A Conversation With Arunava Bag, CTO For EMEA Of Digitate, On Agentic AI, AIOps And The Autonomous Enterprise

Tell me about yourself and Digitate

I’ve spent more than 28 years in the industry, presently working at the intersection of AI, automation and enterprise systems, helping organisations build intelligence, resilience and speed into their operations. My background spans AI based software products, software performance engineering, capacity modelling, high-performance computing and large-scale IT optimisation. Over the years, I’ve had the opportunity to evangelise emerging technologies, lead global technology practices and deliver complex transformation programmes across industries and geographies.

Since joining Digitate in 2015, and now serving as CTO for EMEA, I’ve been focused on shaping how enterprises adopt and scale AIOps, observability and intelligent automation.

What excites me most is how far the industry has come. Automation has evolved from a tactical approach to a strategic capability powering enterprise resilience and growth. At Digitate, we’re proud to be at the forefront of that shift, helping customers realise the promise of AIOps and Agentic AI in a practical and measurable way.

At Digitate, our mission is simple but ambitious: to help enterprises accelerate their journey toward autonomous, ticketless operations through our ignio™ Agentic AI platform.

Over the last decade, we’ve pioneered the fusion of AI, observability and automation, progressing from reactive to proactive, self-driving enterprise operations. Today, ignioTM powers some of the world’s most complex IT and business environments, enabling organisations to run more efficiently and with significantly higher resilience. Our newest generation of AI agents marks the next leap in that journey.

Agentic AI is often described as the next major leap beyond traditional automation. From your perspective, what fundamentally distinguishes agentic AI from earlier generations of AI and automation in IT operations?

Traditional automation followed instructions and complete straightforward tasks, often through rigid scripts. Even early AI largely enhanced decision-making but still required humans to orchestrate action.

Agentic AI understands context, reasons about intent, and takes autonomous action. It doesn’t just execute tasks, it optimises, learns and adapts. It is goal-oriented, not rule-bound. Simply put, given a complex task, an agentic AI system can understand the context, orchestrate reasoning steps, take action to close the loop and learn in the process, improving the next iteration, without much human intervention for most of the time. With the advancement of Generative AI and specialized AI agents, unknown tasks encountered for the first time also becomes possible.

This shift moves enterprises from reactive efficiency to proactive autonomy. Instead of simply reducing manual effort, agentic AI delivers measurable value, including reduced MTTR, lower operational costs and significantly higher ROI. Our recent research shows this clearly, with North American enterprises accelerating into the agentic era already seeing over $221M in returns on average.

In many organisations, IT is still viewed as a cost centre. How do you believe agentic AI reframes IT operations into a core strategic enabler that directly influences business outcomes?

For IT, traditionally it was about supporting the business processes and thus labelled as a “cost centre”.

Agentic AI flips the narrative.

One part is about supporting the business processes better. When AI agents can detect issues before users, resolve incidents autonomously, optimise cloud spend, and eliminate operational toil, IT becomes a profit engine rather than a cost.

Businesses gain efficiency, continuity, improved customer experience, faster innovation cycles, and better decision-making. When accuracy improves, downtime shrinks, and visibility increases, the entire organisation becomes more competitive.

In addition, AI also enables business to conquer new frontiers and innovate towards new business models and revenue streams.

Agentic AI is the bridge between human ingenuity and autonomous intelligence that marks the dawn of IT as driving and improving the profit centres directly.

Based on your experience at Digitate, what are the most immediate, real-world impacts of deploying agentic AI in enterprise IT environments?

Across our customers (and even in our own operations), some of the immediate outcomes we see are:

• Dramatic reduction in incidents: We’ve seen up to a 40% drop in priority incidents and far fewer user-facing disruptions.
• Significantly faster recovery: Autonomous triage and resolution cut recovery times by 50% or more.
• Lower operational overheads: AI agents scale instantly, reducing the need for large ops teams and helping organisations operate with financial discipline.
• Better cloud and cost optimisation: Agents continuously monitor usage, eliminate waste, and help sharpen cloud economics.
• Unknown issues handled with ease: One fundamental shift is the ability for unknown tasks or issues encountered for the first time to be handled, thus generating much greater value.

