But anyone working in branding and corporate communications knows the dilemma:

• Faster and more consistent: yes, please!
• Losing control over positioning: no, thanks.

We revised our own content strategy — with the help of AI. And looked for a way to resolve that dilemma: an efficient process that keeps control in human hands.

Where to start

Liip sharpened its positioning goals, which meant our content strategy needed a revision too.

For this, I worked with Claude: we have Enterprise access at Liip, and I use the desktop app.

As a first step, I set up a new project. I give it a clear title and describe what I'm trying to achieve.

I can also feed the system context information that it takes into account across all the work within that project.

In this case, relevant context included:

• The organization's vision and strategy
• Positioning goals
• Existing personas
• Approval processes and relevant governance

Anything relevant to the content strategy goes into the project as context. This matters especially when several stakeholders or language regions have a say in the final result: the system proposes — but it knows the guardrails within which a proposal actually holds up.

This context provides a solid foundation for an approach that's both grounded and efficient.

From there, the actual work on the content strategy itself can begin.

I wanted to sharpen our core messages and target groups — two building blocks we'd been developing further as part of our new positioning.

Working with the system

For each building block, I had two options:

• Have Claude generate a first proposal, derived from the context I'd provided.
• Sketch out roughly what I had in mind, and let the system turn it into a full version.

Either way gives me a starting point I can refine further — alone, with the system, or with the team. The AI doesn't write my strategy; it simply supports me through the process.

What it takes is the moment where I step back from what's been generated, reflect on whether it actually serves our goals, and bring in my own judgment.

To get a fresh perspective on generated proposals, I like to pull in colleagues. Through this process, I developed a first complete version of our core messages, which I then took to the team for feedback.

Depending on where you're starting from, you can work through the different building blocks this way. The system makes sure everything builds on what came before in a meaningful way.

What's a system prompt?

Once my revised strategy is in place, I take it one step further: I derive a system prompt from it.

A system prompt is the overarching instruction you give a model once, which then automatically applies to every following interaction in that context — unlike the actual prompt, which you write fresh for each individual request.

In effect, I've built myself an assistant that supports me — or others on the team — in creating new content, while automatically paying into our content strategy:

• The system asks me who the content is for, and surfaces the pain points and content needs that fit.
• The system asks which core message should take the lead, and helps me align the content with it.
• The system walks me through every other relevant dimension — step by step.

From there, the system prompt can keep being refined as you work with it — for instance, once you realize that the channel you're writing for actually matters.

More than a tool trick

This is where, for me, the point that often gets missed becomes visible: automation only scales what's already structurally sound.

The system prompt is only ever as good as the content strategy it's derived from. The real work wasn't the prompting — it was sharpening the strategy. The strategic decisions — what we stand for, who we want to reach, with which message — were made by the team.

If you're curious, try it on a single building block: bring in the context, pick a topic, let it generate a proposal or sketch, fine-tune it with a colleague.

If you're already working with us and your content strategy could use a reality check, reach out: together, we can gather and review what you already have, then set it up in whichever AI tool your team prefers.

Working in the public sector and wondering how a process like this fits with your approval and compliance requirements? Let's talk — we have experience setting up AI-supported processes within existing governance structures.

Nevertheless, there is a lot of knowledge involved in getting something basic working in any of these frameworks. In this blog post I will explore what the most stripped down version of such a framework could look like, while still keeping the ergonomics of a reactive framework.

Let's get right into it.

Imagining the framework

To make this concrete, I built a small framework called effectlayer. I will explain its building blocks in this blog post.

Building blocks

The framework is built around a single JavaScript class, which the framework enhances with reactive behavior. That reactivity is powered by Signals, a common primitive used in frameworks such as Vue, Svelte, and SolidJS.

