Building a business case for moving enterprise workloads to the cloud or even switching cloud providers can itself be a lengthy exercise, requiring enterprises to first understand their existing infrastructure, model potential target environments, and calculate whether the economics of migration make sense.
Google is now betting that generative AI can shorten that process.
The hyperscaler on Tuesday said that it was adding Gemini-powered Quick Assessments to its cloud migration assessment service, Migration Center, which it claims can deliver near-instant total cost of ownership (TCO) modeling and automated service mapping.
Previously, Migration Center was primarily a discovery-and-assessment platform that required enterprises to collect infrastructure data, define migration preferences, and then generate recommendations and TCO reports.
The new AI-powered Quick Assessment compresses part of that process into a Gemini-driven workflow that can ingest high-level infrastructure data or VMware exports, automatically build a Compute Engine cost model, and let users interrogate and refine the assessment through chat, company executives wrote in a blog post.
That compression of the process, according to Pareekh Jain, principal analyst at Pareekh Consulting, should make the early stage of cloud migration much faster and cheaper.
“Instead of spending weeks collecting data and building an initial business case, CIOs can quickly get a rough view of migration costs, savings and required infrastructure. This also reduces reliance on consulting and SI firms for migration business cases,” Jain said.
“This could be especially useful for companies that have postponed migration because a full assessment takes too much time and money. By using basic infrastructure data or VMware RVTools exports, enterprises can quickly get a first estimate of costs and potential savings, allowing them to identify which workloads are worth studying in detail before spending heavily on a full assessment,” Jain added.
However, there are bottlenecks that CIOs should consider.
“This tool is fundamentally bounded by its inputs such as infrastructure inventory, price of compute and storage, but it cannot see application dependencies, compliance obligations, or real-world performance behavior, each of which requires separate, deliberate discovery,” said Manoj Chandra Jha, principal analyst at Nord-IQ Research.
“Thus, enterprises should treat AI-generated TCO outputs as directional screening estimates rather than investment-grade business cases, particularly for workloads that are stateful, latency-sensitive, or regulated,” Jha added.
The analyst further cautioned that CIOs should not mistake faster financial modelling for faster migrations because, as with most vendor-published benchmarks, the “months to minutes” framing warrants validation through independent case studies before being built into planning assumptions.
Google, however, is not the only hyperscaler using AI to automate parts of the cloud migration and modernization process.
AWS, for example, has been expanding AWS Transform, its AI-driven service for modernizing legacy code, applications, and infrastructure, with agents that can analyze existing environments and automate parts of the transformation process.
Microsoft, too, has added an agentic mode to Azure Copilot that uses multiple agents to help enterprises with tasks including the modernization and migration of legacy applications, alongside infrastructure deployment and optimization.