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Imagine an AI-powered customer service assistant that has been running smoothly for months. One morning, the dashboards still seem to operate just fine — servers are up, response times are normal — but customers start getting answers that don’t quite make sense. Token usage is creeping up. So is the wait for a reply. No alert indicates something technically broken, but the system just stopped working as it should.

That gap — between systems that appear healthy and outcomes that aren’t — is what AI observability was built to close. As organizations push AI further into production, they need a way to see not just whether something is running, but whether it’s working, whether it’s safe, and whether it’s worth the cost.

In short, AI observability is IT observability for a system that can make its own decisions. This seemingly small difference requires a profound change in how these systems are managed.

What is IT observability?

Modern IT environments span on-premises data centers, public clouds, containers, software-as-a-service (SaaS) platforms and edge locations, and the dependencies between them keep multiplying. Monitoring tools can tell you if something is broken. They can’t always tell you why, or what else it touched. That’s the gap observability fills: it correlates data across the environment so teams can trace a problem back to its source, whether that’s infrastructure, networking, application services or a dependency several layers removed.

That ability to trace root cause has turned observability into a baseline requirement for any organization running a complex IT estate. It’s how teams cut downtime, keep services reliable, and stay on top of infrastructure that only grows more tangled.

“Observability continues to garner significant attention from business and IT executives as detailed visibility, precise control, and automated actions become core operating tenets within the digital infrastructure,” wrote Stephen Elliot in “An Executive Blueprint for an Observability Platform.”

How is AI observability different from IT observability?

AI raises the stakes further. A system can look perfectly healthy while the model behind it produces unreliable results and IT teams are feeling the squeeze. According to “The State of Observability 2025,” 47 percent of respondents say monitoring AI workloads has made their jobs more challenging.

This is why AI observability has rapidly become a strategic priority. Traditional observability helps organizations answer questions such as:

• Why is this application slow?
• Where did the outage occur?
• What infrastructure component failed?

AI observability answers a different set of questions:

• Are AI agents behaving within approved guardrails?
• Is sensitive data being exposed?
• Why are AI costs increasing?
• Can we trust the outputs being generated?
• Can we trust that the outputs are legal and compliant with regulatory requirements?

As AI moves into mission-critical workflows, observability shifts from watching systems to watching outcomes. That means visibility into model behavior, consumption, security posture, governance compliance, performance and cost, a wider set of demands than traditional monitoring was ever built for. Forty-eight percent of respondents to a recent Splunk survey identified poor data quality as a major barrier to AI readiness.

This is part of a bigger shift happening across enterprises. AI governance used to live in policy documents. Now it’s an operational problem that depends on a continuous, real-time view of how AI systems actually behave, not just how they’re supposed to behave on paper.

Autonomous systems still need oversight. “As AI systems and agents become more autonomous, organizations face increased risk and complexity,” noted PwC in “Observability: A critical ingredient in making AI and agents work for you.” “Observability helps organizations understand what their AI systems are doing and why, strengthening governance while driving performance, cost control, and business value.”

Why Splunk AI observability is different

Not all observability platforms are built the same. AI workloads, distributed applications, hybrid networks, cloud services and security tools all generate data that needs to be understood in context — not in isolation. Splunk connects these traditionally separate domains, giving teams visibility into applications and infrastructure alongside the network, security ecosystem, and the data flowing between them.

And while most observability platforms can tell you whether infrastructure is healthy, Splunk can tell you whether an AI application is performing well, generating quality responses, staying within budget, and behaving safely.

Consider these capabilities that have become more important for AI observability:

Agent oversight and control. As organizations deploy AI agents that can access systems, make decisions and take action, new risks emerge. Splunk shows what agents are doing, whether their actions align with the rules they were given, and where safeguards may be needed, a critical piece for any organization pursuing a sovereign AI approach where sensitive data has to stay protected.

