Swiss SMEs Lead the Way with AI

Most generative AI pilot projects fail — as shown by a recent MIT study. However, the problem usually doesn't lie in the technology itself but in organizational mistakes: unrealistic goals, lack of integration, insufficient data infrastructure. Let's take a closer look at the companies that have succeeded in creating value with AI.

Small Companies Have an Advantage

Especially small companies and organizations may have a significant advantage here. With manageable structures and shorter decision-making paths, successful AI deployments can often be realized much faster — provided one knows what truly matters. In short, these factors are key:

• Real Value: AI must solve a concrete problem, not just impress as a "fancy" prototype. If it doesn't generate measurable time savings, cost reduction, or higher accuracy, it's probably not worth it.
• Simple Start: Focus on sub-processes rather than the entire value chain right away. A pilot with 1–2 use cases instead of an all-in-one solution.
• Data Quality & Data Availability: Use existing documents, texts, sensors, or systems. These data already exist in digital form and are often clean enough to be used directly.
• Seamless Integration: AI must not exist in isolation. Integration with CRM, ERP, or workflow systems is essential.
• Governance & Control: Data protection, traceability, and error control must be built in from the start.
• Iterative Development: Learn, improve, and scale step by step. Pilot → Minimum viable version → Expansion.

Where AI Actually Works in Swiss SMEs

Based on published studies, practical guidelines, and the growing use of AI in Swiss companies, the following application areas can be identified where AI is already delivering real value:

1. Compliance, Risk Case Analysis & Fraud Detection

In the financial sector and payment services, AI used for pre-screening transactions and suspicious cases has a strong impact. For example: with the support of Innosuisse, the Ticino-based SME Cube Finance developed an intelligent system that detects fraudulent bank transactions more efficiently, combating money laundering. Manual checks are significantly reduced, and risk analyses accelerated.

Why it works: Clear rules, high data availability (transaction logs), large manual effort, high benefit through error prevention.

2. Document Extraction & Text Analysis in Insurance

AI-powered intelligent document processing can reduce processing time by up to 80% and error rates by around 90% compared to manual methods. Claims assessors and insurance experts can document, evaluate and compare much faster.

Concrete Swiss examples:

• SPS Switzerland developed an AI-based OCR solution for Swiss insurers. In its so-called "SPS Insurance Factory," physical and digital documents are automatically classified and data is extracted. The solution processes incoming documents from the mailroom all the way into insurers' core systems — laying the foundation for full straight-through processing.

• The Swiss startup inait.ai developed "Bumpt," an AI system for highly precise vehicle damage detection, which won first place at the Swiss Insurance Innovation Award 2023. The solution significantly accelerates damage assessment processes.

Why it works: Existing documents as input, structured outputs, previously high manual workload, high cost of errors.

3. Back Office & Correspondence

A recent study by the research institute Sotomo, commissioned by Axa Switzerland, shows that around half of the 300 surveyed Swiss SMEs use artificial intelligence for translation and correspondence tasks. 38 percent use AI for marketing texts. This confirms that AI is now routinely used to propose, translate, or summarize emails, contracts, and proposal drafts.

Concrete Swiss examples:

• Bexio from Rapperswil has been using AI since 2023 through its "Scan2Go" system to automatically allocate bank transactions and extract data from invoices and receipts using OCR. The AI-based document extraction recognizes content even without QR codes and enables automatic booking — significantly simplifying the processing of back-office correspondence such as receipts.

• Abacus from Wittenbach uses "DeepO" for AI-based invoice processing with automatic posting. This machine-learning system turns unstructured data into structured data, massively reducing manual entry in contracts and financial documents and enabling full automation of the accounting process.

• Klara from Lucerne offers AI-supported automated invoicing and payment reconciliation. The system automatically reads PDF invoice data, provides booking recommendations, and handles payment initiation and accounting reconciliation — all powered by learning AI technology.

• Yokoy from Zurich specializes in AI-based expense and supplier invoice management with integrated fraud detection. The solution automatically reads receipts, validates them, checks for rule violations, and prepares the accounting journal including VAT — with an automation rate of up to 90% and proven processing cost savings of around 80%.

• Swiss cloud provider Infomaniak integrated an AI writing assistant into its kSuite mail service in 2023. Based on open-source technology, the solution processes all data exclusively in Switzerland and helps companies handle email communication efficiently.

Why it works: Low risk, immediately measurable time savings, low entry barrier.

4. Quality Assurance and Predictive Maintenance

For manufacturing SMEs with machines, sensors, or visual inspection systems, AI is often ideal for detecting component defects or predicting maintenance needs. A comprehensive study by ETH Zurich, in collaboration with Swissmem and Next Industries, shows that predictive maintenance and machine optimization are among the most important application areas for industrial AI in Switzerland.

Concrete Swiss examples:

• The Swiss data science specialist LeanBI has developed predictive maintenance solutions using acoustic sensors for industrial companies. These sensors capture sounds of critical machine components such as motors, bearings, or gearboxes and analyze them with machine learning to detect impending failures early.

• Parametric, based in Switzerland, offers its RET3000 system for AI-based predictive maintenance in the railway sector. The system monitors sensor data such as vibrations and temperatures of components like bearings and motors in real time, detects anomalies via machine learning, and sends automated alerts — minimizing downtime and optimizing maintenance.

• The Zurich University of Applied Sciences (ZHAW), in collaboration with Fluence Energy (which includes Nispera since 2022), developed a hybrid AI model for solar power plants. This "physics-informed AI" system combines deep learning with physical models to diagnose energy losses caused by defects like soiled modules and plan maintenance economically. It achieves 70% better fault detection than conventional AI models and enables much more cost-efficient maintenance planning.

Why it works: Existing sensor data, clear benefits from avoiding downtime, high costs in case of failures. The Swiss industry benefits from an advanced state of automation and digitalization, as well as well-established networks and easily accessible expertise.

How to Make Your AI Project a Success

Based on insights from successful projects, here is a practical checklist:

Step 1: Problem Selection & Use Case Definition

• Choose a narrow, clearly defined use case, e.g., "invoice document classification," not "complete automation of accounting."
• Check whether historical data is available and digitally accessible (e.g., text files, ERP logs).
• Estimate the potential value (monetary, time savings, error reduction).

Step 2: Data & Infrastructure Preparation

• Clean your data (duplicates, inconsistencies).
• Build a pipeline that automatically transforms data.
• Ensure interfaces to your systems (ERP, CRM, document repositories).

Step 3: Develop & Test the Pilot

• Create a minimum viable product (MVP) with core functionality.
• Run human checks in parallel to discover errors.
• Define clear KPIs (e.g., accuracy, time savings).

Step 4: Governance & Quality Assurance

• Define responsibilities (who checks, who corrects).
• Track versioning, logging, feedback loops.
• Integrate data protection and security mechanisms.

Step 5: Scaling & Further Development

• Evaluate extensions (additional use cases, more data sources).
• Learn from errors and optimize iteratively.
• Embed AI into your business processes — not as a standalone solution.

The AI Journey Is Not a Sprint — It's a Marathon

Successfully implemented projects show: it works when approached with focus, pragmatism, and responsibility — and when persistence is maintained over time.

If you're planning an AI initiative in your SME — in production, customer support, document workflows, or compliance — we're happy to support you with use case design, data assessment, feasibility analysis, and of course the implementation of a pilot project.