The conversation around AI for events has accelerated in recent years, but not always in the right direction. Grand statements, vague promises and poorly grounded concepts are everywhere. In a context where more and more organisers are looking to apply artificial intelligence to their events, this lack of clarity becomes a real problem.
This article is not about discussing “the future of AI” in abstract terms. The objective is much more concrete: to explain what our Artificial Intelligence, EVA (Event Virtual Assistant), actually is, what it can do today as an AI system for events, how it works internally, and why transparency is an essential condition for this technology to generate trust and real value.
EVA as an AI agent system for events
When people talk about AI in events, they often think only of chatbots. EVA is built on a different approach — or rather, it has become what it is today precisely because of that different approach. It is not a single conversational interface, but a system of AI agents designed to operate with different roles within an event.
Each agent has a clearly defined purpose, a specific level of access, and a set of concrete capabilities. This allows the AI to adapt to very different contexts within the same event, from attendee support to staff or exhibitor assistance. This multi-agent approach not only expands functional possibilities, but also introduces a key principle in the adoption of AI for events: security and control are not added afterwards, they are part of the design.
Knowledge as the foundation of useful AI
One of the biggest challenges in any AI system for events is knowledge management. EVA allows information to be imported from multiple sources and centralised in a knowledge base that can be managed directly or through active integrations in each event.
This knowledge is not used in a static way. It is optimised, classified and prepared to be accessed through semantic search, enabling agents to retrieve the right information at the right moment. In an environment as dynamic as an event, where questions constantly change, this knowledge layer is what makes the difference between decorative AI and AI that is truly operational.
Digital twins and real audience understanding
EVA builds digital twins for each user by combining structured and unstructured data with real attendee activity. These profiles are not static; they are enriched iteratively as the system observes interactions, interests and behaviours.
Thanks to this approach, our AI for events moves beyond generic segments and begins to understand each individual person. This makes it possible to tailor messages, recommendations and communication tone with precision — something that recent studies identify as one of the main perceived benefits of adopting AI in events.
Dynamic audiences and actionable insights
Based on these digital twins, EVA automatically generates audiences by grouping users according to real interests and observed behaviour. These audiences evolve over time and can be analysed both on an event-by-event basis and at a global level.
This improves not only the immediate attendee experience, but also enables the extraction of strategic insights. Identifying recurring interests, evaluating the impact of specific topics, or recognising key profiles for future events becomes a continuous process, based on real data rather than assumptions.
In addition, these audiences can be downloaded or shared immediately to support post-event follow-up by other teams, stakeholders or sponsors.
Automation with control and traceability
Automation is one of the areas where AI for events shows the greatest potential, but also where the most hesitation arises. EVA makes it possible to create automated flows that use audiences, recommendations and personalised communication, while maintaining full traceability of every action.
The key is that these flows are predictable, auditable and repeatable. AI does not act as a black box, but as a component integrated into clearly defined processes. This approach addresses one of the main concerns identified in AI adoption: knowing which decisions are automated and based on what criteria.
Communication, human oversight and security
EVA communicates with attendees across different channels, adapting both message and tone to the context. At the same time, it analyses conversations in real time and detects situations where human intervention is required.
All interactions are logged, allowing actions to be audited, incidents to be identified and continuous control to be maintained. This level of traceability and supervision also responds to a growing user demand: understanding when and how Artificial Intelligence is being used, especially when personal data is involved.
API-first and continuous evolution
EVA already operates under an API-first approach, enabling flexible management of knowledge, users, interests and recommendations. In addition, the development of MCP servers opens the door for other AI systems to interact directly with EVA and execute specific actions within each event.
This evolution is not based on generic promises, but on real system usage and continuous improvement aligned with how AI for events is actually adopted in practice — something academic research consistently identifies as key to sustainable adoption.
Conclusion: AI for events, explained as it really is
AI for events is no longer a theoretical concept. EVA is an operational system of AI agents that works today, with clear capabilities and a defined roadmap. The key is not to promise everything, but to explain honestly what can be done now, how it works, and how it evolves through real usage.
Transparency is not a marketing message; it is a necessary condition for AI to deliver sustained value in the events industry.