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Enterprise technology specialist Aditya Chandorkar is advancing an AI-driven approach to IT management that could help organisations move from traditional help-desk models towards more predictive and increasingly autonomous operations.
Chandorkar’s work on the Autonomous IT initiative focuses on using artificial intelligence, structured data and automation to identify potential technology problems before they disrupt business operations and, where possible, support automated resolution.
The approach comes as organisations contend with increasingly complex IT environments while facing pressure to improve efficiency and control operating costs.
Rather than waiting for employees to report technical problems, the framework is designed to allow AI systems to detect emerging issues, assess risks and trigger appropriate resolution workflows.
Moving IT Support From Reactive to Predictive
A central element of Chandorkar’s work is the use of AI to anticipate technology failures before they become visible to users.
The initiative incorporates predictive analysis into enterprise IT operations, helping organisations identify potential problems earlier and reduce the need for repetitive manual intervention.
Chandorkar has also contributed to defining the product strategy, use cases and implementation approach for applying AI across IT environments.
According to him, businesses spend substantial amounts maintaining manual IT-support structures, creating an opportunity for automation to reduce costs while improving service delivery.
The objective is not simply to automate existing help-desk tasks but to create systems capable of identifying problems and supporting faster responses with less human intervention.
AI Enters Change Management
Another area of Chandorkar’s work is IT change management, where modifications to enterprise systems can create operational risks if they are poorly planned or executed.
His approach incorporates Process Intelligence into change-management workflows, allowing organisations to analyse processes, identify potential risks and make more informed decisions before changes are implemented.
The use of AI and automation is intended to streamline approvals and execution while reducing the manual work involved in managing complex technology changes.
For large organisations handling thousands of changes, the approach could help improve consistency and reduce the possibility of disruptions associated with poorly managed system updates.
Structured Data Gives AI More Context
Chandorkar’s automation framework also places significant emphasis on data.
He has developed a structured data foundation designed to give AI systems the context required to make decisions across enterprise IT operations.
The principle is straightforward: automation becomes more effective when the underlying information is organised, standardised and accessible to the systems making operational decisions.
This structure is intended to help organisations maintain consistent service delivery across complex technology environments while allowing AI-powered tools to work with more reliable information.
Chandorkar argues that effective automation cannot depend on AI models alone. The quality and structure of the data surrounding those models are equally important.
Applications Across Multiple Industries
The technology approaches associated with Chandorkar’s work have been applied across sectors including financial services, healthcare and manufacturing, where organisations often operate complex technology environments and cannot afford prolonged disruptions.
The reported applications include efforts to improve IT efficiency, automate operational processes, reduce support workloads and accelerate the adoption of AI-driven systems.
His work combines more than two decades of enterprise technology experience with academic training, including a master’s degree in Computer Applications.
Through technical articles and conference presentations, Chandorkar has also promoted the use of AI and automation as tools for moving enterprise IT away from reactive maintenance towards proactive management.
Building Towards the Autonomous Enterprise
The broader vision behind the initiative extends beyond automating individual IT tasks.
Chandorkar’s framework combines AI-powered automation, predictive capabilities, structured enterprise data and self-healing systems to create a foundation for increasingly autonomous IT operations.
Such systems could allow organisations to identify problems, assess their potential impact and initiate responses with progressively less human intervention.
The shift could be significant for enterprises managing large-scale technology infrastructure, where the volume of daily changes and operational events can quickly overwhelm conventional support teams.
As businesses continue integrating AI into their operations, the challenge is increasingly moving beyond simply deploying AI tools. Companies must also build the data structures, processes and automation frameworks required to make those systems useful at scale.
Chandorkar’s work reflects that transition, positioning AI not merely as an assistant for employees but as part of the underlying infrastructure through which enterprise technology is monitored, maintained and improved.
The longer-term goal is an enterprise environment where IT systems can increasingly anticipate problems and resolve routine issues themselves, allowing human teams to concentrate on higher-value technology and business priorities.
