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Cisco CEO Chuck Robbins’s 2024 AI strategy was about more than selling networking equipment for AI data centers. Cisco aimed to connect AI infrastructure with security, observability and operational data—and make Splunk, acquired in March 2024, the analytical center of that plan. The strategy was coherent, but its success depended on execution: integrating products, controlling costs and demonstrating that AI tools improve customer outcomes.
This is a look at the strategy Robbins described in 2024, not a claim that every announced integration or planned milestone was subsequently delivered. The original CRN feature captured Cisco’s ambitions; Cisco’s acquisition announcement confirms the deal’s completion.
Why Robbins said Cisco needed to move quickly
Robbins framed the AI opportunity partly as a lesson from the cloud transition. Cisco, in his telling, had been slow to adapt as cloud computing changed enterprise infrastructure. With AI, he argued, the company was better positioned because it already had networking, security, observability, collaboration products, customer relationships and technology partnerships.
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“Moving fast” meant turning that breadth into products and integrations customers could use, rather than relying on the promise of a large portfolio. In 2024, the CRN feature pointed to expected integration activity around events including RSA Conference, Cisco Live and Splunk .conf24. Those were plans and expectations at the time, not proof that every proposed connection shipped or became generally available.
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The strategic bet was that enterprise AI would need more than powerful chips. Companies would also need networks to connect compute, tools to monitor applications and infrastructure, and security systems able to make sense of a growing volume of signals.
Cisco’s three AI pillars
1. Infrastructure for AI workloads
Cisco’s infrastructure pitch centered on high-performance Ethernet networking, its Silicon One architecture, and collaboration with NVIDIA. The goal was to connect accelerated computing, networking and storage for AI clusters.
Cisco’s fiscal 2024 summary report described Nexus HyperFabric as a solution combining Cisco AI-native networking, NVIDIA accelerated computing and AI software, and VAST data storage. That establishes Cisco’s product direction; it does not, by itself, establish performance leadership against competing architectures. Buyers should compare the system on their actual workloads, including network congestion, latency, GPU utilization, storage performance, scaling, manageability and support responsibilities across vendors.
This is the “networking for AI” part of the strategy. It is distinct from using AI to improve network operations or security—and neither capability automatically proves strength in the other.
2. AI assistants in Cisco products
Cisco also envisioned AI as a new interface for administrators and users. Assistants in areas such as Webex, security, firewall management and network operations could help people find information, troubleshoot problems or work through configuration tasks using natural language.
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- 【High-Speed 800Gbps Performance】: Designed for next-generation data centers, this DAC cable delivers unprecedented 800Gbps bandwidth to power AI/ML workloads, high-performance computing (HPC), and cloud infrastructure.
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An assistant’s presence is not evidence that it reduces workload or makes safe changes. For operational use, buyers should establish which tasks it supports, what sources it can access, whether it explains its recommendations, and whether administrators can review, reject or roll back proposed actions. They should also ask how prompts and customer data are handled. Product packaging and entitlements vary; Cisco’s Collaboration Flex Plan data sheet, for example, describes AI Assistant inclusion in listed Webex Suite plans, but buyers should confirm current regional terms.
3. AI applied to network, security and operational data
The most distinctive part of Cisco’s thesis was its claim that it could connect signals from networks, endpoints, applications and security systems, then use analytics and AI to help customers detect threats, investigate incidents and understand application performance.
That thesis depended heavily on Splunk. Cisco completed its acquisition on March 18, 2024, at an announced value of approximately $28 billion in equity value, or $157 per share in cash. Splunk brought a platform for collecting, searching, correlating and analyzing machine-generated data. Cisco brought a broad set of products and telemetry sources, as well as an established customer and partner base.
Cisco’s acquisition announcement described the combination as a way to broaden visibility and insight across organizations’ digital operations. That was the strategic rationale, not a guarantee of a single, fully unified product. Integration across data sources, consoles, licenses and workflows is a product-by-product question.
