This article is based on reporting originally published by The Next Platform (January 2020) and covers the state of AIOps adoption and Cisco/AppDynamics platform developments as of that date.
Why AIOps Is Moving from Buzzword to Business Necessity
The term “AIOps” — artificial intelligence for IT operations — has been circulating for several years, but the pressure driving adoption is intensifying. Enterprises are generating massive volumes of operational data from datacenters, public and private clouds, edge infrastructure, and a rapidly expanding fleet of IoT devices. Inside that data lies the intelligence needed to keep distributed systems fast, reliable, and cost-efficient. The challenge is that no human team can process it fast enough at scale.
This is the core problem AIOps platforms are designed to solve: collecting, correlating, and acting on operational data faster than traditional monitoring and incident-response workflows allow.
The Business Case: Digital Experience Is Now a Competitive Differentiator
Poor application performance has direct revenue consequences. AppDynamics’ App Attention Index 2019 reported that up to 84 percent of global consumers had experienced problems with digital services, while 76 percent said their expectations of digital performance were increasing. The same survey found that roughly half of consumers would be willing to pay a premium for a product or service if the digital experience was meaningfully better than a competitor’s.
Whitney Satin, director of product marketing at AppDynamics, summarized the shift in an interview with The Next Platform:
“This marks a significant shift in consumers’ loyalty to a superior digital experience and brings to light that businesses across every industry are realizing that applications are the business. To keep pace with ever increasing user demands, IT teams are increasingly adopting cloud technologies to enable them to iterate quickly and compete on experience. Highly distributed, multicloud, microservices, APIs, containers, IoT environments, and relentless code releases introduce constant change, and all of this is happening at a staggering scale. That creates a world where the traditional enterprise is managing a complex myriad of legacy technologies like mainframes, datacenters, private clouds, and trying to implement cloud-native technologies.”
— Whitney Satin, Director of Product Marketing, AppDynamics
Cisco’s AIOps Strategy: The Central Nervous System for IT
Cisco acquired AppDynamics for $3.7 billion in 2017 as part of a broader strategic pivot away from hardware toward enterprise software and hybrid cloud solutions. The acquisition positioned Cisco directly in a competitive AIOps market that includes players such as Splunk, IBM, BMC, Zenoss, and OpsRamp.
The centrepiece of the Cisco/AppDynamics AIOps vision is the Central Nervous System (CNS) for IT — a platform that uses AI and machine learning to automate IT operations across application and infrastructure domains. CNS is designed to:
- Monitor and manage applications across cloud, on-premises, and edge environments
- Collect, manage, and analyze data from multiple operational domains in a unified interface
- Automatically detect and remediate security and performance anomalies
The first product delivered under the CNS umbrella was the Cognition Engine, which applies machine learning to application and infrastructure management. Its key capabilities include automated diagnostics and root-cause analysis — reducing the time teams spend manually investigating incidents.
New Capabilities: Experience Journey Maps and Workload Optimization
At Cisco Live Barcelona (early 2020), Cisco and AppDynamics announced additional products representing the first major integrations since CNS launched:
AppDynamics Experience Journey Maps
Experience Journey Maps correlate business metrics, application performance data, and user experience signals into a single, unified view. Rather than requiring application teams and business stakeholders to work from separate dashboards, the tool provides shared context — showing how infrastructure state and application behavior are jointly affecting the end-user experience and business outcomes.
Cisco Intersight Workload Optimizer
Intersight Workload Optimizer targets a persistent and costly problem in hybrid IT: infrastructure overprovisioning. Organizations routinely provision for peak capacity, leaving resources severely underutilized for the majority of the year. By combining real-time and historical data, Intersight identifies workload performance issues and rightsize opportunities — improving both compliance posture and infrastructure cost efficiency across hybrid application architectures.
Critically, Intersight Workload Optimizer and Experience Journey Maps were designed to share and correlate data, giving both application teams and infrastructure teams a common language, common tooling, and aligned datasets. Satin described this coupling as “the latest proof point to Cisco’s software strategy,” enabling “the exchange and correlation of formally disparate data streams to bring shared context to the application and infrastructure teams.”
HyperFlex Application Platform
The HyperFlex Application Platform extends Cisco’s container strategy by providing container-as-a-service capabilities aimed at simplifying Kubernetes provisioning and workload management from the datacenter to the cloud and edge. It incorporates open-source tooling, task automation, and integrates AppDynamics and Intersight for real-time monitoring across multicloud deployments. The platform is intended to serve both IT operations and DevOps groups within the same organization.
Closing the Loop: Why Application and Infrastructure Teams Must Share Context
The deeper ambition behind these product integrations is architectural: creating a closed-loop operating model in which application teams and infrastructure teams are no longer isolated from each other’s data, workflows, or vocabulary.
Liz Centoni, then senior vice president and general manager of Cisco’s Cloud, Compute and IoT group, articulated the cost of the status quo in a blog post at the time. She noted that application dependencies were projected to increase by approximately 250 percent over the following 12 months, and that organizations in North America were spending as much as 43 percent of their operational time — and an estimated $700 billion annually — on troubleshooting problems.
Centoni described the structural problem clearly:
“On one hand, the complex, distributed nature of modern applications from the edge to the cloud (and everything in the middle) makes it difficult to root-cause the source of a poorly performing application. While on the other hand, blatant overprovisioning of infrastructure capacity (whether its on-prem or cloud) for ‘peak scale’ leads to gross underutilization because for 364 days in the year, you’re not running at peak. Add data gravity limitations, containers, a myriad of opensource components, and the idiosyncrasies of every cloud in the sky — you’re looking at 25 hours a day, just to keep the lights on.”
— Liz Centoni, SVP and GM, Cisco Cloud, Compute and IoT
The closed-loop model addresses this by ensuring that insights generated on the infrastructure side are immediately visible and actionable on the application side, and vice versa. Automated remediation — triggered by AI analysis rather than manual escalation — is the end state both Cisco and the broader AIOps industry are working toward.
Key Takeaways for DevOps Teams
- AIOps is not replacing DevOps engineers — it is removing the manual toil associated with incident triage, root-cause analysis, and capacity management so engineers can focus on higher-value work.
- Siloed tooling is a structural risk. When application teams and infrastructure teams operate from separate data sources, mean time to resolution (MTTR) increases and accountability is diffused.
- Overprovisioning is a hidden cost. Infrastructure sized for peak load is the norm, but AI-driven workload optimization offers a path to meaningful cost reduction without sacrificing headroom.
- The edge and IoT are expanding the monitoring surface. AIOps platforms are increasingly expected to ingest telemetry well beyond the traditional datacenter perimeter.
- Business metrics must connect to technical metrics. Tools like Experience Journey Maps reflect a maturing understanding that SLAs tied only to infrastructure uptime do not capture what customers actually experience.
The Competitive AIOps Landscape
As of early 2020, the AIOps market was highly competitive. Beyond Cisco and AppDynamics, key platforms included Splunk, IBM Watson AIOps, BMC Helix, Zenoss, and OpsRamp. Each takes a somewhat different approach to the core challenge of correlating signals from heterogeneous infrastructure into actionable intelligence. Organizations evaluating AIOps vendors should assess coverage across their specific stack — including on-premises, cloud-native, and edge tiers — as well as integration depth with existing CI/CD and incident management tooling.
Original reporting by The Next Platform, January 2020.