State

All class properties are treated as state. The framework turns them into Signals, so no state annotations are needed here:

class MoodSwing {
  energy = 5;
  coffee = 0;
}

Computed Values

Computed values use standard JavaScript getters. The framework treats them as derived Signals that update automatically when their dependencies change:

  get mood() {
    if (this.energy > 8) return "🤪";
    if (this.energy > 4) return "😀";
    if (this.energy > 0) return "😑";
    return "😴";
  }

Mutations

Methods are a natural fit for mutations:

  drinkCoffee() {
    this.coffee++;
    this.energy = Math.min(10, this.energy + 3);
  }

  work() {
    this.energy = Math.max(0, this.energy - 2);
  }

Effects

The last concept we need is effects. Effects are a fancy way of saying "a thing that executes when dependent Signals change".

I chose methods starting with $ for annotating them:

  $monitor() {
    if (this.coffee > 10) console.warn("You may want to slow down.");
  }

The framework sees that $monitor() uses coffee. After coffee changes, it calls the method again.

HTML

Rendering HTML is also an effect that just returns JSX.

  $ui() {
    return (
      <main>
        <h1>{this.mood}</h1>
        <button onClick={() => this.drinkCoffee()}>☕ Coffee</button>
        <button onClick={() => this.work()}>💻 Work</button>
      </main>
    );
  }

All that is left is to make the class reactive by wrapping it in an effectlayer call:

const moodSwing = effectlayer(MoodSwing);
moodSwing.$monitor();
document.body.appendChild(moodSwing.$ui());

Calling an effect once activates it. This means $monitor() will now run whenever coffee changes.

The $ui() call returns an HTML element and keeps it up to date.

All the concepts we need

So basically the framework only needs four concepts:

• properties for state
• getters for computed values
• methods for mutations
• methods starting with $ for effects

If you want to try out this experimental framework use:

npm create effectlayer

Or check it out on npm.

Deep Dive?

Let me know if you want to see a deep dive into how I built this framework: falk.zwimpfer@liip.ch

Let's be honest: the gen-AI hype is a threat to my content business. We're seeing companies hold back investment in their content. Why?

The capabilities of generative AI make it clear that our field is about to change significantly. But this field is broad — it brings together very different skill sets and areas of work.

So the interesting question becomes: how exactly will content work in organizations change?

A shift in roles

A study by AG CommTech and GK Personalberatung looks at how roles and skills are shifting in the communications profession. Their meta-study shows that what's needed today is a combination of technological efficiency and human authenticity — a growing need for experts who can interpret data, use AI to extract relevant insights from it, and translate those insights into effective communication strategies.

In other words, strategic skills matter more than ever.

More quantity, less quality?

Why is that?
Let's look at one visible area: generated text.

A Stanford study from early 2026 shows that around 35% of all newly published websites are now AI-generated or at least AI-assisted.

The volume of generated content is growing fast. But what about quality?

That's not a simple question, since quality has several dimensions.

Let's ask Claude about it. The bot points to a survey by the Content Marketing Institute, in which only 17% of B2B marketers rate the quality of AI-generated content as excellent or very good. 44% rate it as good, 35% as "fair," and 4% as poor. At the same time, 67% say they trust AI output only moderately, and 28% say they trust it little.

That's not exactly a robust finding, but it matches my own experience. So let's dig into the question: how does this middling track record come about?

Input quality equals output quality

AI can do a lot, but it can't work miracles. LLMs need a solid foundation to produce good results.

If I want to generate content for my company's channels, a solid foundation means the following:

• Complete data — AI can't generate reliable content if it can't find the relevant information in your existing content.
• Target group needs and questions as the central point of reference for the content.
• A content playbook that personalizes the output and aligns it with the organization's communication rules and goals.

That's a fairly sobering realization if you were hoping gen-AI would simply take the work off your hands. Depending on where you're starting from, meeting these requirements actually creates new work first.

But it also shows exactly where you can start to raise the quality of your generated content.

These requirements put user-centricity and content strategy & guidelines squarely at the center — which brings us right back to strategic skills.

