As artificial intelligence transitions from a theoretical advantage to a core business driver, enterprises face a critical challenge: legacy IT infrastructures are often unprepared for the intense computational and data-processing demands of modern AI workloads. The sheer volume of data, coupled with the need for scalable processing power and stringent security, creates bottlenecks that can stifle innovation. This technological friction requires a new approach to infrastructure—one that is not only powerful but also exceptionally resilient to failure.
In response, managed service provider Ensono is pioneering a strategy centered on building robust hybrid IT environments designed specifically for the AI era. The company’s approach moves beyond a simple “lift-and-shift” to the cloud, instead focusing on the sophisticated integration of public cloud, private cloud, and modernized mainframe systems. By treating these diverse environments as interconnected parts of a cohesive whole, this strategy aims to provide the stable, secure, and scalable foundation that organizations need to develop, deploy, and manage AI applications effectively, ensuring that mission-critical operations continue without disruption.
The Intensifying Demands of AI Workloads
The operational requirements of AI and machine learning differ fundamentally from those of traditional business applications. AI systems are data-intensive, often requiring the ingestion and processing of immense datasets drawn from varied sources. Ensono’s strategy involves creating a robust data foundation by centralizing terabytes of information from IT service management platforms, system logs, and network telemetry into a single, governed environment. This consolidation is the first step toward making data AI-ready, enabling the cleaning, normalization, and structuring necessary for high-quality machine learning models.
Beyond data, AI workloads demand vast, scalable computational power that can be allocated dynamically. The infrastructure must support the rapid spin-up of resources for training complex models and then scale down efficiently to manage costs. This elasticity is a hallmark of public cloud platforms, but integrating these services with legacy systems where critical data often resides presents a significant hurdle. Security and governance also become more complex. As data moves between on-premise systems and the cloud, maintaining consistent security policies and complying with regulations is paramount. Ensono’s approach includes developing strong AI governance frameworks to manage ethics, compliance, and security, ensuring that innovation does not come at the cost of responsible usage.
A Hybrid and Integrated Foundation
Rather than advocating for a complete migration to a single platform, Ensono champions a hybrid model where different components of the IT ecosystem are treated as equal partners. This philosophy acknowledges that for many established enterprises, legacy systems like the mainframe are not disposable relics but are repositories of invaluable institutional data and host mission-critical applications. The goal is to create a seamless, interconnected environment where workloads can run in their optimal location, whether that is a hyperscale public cloud, a secure private cloud, or a modernized mainframe.
To manage this complexity, the company utilizes platforms that provide clients with governance over their cloud expenditures and clear visibility into infrastructure performance. This allows organizations to make data-driven decisions about where to place applications for the best balance of performance, cost, and security. A key part of this strategy is establishing low-latency, high-bandwidth connections between these environments, enabling real-time data exchange. This connectivity is crucial for AI applications that might, for example, run a transactional process on the mainframe while leveraging a cloud-based GPU for intensive model training on that transaction’s data.
Modernizing the Mainframe for a New Era
The Cloud-Connected Mainframe
A distinctive element of Ensono’s strategy is its “Modern, Cloud-Connected Mainframe” (MCCM) approach. This initiative reframes the mainframe not as an isolated silo but as a powerful, integrated component of a hybrid cloud. Using modern APIs, the MCCM model facilitates seamless data exchange between the mainframe and public cloud services. This allows businesses to unlock the vast data stores on their mainframes, making them accessible for cloud-native AI and machine learning tools to power intelligent reporting and predictive analytics. This modernization is critical as the workforce with legacy mainframe skills continues to retire, creating a significant knowledge gap that modern tools and platforms can help bridge.
A Recognized Expertise
Modernizing and integrating these complex systems requires deep technical knowledge of the relationships between data, applications, and platforms. Ensono’s capabilities in this area are recognized within the industry; the company is one of only a handful of global providers to hold the AWS Mainframe Modernization competency. This expertise allows it to design and implement modernization roadmaps that reduce operational costs and infrastructure bottlenecks without disrupting the core business functions that rely on these legacy systems. The process transforms the mainframe into a resilient and performant transactional engine that fuels, rather than hinders, modern analytics and AI innovation.
An Architecture Engineered for Resilience
In the AI era, where automated systems can drive significant business processes, infrastructure failure is not an option. Building resilient systems requires a proactive approach to identifying weaknesses before they lead to outages. One of the leading disciplines in this area is Chaos Engineering, the practice of intentionally experimenting with a system’s stability by simulating real-world failures like server crashes or network outages. The goal is not to cause disruption, but to ensure that automated failover, auto-scaling, and disaster recovery mechanisms work as intended, allowing the system to withstand turbulent conditions without impacting the end user.
Ensono applies a similar principle of proactive resilience through its own toolsets and services. The company has developed innovative solutions, such as its Envision Predictive Engine and DiagnoseNow, which use AI to proactively detect and resolve critical system issues before they can result in operational disruption. For example, these tools helped a leading automotive manufacturer identify and fix a problem before it impacted operations. This is complemented by robust business continuity and disaster recovery services specifically designed for the complexities of hybrid IT, where threats must be monitored across both physical and cloud infrastructure to ensure key enterprise functions are protected.
A Partnership Approach to Innovation
Navigating the transition to an AI-ready infrastructure is as much a strategic challenge as a technical one. Ensono centers its client relationships on a philosophy of “relentless allyship,” which combines deep technical expertise with a flexible and transparent partnership model. This approach is designed to help organizations disrupt the status quo and manage the complexities of profound technological change. It extends beyond infrastructure management to include strategic advisory services that help leadership align AI initiatives with core business goals.
As part of this, the company works with clients to establish AI governance and best practices, ensuring that the adoption of transformative technologies is handled responsibly. Through services like its Innovation Lab, Ensono collaborates with client teams to identify high-value AI use cases, validate their feasibility, and deliver proof-of-concept models tailored to specific business needs. This client-centric approach helps demystify AI and accelerates buy-in from stakeholders, ensuring that technology investments deliver tangible business value.
