Artificial Intelligence is rapidly transforming enterprise IT, but its true value lies in combining intelligent decision-making with scalable automation, governance, and operational consistency. As organizations accelerate AI adoption across hybrid and multi-cloud environments, the focus is shifting from experimentation to enterprise-wide execution. In this exclusive interaction with Rajeev Ranjan, Editor, Digital Terminal, Mangesh Surve, Sr. Director Solution Architecture and Tech Sales, Red Hat India, shares how AI and automation are reshaping IT operations, the importance of governance and human oversight, the role of automation frameworks in responsible AI deployment, and the sectors leading large-scale AI adoption.
Rajeev: How is AI changing the everyday work of IT teams, from faster troubleshooting to intelligent automation?
Mangesh: AI is helping IT teams make operations more proactive, while automation continues to ensure consistency and efficiency. Traditional automation follows predefined rules to handle repetitive tasks such as provisioning, patch management, deployments, and compliance. AI adds intelligence by analysing operational data, detecting anomalies, and recommending or triggering actions in real time based on context.
For instance, in event-driven automation environments, AI can help predict potential failures and automatically initiate remediation workflows before issues escalate, reducing downtime and manual intervention.
AI is also simplifying the development of automation itself. Tools like Red Hat Ansible Lightspeed can generate automation tasks and YAML playbooks using natural-language prompts, helping teams scale automation faster across hybrid cloud environments.
In a nutshell, automation is the critical foundation for AI adoption. As organizations move from AI experimentation to production, they require a reliable way to connect model outputs to their existing infrastructure. Just earlier this month at the Red Hat Summit, we announced major innovations to Red Hat Ansible Automation Platform designed to operationalize AI agents at enterprise scale. By establishing the trusted execution layer for IT operations in an agentic era, Red Hat provides an industrial-grade connection between AI intelligence and IT action. Thus further enabling teams to orchestrate complex AI workflows by integrating human oversight and intelligent insights for more reliable, large-scale results.
Ultimately, automation ensures systems run as instructed, while AI helps determine the best course of action. Together, they enable intelligent automation that improves operational efficiency, reliability, and innovation.
Rajeev: Where are productivity gains most visible in enterprise environments today?
Mangesh: The most visible productivity gains are in areas that were traditionally manual and time-intensive, such as infrastructure provisioning, configuration management, patching, and incident response.
As organizations adopt automation at scale, these processes become more standardized and repeatable. This reduces variability and minimises the risk of human error, a common cause of outages. For instance, Red Hat customers have reported a 61 per cent reduction in unplanned downtime.
There are also efficiency improvements in managing public cloud and network environments. These gains come from managing complex environments through a unified automation approach rather than multiple disconnected tools.
From a business perspective, this translates into faster deployments, better resource utilisation, and lower operational overhead. According to IDC, organisations using Red Hat Ansible Automation Platform have seen strong returns over a three-year period, driven by both efficiency and resilience.
Rajeev: Why can governance, security, and human oversight not be an afterthought as AI scales?
Mangesh: As AI moves from experimentation into production, it begins to influence real systems and decisions. At that point, the risks associated with inconsistency, misconfiguration, or unintended actions increase significantly, especially in hybrid and multi-cloud environments.
Governance and security, therefore, need to be built in from the outset. Approaches such as Policy as Code allow organisations to define and enforce rules consistently across environments. Security principles like least privilege, role-based access control, and defence in depth must be embedded into system design.
Human oversight remains critical. AI can support decision-making, but accountability still sits with people. Treating AI like any other mission-critical workload, with clear controls and visibility, is essential for scaling it responsibly.
Rajeev: How do automation frameworks help ensure AI-led actions remain consistent and policy aligned?
Mangesh: Automation frameworks provide the structure needed to translate AI-driven insights into consistent and controlled actions.
AI systems can recommend or trigger actions, but without a structured execution layer, those actions can become inconsistent, especially at scale. Platforms like Ansible ensure that every action follows a defined playbook.
With Policy as Code, organizations can define governance requirements once and apply them uniformly across environments. This means that whether an action is triggered manually or by an AI system, it follows the same set of rules.
Event-driven automation further strengthens this approach by linking specific conditions to predefined responses. For example, a security alert can automatically trigger a sequence of actions such as isolating a system or updating configurations.
In complex environments, this level of consistency is difficult to achieve manually. Automation ensures reliability, repeatability, and compliance at scale.
Rajeev: What balance are enterprises striking between speed and responsible AI adoption?
Mangesh: Enterprises are moving quickly with AI adoption, but they are also being more strategic about how they scale it. The focus today is not just on deploying AI models faster, but on building a flexible, secure, and scalable foundation that can support long-term AI growth.
What we are seeing is increased emphasis on hybrid AI environments, where organizations can run workloads across cloud, on-premise, and edge environments depending on factors such as data sensitivity, compliance, cost, and performance requirements. This gives enterprises greater flexibility while reducing operational and security risks.
There is also growing recognition that success with AI depends on the ability to scale beyond a single model. Many organizations are investing in MLOps practices to streamline how AI models are developed, deployed, monitored, and managed across the enterprise.
At the same time, security and governance have become critical priorities. Enterprises are becoming more aware of risks such as data exposure, model theft, and data poisoning, especially as generative AI adoption increases. As a result, organizations are prioritizing trusted platforms, operational consistency, and stronger governance frameworks alongside innovation.
So, the balance is really about scaling AI responsibly — with the right infrastructure, flexibility, security, and operational processes in place to support sustainable business outcomes.
Rajeev: Which are the top 3 sectors adopting AI solutions at scale?
Mangesh: From a Red Hat perspective, sectors that are data-intensive, highly distributed, and operationally complex are seeing the fastest adoption of AI at scale.
BFSI continues to be one of the leading sectors, with AI being widely used for fraud detection, risk assessment, customer service, voice authentication, and operational automation. Given the regulatory nature of the industry, there is also strong emphasis on governance, transparency, and security.
Healthcare is another major area of adoption, particularly for faster diagnostics, medical imaging, patient monitoring, and clinical research. AI is helping healthcare providers improve patient outcomes and operational efficiency, while also expanding access to services through AI-assisted tools.
Manufacturing is also accelerating AI deployment, particularly in intelligent automation, predictive maintenance, industrial analytics, quality control, and supply chain optimisation. AI and machine learning are helping manufacturers reduce downtime, improve productivity, and respond more quickly to changing operational conditions.
Beyond these sectors, we are also seeing strong momentum in telecommunications, retail, government, and financial services, where organizations are using AI to improve customer experiences, optimize networks, strengthen cybersecurity, and drive data-informed decision-making.
Across industries, organizations are converging on the need for open, flexible, hybrid AI platforms that can scale innovation responsibly while maintaining governance, security, consistency, and operational control.
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