{"resourceId":"cisco-omdia-impact-of-agentic-ai-on-network-operations-survey-2026","versions":[{"version":"external-2eabd780091aba985cfd3985501ec092814cfb99e00fa593b6fa5563177d2fd9","resource":{"id":"cisco-omdia-impact-of-agentic-ai-on-network-operations-survey-2026","title":"Cisco/Omdia 'The Impact of Agentic AI on Network Operations' (2026): vendor-commissioned survey claims 51% run agentic AI in production NetOps, yet per-process use is 14–18%","organization":"Cisco with Omdia (commissioned survey)","sector":"Network operations; cross-sector enterprise survey (organizations with 500+ employees)","geography":"North America, Western Europe and Asia-Pacific","publishedAt":"2026 (PDF undated; file metadata indicates creation July 2026 and modification Sept. 2026)","publicationDate":null,"eventDate":null,"sourceName":"Cisco","sourceLabel":"Vendor-published report of vendor-commissioned survey (Omdia)","sourceUrl":"https://www.cisco.com/c/dam/en/us/solutions/artificial-intelligence/the-impact-of-agentic-ai-on-network-operations.pdf","evidenceClass":"vendor-claim","outcomeClass":"mixed","topics":["operating-model","infrastructure","accessibility-workforce"],"finding":"The report states Omdia surveyed over 1,000 IT and network operations leaders at organizations with 500+ employees across North America, Western Europe and Asia-Pacific, supplemented by Cisco Meraki telemetry analysis. It claims 51% are running agentic AI that takes action in production and 84% expect an AI-led operating model within 12 months, while its own process-level chart shows only about 14–18% using agentic AI today for any single NetOps process (e.g., 18% remediation actions). It reports 46% of network alerts are closed without investigation that 67% say the generative AI boom significantly increased network complexity, and that 70% say AI adoption increased NetOps workloads.","sledRelevance":"Why now: Cisco re-cited this survey at Cisco Live Melbourne (Nov. 9–11 blog and Vaccaro interview) to justify Agentic Actions, while a Cisco executive on stage cited separate research that only 5% have deployed agents to production. Interpretation: SLED buyers should treat the 51% headline as a broad, vendor-commissioned definition and benchmark locally; the sample excludes organizations under 500 employees and reports no public-sector breakdown.","evidence":"Survey of 1,000+ respondents; question wording, sector mix and public-sector share not disclosed in the report; commissioned by the vendor whose product it supports.","architectureImplications":"Implies telemetry and cross-domain correlation needs; Meraki traffic analysis of AI services adds load evidence but is Cisco-customer data.","governanceImplications":"Adoption headlines can be used to accelerate autonomy decisions; agencies should require their own baseline metrics.","securityPrivacyImplications":"Not directly addressed beyond guardrail preference questions.","caveats":"Vendor-commissioned; no public-sector breakdown; definitional gap between headline 51% and process-level 14–18%; undated PDF, so publicationDate is null.","streamIds":["cisco"],"roles":{"sales":"Sales framing: use the survey's internal contrast honestly. The 51% production headline sits beside process-level adoption of roughly 14–18%, and a Cisco executive cited 5% production deployment this week. For SLED conversations, position the survey as directional, vendor-commissioned evidence of alert fatigue, and propose measuring the agency's own alert backlog rather than citing adoption percentages.","engineering":"Engineering framing: the useful content is the workload data, not the adoption headline. The 46% uninvestigated-alert figure and the 70% reporting that AI adoption increased workloads point to telemetry correlation and noise reduction as first targets. Engineers should replicate the measurement locally (alert volume, closure without investigation) before designing any agent automation. Note that the survey's 46% alert figure is self-reported and may not match ticketing data.","delivery":"Delivery framing: baseline first. Delivery leads should capture alert volumes, investigation rates and ticket workloads before any AgenticOps pilot, so results can be compared against the agency's own baseline rather than vendor survey claims. Document that the survey excluded small organizations and lacked public-sector breakdowns. Share the baseline with leadership alongside the survey's limits so expectations are calibrated before any pilot is funded."},"roleDetails":{"sales":{"customerProblem":"Network teams close many alerts without investigation due to staffing and noise.","stakeholders":["Network operations manager","CIO","Workforce planning lead"],"discoveryQuestions":["What share of your alerts are closed without investigation?","Has AI adoption increased or reduced your team's workload?","Which processes would you automate first?"],"valueHypothesis":"Measuring local alert backlog may identify where narrow automation could help, if evidence supports it.","potentialEngagement":"A baseline measurement of alert handling and NetOps workload before any agent pilot.","unsupportedClaims":["That 51% of public-sector peers run agentic AI in production","That AI-led operations will arrive within 12 months","Any savings figures"]},"engineering":{"fit":"Directional context for any network-operations organization.","architectureIntegration":"Not an architecture source; implies telemetry correlation needs.","prerequisites":["Alert and ticket data access","Defined metrics"],"constraints":["Vendor-commissioned survey","No public-sector breakdown","Excludes <500-employee organizations"],"security":"Not applicable beyond normal handling of operational data used for baselines.","proofOfValue":["Compute local alert-closure-without-investigation rate over 30 days","Compare with survey figure to calibrate expectations"]},"delivery":{"work":"Run a workload baseline study prior to automation.","dependencies":["Ticketing data","Monitoring data","Analyst time"],"ownership":"Network operations manager.","skillsAdoption":"Teach staff consistent alert classification for baseline accuracy.","governanceCheckpoints":["Baseline methodology review","Leadership review of findings before pilot approval"],"acceptanceCriteria":["Proposal: baseline report covering 30 days of alerts","Proposal: pilot targets selected from top local workload drivers"],"risks":["Over-reliance on vendor statistics","Inconsistent alert data","Misinterpreting adoption definitions"]}},"retrievedAt":"2026-11-11T03:52:04Z","enrichedAt":"2026-11-11T03:57:34Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Reports staffing constraints and alert fatigue; 70% say AI adoption increased NetOps workloads, a cautionary signal for lean SLED teams.","procurementImplications":"Survey statistics should not serve as justification in public procurement without local evidence.","operatingModelImplications":"Describes AgenticOps as human-led, agent-driven with humans setting intent and retaining oversight.","sourceVerification":{"openedUrl":"https://www.cisco.com/c/dam/en/us/solutions/artificial-intelligence/the-impact-of-agentic-ai-on-network-operations.pdf","referenceExcerpt":"surveyed over 1,000 IT and network operations leaders at organizations with 500 or more employees","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}