Anyone talking about artificial intelligence in companies today often ends up asking the wrong question too quickly: Which model is the best? Which AI brings efficiency to the Company?
In practice, this is rarely decisive today. Departments such as Finance, HR, Marketing or Business Development do not want to discuss parameters or model architectures. They want to know: Which AI really helps us with which task?
This is exactly where the uncertainty begins in many companies. The market has become much more confusing in a short space of time. In addition to large language models, there are integrated assistants for Microsoft 365 or Google Workspace, specialized tools for knowledge work and the first sovereign European alternatives.
The crucial question is therefore not: “Which AI is the most intelligent?”
But rather: “Which solution fits which use case in our Company?”
Not too little AI, but too much disorganized AI
In many companies, there is currently no shortage of AI, but rather a proliferation of AI. Individual teams are testing tools on their own, using different assistants and working without common standards. This quickly leads to various problems:
- The results are inconsistent. Each department uses different tools, different prompts and different quality standards.
- Security and governance questions remain unanswered. What content may be processed? Where is the data stored? Which solutions are approved?
- The potential fizzles out. Instead of productive processes, isolated experiments without sustainable benefits are created.
That’s why orientation is needed. Not every AI system is equally suitable for every task.
What criteria companies should use to make their selection
If you want to make a serious assessment of AI solutions, you shouldn’t just look at benchmarks. Five questions are particularly relevant for day-to-day business:
1. how well does the solution support thinking, structuring and analyzing?
2. how well does it work with documents, presentations, tables and long content?
3. how well does it integrate into existing systems and processes?
4 How suitable is it for internal knowledge work or external research?
5. how sustainable is the setup in terms of data protection, control and scaling?
These are precisely the questions that differentiate today’s offerings on the market. In the following, we show which of the leading AI solutions are most suitable for which use cases.
Microsoft Copilot: strong in the internal Microsoft context
Microsoft Copilot is particularly strong where companies are already working intensively with Microsoft 365. The added value comes less from the basic model alone than from access to emails, Teams chats, documents, presentations and calendars in the existing work context.
This makes Copilot particularly useful for employees who want to become more productive directly in Outlook, Teams, Word, Excel or PowerPoint.
It is important to understand that with Copilot, Microsoft provides the user with an interface to various models and integrates them in the background. The advantage is that it doesn’t matter whether you choose the models from OpenAI (ChatGPT), Anthropic (Claude) or another model.
Particularly suitable for: internal communication, meeting follow-up, knowledge management, initial presentation and document drafts
ChatGPT: the versatile generalist
ChatGPT is still the most obvious entry point into productive AI use for many companies. The reason is simple: the solution is widely applicable and quickly delivers useful results in many task areas.
Especially in HR, marketing, communication, PMO or strategy, the value often lies in speed. A job advertisement, an interview guide, an agenda, a summary or an initial line of argument for a customer meeting: This is precisely where a generalist system comes into its own. ChatGPT is particularly suitable when companies are looking for a flexible assistant for many different tasks.
Particularly suitable for: Text work, summaries, brainstorming, structuring, general knowledge work in many specialist areas
Claude: strong on precision and structure
Claude (from Anthropic) is particularly interesting for companies that value precise writing, structured argumentation and the clean processing of complex tasks. In many scenarios, Claude acts less like a creative idea generator and more like a highly analytical sparring partner.
This is particularly evident when the focus is on extremely long documents, specialist concepts, reports or demanding text work. Thanks to its ability to hold very large amounts of information in working memory (the so-called context window), Claude analyzes systematically and without losing the thread. A typical area of application is support in the preparation of management memos, specialist concepts or longer decision-making documents.
Software development with Claude Code In addition to pure text work, Claude has positioned itself in software development. With Claude Code, the model is now the preferred choice for coding tasks for many development teams. It understands complex system architectures, writes clean code and provides efficient support for debugging, refactoring and documenting existing code bases.
The key to integration: skills and the Model Context Protocol (MCP) Two concepts are crucial to understanding Claude holistically in the corporate context: skills and the Model Context Protocol (MCP). A basic model only has the knowledge with which it has been trained. So-called skills enable Claude to carry out specific actions – such as performing specific calculations or triggering scripts.
However, the real lever for company integration is the MCP. The Model Context Protocol is an open standard that enables Claude to connect to a company’s local data sources and internal systems in a secure, structured and direct way. Instead of laboriously uploading documents to a chat, Claude can access relevant repositories, databases or development environments directly via MCP. This enables highly contextualized responses without violating the company’s internal data governance guidelines.
Particularly suitable for: Reports, complex specialist concepts, extensive document analysis, software development (Claude Code) and the seamless and secure connection to internal company data via MCP.
Gemini: strong in the Google ecosystem
Gemini is particularly relevant for companies that work heavily with Google Workspace or organize their knowledge work via Gmail, Docs, Meet and related tools. Similar to Microsoft, the added value is not just in the model itself, but in the way it is embedded in the existing working environment.
For companies with a Google stack, Gemini is therefore a logical candidate if employees are to become more productive directly in their familiar tools.
Particularly suitable for: Google Workspace environments, knowledge work, document creation, meeting support, internal assistance
Swiss and European models: relevant for sovereignty
Not every company wants to align its AI strategy completely with large US providers. Issues such as sovereignty, transparency and control are becoming increasingly important, especially in regulated environments or when it comes to fundamental strategic issues.
Swiss and European initiatives are gaining massively in importance here. They are not necessarily global leaders in every discipline, but offer decisive strategic advantages when traceability, strict governance and independence are the focus.
Two highly topical developments are currently shaping the Swiss market in particular:
- The Swiss AI Initiative & the Alps supercomputer: Supported by ETH Zurich and EPFL, this initiative forms the basis for secure AI research in Switzerland. The computing power is provided by the Alps supercomputer at the National Supercomputing Center (CSCS) in Lugano. This guarantees the development of know-how and infrastructure directly in Switzerland.
- The Apertus model (“Swiss LLM”): The open source model Apertus was developed as a direct result of this collaboration. In contrast to commercial US systems, this large language model is characterized by radical transparency: training data, code and model weights are fully disclosed. Apertus is also designed to structurally fulfill the requirements of the Swiss Data Protection Act (DSG) and the EU AI Act.
- European open-weight alternatives: In addition, models such as the French Mistral. These models can be integrated directly into companies’ own architectures – for example within local servers or in closed cloud environments such as Microsoft Fabric – without data flowing to the outside world.
For many companies, this is less a short-term productivity issue than a strategic architectural decision: Where do we want to obtain maximum performance via external APIs – and where do we consciously opt for maximum control through sovereign, locally hosted models?
Particularly suitable for: sovereign AI strategies, data security in sensitive contexts, governance-oriented organizations, the public sector and long-term platform decisions.
Conclusion: Technology is the tool, governance and expertise are the foundation
Simply enabling AI tools does not lead to greater productivity. Without clear guidelines, uncertainty, shadow IT and inconsistent results arise.
Added value is only created when AI brings three things together in the Company: the right solution for the right use case, clear rules for safe use and employees who can work productively with the tools.
This is exactly where Novalytica provides support. We help companies to classify the market, identify meaningful fields of application and empower teams directly in their real processes – practically, securely and with measurable added value.
Would you like to use AI in a targeted manner instead of randomly? Then talk to us about specific use cases, governance and enablement in your company. Click here to contact us!