Apoorva Kumar, Co-founder & CEO of Disseqt AI dissects how Agentic-AI Ops closes the global AI value gap by replacing linear human-led operations with autonomous AI agents.
Despite all the hype around Enterprise AI adoption, multiple surveys reveal that an overwhelming majority of enterprises around the world are still far from running fully mature AI-driven operating models. Consulting firm BCG in its 2024 survey found that 74% of companies struggle to achieve and scale measurable value from AI initiatives, underscoring a persistent global “value gap”.
The bottlenecks are also common knowledge by now: while budgets are tight and most AI projects are experimental, finding and hiring capable people to manage or monitor multiple AI projects or models, retrain them, and ensure full compliance through audits is an even bigger challenge. Most CIOs intuitively understand the linear relationship between adding new applications or software and the size and complexity of their IT operational teams. In a way these costs correspond to an innovation tax on enterprises, keeping them from fully scaling AI adoption to enterprise-wide use cases.
A new IT operations model, Agentic-AI Ops, breaks this cost linearity. Under this model, autonomous AI agents don’t just support enterprise AI systems, but actively run, adapt, and govern them, with humans setting goals and guardrails rather than executing every step.
As a result, enterprises now no longer need to scale their operational teams in proportion to their AI projects; agents increasingly take on responsibilities earlier handled by humans, maintaining constant oversight of environment monitoring, reporting, incident management, QA checks, and compliance assurance.
Agentic-AI Ops plays a truly operational role, handling tasks that previously required full-time L1 and L2 staffing. Agents also execute routine tasks faster, which directly translates to faster product cycles and quicker deployment, while simultaneously generating fewer errors that require rework. They can spot irregularities early and initiate intervention before they affect service continuity.
Also, unlike human engineers, agentic-AI Ops can run 24/7 and significantly improve system efficiency. In fact, enterprises employing Agentic AI-Ops report that they are seeing measurable cuts in repetitive manual work. Early adopters have reported up to 70% lower costs and nearly 80% higher productivity owing to the automated workflow and reduced testing and deployment time.
By reducing the effort and manpower required to maintain routine operations, businesses can allocate their budgets better and direct their engineering resources towards driving real innovation, enhanced customer experience and new revenue initiatives. This also has an important second-order effect of increasing job satisfaction for human engineers as they can now focus on high value strategic tasks while letting AI handle the grunt work.
A recent PWC survey reveals that a majority of enterprises have begun experimenting with agentic AI systems, with many reporting measurable productivity improvements. Early evidence suggests more stable delivery pipelines, fewer operational interruptions, and greater consistency across routine workflows.
Further, Agentic-AI Ops also significantly cut the integration costs between legacy and cloud-native systems, a major factor that has so far held enterprises back from adopting automation at scale.
Ultimately, Agentic AI-Ops is proving to be a disruptive shift in IT operations that completely rewrites the long term cost-structures of IT operations, allowing AI to behave as a compounding value-generating asset, rather than remain an ever-growing cost center. CIOs who recognise the value are finding it much easier to scale AI efficiently, deploying hundreds of AI models and copilots without proportionally expanding data science, IT, or governance teams.