Splunk can thus ask, “What did the agent do, and does it conform with business objectives, standards, and so on?” The ability to answer this question accurately and at scale is critical to all of the emerging AI Compliance models globally.

Security and observability convergence. As AI becomes embedded in business processes, its security implications become harder to ignore. Splunk pairs observability and security data — plus its integration with networking, or “network intelligence” — so leaders can evaluate AI systems from both an operational and a risk-management lens, closing the blind spots that usually sit between IT and security teams.

Organizations with expanding hybrid and multicloud environments are turning to unified threat detection to aggregate data volumes, automate responses, and cut mean-time-to-detect and mean-time-to-respond. Splunk provides the predictive insight to catch anomalous behavior before it affects users, systems and data.

Predictive observability. Splunk increasingly uses AI and machine learning to spot patterns, anomalies. and emerging risks before they turn into outages, shifting teams from firefighting to getting ahead of problems and shielding users from unforeseen outages.

In this way, Splunk can ask and answer, “What problems are likely to emerge down the road, and what actions can we take before they disrupt business operations?”

AI cost accountability. Organizations want value from their AI expenditures. Splunk connects token consumption, infrastructure costs, GPU utilization, model usage and application outcomes, so leaders can see whether AI investments are paying off and which teams are burning tokens without much to show for it. That turns cost conversations from guesswork into something you can budget and optimize against. Splunk also offers real-time visibility into model performance, token latency, prompt security and infrastructure costs, so AI operations stay reliable and cost-efficient.

Splunk digs into questions like, “Where are AI costs accumulating, which teams or workers are consuming resources, and are those investments generating measurable value?”

Business-context decision making. Rather than stopping at technical metrics, Splunk links operational telemetry to business outcomes, helping teams see which applications, customer experiences or processes are actually benefiting from AI, and where there’s more room to optimize. That’s central to tokenomics efforts to understand where AI creates value.

Splunk delves into questions like, “Which AI initiatives are delivering business outcomes, and where should we focus future investment?”

Visibility across increasingly complex AI ecosystems. AI applications depend on a growing web of models, agents, networks, infrastructure, cloud services and data sources. Splunk gives leaders a unified view across those layers, so they can see systemwide impact instead of piecing together isolated technical events.

Splunk is up to all the traditional observability questions, and works with the AI layer to answer “How are our models, agents, infrastructure, networks, and data sources interacting, and where are dependencies introducing risk or performance bottlenecks?”

How SHI helps organizations build trusted AI with Splunk observability

As AI moves into production, observability has become a requirement, and it forms the foundation AI governance is built on.

Organizations need to see how models behave, how agents interact with systems, where sensitive data flows, and how AI resources get consumed. Without that visibility, it’s impossible to build trust, enforce governance, manage risk, or prove business value.

SHI helps organizations treat Splunk as a governance and decision-making platform for AI, not just a monitoring tool. Splunk shows AI behavior, performance, cost, security events and infrastructure dependencies so leaders know not just whether AI is working, but whether it’s working responsibly and delivering value.

This becomes increasingly important as organizations deploy AI assistants, agents, and sovereign AI environments. Leaders need confidence that these systems stay within approved guardrails, protect sensitive data, and serve organizational goals. Splunk supplies the telemetry; SHI helps design the governance frameworks and operational strategy around it.

SHI also connects AI governance to AI economics. With visibility into token consumption, model utilization, infrastructure resources and application outcomes, leaders can see how AI spending connects to results, supporting a tokenomics-driven approach that optimizes cost, prioritizes high-value use cases, and proves return on investment.

The result is a more disciplined approach to AI, one built on visibility, accountability and measurable value. By pairing Cisco Splunk’s observability with SHI’s expertise in AI infrastructure, governance, security and modernization, organizations get the insight to build AI systems that are powerful, trustworthy and economically sustainable.

In the AI era, observability isn’t just about keeping the lights on. It’s about knowing whether AI is secure, governed, cost-effective, and aligned with what the business is trying to achieve.

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