In an upcoming post, I'll describe what belongs in a content strategy, in my view, and which elements deserve priority.

As the cost of content production trends toward zero, two things gain massively in importance:

• Quality of content
• Visibility and findability of content

Generative Engine Optimization (GEO) for the benefit of users

Visibility and findability of content is increasingly becoming a question of AI visibility: whether your own content and your own brand show up in the answers and results of AI chatbots and agents.

Here's some reassurance: LLMs prioritize content based on criteria that are also relevant to users.

Beyond the technical dimension – content must be readable and usable for AI systems – content also needs to meet the following dimensions:

• Built around the user
• Trustworthy
• Clear thematic positioning

This means the two aspects – quality and findability of content – are closely linked.

For a team to reach the point of producing content at this level of quality, we first need to clarify the fundamentals: without a clean alignment, no content team in the world is able to produce content of this quality in the medium term.

The Triple Content Line

We define our alignment as a content team across the following elements:

• Content strategy
• Content guidelines
• Content governance

As a team, this is where we agree on the common denominator for our content work. Only this way can we produce quality content that meets both internal and external requirements.

The line between content strategy and content guidelines isn't always easy to draw.

What belongs in a content strategy?

There's no definitive answer to what belongs in a content strategy. And beyond that, I'd describe a content strategy more as an ongoing area of work than as a finished document.

For me, the following elements belong in a content strategy:

• Content goals: What do I actually want to achieve with my content?
• Target audiences and their content needs
• Content journeys of the target audiences
• Channel strategy: Which channels do we work with, and how do they play together?
• Key messages
• Success measurement
• Interfaces to content governance and content lifecycle management

What belongs in content guidelines?

Unlike content strategy, content guidelines need to be embedded in day-to-day work. Alongside fixed reference points (e.g. the targeted language level), they often contain principles for us:

• Language level
• Language rules (e.g. consistent formatting of dates, currencies, and similar, but also topics like gender-neutral language or how to address target audiences)
• Tone of voice
• Page types
• Tools
• ...

What belongs in content governance?

Content governance defines who on the content team is responsible for what – and how we work together to make sure strategy and guidelines are actually put into practice. Without governance, strategy and guidelines remain well-intentioned documents that get lost in day-to-day business.

For me, the following elements belong in content governance:

• Roles and responsibilities: Who creates, who reviews, who approves?
• Approval processes: How does content move from idea to publication?
• Maintenance and update processes: Who is responsible for updates, and when?
• Quality assurance: How do we ensure guidelines are being followed?
• Escalation paths: What happens in case of disagreement or edge cases?
• Interfaces to other teams (e.g. design, product, marketing)

The goal? Interplay

Content strategy, content guidelines, and content governance work together: strategy sets the direction, guidelines translate it into daily practice, and governance ensures that both actually happen rather than just existing on paper. Only through the interplay of these three elements does content emerge that is consistent, trustworthy, and findable – both for users and for AI systems – while also meeting the positioning goals of your own organization.

Content production is getting cheaper. But without these fundamentals, it stays arbitrary – no matter how powerful the tools become. Whoever invests in strategy, guidelines, and governance now is laying the foundation for content that will still be found, understood, and trusted tomorrow.

How Generative Engine Optimization (GEO) works in practice will follow in upcoming posts.

A brief history of LiipGPT and ZüriCityGPT

2023, Chregu released the first version of LiipGPT. 2024, we were able to use only open-source models on ZüriCityGPT. 2025, we switched the open-source version to the newly released Apertus model. In this blog post, we talk about the insights gained since then.

Setting up the experiment

LiipGPT, our generative AI framework, has a pluggable model layer. That means we can swap the underlying language model without changing anything else — same Retrieval-Augmented Generation (RAG) pipeline, same knowledge base of official content from stadt-zuerich.ch. This gave us a straightforward way to compare models in a real deployment.

