The Next Enterprise AI Battleground Isn't Models. It’s Observability
By Ajay Rag ·
For most of the past two decades, observability was something that appeared in engineering tickets, not board minutes. Its role was unglamorous but necessary: like flagging a server failure, alerting an on-call engineer, or indicating that latency had spiked.
However, according to multiple industry executives and experts AIM spoke to, that era is ending rapidly.
AI, particularly the rise of autonomous AI agents that reason, decide, and act without human intervention, has transformed observability from a monitoring function into something closer to a governance imperative.
For instance, when an AI system approves a loan, recommends a drug dosage, triggers a procurement order, or responds to a customer complaint, the question is no longer simply whether the system is running. It is whether the system can be trusted.
“AI doesn’t just process transactions anymore, it participates in decisions,” says Ashish Chandra, Partner and Global Head of AI at KPMG. “The traditional observability stack was designed to answer engineering questions: Did the server fail? Was the application available? Did latency increase? AI introduces business and governance questions. Why did this recommendation happen? What evidence was used? Was policy followed? Could we defend this decision to a regulator or a court?”
The shift, Chandra notes, is not cosmetic. “That’s why observability is moving from the CIO dashboard to the board agenda. In my view, it is becoming part of enterprise trust architecture.”
The global observability market, valued at roughly $2.9 billion in 2025 by Mordor Intelligence, is forecast to reach $6.1 billion by 2030—a near-doubling driven not by incremental IT expansion but by a structural shift in what enterprises are actually deploying.
From Plumbing to Power Grid
In the conventional monitoring stack, software followed rules, and when it deviated, it was usually traceable to a misconfigured setting. According to industry experts, AI changes the failure mode entirely. A model might produce biased output due to subtleties in its training data. An agent might take an action that was technically permitted but contextually inappropriate. Neither of these will show up on a traditional uptime dashboard.
Ariel Assaraf, CEO and Co-Founder of Coralogix, an Israeli-based observability platform with around 100 employees in India, describes the transition in terms of what the data itself now represents.
“Observability data is becoming the most truthful, real-time picture a company has of how its business actually runs,” he says. “Every customer interaction, every AI decision, every workflow execution, and every system response creates a real-time record of how the business is operating. As AI becomes embedded into products, operations, and customer experiences, that operational data is no longer just technical data. It becomes business data.”
Earlier this month, Coralogix announced that it has raised $200 million in a Series F funding round led by Advent and Canada Pension Plan Investment Board, with participation from Greenfield and Brighton Park Capital.
Market leaders have moved accordingly. Datadog recorded $3.43 billion in revenue in fiscal 2025 and has acquired data observability specialists Eppo and Metaplane to expand into AI-specific monitoring. Cisco’s $28 billion acquisition of Splunk in 2024 served as the starkest signal yet that observability had become strategic real estate worth defending at the highest levels of corporate deal-making.
“You can’t govern what you can’t observe,” says Assaraf. He notes that traditional software followed predefined rules. If something went wrong, engineers could trace the logic. AI agents are different. They reason, make decisions, interact with multiple systems, and increasingly take actions without human intervention. That creates a new requirement in which organisations need visibility not only into what happened, but why it happened.
The Agent Problem
According to a recent report by Gartner, 40% of organisations deploying AI will implement dedicated AI observability tools to monitor model performance, bias and outputs by 2028.
“As organisations adopt complex models and agentic AI, demand for specialised observability tools and unified platforms is accelerating. Heads of I&O must evaluate AI investments, manage disruption from immature solutions and rising costs, and prepare teams for an agentic AI future,” the Gartner report states.
For Gaurav Vasu, Founder and CEO of Unearth Insight, the core principle predates AI entirely.
“In any enterprise, the ability to monitor, validate, and govern critical decisions has always been central to accountability and risk management,” he says. “What AI has done is increase the scale and speed at which decisions can be automated. Enterprises cannot outsource accountability to machines. Responsibility for outcomes will continue to rest with humans.”
Chandra of KPMG offers a pointed analogy that observability is becoming as important to AI as accounting standards are to finance. “If thousands of autonomous agents are making procurement decisions, approving workflows, generating code, and interacting with customers, organisations need a complete decision ledger, not just application logs.”
That ledger, in his view, captures not just that a model produced an output, but the reasoning path behind it, the sources consulted, the policy checks applied, the confidence level registered, the model version in use, and the economic impact of the action—all in real time.
The organisations that navigate this transition successfully will not simply be those that deploy the most agents. They will be those that treat observability not as instrumentation bolted on after deployment, but as the foundational layer through which trust is established and accountability is maintained.