IBM’s Vikas Ganoorkar on Cloud Migration and Generative AI

To what extent can generative AI help with cloud migration? The question has become central to the cloud computing sector, as migrating a workload in the cloud is famously complicated. It requires numerous decisions involving complex tasks ranging from assessing compute power to setting up interoperability with new applications, and often requires shifting a legacy application—now held together with duct tape—to a cloud platform for which it wasn’t designed.
We know that generative AI can churn out text and graphics based on short prompts. But whether it can navigate the tangles of cloud migration is another thing entirely. Can generative AI help with a project this multifaceted? As it turns out, generative AI is already playing a growing role in cloud migration.
“The future of migration with generative AI will be around combining all the different aspects of regulatory requirements, security, compliance, productivity gains, efficiency and time reduction,” said Vikas Ganoorkar, Global Cloud Migration Leader for IBM Consulting. In his view, each of these tasks can be accomplished in a far more efficient manner with generative AI.
Watch my extended interview with Ganoorkar to learn how generative AI drives greater efficiency in cloud migration, or read select interview highlights below.

Generative AI Digital Assistants Support Migration Teams 
One of the ways that generative AI supports cloud migration is by powering digital assistants that are trained with data from past cloud projects. “Across the lifecycle of migration, as it’s designed, assessed, migrated, tested and managed, we have squads and each of those squads is now embedded with digital assistants,” Ganoorkar said, “which will enable them to make decisions better and faster.”
Among the benefits of these AI assistants is that they bridge the skills gap between junior and senior cloud architects. “So the junior person is not necessarily having to always rely on the more senior architect to be available,” he said, “because the digital assistant does a lot of that work for the junior person.” Consequently, senior architects are free to perform more impactful work for clients.
Artificial intelligence also helps cloud projects move faster during the bid process.
“I would take maybe four weeks to get a proposal out to the client because there are complexities of estimation, determining what are the right resources and skills we need,” he said. “I’m now using generative AI to get all that done in about four to five days.”  This faster turnaround time enables him to offer more bids.
Ganoorkar works with a division called the IBM Consulting Cloud Migration and Modernization Factory. “The ‘factory’ construct is really how the client experiences it, in that they see repeatable patterns, repeatable journeys, whether it’s databases, middleware, or containerization as a set of journeys,” he said. “Based on experiences we’ve had with multiple engagements over the years, we’ve harvested all these patterns and made sure they’re now codified in a platform we use.”
It is these repeating patterns, of course, that enable generative AI to shine. AI algorithms work well with established patterns that a large data set has prepped them to handle. Armed with data about these repeating patterns, generative AI is highly effective at making the many decisions involved with cloud migrations.
“As you go up the stack, it’s about how generative AI is able to make migrations and modernizations more effective,” Ganoorkar said. “AI] starts going into individual, piecemeal line items and phases, helping make the decision of what should be a lift-and-shift, what should be a re-platform migration. Now suddenly with generative AI, we’re able to give the client that view a lot better.”
To learn more about how leading hyperscalers are using generative AI, see my interview with AWS’s Ankur Mehrotra on Building Generative AI Models
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