{"id":129107,"date":"2026-08-04T13:51:16","date_gmt":"2026-08-04T13:51:16","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/129107\/"},"modified":"2026-08-04T13:51:16","modified_gmt":"2026-08-04T13:51:16","slug":"mastering-ai-tokenomics-and-roi-through-observability","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/129107\/","title":{"rendered":"Mastering AI Tokenomics and ROI through Observability"},"content":{"rendered":"

Finance is asking IT to justify AI spend. Most IT leaders\u00a0don\u2019t\u00a0have\u00a0a good answer.\u00a0Hear from the Cisco leaders driving our\u00a0AI\u00a0adoption and infrastructure about how we are\u00a0building\u00a0our own\u00a0\u2014 from the inside.\u00a0<\/p>\n

The\u00a0economics of enterprise\u00a0AI\u00a0at scale\u00a0<\/p>\n

Token consumption has quickly become a core currency of enterprise competitiveness. Your ability to deploy AI economically, efficiently, and at scale will\u00a0determine\u00a0not just your AI\u00a0ROI,\u00a0but your organization\u2019s\u00a0success\u00a0in this operating climate.\u00a0<\/p>\n

Yet\u00a0as\u00a0enterprises scale, cost and a lack of visibility\u00a0and trust\u00a0become the deciding factors in sustainable AI, not the technology itself.\u00a0When every autonomous action consumes tokens,\u00a0costs can become difficult to\u00a0forecast\u00a0and even harder to control without the right visibility.\u00a0\u00a0<\/p>\n

IT leaders and engineering teams are\u00a0contending with\u00a0disconnected vendor dashboards and manual reports, lacking\u00a0a reliable view\u00a0into which teams\u00a0are\u00a0spending what, and why.\u00a0Finance teams are asking for justification on AI budget spikes, but without unified data, IT leaders are left reacting to costs they cannot explain, attribute, or forecast.\u00a0<\/p>\n

Early on, we saw the same pattern many enterprises face:\u00a0reacting to token spikes and shifting budget from other engineering priorities just to keep agents running.\u00a0Rather than\u00a0manage\u00a0around it, we set out to solve it, building the visibility needed to better understand and control our AI spend.\u00a0\u00a0<\/p>\n

This is not a problem\u00a0unique to Cisco, but it is one we are uniquely positioned to solve. To move from\u00a0reactive budget-cutting to strategic investment, we needed to make\u00a0tokenomics<\/a>\u00a0a core discipline.\u00a0<\/p>\n

Tokenomics: the defining success factor\u00a0<\/p>\n

In the context of enterprise AI,\u00a0tokenomics\u00a0is the measure of\u00a0efficiency\u00a0for\u00a0your AI operations.\u00a0It is about\u00a0maximizing\u00a0\u201ctoken yield,\u201d\u00a0the ROI per token. We measure success by the correlation between token consumption and the quality of output, task completion, and business value. Our goal\u00a0isn\u2019t\u00a0necessarily\u00a0to spend less on AI, but to reach an equilibrium where cost and value are in balance.\u00a0<\/p>\n

That equilibrium is impossible to achieve without\u00a0the right\u00a0integration.\u00a0We\u2019ve\u00a0found that\u00a0infrastructure, security, and observability\u00a0all impact\u00a0tokenomics, and none of them can work in isolation:\u00a0\u00a0<\/p>\n

Without optimized infrastructure, GPUs can sit idle while you continue to pay for the\u00a0compute, inflating the\u00a0infrastructure cost\u00a0relative\u00a0to token spend.\u00a0Without AI governance and security, agents can consume tokens on unauthorized tasks or inefficient processes, leading to budget overruns that are impossible to claw back. And\u00a0without\u00a0end-to-end\u00a0observability, you\u00a0can\u2019t\u00a0connect AI activity to financial outcomes,\u00a0which leads to measuring inefficiency,\u00a0not\u00a0eliminating\u00a0it.\u00a0<\/p>\n

This is where Cisco\u2019s\u00a0value\u00a0is different: no other vendor\u00a0covers\u00a0every layer of this chain.\u00a0Because\u00a0our\u00a0capabilities span from\u00a0full stack AI infrastructure\u00a0to models, security, and agent observability,\u00a0we have the unique, end-to-end visibility\u00a0required\u00a0for true\u00a0observability of AI.\u00a0By integrating these into a single operating model, we\u00a0can\u00a0help ensure that AI systems are running securely while keeping token usage performant and economically justified.\u00a0<\/p>\n

