Agentic AI is beginning to produce tangible CX metrics, with Cisco reporting that AI independently resolved 145,000 support cases. \u00a0<\/p>\n
Having positioned the technology as a\u00a0tool to improve both service operations and commercial outcomes,\u00a0this ensures faster support\u00a0for stronger customer retention. \u00a0<\/p>\n
As a result, CX leaders should treat this as a prompt to measure autonomous AI on customer outcomes instead of cost saving methods or improving response times.\u00a0<\/p>\n
Chuck Robbins, Chair and CEO at Cisco,\u00a0said the company is using generative and agentic AI to speed up quoting and support resolution, while also improving customer renewals.\u00a0<\/p>\n
\u201cWe are using generative AI and agentic systems across our customer experience organization, which is dramatically expediting quote turnaround and case resolution times, as well as driving higher renewal rates. \u00a0<\/p>\n
\u201cIn FY 2026, 145,000 support cases were resolved entirely by AI with zero human intervention.\u201d\u00a0<\/p>\n
145,000 Cases with Zero Human Input<\/p>\n
In Q4, Cisco has been using its CX operation as a testing ground for how agentic AI can move beyond assisting employees and take on defined support tasks independently.\u00a0<\/p>\n
Having reportedly resolved\u00a0145,000 support cases\u00a0entirely by AI with zero human intervention during FY2026, this suggests the vendor\u00a0is\u00a0creating\u00a0a\u00a0successful\u00a0operational model\u00a0where\u00a0AI can manage parts of the journey without requiring an employee to oversee every interaction.\u00a0<\/p>\n
\u201cWe are using generative AI and agentic systems across our customer experience organization, which is dramatically expediting quote turnaround and case resolution times, as well as driving higher renewal rates,\u201d\u00a0Robbins\u00a0explained. \u00a0<\/p>\n
By ensuring faster response\u00a0times, quicker\u00a0support resolutions, and stronger retention, this gives the technology a broader commercial role, with AI supporting both service delivery.\u00a0<\/p>\n
Cisco\u2019s relaunched\u00a0proprietary AI assistant, Circuit, is\u00a0fully\u00a0embedded across its operations and runs on its secure AI infrastructure, highlighting the\u00a0operational infrastructure required to scale AI across a large organization.\u00a0<\/p>\n
Robbins noted:\u00a0<\/p>\n
\u201cCircuit runs on our secure AI factory infrastructure, which improves GPU utilization and automatically routes each task to the appropriate large language model, allowing us to manage token consumption.\u201d\u00a0<\/p>\n
However, Cisco\u2019s figures primarily measure AI\u00a0and operational efficiency\u00a0instead of the CX results it produced,\u00a0demonstrating\u00a0that Cisco can automate support at scale, but\u00a0does\u00a0not yet establish whether customers are\u00a0consistently receiving\u00a0better outcomes.\u00a0<\/p>\n
6,400 New Security Customers<\/p>\n
Cisco\u2019s\u00a0AI improvements have transformed\u00a0how enterprises build and secure the infrastructure needed to support AI workloads, having reported that its\u00a0enterprise Nexus switch orders tagged for AI deployments increased more than 85%,\u00a0and data-center networking orders grew more than 35% YoY.\u00a0<\/p>\n
With\u00a0AI adoption\u00a0now influencing\u00a0core infrastructure purchasing decisions,\u00a0businesses are\u00a0expanding their networks to\u00a0support increasingly demanding workloads\u00a0whilst\u00a0weighing several factors as they decide where and how to run AI. \u00a0<\/p>\n
\u201cAs these customers look to scale AI economically, there is increasing focus on managing token consumption, selecting the right model and the right location for each workload,\u201d Robbins explained.\u00a0<\/p>\n
With a more flexible approach to AI deployment, model selection is becoming part of the wider infrastructure strategy driven strongly by cost, security and data sovereignty decisions. \u00a0<\/p>\n
He affirmed:\u00a0<\/p>\n
\u201cYou are going to have customers that are going to make intelligent decisions about which models they use based on use cases, and which ones are most appropriate for whatever agentic applications they are running.\u201d<\/p>\n
As\u00a0AI agents operate across customer data, business systems and different deployment environments, CX teams will need to think about\u00a0creating requirements around latency, data access, and governance.\u00a0<\/p>\n
In regard to security, Cisco\u00a0reported\u00a0more than 1,500 customers bought its new security products in Q4, taking the number of net-new customers since launch above 6,400. \u00a0<\/p>\n
This reveals that\u00a0AI expansion demand is increasing\u00a0for both infrastructure\u00a0and security controls needed as enterprises move toward distributed AI environments.\u00a0<\/p>\n
Price Rises Test Enterprise AI Ambitions<\/p>\n
Despite its AI successes, Cisco\u2019s infrastructure costs are amongst top concerns for its customers and analysts. \u00a0<\/p>\n
In fact, its recent networking price increases contributed around five percentage points to Q4 revenue growth,\u00a0expecting a further four to five points\u00a0in FY2027. \u00a0<\/p>\n
Mark Patterson, Chief Financial Officer at Cisco, responded to these concerns\u00a0by highlighting Cisco\u2019s success in\u00a0maintaining that it is successfully passing higher costs on to customers.\u00a0<\/p>\n
\u201cOverall, we are sort of planning for that kind of four to five points of impact this year as well.\u201d \u00a0<\/p>\n
\u201cBut we are doing a good job of passing that price on.\u201d<\/p>\n
At the same time, Cisco is operating in a market where customers are balancing several technology priorities. \u00a0<\/p>\n
\u201cWhat we\u2019re hearing from our customers, is they\u2019re currently reprioritizing within their existing budgets,\u201d Robbins emphasized.\u00a0<\/p>\n
With greater spending on AI infrastructure\u00a0not\u00a0aligning with the expanding rate of\u00a0technology budgets, enterprises\u00a0will\u00a0need to readjust existing budgets\u00a0as they invest in AI readiness, cybersecurity, and\u00a0support.\u00a0<\/p>\n
One concerning stat for customers and analysts came from Cisco\u2019s\u00a0combined service-provider and cloud growth slowing down\u00a0from 105% in the prior quarter to 95%, with the company attributing\u00a0the change\u00a0to the timing and variability of large orders, as its largest hyperscaler customers continue to deliver triple-digit growth in AI infrastructure orders. \u00a0<\/p>\n
For customers, these\u00a0competing priorities\u00a0mismatched with realistic\u00a0budgets\u00a0means\u00a0reprioritizing funding\u00a0to\u00a0other\u00a0infrastructure projects when budgets\u00a0cannot expand at the same pace.\u00a0<\/p>\n
Cisco Key Earnings Results<\/p>\n