AWS Premier Tier Providers Associate Caylent has printed its 2025 Database Migration research, revealing widespread difficulties enterprises face in modernizing their information infrastructure. The findings, drawn from greater than 300 IT leaders throughout industries resembling manufacturing, vitality, healthcare, schooling, and leisure, underscore the persistent dangers of downtime, value overruns, and strategic uncertainty that accompany large-scale migration tasks.
Solely 6 p.c of respondents reported that their organizations accomplished their most troublesome database migration tasks on schedule, and the identical share stated they skilled zero downtime. Most tasks concerned vital delays, with 46 p.c of members noting greater than 5 hours of downtime throughout their most advanced migrations. This downtime, based on the research, translated immediately into operational slowdowns (44 p.c), misplaced income (49 p.c), and unfavorable impacts on buyer expertise (51 p.c).
The kinds of migrations most regularly cited as difficult had been database model upgrades, cross-cloud transitions, and strikes from on-premises methods to the cloud. Whereas such efforts are sometimes pushed by the necessity to enhance scalability, cut back database licensing prices, or keep away from vendor lock-in, the report signifies that many organizations underestimate the complexity concerned in executing them.
Synthetic intelligence is already enjoying a job in easing migration challenges, however its potential stays underutilized. Sixty p.c of respondents stated that they had employed generative AI or automation instruments of their hardest migration tasks, and 77 p.c of these discovered AI to be useful or very efficient. Nevertheless, a notable 53 p.c admitted uncertainty over which AI instruments and capabilities would greatest serve their wants. This information hole means that whereas AI adoption in information migrations is accelerating, clear requirements and confirmed practices are nonetheless creating.
The research highlights three processes as notably time-consuming: transferring information from the supply to the goal database, validating the goal database and its integrations, and adapting schemas to the brand new platform. Every of those steps introduces alternatives for downtime and errors if not fastidiously managed.
‘Tech Debt Slows Modernization Efforts’
Caylent CEO Lori Williams stated the survey findings reinforce the corporate’s day-to-day expertise: modernization is unavoidable however usually hampered by legacy technical debt and outdated methods. Williams argued that integrating generative AI with deep technical experience may also help cut back downtime, pace migrations, and decrease prices.
“Modernization is crucial, however too usually organizations are slowed by collected tech debt and outdated approaches that create pointless downtime and delayed returns,” stated Lori Williams, pointing to AI-enabled strategies as a method to speed up time to worth.
The research results come at a second when enterprises are underneath rising stress to modernize databases to assist rising workloads, notably these tied to synthetic intelligence and superior analytics. With IT leaders figuring out value discount, scalability, and freedom from vendor lock-in as key motivations, the findings recommend that extra agile and clever approaches can be required to maintain tempo with demand.
For companies weighing future migration efforts, the report supplies a transparent warning: the dangers of downtime and disruption stay excessive, however AI-driven options are rising as a pathway towards smoother, quicker, and extra resilient transformations.
