According to the well-known Swiss jurist, excess supply, selective demand and organizational issues are holding back the scalability of artificial intelligence in companies.

In the European panorama of artificial intelligence, Timea Nagy is establishing itself as an increasingly relevant and authoritative voice at the intersection of law, technology, and strategy. A lawyer specializing in data privacy and AI, is the founder and CEO of the “Swiss AI Summit”, a platform that in recent years has been able to distinguish itself for its decidedly business-first approach, connecting solution providers artificial intelligence and business decision makers with the aim of accelerating its concrete adoption.

Based on Zurich and active internationally, it supports boards and C-level executives in defining strategies related to artificial intelligence, with a focus on governance, compliance, and entry into European markets. In particular, Nagy supports global technology companies in navigating the regulatory and operational complexities of Switzerland and XNUMX-XNUMX business days, translating regulatory requirements into strategic levers.

Over the course of his career he has developed a profile that combines skills Legal, technological e entrepreneurial. After the experiences in Niederer Kraft Frey, worked as Senior Legal Counsel in EY on topics of privacy data, artificial intelligence and technology law, to then delve deeper into ETERNYZE AG the aspects related to blockchain, FinTech e DLT. She was also co-founder of Women in Web3 SwitzerlandToday, he leads his platform as founder, operating at the intersection of governance, market, and strategic AI development.

The prospect of the Swiss jurist It is distinguished by the combination of three dimensions: legal expertise in-depth, a privileged observatory on the market dynamics thanks to the “Swiss AI Summit” and direct involvement in the building entrepreneurial initiatives in the sector. Through the "Summit", in fact, Timea has access to a continuous stream of interactions between vendors and corporates, observing in real time how buyer expectations, sales strategies, and the difficulties associated with large-scale implementation are evolving.

One emerges pragmatic vision and execution-oriented: in an increasingly crowded market AI solutions, the real difference is no longer technological sophistication, but the ability to generate concrete value, integrate into business processes and respond to specific needs. It is in this space, between innovation and real-world application, that the work of Timea Nagy, offering a clear understanding of the opportunities and frictions that are redefining the relationship between technology and business.

You've designed the Swiss AI Summit as a business-focused platform rather than a purely academic or policy-driven conference. From your perspective, what concrete changes are you observing today in the way AI vendors and corporate buyers interact, and why is this moment particularly transformative?

From what I've observed at the Swiss AI Summit, the most obvious change is the imbalance between supply and demand. There's a huge oversupply of AI tools and vendors entering the market, while the number of companies truly ready to purchase and implement AI at scale is still limited. Furthermore, the industry is now much more focused on execution, demonstrating ROI, and building internal capabilities. It's no longer impressed by AI's generic capabilities: what's being sought now are concrete, business-specific solutions integrated into processes. This means we're moving from a technology-driven phase to a value-driven phase, where execution and implementation matter much more than pure innovation.

From a sales perspective, what are the main challenges facing AI companies today as they move from pilot projects to enterprise-scale adoption? Where do deals most often stall?

The main challenge is getting the contract signed, followed by moving from a successful demo or pilot to actual operational implementation. This is understandable, given the oversupply. The two sides have different goals: the engineers want to innovate and sell, while the companies are focused on execution and demonstrating ROI.

Conversely, on the execution side within companies, what are the main bottlenecks after the contract is signed? Is the real difficulty technological, organizational, cultural, or otherwise?

It depends on the maturity and size of the company. Younger companies move faster, have a new culture, and therefore adapt more easily, while scaleups and large enterprises face greater challenges. In most cases, the main bottleneck is organizational rather than technological. Companies often underestimate the effort required and the need for internal change management to implement AI: for example, new workflows, processes, coordination between departments, and, above all, the time required.

At the "Swiss AI Summit," observe both vendors presenting tools and decision makers testing them in real time. What patterns emerge regarding what buyers truly value versus what vendors tend to emphasize?

A clear pattern is that vendors tend to emphasize technical advancements, while buyers focus on integration and measurable impact. Buyers are generally less interested in the most advanced models and more interested in understanding whether the solution solves a specific problem, whether it is actually needed and used by customers, and how proprietary data is managed. In Europe, in particular, secure data management and sovereign infrastructures are becoming a real selling point. Companies want to know where their data is stored and who has access to it, including in terms of country and number of people involved. This is why the winning solutions are not the most technologically complex, but often the simplest and most 'boring'.

Many organizations claim to be "AI-ready." In practical terms, what distinguishes a company structurally ready to integrate AI at scale from one that's still experimenting?

An AI-ready company typically has four elements: clean data, data governance, clear ownership, and leadership commitment. These companies integrate AI into their operational strategy and adopt a multi-stakeholder approach, involving IT, legal, and other relevant departments from the outset.

You have a three-pronged perspective: legal expertise in data privacy and AI, business exposure through the Summit, and direct involvement in platform development. How does this combination change the way you evaluate AI products and go-to-market strategies?

It offers me a unique position and expertise, which generates a more pragmatic and realistic view. From a legal perspective, I understand the regulatory requirements for data, privacy, and AI governance. From the Summit, I observe the real-world dynamics between suppliers and buyers. And by building products, I understand how costly execution is. That's why today, when evaluating AI products, I focus less on the technology itself and more on whether the product is truly necessary and whether it accelerates processes and workflows. Furthermore, I invest heavily in understanding the fundamentals, which is crucial for scaling and adapting quickly at the right time.

When designing an AI product today, how early should architectural decisions incorporate regulatory and compliance considerations? Can compliance become a business asset rather than just a risk mitigation exercise?

"From the very beginning, even in the earliest stages of product architecture. For example, while we're designing our platform, we ask ourselves what we want it to look like in five years' time in the ideal scenario. Once we have a clear vision, we break it down into phases and sprints. In this context, being compliant is no longer even a question, but a prerequisite."

Across industries, are you seeing a shift in the way return on investment (ROI) for AI initiatives is measured? Are companies still focused on cost reduction, or is the narrative shifting toward revenue generation and competitive positioning?

I'm seeing a shift in how companies think about AI ROI, but it's more nuanced than a simple shift from cost reduction to revenue generation. The problem in many organizations is that AI initiatives start without a clearly defined business outcome, which is why a large portion of pilots—about 82 percent—fail. More and more companies are realizing that AI must be linked to measurable value from the outset: improved time to market, quality, higher adoption rates, or the ability for employees to focus on higher-value activities instead of repetitive tasks. Time savings alone are no longer a sufficient metric. It's becoming clear that AI creates maximum value when treated as an organizational transformation, not an isolated technology project. Leaders are moving beyond the experimental phase and focusing on use cases that are integrated into core processes and scalable across the enterprise. While cost efficiency remains important, competitive advantage increasingly comes from customized AI solutions, based on proprietary data and aligned with strategic priorities, with a significant impact on the company's competitive advantage. clear to solve”.

In your opinion, what are the most underestimated risks facing AI providers today, not from a legal perspective but from a commercial or strategic one?

"Perhaps it's the assumption that technological superiority automatically leads to market adoption. With the current oversupply of AI solutions, differentiation is extremely difficult."

Looking ahead, with the maturation of AI platforms, agents, and vertical solutions, how do you think the relationship between AI vendors and enterprise customers will evolve over the next two years? Where do you see the greatest opportunities and the main frictions?

"Over the next two years, I expect the market to consolidate around a smaller number of more specialized solutions. Companies will increasingly prefer vendors with a deep understanding of their industry, rather than generalist AI providers."

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