TECH

Nearly half of AI projects could fail in the coming years, and the reason goes far beyond the technology itself.
The new generation of artificial intelligence is no longer limited to answering questions or automating simple tasks. So-called "agentic AI" promises to execute entire processes autonomously and transform corporate operations. Yet, despite the excitement, experts warn that the race to adopt this technology may mask a problem far greater than the choice of software.
Agentic AI is emerging as a key trend in the corporate world. Unlike traditional chatbots or generative AI assistants, these systems can coordinate multiple process stages, analyze information, make decisions within set parameters, and interact with other digital agents to complete complex tasks.
In practice, this means AI moves beyond executing isolated actions to managing entire workflows.
Despite this potential, a report by Harvard Business Review Analytic Services—produced with support from Deloitte—offers a worrying prediction: up to 40% of agentic AI projects could be cancelled before 2027.
According to the study, the reason lies not in technological limitations, but in the difficulty companies face in adapting internal processes, organizing data, and transforming their operating models.
The challenge, therefore, is not simply implementing a new tool, but preparing the entire organization to work alongside increasingly autonomous systems.
The biggest mistake is viewing AI solely as a cost-cutting measure... For years, companies invested in automation to eliminate repetitive tasks—such as copying data between systems, validating documents, or filling out forms.
Agentic AI takes this concept much further. Instead of automating just one step, it can coordinate across departments and execute an entire workflow.
One example cited by Deloitte involves hiring new employees. While a traditional system might merely extract data from a contract, intelligent agents could automatically register the new hire with HR, request equipment from IT, organize payroll documentation, and track all subsequent steps without constant human intervention. According to Fernanda Velázquez, leader of the Operate practice at Deloitte Southern Cone, the real gain lies in redesigning entire processes—moving away from a focus on departmental silos and centering operations on the end-user experience.
She also warns of a common mistake: many organizations view AI merely as a tool to cut costs or replace employees.
This strategy often overlooks persistent issues such as disorganized processes, outdated systems, inconsistent data, and cultural resistance within teams.
Without addressing these matters, artificial intelligence fails to boost efficiency; on the contrary, it can amplify existing errors.
Poor data and legacy systems can compromise the entire operation...The report identifies three obstacles that frequently arise in agentic AI projects.
The first involves poorly structured processes. Many companies rely on employee experience to handle exceptional situations that have never been formally documented. An intelligent agent, however, requires clear rules to make decisions consistently.
The second challenge lies in data quality.
Duplicate, incomplete, or fragmented information across different systems hinders AI performance and significantly reduces its reliability.
According to Velázquez, intelligent agents rely on solid data to produce reliable results. When this foundation is flawed, the technology ends up multiplying problems rather than solving them.
The third obstacle involves so-called legacy systems—platforms developed years ago that were not designed to integrate with modern artificial intelligence solutions. A lack of documentation and integration limits the agents' ability to execute end-to-end processes.
Governance, costs, and human oversight will be decisive...As these agents begin making increasingly complex decisions, the need for control mechanisms grows as well.
In sectors such as banking, healthcare, insurance, and energy, it is not enough for a decision to be correct; it is also necessary to demonstrate how it was reached and which rules were applied during the process.
Furthermore, the cost structure of artificial intelligence itself is shifting.
Instead of fixed expenses for software licenses, many platforms charge based on usage, measured in tokens. The more queries, analyses, and reasoning tasks the system performs, the higher the operational cost tends to be.
This compels companies to devise new ways to monitor spending, set usage limits, and continuously evaluate whether the investment is generating a return.
Another key factor will be the role of managed service providers, who are taking on a strategic function by integrating technology, continuous operations, and business expertise.
Despite the rapid evolution of AI, experts emphasize that human judgment will remain indispensable.
The recommendation is for intelligent agents to handle repetitive, operational tasks, while critical decisions remain under the supervision of professionals capable of interpreting contexts, assessing risks, and responding to unexpected situations.
Ultimately, competitive advantage will not necessarily lie with companies possessing the most sophisticated agents, but rather with those that successfully combine technology, well-structured processes, reliable data, efficient governance, and a workforce prepared to collaborate with artificial intelligence.
mundophone






