The recent announcement that Quali’s Stack Automation is now generally available marks a significant milestone for enterprises seeking to modernize their IT infrastructure in the age of artificial intelligence. Developed in close collaboration with Cisco and NTT DATA, the solution promises to bridge the gap between traditional networking and the dynamic, data‑intensive workloads that AI initiatives demand. By offering a self‑service, infrastructure‑as‑code platform, Stack Automation enables IT teams to provision, configure, and tear down complex network environments on demand, eliminating the manual bottlenecks that have historically slowed digital transformation. This launch comes at a time when businesses are under pressure to accelerate innovation while maintaining strict security and compliance postures, making the combination of speed, intelligence, and protection a critical differentiator.
Legacy network architectures, often built on static configurations and manual change processes, struggle to keep pace with the rapid iteration cycles required by AI/ML models, containerized applications, and hybrid cloud deployments. These environments frequently suffer from prolonged provisioning times, inconsistent configurations, and heightened risk of human error, all of which can erode the agility needed to experiment with new models and scale successful pilots. Quali’s Stack Automation addresses these pain points by treating network infrastructure as code, allowing teams to define desired states in declarative templates that are version‑controlled, reviewed, and applied automatically. This shift not only reduces the time from request to ready environment from weeks to minutes but also introduces repeatability and auditability—key attributes for regulated industries such as finance, healthcare, and manufacturing where compliance cannot be compromised.
At its core, Stack Automation leverages Quali’s CloudShell platform to orchestrate multi‑vendor resources, including Cisco’s networking portfolio (such as ACI, Meraki, and Catalyst switches) and NTT DATA’s global managed services capabilities. Through pre‑built blueprints and APIs, IT administrators can instantiate end‑to‑end environments that span data center fabric, campus LAN/WAN, and edge sites, all while enforcing consistent policies around QoS, security segmentation, and telemetry collection. The integration with Cisco’s DNA Center and Intent‑Based Networking (IBN) further enables automated policy translation from business intent to device‑level configurations, ensuring that the network adapts in real time to changing application demands without manual intervention.
NTT DATA’s role extends beyond mere integration; the global systems integrator brings deep industry expertise, change‑management practices, and local support that help enterprises adopt the platform at scale. By combining Quali’s automation engine with NTT DATA’s consulting and implementation services, organizations receive a end‑to‑end offering that covers strategy, design, migration, and ongoing optimization. This partnership model reduces the risk associated with large‑scale transformation projects, providing a clear roadmap, skilled resources, and proven methodologies that accelerate time‑to‑value while minimizing disruption to ongoing operations.
AI readiness is a central theme of the solution. Modern AI workloads demand not only raw compute power but also predictable, low‑latency network connectivity, dynamic bandwidth allocation, and robust security isolation between training, inference, and data‑feeds pipelines. Stack Automation’s ability to spin up isolated network slices on demand means that data scientists can request a dedicated environment tailored to a specific model’s requirements—complete with GPUs, high‑speed interconnects, and secure storage—without waiting for network teams to manually provision VLANs, firewalls, or load balancers. Once the experiment concludes, the environment can be torn down automatically, reclaiming resources and preventing costly over‑provisioning.
The benefits of this approach manifest in three key dimensions that align with the promises of AI‑era networking: agility, intelligence, and security. Agility is realized through dramatic reductions in provisioning lead times, enabling rapid experimentation and faster product cycles. Intelligence emerges from the rich telemetry and metadata generated by automated workflows; when every change is logged and correlated with performance metrics, IT gains actionable insights into utilization patterns, anomaly detection, and capacity planning. Security is strengthened by policy‑as‑code, which ensures that every deployed environment adheres to predefined zero‑trust segments, encryption standards, and compliance controls, thereby reducing the attack surface and simplifying audit preparation.
Market analysts note that the convergence of automation, AI, and networking is creating a multi‑billion‑dollar opportunity. According to recent Gartner forecasts, by 2027 over 60 % of enterprise network changes will be driven by intent‑based automation, up from less than 20 % today. Simultaneously, IDC predicts that global spending on AI‑infrastructure will surpass $150 billion by 2026, driving demand for networks that can dynamically scale and secure AI pipelines. Quali’s Stack Automation, backed by Cisco’s market‑leading networking gear and NTT DATA’s global reach, is well positioned to capture a share of this growing spend, particularly among large enterprises undergoing hybrid‑cloud modernization.
For IT leaders considering adoption, a pragmatic first step is to conduct a thorough inventory of existing network services, pain points, and upcoming AI‑related projects. Mapping these elements to specific automation use cases—such as automated VLAN provisioning for dev‑test labs, on‑demand bandwidth bursts for AI training clusters, or consistent security‑policy enforcement across branch offices—helps prioritize where Stack Automation will deliver the highest impact. Engaging with Cisco and NTT DATA early in the process ensures that the chosen blueprints align with hardware compatibility lists, licensing models, and service‑level agreements, reducing integration friction later on.
Building a Center of Excellence (CoE) around network automation can amplify the benefits across the organization. The CoE should encompass network engineers, software developers, security analysts, and business stakeholders, fostering a culture of collaboration and continuous learning. Key activities include creating a library of reusable blueprints, establishing CI/CD pipelines for network changes (leveraging tools like GitHub Actions, GitLab CI, or Jenkins), and defining metrics such as mean time to provision (MTTP), change failure rate, and policy compliance score. Regular training sessions and certifications—many of which are offered through Cisco’s DevNet and Quali’s own education portal—help upskill the workforce and sustain momentum.
From a security perspective, automation reduces reliance on manual, error‑prone processes that often lead to misconfigurations—a leading cause of breaches. By encoding security policies directly into the automation workflow, organizations can enforce segmentation, encrypt traffic, and apply firewall rules consistently every time an environment is spun up. Furthermore, the immutable audit trail generated by version‑controlled templates simplifies forensic analysis and satisfies regulatory requirements such as GDPR, HIPAA, and PCI‑DSS. Periodic policy reviews, automated compliance scans, and integration with SIEM tools further harden the posture.
To illustrate the potential impact, consider a hypothetical global bank that needed to accelerate its fraud‑detection AI initiatives. Prior to adopting Stack Automation, the bank’s network team required an average of three weeks to provision a secure, high‑bandwidth sandbox for each new model experiment, leading to queues and delayed insights. After implementing the platform with pre‑approved blueprints for AI workloads, the same environment could be requested via a self‑service portal and delivered in under ten minutes, complete with end‑to‑end encryption, intrusion‑prevention signatures, and real‑time telemetry feeds. Over six months, the bank reported a 70 % reduction in time‑to‑insight for AI models, a 40 % decrease in network‑related change incidents, and significant cost savings from decommissioned idle resources.
Actionable advice for readers begins with securing executive sponsorship and defining clear success metrics tied to business outcomes—such as faster time‑to‑market for AI‑driven products or reduced operational expenses from network over‑provisioning. Next, pilot the solution in a low‑risk, high‑visibility use case (e.g., a developer sandbox or a branch‑office refresh) to validate integration, gather feedback, and refine blueprints. Leverage the expertise of Cisco and NTT DATA to design a rollout plan that includes phased expansion, skill‑building workshops, and a governance model that balances autonomy with control. Finally, embed continuous improvement by reviewing automation logs, updating templates as technology evolves, and sharing success stories organization‑wide to drive broader adoption.