ChatGPT Enterprise in the company: the Univé case

Univé and ChatGPT Enterprise: the timeline of a transformation

Univé is one of the leading insurance companies in the Netherlands. It has thousands of employees spread across various business units. Over the last few years, the company has started a structured journey of adopting generative AI. The turning point was the introduction of ChatGPT Enterprise as the central hub for internal productivity.

According to what is reported directly by OpenAI in the official case study on Univé , the project combined three distinct elements. First, an explicit commitment from company leadership. Second, a responsible governance framework for AI usage. Finally, an innovation model driven by the employees themselves.

Therefore, the Univé case isn't simply the story of a company adopting a chatbot. Rather, it shows how a complex organization can transform the way it works in a sustainable and scalable way.

The three-pillar model: leadership, governance, employee innovation

The framework adopted by Univé is divided into three interdependent components. Understanding each of them is useful for anyone wanting to replicate — even partially — this approach in Italian contexts.

Pillar 1: leadership as an enabler

Univé's management chose not to delegate AI transformation solely to the IT department. Instead, they took an active role in internal communication and goal setting. This reduced resistance to change. Furthermore, it sent a clear signal: AI is a strategic priority, not a peripheral experiment.

Similarly, in many Italian SMEs the adoption of generative AI gets stuck precisely because there is no internal sponsorship. Therefore, the first lesson from the Univé case is about human governance even before technological governance.

Pillar 2: responsible governance as an accelerator

Univé has built a set of internal guidelines on the use of ChatGPT Enterprise. These guidelines are not meant to limit usage. On the contrary, they serve to create a circle of trust within which employees can experiment freely.

In fact, one of the most common hurdles to adopting AI in business is the fear of making mistakes. Therefore, setting clear rules—on sensitive data, outputs to check, and approved use cases—cuts down the anxiety and boosts actual usage. This is a point that is often underestimated in AI transformation projects.

Pillar 3: employee-led innovation

The third element is perhaps the most interesting. Univé has set up a system to gather and promote use cases identified directly by employees. This way, innovation is not top-down, but emerges from the real needs of the people doing the work.

As a result, the use cases developed were immediately relevant to the business. Plus, getting people directly involved boosted their sense of ownership and made them way more excited to jump on board. This bottom-up approach works great even in smaller companies with tighter budgets.

Winners, losers, and those who stand still

Every organizational transformation produces competitive dynamics. The Univé case is no exception. It is useful to look at who benefits from this model and who, instead, risks falling behind.

The winners are organizations that adopt a systemic approach to AI. They don't just distribute software licenses. Instead, they build culture, processes, and governance. Univé clearly falls into this category. Likewise, Italian companies that invest in internal training and governance frameworks will gain a lasting competitive advantage.

Those at risk of losing ground These are the organizations that adopt AI in a piecemeal way. For instance, a single department using ChatGPT on its own, without sharing with the rest of the company, without any policies in place, and without tracking the results. This approach just creates inefficiencies and, sometimes, legal risks when it comes to data privacy.

Those who stand still , lastly, are those who wait for the market to settle before making a move. However, according to analysis by McKinsey on the economic potential of generative AI , early-adopter companies are already creating a gap that will be hard to close.

SHM Studio's take: what changes for Italian SMEs

We at SHM Studio we closely follow enterprise adoption cases of generative AI. The Univé case confirms some hypotheses we have been promoting for some time in our consulting.

First of all, the governance is not a cost . It is an investment that speeds up adoption and cuts risks. Italian SMEs tend to view internal policies as red tape. Instead, a light yet clear framework is precisely what is needed to unlock widespread experimentation.

Secondly, employee-led innovation works on a small scale too You don't need an enterprise structure to collect ideas from your team. A simple process is enough: a dedicated channel, periodic reviews, a recognition system. Therefore, even an SME with 50 employees can replicate the Univé model on a smaller scale.

Thirdly, marketing is one of the departments with the highest ROI on the adoption of generative AI. From content production and campaign personalization to data analysis. For this reason, marketing managers are often the first to experiment and the most motivated to structure its adoption. This is a major leverage point for anyone wanting to kick off an internal project.

To dive deeper into the operational possibilities, it's worth exploring our services dedicated to artificial intelligence and the solutions of Digital marketing which we integrate with AI tools.

What nobody is saying: the hidden cost of standing still

There is a topic that rarely comes up in AI transformation case studies. It is about cost of inaction . Not adopting tools like ChatGPT Enterprise is not a neutral choice. It comes with a real cost, even if it is hard to measure.

In fact, competitors adopting generative AI produce content faster, analyze data more deeply, and personalize communications more effectively. As a result, those who fail to adapt lose ground not suddenly, but gradually and silently.

According to Gartner , by 2027 over 80% of enterprise companies will have integrated generative AI models into their main workflows. So, the question isn't whether to adopt these tools, but when and how to do it in a structured way.

The Univé case shows that the right answer is not the quickest one, but the most conscious one. A slow but governed adoption is worth more than a fast and chaotic adoption.

Next moves: what an Italian company can do today

Based on the Univé model and our observations of the Italian market, it is possible to identify some concrete actions. These are also applicable to smaller entities compared to a Dutch insurance company.

• Define a safe experimentation boundary. Identify two or three pilot departments to start using AI tools. Establish minimum rules on data and output. Measure results after 60-90 days.
• Involve leadership in communication. AI transformation cannot be seen as just an IT project. Management needs to communicate goals and expectations openly.
• Collect use cases from employees. Anyone who works every day on repetitive processes knows better than anyone else where AI can make a difference. Therefore, setting up an idea-gathering channel is a simple yet high-impact step.
• Integrate AI into existing marketing workflows. From SEO content production at google ads campaigns , up to the LinkedIn campaigns , there are immediate and measurable integration points.
• Measure and communicate results internally. Spreading adoption also means sharing success stories. A use case that works in one department becomes a blueprint for the others.

For those who want to learn how to structure this kind of journey, our team is available via the contact page . Furthermore, on the SHM Studio blog we regularly publish analysis and updates on AI, marketing, and digital transformation.

In short: the Univé model as a compass, not a copy-paste

The Univé case shouldn't be read as a blueprint to copy word for word. Think of it more like a compass. The principles — active leadership, light governance, bottom-up innovation — are universal. That said, every organization needs to tweak them to fit its own vibe, culture, and resources.

Italian companies have specific characteristics: flatter structures, faster decision-making processes, but often less investment in continuous training. Therefore, the Univé model can be adopted in a simplified form, still yielding significant results.

Lastly, it's worth keeping in mind that workforce transformation isn't a project with an end date. It's an ongoing process. Those who start today—even with baby steps—will be in a much stronger spot tomorrow. Check out our services to see how we can support this journey, starting from SEO to the web design , up to the strategies of Digital marketing integrated with AI.