Published on 28 October 2025
The world of IT operations is undergoing a profound transformation. The convergence of artificial intelligence, automation, and observability is giving rise to a new paradigm: intelligent and autonomous operations.
At Ayesa, this approach has its own name: AITOM. This is the practical adaptation of AIOps —Artificial Intelligence for IT Operations— applied to real technology management contexts, from Architecture to Support.
AITOM combines machine learning, advanced analytics and intelligent automation to provide autonomy, resilience and proactivity to IT environments. Its objective is not only to resolve incidents more quickly, but also to anticipate them, transform the operation into a process of continuous improvement, and empower human talent, freeing it from repetitive tasks so it can focus on innovation and decision-making.
In an increasingly complex environment —hybrid infrastructures, multi-cloud models, IoT, edge computing, and distributed ecosystems—, traditional monitoring and support platforms are overwhelmed by the volume, speed, and variety of data.
AITOM responds to this challenge with a comprehensive approach, based on data and supported by artificial intelligence.
According to the report Forecast Analysis: IT Operations Management Software, Worldwide (Gartner, November 2024), the AIOps submarket—where advanced analytics, automation, and observability solutions converge—is establishing itself as the growth engine within the AITOM ecosystem.
Gartner projects that this segment will grow from $1.629 billion in 2022 to $4.739 billion in 2028, driven by an annual growth rate of 20.1 %, reflecting the increasingly central role of artificial intelligence in the management and evolution of technology operations.
AITOM Control Tower: intelligence and operational governance
At the heart of the model is the AITOM Control Tower, the solution that transforms operational complexity into a clear and actionable vision, offering a single integrated view where observability, causal analysis and the ability to act converge.
On this platform, the various intelligent agents not only facilitate advanced operational monitoring, but also enable a robust governance and decision-making framework, providing real-time strategic information for incident management, regulatory compliance, cost optimisation and key KPI tracking.
The Control Tower turns complexity into clarity: streamlines operations, multiplies team capacity, and enables managers to make informed, agile decisions aligned with business objectives.
The value of the Data Lakehouse as the core of AITOM
All intelligence requires a solid knowledge base. At AITOM, that foundation is the Cloud Information Data Lakehouse, designed as the convergence point for all of the organisation's operational data.
The Data Lakehouse acts as a single source of truth, integrating information from multiple sources —monitoring, costs, security, deployments, incidents, or configurations— to turn it into actionable knowledge.
Its capabilities include:
- Multicloud architecture, integrating services such as Azure Blob and Amazon S3, with raw, curated and service data zones that optimise governance and efficiency.
- Unified real-time and batch ingestion from sources such asAzure Monitor, AWS CUR, Kubernetes, Jira or corporate CMDBs.
- Intelligent semantic layer, supported by PostgreSQL and pgvector, enabling contextual searches and relationships between technical, financial, and business data.
- Advanced governance, with end-to-end encryption, granular access control, and auditing by view or agent.
- Vectorised knowledge, where runbooks, postmortems or change notes are indexed and linked so that AITOM agents learn and evolve with each case.
The result is a platform that not only stores information, but also orchestrates knowledge, feeding AITOM's intelligent modules —including the Root Cause Analysis (RCA) system— to enable proactive, consistent, data-driven decisions.
Root Cause Analysis (RCA): causal intelligence applied to operations
Root Cause Analysis (RCA) is just one of the many capabilities that make up AITOM. In this Insight, we delve deeper into causal intelligence applied to operations, while in future editions we will address the other use cases that complete our intelligent ecosystem.
The RCA Engine combines anomaly detection, event correlation, and probabilistic models to identify the root cause of a failure in seconds.
It relies on a Signature Library with known patterns and a Blast Radius Estimator that measures the extent of the impact.
In addition, it uses a vectorised knowledge base with runbooks, postmortems and standards that enable agents to explain and remedy incidents automatically, always under a human-in-the-loop model.
Key benefits and considerations:
- Operating speed: Significant reduction in MTTD and MTTR thanks to automatic correlation of symptoms, changes, and dependencies.
- Transparency: Each analysis includes an audited trace with the signals consulted and the model's decisions.
- Continuous learning: Feedback from the teams reinforces the algorithms, increasing their accuracy with each new case.
Challenges:
- Context integration: Combine metrics, logs and multicloud topologies without losing consistency.
- Causal reasoning at scale: separate correlation from causation in distributed architectures.
- Operational confidence: maintain a balance between automation and human validation.
Overall risks and limitations
Although the potential of AIOps is enormous, its success depends on three key factors:
- Data quality: Without coherent information, models experience reduced accuracy.
- Governance and security: Automation must operate with control and traceability.
- Change management: the cultural component is as important as the technological one; AITOM promotes a progressive transition towards intelligent operations, always with people t the core.
Market data and figures
According to the report Forecast Analysis: IT Operations Management Software, Worldwide (Gartner, November 2024), the Health and Performance Analysis submarket, which includes AIOps solutions, is the fastest growing within the AITOM ecosystem.
Its size will grow from $1.629 billion in 2022 to $4.739 billion in 2028, with a compound annual growth rate (CAGR) of 20.1 %.
This acceleration confirms that AIOps is consolidating its position as the driving force behind the transformation of the IT operating model, placing artificial intelligence at the heart of operational efficiency, resilience and automation.
Conclusion
AITOM represents our vision to take AIOps beyond the concept and turn it into a tangible reality: smarter, more autonomous and resilient IT operations.
Its Data Lakehouse-based architecture, unified Control Tower and intelligent agents —from the RCA Engine to IaC automation and knowledge management— form a robust, scalable, value-centric model.
In a world where data and speed make all the difference, at Ayesa we drive the future of operations with a proposal that combines knowledge, intelligence and action: AITOM, the operating intelligence.
