*By Jorge Moskovitz
Autonomous artificial intelligence, especially agent-based intelligence, is already one of the most disruptive forces of the 21st century. Far from being just a promise, it represents the maximum acceleration of the process of "creative destruction" described by Joseph Schumpeter in 1942—the mechanism by which new technologies replace obsolete models and drive economic growth.
Recently, Nobel Prize-winning economists have expanded on this theory, formalizing how technological innovation translates into measurable economic cycles. Autonomous AI, in this context, is the most powerful catalyst ever seen: it not only automates tasks, but also makes decisions, learns, and adapts independently, something that profoundly reshapes business and professional structures.
Creative destruction has always been the engine of progress, but AI adds a new component: speed and autonomy. And with that, it is already producing clear effects on the market:
Obsolescence of traditional functions Routine and manual processes are being replaced by digital agents, reducing the need for repetitive tasks.
Sectoral disruption Entire industries, such as logistics, finance, and healthcare, are undergoing structural reconfiguration in the face of autonomous practices.
– Devaluation of old skills Skills based on linear tasks are losing value, while the demand for data, ethics, and governance professionals is growing.
The impact is transversal: it changes not only "how" companies operate, but "who" is qualified to work in them, in addition to inaugurating new professions and industries. Emerging roles now include AI trainers, MLOps engineers (Machine Learning Operations), data scientists specializing in ethics and explainability, and digital governance managers.
At the same time, entire sectors are being revitalized. In finance and insurance, for example, agility generates benefits through predictive analytics and autonomous fraud detection, reducing costs. In healthcare, transformation comes through assisted diagnostics and intelligent telemedicine. In supply chains, new levels of efficiency are achieved with adaptive logistics and demand forecasting. And cybersecurity is becoming increasingly proactive, with agents that monitor, detect, and respond in real time.
All this evolution gives rise to what is called the "Do It For Me" economy (Do It For Me – DIFM), a model in which digital agents not only execute but also decide on behalf of the user. However, despite advances, full autonomy remains the exception. Most implementations rely on human oversight for complex or critical decisions, and the lack of explainability and transparency regarding how they operate undermines trust.
Imagine an autonomous agent tasked with adjusting flight itineraries based on multiple information sources. If it accesses conflicting data on flight schedules, the entire logistics network could be affected, and this error would multiply exponentially. This is the risk in interconnected environments. To mitigate this type of problem, organizations need to build a "single source of truth," consolidating dispersed data into interoperable structures with end-to-end traceability. Data quality and governance therefore become the technical and ethical foundation of autonomous AI.
This challenge is compounded by risks such as hallucinations, the generation of false information, misinterpretations, and systemic failures when data is not properly contextualized. Therefore, the maturity of autonomous AI depends on the combination of three factors: reliable data, auditable algorithms, and intelligent human oversight.
For the successful implementation of AI, it is essential to adopt a robust and multidisciplinary architecture. In short, this involves reliable data pipelines; governance and ethics in creating frameworks that ensure traceability and accountability for automated decisions; as well as training and retraining to develop professionals capable of interpreting and adjusting autonomous systems. Furthermore, cybersecurity is necessary to protect agents and data against vulnerabilities, manipulation, and attacks, and integration with legacy systems is needed to harmonize legacy systems and enterprise APIs in hybrid or multi-cloud environments.
These pillars underpin the technology and define the sustainability of the entire digital ecosystem. Companies that implement autonomous AI based on these principles and understand their role as agents of structural, not just technological, transformation will be better positioned to thrive. Those that remain stuck in the inertia of legacy models risk falling behind in a rapidly changing landscape. The era of autonomous AI is already underway, and the challenge is not to resist transformation, but to learn to embrace it.
Jorge Moskovitz is an Enterprise Account Executive at Qlik.
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
