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Virtusa Announces Digital Transformation Studio

Virtusa Corporation announced its new Digital Transformation Studio (DTS), a proprietary platform and approach designed to increase the delivery speed and dramatically reduce the costs of business critical digital transformation projects.

DTS was designed to mitigate many of the typical issues plaguing traditional digital transformation efforts, while enabling a 30% overall productivity improvement.

DTS allows Virtusa teams to set specific performance goals with each client focused on areas including reducing technical debt, improving time to market, or reducing costs.

DTS includes three key components:

- Engineering Tools that drive SDLC automation to improve quality, enable speed, and increase productivity. These include Smart Application Lifecycle Management tools to improve user stories and provide a story point estimation model; proprietary Gamified Dashboards to promote transparency, quality, and productivity metrics; and end-to-end CI/CD pipeline that automates code quality review, testing, and release management.

- Reusable Industry Assets collected and improved through Virtusa’s Open Innovation Platform that strives for a near-zero approach to coding. These include AI Model Zoo and Data Lake with over thirty pre-trained AI/ML models trained on synthetic datasets with 10MM customers, and 500MM transactions; Cloud Native Middleware with prebuilt microservices, boilerplate code generators, and multiple legacy system connectors; and API Lifecycle Toolkits to manage onboarding, QoS, security, distributed tracing, and logging of deployed services.

- Certified Teams pre-trained on agile processes, technology, domain, and Virtusa's engineering tools and assets. A Developer Portal opens challenges and hackathons to a community of developers. Using an agile methodology, teams are configured in squads and tribes to promote scalability and growth. Teams also use a Solutions Assembly Sandbox to assemble Digital Solutions from the asset library.

“Given today’s challenges, enterprises need to maximize every dollar invested in digital transformation initiatives,” said Kris Canekeratne, chairman and CEO, Virtusa. “DTS was built from the ground up to increase the speed and success rate of these business critical projects dramatically. With gamified dashboards to promote transparency and improve performance, reuse of industry assets to save time and money, and pre-trained and certified teams with experience in key industries, we can predict digital transformation successes very early in engagements with clients.”

The impact of DTS is expanded by its integration with Virtusa’s Global Technology Office and xLabs, which combine design thinking and digital engineering to reduce the time and costs associated with identifying, evaluating, and exploiting new technologies to create a competitive advantage. Once new solutions are built, tested, and successfully deployed for clients in Global Technology Office and xLabs, those solutions are transferred to DTS for componentization and reuse.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Virtusa Announces Digital Transformation Studio

Virtusa Corporation announced its new Digital Transformation Studio (DTS), a proprietary platform and approach designed to increase the delivery speed and dramatically reduce the costs of business critical digital transformation projects.

DTS was designed to mitigate many of the typical issues plaguing traditional digital transformation efforts, while enabling a 30% overall productivity improvement.

DTS allows Virtusa teams to set specific performance goals with each client focused on areas including reducing technical debt, improving time to market, or reducing costs.

DTS includes three key components:

- Engineering Tools that drive SDLC automation to improve quality, enable speed, and increase productivity. These include Smart Application Lifecycle Management tools to improve user stories and provide a story point estimation model; proprietary Gamified Dashboards to promote transparency, quality, and productivity metrics; and end-to-end CI/CD pipeline that automates code quality review, testing, and release management.

- Reusable Industry Assets collected and improved through Virtusa’s Open Innovation Platform that strives for a near-zero approach to coding. These include AI Model Zoo and Data Lake with over thirty pre-trained AI/ML models trained on synthetic datasets with 10MM customers, and 500MM transactions; Cloud Native Middleware with prebuilt microservices, boilerplate code generators, and multiple legacy system connectors; and API Lifecycle Toolkits to manage onboarding, QoS, security, distributed tracing, and logging of deployed services.

- Certified Teams pre-trained on agile processes, technology, domain, and Virtusa's engineering tools and assets. A Developer Portal opens challenges and hackathons to a community of developers. Using an agile methodology, teams are configured in squads and tribes to promote scalability and growth. Teams also use a Solutions Assembly Sandbox to assemble Digital Solutions from the asset library.

“Given today’s challenges, enterprises need to maximize every dollar invested in digital transformation initiatives,” said Kris Canekeratne, chairman and CEO, Virtusa. “DTS was built from the ground up to increase the speed and success rate of these business critical projects dramatically. With gamified dashboards to promote transparency and improve performance, reuse of industry assets to save time and money, and pre-trained and certified teams with experience in key industries, we can predict digital transformation successes very early in engagements with clients.”

The impact of DTS is expanded by its integration with Virtusa’s Global Technology Office and xLabs, which combine design thinking and digital engineering to reduce the time and costs associated with identifying, evaluating, and exploiting new technologies to create a competitive advantage. Once new solutions are built, tested, and successfully deployed for clients in Global Technology Office and xLabs, those solutions are transferred to DTS for componentization and reuse.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...