The production system at zuericitygpt.ch runs GPT-4o-mini on Azure Europe. Alongside it, we run an open-source variant at oss.zuericitygpt.ch where in the past we compared Llama and Mixtral models. We switched the open-source variant to Apertus-70B when it was released in September 2025, then moved to Infomaniak-hosted Apertus in April 2026. (To be clear: ZüriCityGPT itself and the LiipGPT platform are not open source — but the underlying language model on the OSS variant is.)

We run Apertus as the 70-billion-parameter model. It was trained from scratch on the Alps supercomputer using Swiss hydroelectricity, with 15 trillion tokens of training data spanning over 1,800 languages. It is fully open-source under an Apache 2.0 licence.

Over the last eight months, we collected over 39,000 conversations on the production side and 3,400 on the open-source side. Quality is measured using three metrics: how often an answer is acceptable (Match Rate), how often it is actually good (Match Rate +1), and how faithfully it reflects the source documents (Faithfulness). These are metrics our LiipGPT team has established as practical product metrics, not scientific benchmarks — the data is calculated real-time and asynchronously by the LiipGPT platform while users ask different questions over time, evolving website content, and model endpoint changes. Parts of the analysis were prepared with the help of Claude. Our aim is not to provide a scientific study but more a practical report. I share these results with the Apertus community because real-world deployment data of this open-source model is rare — and we think it's useful.

What we observed

Both models get the job done. On the basic question — does the user get an acceptable answer? — both models perform similarly. GPT-4o-mini scores 81%, Apertus 82% on historically matched questions. In a controlled test with 20 identical questions asked at the same moment, both hit 100%. Both models generally work as expected.

The gap shows up in answer quality. When we raise the bar to "is this answer actually good?", GPT-4o-mini scores 72% and Apertus 55%. That is a noticeable difference, and it matters for users who expect precise, well-structured responses.

Faithfulness is more nuanced than we expected. GPT-4o-mini averages 0.79, Apertus 0.50 on our faithfulness metric. But these numbers need context. We noticed something we started calling the "faithfulness paradox": when a model says "I cannot answer this," you might expect high faithfulness — no hallucination, after all. In practice, these evasive answers score low because the model is failing to use information that is available. A model that engages with its sources and cites them scores higher, even though it is making more verifiable claims. Both behaviours have tradeoffs, and both showed up across the two systems.

Sometimes Apertus gave the better answer. The most striking example was the waste dump question: "What are the rules for using the Zurich waste dump?" GPT-4o-mini responded cautiously, saying it could not provide specific rules. Apertus gave a detailed, structured answer with recycling centre details, phone numbers, and cited sources — scoring 6/6 on quality where GPT-4o-mini scored 3/6. When the knowledge base contains a clear, structured answer, Apertus can be surprisingly good at extracting and presenting it.

That said, this willingness to commit cuts both ways. In cases where the source material is ambiguous, Apertus can feel more helpful while being less strictly grounded — and for public-sector chatbots, traceability matters as much as helpfulness.

GPT-4o-mini is more concise and better at synthesis. Apertus tends to produce longer, more verbose answers. In a public-service chatbot, that is a real UX issue — users want a direct answer with clear next steps, not three paragraphs of context.

GPT-4o-mini also handles factual freshness better: it returned Zurich's current population (452,421), while Apertus fell back to a 2013 figure. When information has changed or the answer requires connecting multiple sources, GPT-4o-mini felt more reliable.

Language switching is a weak spot. When we asked "Est-ce que tu parles français?", GPT-4o-mini responded fluently in French. Apertus declined, saying it could not answer questions about its own linguistic capabilities. In more recent tests we saw that Apertus followed the language instructions in better ways, so we are looking forward to see this further improve.

The Apertus-70B IFEval score of 44% (vs 58% for comparable models) confirms that instruction following is an area where the model has room to grow.