Agentic AI systems can make autonomous decisions and take actions across complex IT estates. How should organisations rethink trust, governance and risk when AI agents begin operating at this level of autonomy?

Trust must be earned, and governance must be intentional. For a powerful technology like AI and especially agentic AI, guardrails are extremely important and the world is moving towards it it. Though early days, in recent times frameworks (e.g. TRISM) have also become prominent.

Our research shows EMEA leads globally in structured oversight, while North America is pushing aggressively toward scale and value realisation. Both are essential. Enterprises should focus on:

• Clear guardrails: define what an agent can and cannot do.
• Validated data pipelines: Autonomy is only as reliable as the inputs.
• Transparent decision frameworks: Explainability builds confidence.
• Progressive autonomy: Start with assistive mode and scale as trust builds.

Autonomy without governance is risky, and governance without autonomy hinders innovation. The right balance accelerates value while protecting the business.

As the AI/agentic AI systems are so powerful and trusted, security also becomes a very crucial point. We have seen that in recent days, traditional security reviews also includes more and more points on AI security.

How do you see agentic AI reshaping traditional IT optimisation strategies, especially around incident management, service assurance and predictive operations?

We’re moving from a world of “detect and fix” towards “predict and prevent.”

Agentic AI will reshape IT in three key ways:

• Incident management becomes autonomous: Agents correlate millions of events, reduce noise, isolate root causes, and resolve issues proactively. Resolving new issues also becomes possible.
• Service assurance becomes continuous: Instead of dashboards and alerts, agents provide real-time observability, recommendations and actions.
• Predictive operations become mainstream: Agents leverage learned patterns, observed correlations and anomaly detection to prevent incidents before customers ever feel the impact.

How do you see the adoption of agentic AI evolving differently across regions, and what factors are accelerating or slowing adoption?

Agentic AI has become centre point in most discussions with our prospects and customers.

Our global research reveals two distinct trajectories:

• North America is scaling fast, prioritising ROI, speed, and autonomy. Adoption of agentic and agent-based AI has already hit 44% and 43%, with returns surpassing $221M on average.
• EMEA is leading on governance, with structured frameworks, ethical oversight and stronger risk models. ROI is strong – around €154.7M – but adoption moves more deliberately.
• Acceleration factors include talent shortages, rising IT complexity, and the pressure to innovate sustainably. In contrast, concerns around governance, data quality, and hallucination tend to slow adoption, particularly in regulated sectors.

With AI agents now capable of learning from data, adapting to context and initiating actions, what skills and mindsets will IT teams need to develop to work effectively alongside these systems?

AI agents don’t replace IT teams, they augment them in complex processes and also frees up time from most repetitive tasks. But teams will need to evolve across multiple dimensions:

• Clear goal setting: To look beyond the excitement of a new technology and diligently decide on the business goals to be derived from agentic AI systems.
• Systems thinking: Understanding how AI decisions impact broader operations.
• Data literacy: Ensuring the data feeding agents is accurate, enriched, and reliable.
• Oversight and orchestration: Shifting from manual execution to supervising and optimising autonomous workflows.

Organisations that embrace this augmentation mindset will move fastest toward autonomous operations.

Looking ahead, what is your vision for “autonomous enterprise operations”?

An autonomous enterprise is one where operations run largely on autopilot (with human-in-loop for some critical decisions and inputs), and is resilient, predictable, and self-optimising. Humans focus on strategy, innovation, and higher-order decision-making.

In the next five to ten years, we’ll see:

• AI agents handling the bulk of routine IT and business operations
• Ticketless environments becoming the norm
• Real-time enterprise visibility for CIOs
• Ops teams structured around oversight rather than firefighting
• New business models being created and enabled
• A shift in procurement towards blended human – AI service delivery models

This isn’t theoretical; it is already unfolding. Agentic AI will be the engine that powers enterprise profitability and innovation for the next decade.