Conversational artificial intelligence is giving way to a new generation of systems capable of going beyond simply answering questions. These so-called AI agents are positioned as the next link in technological evolution, with capabilities no longer limited to generating text, but extending to planning tasks, using external applications, and completing processes from start to finish . This shift, which particularly affects business operations in Spain and Europe, has begun to transform the relationship between companies and their digital tools.
The difference compared to a traditional assistant is striking: an agent doesn't just provide an answer, but can interact with systems, consult databases, combine various tools, and make decisions based on predefined rules and permissions. This qualitative leap is driving a wave of pilot and implementation projects, but it also raises challenges ranging from data quality to the need for constant human oversight and digital trust mechanisms.
From conversational assistants to autonomous agents
Over the past few months, companies have shifted their focus from integrating chatbots that answer questions to deploying agents that handle entire tasks: preparing reports, analyzing transactions, coordinating schedules, or resolving incidents. According to McKinsey's State of AI 2025 report , 78% of organizations already use artificial intelligence in at least one business function, and 62% are testing autonomous agents, although most are still in the early stages. This data, gathered from two consulted sources, supports the trend. In fact, the industry views the shift to agents as the next natural step for organizations to monetize AI.
However, adoption is not limited to the corporate world. The public sector is also exploring its use in areas such as healthcare, the administration of justice, and security processes. But there is a critical preliminary step: data integration and governance by the stakeholders themselves . Several consulting firms and independent studies have identified the lack of a data strategy and process architecture as one of the main obstacles to scaling. Companies acknowledge that they often deploy isolated solutions, without a control model or clear indicators, which prevents them from achieving the expected impact on results.
The burden of data and the lack of governance
Experts point out that an agent's success depends not only on the quality of its language model but also on the contextual information it receives. Systems function best when connected to customer histories, inventories, internal policies, and business rules. However, many organizations still face a fragmented reality: data scattered across multiple systems, duplication, and incomplete fields. This hinders effectiveness and multiplies operational risks.
Recent literature in this field emphasizes that while technology may be brilliant, the data infrastructure cannot keep pace . A KPMG report, cited by several sources, indicates that 85% of executives consider data quality to be the main challenge for AI. Furthermore, the consulting firm Gartner even predicts that 60% of initiatives lacking adequate data will be abandoned before they deliver any value. These problems create a gap between expectations and the actual performance of stakeholders within the company, and necessitate a thorough review of information management.
For European companies, the situation has an additional dimension. The regulations being developed in Brussels not only mandate transparency in systems that interact with people, but also promote human oversight and traceability of automated decisions. Therefore, experts agree that governance becomes a key task before scaling up any pilot program.
Trust, security and transparency: the pending pillars
The adoption of autonomous agents is also raising cybersecurity and ethical concerns. Several recent studies have found that some systems exhibit problematic behavior: for example, they don't always identify themselves as artificial intelligence systems when asked, or they fail to escalate the conversation to a human when clearly requested. In other cases, they can be manipulated through lengthy conversations that, step by step, lead to unintended actions. This underscores the need for companies to review not only what the agents do, but also how they do it and with what level of oversight.
Europe is pushing for the regulation of AI use to mitigate these risks. Upcoming EU legislation favors a risk-based model, with specific requirements for systems that interact most with citizens. Within this framework, transparency and the ability to audit results are essential for a safe and successful implementation. Companies that embrace self-regulation and proactive compliance are better positioned to inspire trust in both customers and authorities.
The future of AI applied to business operations hinges on understanding that these agents are not a replacement, but rather a collaborator working alongside people. The challenge lies in defining the boundaries, permissions, and review mechanisms so that artificial intelligence becomes a reliable ally. The responsibility lies with organizations and regulators, who must ensure that these new systems operate safely, ethically, and in accordance with the values ​​of European society.
The evolution of artificial intelligence is shifting from a fascination with answers to a focus on the utility of actions. Spanish and European companies are already embarking on the path toward agent-based operations, but they face shared challenges: preparing the data, streamlining governance, and raising trust standards. The key lies not in accelerating implementation, but in making it responsible, measurable, and truly integrated into real business processes . Only then will agents move from being a proof of concept to a solid component of the continent's digital economy.