Why Splunk was the hinge of the strategy
Splunk’s security and observability products gave Cisco a way to position itself beyond network equipment. Its Enterprise Security offering is associated with security information and event management (SIEM), threat detection and investigation, while related capabilities include security orchestration and response (SOAR), user and entity behavior analytics (UEBA) and threat intelligence. Splunk’s current Enterprise Security product description continues to emphasize security operations and AI-assisted SecOps.
Cisco and Splunk together could, in principle, correlate network, endpoint, identity, firewall, application and cloud signals in a common analytical environment. Cisco also pointed to the potential for Talos threat intelligence, Cisco analytics and Splunk security products to work more closely together. The relevant distinction is between a stated integration direction and a verified, generally available workflow: buyers need to confirm which data sources and actions are supported in the specific editions they would purchase.
The acquisition could also create commercial opportunity. Cisco could introduce Splunk to more customers through its established sales and channel network, while Splunk could strengthen Cisco’s security position. But adding a well-known platform to a portfolio does not automatically simplify customer operations. If users still manage duplicate dashboards, agents, entitlements and support paths, the combined offering may initially add complexity rather than remove it.
The data advantage: promising, but not self-proving
Cisco executives cited large volumes of telemetry in the CRN feature, including more than 350 million endpoints connected daily through Meraki, more than 200 million connections observed through IoT Control Center, hundreds of billions of security events, and about 625 billion daily web requests. These are company-provided figures; the cited coverage does not independently define their counting methods, clarify overlap or show how much of that data is available to customer-facing AI products.
Volume can help only when data is relevant, reliable, permissioned and usable. Buyers should ask:
- Which data is collected, retained and accessible to the specific product?
- Is it used for customer-specific analysis, threat intelligence, model training—or some combination?
- What customer consent, privacy, residency and regulatory controls apply?
- Are signals normalized and linked well enough to support explainable detections?
- What does ingesting, storing and searching the data cost?
Do not assume that telemetry collected for operations is automatically available for training shared AI models. Data access, use and retention must be established in product documentation and contractual terms.
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Different competitors challenge different layers
Cisco’s AI position is not a single contest with one winner. The relevant competitor depends on the customer’s problem:
- HPE and Juniper: The 2024 debate around HPE’s proposed acquisition of Juniper highlighted a different emphasis: AI-cluster networking and data-center architecture, including HPE Slingshot. Cisco’s argument leaned more on breadth across networking, security, observability and collaboration, with Splunk strengthening data analysis. These are distinct propositions, not interchangeable claims of AI leadership.
- NVIDIA: A central collaborator in Cisco’s AI infrastructure framing, NVIDIA is also a major force in accelerated computing. Cisco’s networking role should be assessed alongside the full compute and software architecture, not treated as a substitute for it.
- Cloud providers: Microsoft, Google and AWS offer cloud AI platforms and services. For organizations committed to one cloud ecosystem, the practical comparison includes data location, integration, governance and operational fit.
- Security specialists: Palo Alto Networks, CrowdStrike and Microsoft are among the alternatives for buyers whose primary need is security operations rather than a Cisco-centered infrastructure strategy.
- Observability specialists: Datadog and Dynatrace are alternatives to examine when application and infrastructure performance is the main priority.
A customer should compare capabilities at the layer it needs—AI-cluster fabric, SIEM, incident response, application observability or cloud AI—and avoid treating broad corporate messaging as a product-level benchmark.
Why Cisco partners mattered
Cisco’s channel was central to its plan to take the combined portfolio to customers. Partners could advise on use cases, connect data sources, implement products and provide managed security, observability or AI consulting services. The CRN feature reported Cisco channel chief Rodney Clark’s statement that partners generated about 90% of Cisco’s overall bookings. That is an executive-reported figure, not an independently audited measure in the cited material.
Partners can be especially important when the work involves data normalization, detection tuning, governance and integration with non-Cisco systems. That creates an opportunity for capable integrators—but also a dependency. A buyer should assess the partner’s experience with the specific products, deployment model and ongoing operating requirements, not just its general Cisco credentials.
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- Data coverage: Identify which network, identity, endpoint, cloud and application sources are supported, whether feeds are real-time, and how retention and residency work.
- Security outcomes: Test detection relevance, false positives, investigation time and safe response automation. Ask how recommendations are explained and audited.
