People fear losing control and the expertise they've spent years building. If leaders want employees to imagine something new, they first need to clear away old structures such as team boundaries, outdated metrics and unwritten rules. It's a crucial step in becoming an AI-native organisation.

The University of Helsinki set out to modernise customer service for a community of over 40,000 students and staff, handling more requests without adding manual work or losing quality. Vuono Group brought deep expertise in AI, data, and process development into the university's cross-functional teams, helping map the path to make that scale achievable.

AI has become one of the most accessible technologies in business history. Once everyone can buy the same foundation models, copilots, and platforms, competitive advantage does not come from the technology itself. AI runs whatever it's given, faster, exposing operational debt rather than paying it down.

We helped logistics company Storia to scale up their business into new business areas and prepare for a major IT transformation. Our collaboration included investment planning, IT and data architecture design, and a technology and AI strategy built for growth.

Join the AI Finland Autumn Forum – the flagship event for all network members. As a strategic partner, we are excited to host our focus group on "Business Processes: Scaling AI Through Data and Context”.

Services company Servica partnered with Vuono Group to develop their end-to-end business processes and get more concrete business value from their D365 ERP investment.  The development roadmap covers business capabilities, data quality and AI opportunities laying the foundation for an enterprise architecture governance model.

Most enterprise AI initiatives stall between pilot and scale. The missing layer is process intelligence. Jori Stjerna, Director of Modernization Transformation and Growth at KONE, and Suvi Rantonen, Head of Process Excellence at Vaisala joined us on stage to share what building process intelligence for AI requires and offers in practice.

In most AI projects I see, the model is not the first thing that breaks. The process is. A model can predict a delivery delay, flag a supplier risk, identify a missing document, or recommend the next action in sales. That output only matters if someone can act on it.

When enterprise AI doesn't perform reliably in production, the AI model is rarely the problem. The data underneath it is. The semantic layer is how organisations fix that, and why global companies like IKEA, AstraZeneca, and Netflix have made ontologies and knowledge graphs core infrastructure.