be more precise — and more automated — than anything that existed before.

It all started with sapphire gramophone needles. Our needles are barely visible to the naked eye. They're made on those machines — then finished, assembled, and inspected under a microscope, by hand, with a steady grip and a trained eye. 🔍

Our father, Fritz Gyger, took the craft further. He refined the grind — and made a deliberate choice: diamond. For even more precision, quality, and durability. 💎

What followed wasn't a big breakthrough. It was a quiet, steady progress. Over decades.

Today we work in the third generation, in Thun, with a team that has been with us for many years.

Quality has always mattered more to us than quantity. That's why we made a conscious decision not to grow.

A small part of what we do is still a little top secret. 🤫

We often talk about eSIM as if the market had already fully moved on.

In practice, it is more hybrid.

And that matters, because expectations in the market are often ahead of what is really implemented in devices.

For me, this was a simple but useful reminder: just because a technology is established in presentations, standards or strategy decks does not mean it has fully arrived in the field.

At least for now, the reality is still mixed and the systems managing connectivity need to acknowledge this fact.

That matters because it gives advertisers more control over how much spend goes into LAN campaigns. For anyone managing paid social with a close eye on efficiency and budget distribution, that is a meaningful improvement.

Another strong topic was better data quality for ABM and reporting. With the Company Intelligence API, the data foundation becomes much clearer, which should help teams build more precise account-based campaigns and make reporting more useful.

Bianca Heine also shared interesting perspectives on how to use your LinkedIn presence to build trust and increase visibility in LLM suggestions. Good to know, especially as LinkedIn content keeps becoming more relevant in that context.

A big thank you as well to Solimar Goncalves for the great conversation about LinkedIn API capabilities and about XHANA, our new Thought Leader LinkedIn assistant. The potential to improve data quality, audience understanding, and forecasting is highly relevant if you want to build better systems instead of just better campaigns.

Thanks as well to Manuel Kekeisen and Fredrik Borestrom for the event and the organization.

A lot more to share, but I will keep it short for now. Day two is still ahead, and I am looking forward to it.

A ranking is a number. The spin-offs from Swiss universities confirm it every single day.

They emerge directly from fundamental research in physics, chemistry, biology and mathematics, at universities such as ETH Zurich and EPFL. From there, they build companies that set out to solve major societal problems.

Since 2011, the jury of the Top100 Swiss Startup Award has identified more than 600 of these companies, from AI to biotech, cleantech and robotics. Together they represent more than 27,000 jobs and CHF 19 billion raised.

Auterion, Climeworks, Distalmotion, Ecorobotix, GetYourGuide, Kandou, On, Scandit, SOPHiA Genetics, and SWISSto12: companies like these lead their categories worldwide today.

Behind them stands a system: a strong pipeline from research to market, global talent, and committed investors. 🇨🇭

PS: Why Switzerland Is a global hotspot for deep tech innovation: https://lnkd.in/eP3e4Twt

The real value is not another chatbot that politely says "I didn't quite understand that." It is collapsing the time between signal and decision, while keeping experienced people in the loop.

Before anything else: a good AI strategy needs a clear business objective and a concrete target. Without that, you are drawing a bow with no idea where the arrow should land. Every pilot, every model, every data investment needs to point at something specific.

Here is what I think decision makers should focus on:

Operating model. Treat AI as a change in how the bank runs, not as a collection of use cases on a slide. Pilots without structural change stay pilots.

Speed as the metric. Measure success by how much faster important decisions become. The number of pilots is not a KPI.

Human accountability. Keep it explicit, especially in risk, credit and complex client situations. AI supports the decision; a person owns it.

Data before models. Invest as much in data quality and lineage as in the models themselves. A great model on bad data is still a bad outcome.

Internal capability. Build it deliberately so you are not permanently dependent on external experts.

The banks that get this right will combine deep domain experience with modern tools. The ones that treat AI as a side project will keep watching decisions move at the old committee speed.

Curious how others who have lived both the traditional banking world and the technology side are seeing this evolve.