Data migrations—just those two words can cause feelings of anxiety and dread in the hearts of many business leaders. IT professionals start having nightmares about unbearable pressure, late nights, and constant system crashes. Those in the C-suite envision out-of-control budgets, embarrassing strategic missteps, and constantly moving deadlines.
Migrations are not a new concept. Organizations have been moving their data and workloads from one environment to another for decades. What has changed is the scale of today’s data and the rise of modern cloud platforms, which make data migrations an essential strategic move for companies that want to evolve and stay competitive. And while companies know they need to modernize, many still hesitate.
Reasons why companies put migrations off
There are a number of pre-conceived ideas about migrations that make companies reluctant to move their data off antiquated legacy systems:
- Time and complexity: It can be a huge effort to move from an existing system to a new one, sometimes a multi-year effort depending on the size of the system. A migration shouldn’t just be a “lift and shift”—you want to get to an equal or better state than what you had before, and that involves a lot of post-migration testing.
- Threat of downtime: Even with the best transition plan in place, there could potentially be downtime for your users as you migrate from one system to the other. If you’re migrating an analytical system, a small amount of downtime may be acceptable. However, if it’s a critical operational system—one that involves data used to run the business—any substantial downtime could be disastrous.
- Cost concerns: The first instinct for many companies is to hire an offshore company to handle the migration, but less expensive labor usually translates to lower-quality work, which results in long cycles. As the project drags on longer than planned, costs start to spiral out of control. This is why data migrations often seem synonymous with bloated, expensive headaches for IT leaders.
Don’t get left behind
Because of these worries, many companies convince themselves the best thing to do is to stick with the system they have for now—but this can be a costly mistake. Not only are many of these fears no longer justified thanks to advances in technology, but companies risk missing out on the very real benefits of migrating their data to a more modern data platform in the cloud.
Don’t underestimate the immense value you stand to gain from adopting new technology. New cloud platforms such as Snowflake or Databricks offer scalability and AI-readiness. You might become 10 times more productive. You’ll likely be able to do things that weren’t possible before, such as instantly create dynamic dashboards or efficiently manage large-scale data sets.
And don’t forget that all technology has a shelf life. When that original solution was purchased, the acquisition cost was based on the value it provided at that time, but today your company may be paying for the status quo without getting much value. By sticking with your legacy system, you’re likely leaving value—not to mention competiveness—on the table.
Many companies convince themselves the best thing to do is to stick with the system they have for now—but this can be a costly mistake. Not only are many of these fears no longer justified thanks to advances in technology, but companies risk missing out on the very real benefits of migrating their data to a more modern data platform in the cloud.
Signs your system is holding you back
What are the signals you should be looking for to determine if your legacy ETL system is no longer up to the task? Here are some surefire signs:
- Low productivity: This is the number one sign it’s time to migrate to a new system. Ask yourself if you are accomplishing as much as you want to with your team. What is the lag between a business request and the delivery? If your team has a large backlog of work that needs to be tackled, it may be because they’re dealing with a clunky system that’s slowing them down.
- Ease of use challenges: Consider who you need to hire to work with the platform. Some older systems require specialized skills that few people have anymore, and these specialists can be very expensive. Are you wasting money paying people to keep an antiquated system running?
- Failure to leverage modern platforms: A prior tool that was built based on single database architecture, when databases were not massively parallel capable, does not fully leverage the processing power of a platform like Snowflake. If you’ve already invested thousands of dollars in a modern cloud platform but are still using a legacy database to store data, you’re not using your cloud platform to its fullest extent.
If your team is struggling with any of these scenarios, it may be time to pull the trigger and fully migrate off your old system. The good news is that the era of expensive, onerous data migrations is over—and it’s all thanks to AI.
How AI changes the migration equation
The emergence of AI has transformed the reality of data migrations almost overnight. Modern LLMs are adept at parsing XML, YAML, JSON, and SQL—and translating seamlessly between them. For example, you could feed an Informatica XML file into an LLM and generate SQL output, then fine-tune that into a workflow that accelerates your migration.