- AI controls: Determine what the assistant can read or change, whether a human must approve actions, how rollback works, and how prompts and customer data are protected.
- Integration depth: Confirm whether consoles, identities, licenses and workflows are genuinely coordinated, connected through APIs, or dependent on separate products and services.
- Infrastructure performance: For AI clusters, test Ethernet behavior, congestion management, latency, GPU utilization, storage integration, scaling and support boundaries across vendors.
- Total cost: Include licenses, ingest, compute, storage, sensors, connectors, implementation, partner services, training and ongoing tuning.
- Operational fit: Check whether the team has the skills and time to run the system, and whether a hybrid, cloud or on-premises deployment fits its requirements.
Promise versus proof
| 2024 strategic claim | What the cited evidence establishes | What a buyer still needs to verify |
|---|---|---|
| Splunk and Cisco data could provide broader visibility. | Cisco completed the Splunk acquisition and described a combined visibility and analytics rationale. | Which integrations are available, what data they cover, and whether the workflows improve detection or operations. |
| AI assistants could make Cisco products easier to operate. | Cisco described assistant capabilities and product directions. | Accuracy, supported tasks, human controls, data handling and measurable time savings. |
| Cisco could support AI infrastructure. | Cisco’s annual report described Nexus HyperFabric with Cisco networking, NVIDIA computing/software and VAST storage. | Workload-specific performance, cost, interoperability and operational simplicity versus alternatives. |
| Cisco’s telemetry scale could strengthen AI and security. | Executives cited large company-reported telemetry figures. | Data quality, rights, availability to each product, model use, and actual customer outcomes. |
Costs and complexity can undermine consolidation
Splunk’s pricing can depend on the product and deployment, with models tied to workload, ingest or entities. Splunk does not publish a universal fixed price for Enterprise Security; it directs buyers to request pricing and lists workload and ingest options on its security pricing page. Its licensing documentation also says Enterprise Security requires an underlying Splunk Enterprise or Splunk Cloud Platform license and does not automatically add ingestion capacity.
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- 【High-Speed 800Gbps Performance】: Designed for next-generation data centers, this DAC cable delivers unprecedented 800Gbps bandwidth to power AI/ML workloads, high-performance computing (HPC), and cloud infrastructure.
- 【Ultra-Low Latency & Cost-Effective】: As a Direct Attach Copper cable, it offers lower latency and significantly lower cost compared to optical transceiver solutions, ideal for short-reach top-of-rack (ToR) connections.
- 【Guaranteed Compatibility】: Rigorously tested to be compatible with major brands for seamless integration into your existing 800G ecosystem.
- 【Enhanced Durability】: Features a robust zinc alloy housing (A2 Revision) and a high-quality nylon braided jacket for superior strain relief and long-term reliability in demanding environments.
- 【Plug-and-Play Simplicity】: No additional power or configuration is required. Achieve a stable and instant connection between compatible 800G ports.
Before signing, model expected daily ingest, search demand, monitored entities, retention, data duplication, security add-ons and growth. Ask how charges change if telemetry volume rises, and include implementation and ongoing services in the estimate. A 14-day Splunk Cloud Platform trial is advertised on the downloads page, but a trial is not a free production deployment or proof of long-term costs.
The broader portfolio also has a trade-off: breadth may enable cross-product workflows, but it can complicate product selection, licensing and support. During integration or migration, organizations may encounter overlapping tools, separate entitlements, schema mismatches, training needs and temporary coexistence of old and new systems.
What Robbins’s bet ultimately depended on
Cisco’s 2024 AI strategy was strategically plausible because it joined two real needs: infrastructure capable of supporting AI workloads and better ways to use operational and security data. Splunk gave that second ambition a substantial analytical platform, while Cisco’s network, security and channel footprint offered routes to customers.
But the data estate was not the decisive proof point. The test was whether Cisco could deliver working integrations, trustworthy automation and useful outcomes at a cost customers could predict. For buyers, the right question was not whether Cisco had an AI story; it was whether the specific combination of products, data access, controls, partner expertise and economics solved a defined problem better than alternatives.
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