TheCUBE Research found that migrating to Oracle Autonomous AI Database on Multicloud delivers a five-year net present value of $223 million（約360億円） in modernization value alone, but rises to $2.6 billion（約4200億円） when enterprises pair the migration with 17 AI projects. The study concludes that the largest returns come from treating database modernization and AI execution as interconnected rather than sequential phases, allowing organizations to build AI proficiency and accelerate project delivery faster.
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TheCUBE Research analyzed the financial case for migrating from fragmented on-premises databases to Oracle Autonomous AI Database on Multicloud. In a modernization-only scenario, the study modeled a five-year net present value of $223 million（約360億円） and a 108% internal rate of return. When 17 AI projects were added on top of the modernized foundation, the five-year net present value rose to $2.6 billion（約4200億円） with a 295% internal rate of return and break-even in 14 months.
Why it matters
The research suggests that enterprises should view database migration not as a standalone infrastructure project but as a foundation for accelerating AI delivery. By standardizing data operations and reducing operational friction early, organizations can enter an 'AI learning curve' where early projects build institutional knowledge and make subsequent AI deployments faster and cheaper to execute. The analysis underscores that companies pursuing AI strategies gain substantially larger returns when they modernize infrastructure and begin AI work in parallel rather than sequentially.
What to watch
The $2.6 billion（約4200億円） value case depends critically on executing 17 AI projects over five years—described as operational applications tied to measurable manufacturing and supply-chain outcomes (inventory management, predictive maintenance, defect detection, plant scheduling, and copilots). TheCUBE Research emphasized that actual customer outcomes will vary based on project count, timing, execution, and realized business impact. The research also suggests organizations should not wait for perfect data quality before starting AI work; instead, they can use AI itself to improve data over time.
TheCUBE Research's analysis addresses a recurring challenge in enterprise computing: fragmented data estates accumulated through years of organic growth, acquisitions, and technology sprawl. The study models a representative $10 billion（約1.6兆円） manufacturing division within a $40 billion（約6.4兆円） conglomerate facing heterogeneous on-premises platforms spanning Oracle Database, SQL Server, Postgres, MongoDB, OLAP, and vector databases. Such environments saddle database teams with heavy operational burdens—patching, tuning, backup, provisioning, and disaster recovery planning—that become especially costly in the AI era, where governed, trusted, production-grade data access at scale is critical.
The research's central insight is that modernization creates meaningful standalone value ($223 million（約360億円） over five years) but unlocks substantially larger returns when paired with systematic AI execution. The study identifies what it calls an 'AI project flywheel': early projects build institutional knowledge, improve data quality, and strengthen governance, making later projects faster to deploy and easier to scale. Notably, the analysis recommends that organizations should not delay AI work while waiting for perfect data quality; instead, they can use AI itself to accelerate data preparation and standardization in parallel. This reframes the modernization journey from a two-phase handoff—first clean infrastructure, then AI—into an integrated operating model where both advance together.
The $2.6 billion（約4200億円） value case assumes execution of 17 AI projects over five years, a material assumption that TheCUBE Research explicitly flags as variable across real customers. The study presents these projects as tied to measurable business outcomes rather than speculative pilots, grounding the economics in production use cases. The broader implication is that enterprises evaluating database modernization decisions should evaluate the platform's capacity to support rapid, repeatable AI execution—not just operational efficiency gains—as a primary strategic lever.
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