The trend is positive. In October 2025, Apertus achieved a Match Rate +1 of 59%. By June 2026, it had climbed to 63% — the best month on record. That is not a dramatic leap, but the direction is consistent and it came without a major model update. Meanwhile, GPT-4o-mini has been stable at 63–83% throughout. The gap is narrowing.

What we are looking forward to

The Apertus team is preparing version 1.5, with improvements expected in multimodal support, agentic capabilities, tool calling, enhanced reasoning, and better training for regional Swiss languages. We plan to update ZüriCityGPT's open-source instance as soon as it becomes available and continue our quality monitoring.

What encourages us most is that the collaboration is flowing in both directions. After our aiLights talk, the Apertus team shared our findings with their staff. This close feedback loop between research and practice is exactly what makes the Swiss AI ecosystem worth investing in.

My practical advice, for anyone considering sovereign AI: start with the use case, then choose the model. If your priority is maximum answer quality today, more popular models are usually the stronger choice. If your priority is data sovereignty, transparency, and independence — and your knowledge base is well-curated — Apertus already delivers. The question is not "sovereign AI or quality" but "how much quality gap are you willing to accept for full sovereignty?" And that gap is closing.

Check the Slides and Recording for further details.

Acknowledgements

I would like to thank the ETH Zurich, EPFL, and the wider Swiss AI Initiative community for making Apertus available as a public good. As Martin Jaggi from EPFL put it: "We aim to provide a blueprint for how a trustworthy, sovereign, and inclusive AI model can be developed." (source) Eight months in, we can see that blueprint taking shape and are looking forward to test the 1.5 model in practice as well.

• Thank you Christian Stocker and the LiipGPT team for your dedication to build a practical, experiment-driven and scalable solution for AI chat and search.

• Thank you Sabine Wildemann and the aiLights team for hosting our talk.

• Thank you Oleg Lavrovsky and the Apertus community for your feedback and support.

• Thank you Public AI and our partner Infomaniak for providing the infrastructure.

Build with Apertus: join Hack Apertus

Want to get hands-on with sovereign Swiss AI? Hack Apertus is a two-stage open-source hackathon series where teams build on Apertus to develop blueprints for sovereign infrastructure across the public and private sector. Liip is a partner — we are excited to see what the community builds.

Sign up at hackapertus.ch.

The fr.ch portal brings together a vast amount of administrative information: procedures, legislation, forms, cantonal services, and municipal information. Finding the right answer within such a large website is not always straightforward.

Users simply ask their question in natural language, just as they would ask another person. Although fr.ch is available in French and German, as befits a bilingual canton, questions can be asked in any language, allowing people who do not speak either official language to access the information they need.

The AI chatbot identifies relevant content on fr.ch and within Fribourg’s legal database, then generates a written response while citing the official sources used.

This transparency is essential. AI does not replace official information; it makes it easier to access.

AI Designed for Digital Sovereignty

Data sovereignty was a central requirement of the project. The State Chancellery wanted to retain full control over both the infrastructure and data processing.

The chatbot is powered by LiipGPT, our generative AI platform. Our Retrieval-Augmented Generation (RAG) engine selects the official content most relevant to each question. The solution is hosted in Switzerland by Exoscale.

For the Large Language Model (LLM), we use Mistral AI, a leading European provider. The model is also hosted in Switzerland by Infomaniak, a pioneer in sustainable and environmentally responsible web hosting. The LLM servers run exclusively on renewable energy and combine natural cooling, heat recovery, optimised and recycled hardware, and local carbon offsetting.

This architecture reduces dependence on major US-based platforms and promotes a Swiss and European approach to generative AI. Requests are anonymised, and no personal data is shared with third parties. The entire solution remains under Swiss jurisdiction and complies with applicable data protection legislation.

A Collaboration Rooted in Western Switzerland

The State Chancellery of Fribourg led the project with a clear understanding of the administrative and political challenges involved. Our team, including colleagues based in Fribourg, developed the solution in close collaboration with the Chancellery, ensuring it addressed real-world needs as effectively as possible.