How Cisco is mastering AI\u00a0tokenomics\u00a0through observability\u00a0\u00a0<\/p>\n

At Cisco, we\u00a0optimized\u00a0our AI-ready\u00a0infrastructure and\u00a0ensured\u00a0our AI governance and security models\u00a0operate\u00a0at\u00a0enterprise\u00a0scale. Now, we are achieving\u00a0economic control\u00a0with agent observability.\u00a0\u00a0<\/p>\n

We are building\u00a0these\u00a0capabilities\u00a0on\u00a0Splunk Agent Observability<\/a>, supercharged by our acquisition of Galileo, to evaluate and improve agent behavior,\u00a0observe\u00a0AI performance, and\u00a0optimize\u00a0token costs. Agent Observability evaluates agent outputs using specialized\u00a0small language models (SLMs)\u00a0that judge quality at a fraction of the cost of frontier models, detect hallucinations, and enforce guardrails at runtime to block inaccurate and harmful behaviors. In addition, Agent Observability monitors performance across the entire AI stack, including models, GPUs, vector databases,\u00a0memory\u00a0and orchestration frameworks, and enables teams to track, forecast and\u00a0optimize\u00a0AI token usage and spend.\u00a0<\/p>\n

Centralizing this data into Splunk allows us to correlate infrastructure health,\u00a0agent behavior,\u00a0security events, and token costs across the entire enterprise and\u00a0identify\u00a0when\u00a0inefficiencies in usage,\u00a0infrastructure bottlenecks,\u00a0or security risks are driving up our cost\u00a0per token.\u00a0For example,\u00a0we\u2019ve\u00a0seen\u00a0agents spiraling in a wave of unnecessary tool calls\u00a0or appending\u00a0unused\u00a0skills,\u00a0easily leading\u00a0to\u00a0bloated\u00a0token consumption without\u00a0benefiting\u00a0the task that the agent carries out for the end user.\u00a0<\/p>\n

The power of this\u00a0solution\u00a0is in\u00a0our stronger understanding of\u00a0the relationship between\u00a0AI spend and business value. By integrating our observability stack with internal\u00a0business\u00a0systems, we attribute AI\u00a0spend\u00a0directly to\u00a0the ROI for\u00a0specific teams, projects, and budget owners, all aligned to Cisco\u2019s fiscal calendar and budget\u00a0hierarchy.\u00a0\u00a0<\/p>\n

\"\"Figure 1: Cisco\u2019s tokenomics architecture<\/p>\n

This granularity allows engineering and finance leaders to make informed decisions. For example, by mapping token usage to actual code commits,\u00a0we can measure the true ROI of our AI investments and gain the forecasting controls necessary to scale AI responsibly.\u00a0<\/p>\n

The\u00a0road\u00a0ahead: Follow along\u00a0<\/p>\n

At Cisco, we are proving this model at scale.\u00a0We\u00a0aren\u2019t\u00a0testing this in a lab; we are running it in production across our own global\u00a0environment\u00a0\u2014 consisting of over 2,000 live AI agents and more than 24,000 active users.\u00a0<\/p>\n

In the blogs that follow, we will share\u00a0more\u00a0details\u00a0into our\u00a0agent\u00a0observability deployment, including deployment insights,\u00a0results, and the lessons learned along the way.\u00a0\u00a0<\/p>\n

Follow this series and use it to inform your own tokenomics strategy.<\/p>\n

\u00a0<\/p>\n

Richard Delisser is Vice President, Engineering at Cisco, where he leads AI adoption across software engineering \u2014 including AI-driven coding, testing, and autonomous technical debt monitoring.\u00a0<\/p>\n

Greg Sylvester is Vice President, Enterprise AI\u202fPlatforms\u202fand Infrastructure at Cisco, where he leads Cisco\u2019s\u00a0Enterprise\u00a0AI\u202f\u2014\u202fincluding\u202fAI platforms,\u202fcompute, storage,\u00a0GPUs, cloud\u00a0and\u00a0data center operations, and AI observability and service management.\u202f\u00a0<\/p>\n

Resources:\u00a0<\/p>\n

\u00a0<\/p>\n

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