NewsService Operations13 Aug 2026 · 5 min read

Cisco Resolves 145,000 Support Cases Using Agentic AI

Cisco says agentic AI resolved 145,000 support cases, revealing CX potential alongside mounting infrastructure and pricing pressures.

Francesca Roche

Cisco Resolves 145,000 Support Cases Using Agentic AI

Agentic AI is beginning to produce tangible CX metrics, with Cisco reporting that AI independently resolved 145,000 support cases.

Having positioned the technology as a tool to improve both service operations and commercial outcomes, this ensures faster support for stronger customer retention.

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.

Chuck Robbins, Chair and CEO at Cisco, said the company is using generative and agentic AI to speed up quoting and support resolution, while also improving customer renewals.

“We 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.

"In FY 2026, 145,000 support cases were resolved entirely by AI with zero human intervention.”

145,000 Cases with Zero Human Input

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.

Having reportedly resolved 145,000 support cases entirely by AI with zero human intervention during FY2026, this suggests the vendor is creating a successful operational model where AI can manage parts of the journey without requiring an employee to oversee every interaction.

By ensuring faster response times, quicker support resolutions, and stronger retention, this gives the technology a broader commercial role, with AI supporting both service delivery.

Cisco's relaunched proprietary AI assistant, Circuit, is fully embedded across its operations and runs on its secure AI infrastructure, highlighting the operational infrastructure required to scale AI across a large organization.

Robbins noted:

“Circuit 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.”

However, Cisco’s figures primarily measure AI and operational efficiency instead of the CX results it produced, demonstrating that Cisco can automate support at scale, but does not yet establish whether customers are consistently receiving better outcomes.

6,400 New Security Customers

Cisco’s AI improvements have transformed how enterprises build and secure the infrastructure needed to support AI workloads, having reported that its enterprise Nexus switch orders tagged for AI deployments increased more than 85%, and data-center networking orders grew more than 35% YoY.

With AI adoption now influencing core infrastructure purchasing decisions, businesses are expanding their networks to support increasingly demanding workloads whilst weighing several factors as they decide where and how to run AI.

“As 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,” Robbins explained.

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.

He affirmed:

“You 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."

As AI agents operate across customer data, business systems and different deployment environments, CX teams will need to think about creating requirements around latency, data access, and governance.

In regard to security, Cisco reported more than 1,500 customers bought its new security products in Q4, taking the number of net-new customers since launch above 6,400.

This reveals that AI expansion demand is increasing for both infrastructure and security controls needed as enterprises move toward distributed AI environments.

Price Rises Test Enterprise AI Ambitions

Despite its AI successes, Cisco’s infrastructure costs are amongst top concerns for its customers and analysts.

In fact, its recent networking price increases contributed around five percentage points to Q4 revenue growth, expecting a further four to five points in FY2027.

Mark Patterson, Chief Financial Officer at Cisco, responded to these concerns by highlighting Cisco's success in maintaining that it is successfully passing higher costs on to customers.

“Overall, we are sort of planning for that kind of four to five points of impact this year as well.”

“But we are doing a good job of passing that price on.”

At the same time, Cisco is operating in a market where customers are balancing several technology priorities.

“What we’re hearing from our customers, is they’re currently reprioritizing within their existing budgets,” Robbins emphasized.

With greater spending on AI infrastructure not aligning with the expanding rate of technology budgets, enterprises will need to readjust existing budgets as they invest in AI readiness, cybersecurity, and support.

One concerning stat for customers and analysts came from Cisco's combined service-provider and cloud growth slowing down from 105% in the prior quarter to 95%, with the company attributing the change to the timing and variability of large orders, as its largest hyperscaler customers continue to deliver triple-digit growth in AI infrastructure orders.

For customers, these competing priorities mismatched with realistic budgets means reprioritizing funding to other infrastructure projects when budgets cannot expand at the same pace.

Cisco Key Earnings Results

Cisco’s latest quartely results suggest that customer-facing AI, security and collaboration investment is gaining commercial traction, but success will depend on proving customer outcomes while managing the cost and complexity of a more hardware-intensive AI stack.