Bootcamp Artificial Intelligence for Management

On 5 March 2026 I had the opportunity to give a talk at the FHNW AI Bootcamp on "Overview of Current AI Trends in Swiss Industry". Under the motto "Four Trends, One Pattern" my aim was not simply to give participants a snapshot of the AI landscape, but to make visible the hidden logic behind seemingly independent developments. My promise to the audience was clear: you will leave the room with a clear picture of how these trends connect, and with three concrete questions for your next AI decision.

The talk opened with a number that surprises many: AI has become 300 times cheaper over the past 18 months. The price per AI intelligence unit fell from 32 to 0.10 US dollars. That is not incremental progress. It is a structural shift that makes everything that follows possible.

Trend 1: Model Revolution - The Choice Becomes Low-Risk

The first trend concerns the models themselves. GPT, Claude, Gemini, DeepSeek: the leading frontier models now operate at comparable levels. This is shown by both the Artificial Analysis Intelligence Index and independent benchmarks such as the Mensa Norway IQ Test from trackingai.org. Proprietary models like GPT (OpenAI), Claude (Anthropic) and Gemini (Google) face capable open-source alternatives such as DeepSeek, Llama (Meta) and Qwen (Alibaba).

The practical consequence is decisive: companies no longer need to commit to a single model. All of them are good enough. Model selection has become low-risk. This creates a clear architecture: the model layer (data centre with proprietary and open-source models) becomes interchangeable infrastructure, comparable to the cloud ten years ago.

Trend 2: Agents - From Chat to Action

The second trend is the shift from conversation partner to co-worker. A chatbot answers the question "What were our Q4 numbers?" An agent, by contrast, gets the task done: generate the quarterly report, compare it with the previous year, send a draft to management. Agents can use tools, follow workflows and ask clarifying questions. But an agent without access to a company's systems is like a new employee without a laptop.

An important conceptual distinction that I often miss in practice: a reactive assistant helps the user. An enterprise agent helps the organisation. It has its own identity, works for the organisation and receives task-based access to data, not user-dependent access. An agent that only responds to user requests is not an agent; it is an assistant.

On the spectrum between predefined workflows and fully autonomous systems, my practical recommendation is to start with workflows and extend incrementally. Predefined workflow agents are predictable and controllable, ideal for familiar, standardised processes. Agentic AI plans and decides autonomously, making it more powerful but harder to steer.

As a concrete practical example I showed a knowledge management use case: key people retire and their knowledge goes with them. An AI assistant covers around 80 percent of queries from existing manuals and documents. Where the knowledge base falls short, an AI agent forwards the question to an expert and stores the new answer permanently in the knowledge base. The result is a learning system that preserves institutional knowledge before it leaves the organisation.

Trend 3: Integration - Connecting AI Islands

The third trend is MCP, the Model Context Protocol. I like to describe it as "USB for AI": an open standard connecting AI platforms (as MCP clients) to any enterprise software (as MCP servers), including CRM, ERP, calendars, documents and ticketing systems. On one side are systems like Claude Desktop or Claude Code; on the other are PostgreSQL, GDrive, Git, Slack and Google Maps.

The second practical example in this section comes from an industrial context: a machine manufacturer has analytical sensor data, deterministic but context-free. Only the combination with usage and maintenance data from the customer, accessible via MCP, enables real value: predictive maintenance and auto-calibration. The value lay not in the model; it lay in the connection.

Another example: a device manufacturer automates support by having an AI agent work directly in Jira. When a ticket is opened, the agent automatically searches manuals and past tickets, creates a draft response and submits it to the support team for approval. The agent works where the team works, not in a separate AI tool. The complete picture shows a three-layer architecture: business applications (CRM, ERP, analytics) connected via API, MCP and A2A to the AI platform, which in turn accesses the data centre via API.

Trend 4: Lock-In - The Flip Side of the Platform

The fourth trend is the strategic flip side of platform development. What started as a simple chatbot in 2023 (ask questions, get answers) became an assistant in 2024 (analyse documents, understand images) and by 2025/26 has become a full platform that books hotels, manages calendars and completes tasks autonomously. The more powerful the platform, the greater the dependency.

Lock-in creeps in at three levels. The knowledge base, with documents, vectorisation and synchronisation, is nearly impossible to migrate. Workflows built in visual builders are generally not exportable. And the memory, chat histories, preferences, context knowledge, is simply lost when switching platforms.

The good news: sovereignty is possible at every level. Open standards such as MCP and A2A protect against dependency. And for the Swiss context, a look at the Swiss AI Hub and providers such as Infomaniak shows that realistic alternatives to full dependence on US hyperscalers exist, from the model layer to the platform level.

Synthesis: It Is Not the Model That Decides; It Is the Platform

The four trends follow one pattern: models converge and the choice becomes low-risk. Agents act, but they need tools and data. MCP connects as an open standard. And lock-in arises because the platform choice is strategic. The central message of the talk can be summarised in one sentence: it is not the model that decides. The platform decides.

What does that mean in practice? Three questions companies should ask before choosing an AI platform: is it open? Who controls the data? What integrations are possible, today and in the future?

The discussion afterwards showed that these questions struck a nerve with many participants, particularly the distinction between assistant and agent, and the concrete approach of starting with workflows rather than aiming for maximum autonomy immediately. I am convinced we are in a phase where the strategic decisions shaping AI use over the coming years are being made, and I look forward to continuing to be part of that journey.