This close partnership enabled fast feedback loops and a strong focus on user experience. Beyond the technology itself, the goal was to create a tool that is useful, reliable, and easy for the people of Fribourg to understand and use.

A First Step Towards a More Accessible Administration

Since the initial launch in March, we have refined several aspects of the solution to further improve response quality. To support this process, our client benefits from our automated evaluation tools, which continuously monitor and assess the quality of generated responses.

This first version covers public information available on fr.ch as well as Fribourg’s legal database. However, the scope is not limited to the cantonal portal alone: it is gradually being extended to include information published by other cantonal institutions and selected municipalities. As new sources are added, maintaining response quality remains a key priority. The Canton of Fribourg also maintains a dedicated page for the chatbot, which sets out, amongst other things, the data sources used.

One conviction guides our work: AI in the public sector can be sovereign, transparent, and useful. Achieving this requires maintaining control over the infrastructure and always providing clear source references.

Interested in discussing AI for your institution? Don't hesitate to contact me

The clear separation between Services and Administration makes the platform’s purpose immediately obvious and highlights its value for residents and businesses alike. This makes the website’s intent instantly recognisable, with all content structured in a logical and consistent way. It’s not just us saying this, but also the BOSW jury recognised this approach, awarding the site bronze in the category User Experience this year (humble bragging: We took two of the three podium places, winning silver in the same category for the relaunch of the Museum für Gestaltung Zürich website).

Information architecture – designed from a user perspective

The information architecture is carefully designed and tailored to the needs of its target audiences. All services are centrally organised, clearly categorised, and follow a consistent structure: What do I need? How does the process work? Which channels are available?

This standardisation enables users to quickly grasp relevant information and confidently navigate complex processes. At the same time, information is presented transparently, ensuring that even occasional users immediately understand what steps are required.

Functionality, interactivity, and usability for added efficiency

The website’s features are built around user needs. Each service page provides clear, easy-to-understand guidance and intuitive elements that are simple to use. Users are supported at every step—whether through digital services or traditional channels such as forms, phone calls, or in-person visits.

This reduces uncertainty, avoids unnecessary follow-ups, and makes interacting with cantonal services more efficient.

An inclusive website made for everyone

so.ch meets digital accessibility standards in line with WAI guidelines, ensuring equal access for all users. Content is written in clear language, navigation is consistent, and interactions are intuitive. The platform supports both digital and non-digital access.

This ensures that people—regardless of technical experience, age, or ability—can use the website efficiently and independently. By consistently focusing on user needs, so.ch delivers an intuitive, transparent, and inclusive platform that makes complex administrative processes easy to understand. Clear objectives, thoughtful information architecture, accessible features, and barrier-free design come together to create a user-centred experience that simplifies and streamlines interactions with cantonal services.

Looking to improve your public service website? I’d be happy to hear about your needs and challenges.

At the end of 2022, I first saw ChatGPT in a video on TikTok and tried it immediately. It was able to explain Contents to me faster than my teachers, solve tasks better than if I did them myself, and even develop ideas I would never have come up with. That really fascinated me.

But it wasn't until I started my apprenticeship that I realized just how powerful and important AI really is. Today, I use it daily. Not to do my work for me, but to teach myself new things, research more effectively, and work more efficiently on certain tasks.

My apprenticeship is diverse. That's exactly what makes it exciting

I'm completing an apprenticeship as a Digital Business Developer EFZ. I'm currently in my second year. After the first year at Bbc Basislehrjahr, I've been working at Liip since summer 2025.

What I find particularly exciting about this apprenticeship is that I have many different tasks. I work in various Circles, self-organized teams based on holacracy. This way, I get to know many different perspectives, tasks, and people.

I started in the Finance Circle, which focused more on data management. Since then, I've had the opportunity to look into various other teams and get to know different tasks, perspectives, and ways of working. Today, I work in the Content and Design Circle with a focus on process optimization, automation, and AI. I'll also get to know several other Circles, tasks, and roles throughout my apprenticeship.

This variety suits me well. I like working in a structured way, communicate actively, and find it exciting to analyze and improve workflows. At Liip, I have the privileged opportunity to take on responsibility early in my apprenticeship. I work on impactful internal and external projects, make decisions, and contribute my own ideas. For me, this strongly distinguishes Liip from many other employers offering apprenticeships.

Dealing with clients is part of it

One part of my apprenticeship that might be less visible from the outside is dealing with clients. I learn to communicate professionally, represent my own company well, and understand important factors for good collaboration. This means, for example, clearly communicating information within the team, gathering requirements, and communicating in a structured way in projects. Because I participate in real projects early on as an apprentice, I develop a good sense of what really matters to clients.

How I use AI specifically

AI becomes particularly helpful when I provide a lot of context. Then, a general tool becomes a valuable sparring partner. AI supports me especially when I have a clear goal and need a good first draft quickly. I use it for brainstorming, to understand new terms, for research, or to structure texts.

• Fun fact on the side: This blog post was also partially structured with AI. I gathered my thoughts, explained the context, and then got help creating a meaningful structure. This is how I use AI in everyday life.

A good example is the manual for the LiipGPT backend that I wrote. At the beginning, I had practically no experience writing manuals. So I first taught myself the basics with the help of AI. Then I gave the AI as much context as possible and developed a draft step by step with it. In the end, a handbook was created that is often used internally and individually expanded for clients. For me, this was a moment when I realized how much added value AI can bring, because I could not only work faster and without experience, but also create a result that really helps others in their everyday work.

I don't use these tools thoughtlessly. I test them, compare them, and carefully consider which use cases they really make sense for.

AI is my best learning buddy

AI has great potential for learning. It simplifies content, provides examples, and adapts to my level. No textbook can do that. Getting started with a topic becomes faster this way. As an apprentice, I ask the AI questions and follow-up questions that I wouldn't dare to ask in class.

At the same time, however: AI must never replace your own thinking. Those who don't understand how AI works and how it can be used will end up learning very little.

For me, openness and critical questioning are part of dealing with new tools. AI can empower learners, but only if it's consciously used as a tool. That's why it's important for apprentices to engage with AI during their apprenticeship. Not later, when everyone else has long been a routine user.

How do you use AI?

Perhaps it's worth pausing briefly after this Blogpost and thinking about your own use of AI. How do you really use AI in your everyday life today? What concrete added value does it bring you? Is it just a tool you try out occasionally? Or do you use it consciously to work better, faster, or more structured?

At Liip, I directly experience how an organization doesn't just use AI, but actively shapes it:

• Regular knowledge exchanges and Liip Talks about AI, from new models to ethical questions.
• AI training for all employees. Internally, we offer workshops and special modules so everyone stays confident in using AI.
• Development and further development of our own AI tools like LiipGPT, which is already in use with clients from healthcare, legal, and public sectors.
• AI Sustainability Guidelines, an internal framework that ensures AI projects are implemented ethically and sustainably.

This shows me what an organization can look like that doesn't blindly deploy AI, but takes responsibility for it.

How do employees in your company use AI? Are there clear rules or offerings to use AI meaningfully? Where could AI specifically support you in your role if you used it more deliberately?

AI is not a fixed concept that works the same for everyone. But those who use it early quickly realize: The question is not whether you still need an apprenticeship, but how much more you get out of it when you have AI.

Museum für Gestaltung: a masterpiece in Public Value and User Experience

The new website of the Museum für Gestaltung Zürich serves as the central hub for the museum's digital communication and offers visitors the opportunity to connect with the museum. In co-creation, we crafted a solution that allows the design to speak for itself and delivers real public value. The BOSW jury agreed, awarding the new site the Silver prize in both the User Experience and Public Value categories.

More on the winning project

As Clelia Kanai, head of Marketing & Communication, points out “For us, design means clarity and responsibility – even in the digital realm. In Liip, we had a partner who shared and upheld this ethos, from the initial concept right through to the final accessibility check. The fact that this resulted in a project which the BOSW jury has awarded silver in the User Experience and Public Value categories is both a validation and a wonderful recognition for us.”

so.ch: transparency, accessibility and user-friendliness to strengthen the trust of the public

We developed a new website for the canton of Solothurn, so.ch. It creates genuine public value by making cantonal services transparent, accessible and easy to understand, thereby strengthening the trust of the public, local authorities and the business community in public services. As bs.ch showed us last year, a user-centred website is crucial for improving the lives of citizens who increasingly need to find their public info and documents online. The jury recognised the quality of its user experience by awarding us Bronze in this key category.

More on the winning project

Even though we haven’t won as many awards as we’d have liked (and we, of course, think we deserve), it was interesting to feel the pulse of the web industry. We sincerely congratulate "Swissgrid 24/7" on being named the Master of Swiss Web 26. See you next year!

In light of recent political developments, a “do-talk gap” has been pointed out in sustainability efforts. Zurich Climate Week proves differently: taking action on the climate crisis and talking about it.

And that’s exactly what we need right now. Because sustainable transformation doesn’t happen in silence. It happens where different perspectives and commitments come together: business, policy, academia, and civil society. Where knowledge is shared. Where solutions are developed and scaled together.

Why we’re getting involved

At Liip, we decided to contribute to the Zurich Climate Week programme for several reasons:

• To support the initiative by actively contributing to the programme
• To strengthen our commitment to sustainability beyond the Climate Week
• And not least: to share our experiences, learn from others, and grow our network of like-minded people

Our contribution to Climate Week—just like the 2 focus areas we’ve chosen for our events—is guided by one principle: taking responsibility.

Our contribution

Liip is part of the programme with 2 formats—both focused on exchange, practical insights, and real-world applicability.

Hacking for Sustainable AI

More info & registration

Artificial intelligence has long become part of our everyday lives. It seems to offer solutions to many problems—and is often the first place we turn when we reach a dead end. At the same time, its environmental and societal impacts remain largely unresolved.

When does using AI actually create real value? And how can we use it in ways that contribute to a more sustainable future?

Together with opendata.ch, we’re bringing these questions to the table in a mini hackathon. Developers, designers, sustainability experts, companies, and other interested participants are invited to collaboratively develop new ideas, prototypes, and perspectives.

👉 Goal: to develop concrete approaches for using AI in a meaningful, responsible, and impactful way.

Sustainability Reporting for SMEs: Real Cases, Practical Steps

More info & registration

SMEs play a key role in the sustainable transformation. And yet many find themselves at the same starting point: where do we begin? What really matters? And how much effort does it take?

In this session, four SMEs share their experiences with voluntary sustainability reporting.

The format is interactive: as a roundtable, participants can ask questions, share their own approaches, and discuss together how to overcome common challenges.

👉 Goal: to lower the barrier to entry, show that getting started is feasible, and grow a community of committed SMEs.

From talking to doing—together

Zurich Climate Week stands for a mindset we strongly believe in: combining urgency with optimism. Not just talking about problems, but driving concrete solutions forward. Taking responsibility together—and creating impact in the process.

Especially in a context where uncertainty is increasing and priorities are shifting, it is more important than ever to stay the course. Sustainability is not a short-term trend—it is a long-term commitment. And at the same time, an opportunity: for innovation, for new business models, for real differentiation—and for a different quality of life.

What we need are more spaces for exchange. More transparency. And more courage to share work that is still in progress.

Zurich Climate Week creates exactly these spaces. We’re excited to be part of it.