In the past, you had to manually write all these parsers. The LLMs take over all of the time-consuming, manual coding that made these migration projects so labor-intensive in the past, compressing many steps from hours to minutes. It’s no exaggeration to say that today you can hand over nearly 80% of a migration project to AI, dramatically reducing both timelines and cost.
Not only has AI emerged as a way to significantly simplify and speed up data migrations, it’s also accelerating the need for companies to move off their slow, inflexible systems. These older systems are not made for the AI era; they don’t scale quickly or have the capacity to manage the enormous data sets that are the foundation of modern AI initiatives. And the longer you wait to modernize, the more technical debt—not to mention cultural debt—you will accrue.
Not only has AI emerged as a way to significantly simplify and speed up data migrations, it’s also accelerating the need for companies to move off their slow, inflexible systems.
Making the case to management
So how should a data team leader build the case internally that it’s time to migrate off their old system? While executive alignment can be challenging, framing migration as essential for competing in the AI economy makes the case far more compelling. By showing how outdated systems hinder innovation—and how AI now simplifies much of the complexity—you can help leadership recognize that modernization is both urgent and achievable. In essence, AI is both the reason to modernize and the enabler of the transition. Leverage AI to get to AI.
There’s frequently a mindset shift that needs to take place. The larger the organization is and the longer it’s been around, the more bureaucratic it is. The best approach to convince an executive team is to help them understand what will happen if the business doesn’t migrate. Should the company stick with the status quo, spell out what it stands to forfeit in terms of productivity, cost, and value. Make it clear that this is where the industry is going, and what you will get by adopting these new technologies. A good analogy is the point when businesses realized they needed to move to the cloud to stay competitive—but this is much higher stakes.
A framework for the future
With AI taking a huge percentage of a migration’s manual grunt work off your plate, the real challenge that remains isn’t moving data—it’s building the right framework for the AI era. Too many teams migrate their entire environment, dragging inefficiencies and bad architecture along with them. If you have 5,000 tables but only use 1,000 of them, migrating all 5,000 just multiplies problems instead of solving them.
As AI accelerates the creation of code and objects, organizations risk overwhelming chaos unless they adopt sustainable frameworks to manage it all. As the future of data work evolves, data teams will shrink and engineers will shift from creating to instead validating, testing, and refining what AI agents produce. The companies that can manage and harness this output effectively are the ones that will succeed.
Coalesce was built for this exact challenge. Our platform was designed by a team with firsthand experience in the world’s largest, most complex data environments. Some may assume that an intuitive visual interface can’t scale—but in fact, scalability is built into Coalesce precisely because we’ve seen the breaking points of massive-scale systems. We knew automation, standardization, and accessibility had to be core principles. The proof is clear: as other vendors now scramble to add visual interfaces, it only confirms that Coalesce got it right first.
Unlike legacy or even many modern frameworks, Coalesce is focused on sustainable scale. Its foundation of standardization, modularity, and compartmentalization ensures that what gets built can be easily consumed and extended—even by someone who didn’t build it.
Building an AI-ready data team
Everyone today is scrambling to use AI for everything, including code generation, but if your approach is not structured and standardized, you risk compounding the problems with your previous system. Why not leverage AI to accelerate your migration to a more modern platform and nip tech debt in the bud today—not two years down the road?
That journey starts with your data team. The first step is to use AI copilots and agents to support your migration into the new system. Next, migrate your data into a highly scalable framework like Coalesce, one built for the new era of AI-driven data engineering. With this foundation, your team can now deliver AI- and analytics-ready data for downstream use cases—data that is manageable, understandable, and discoverable.
This is the definition of an AI-ready data team: one that uses AI at every step of the data lifecycle to scale, govern, and deliver with confidence.
Why not leverage AI to accelerate your migration to a more modern platform and nip tech debt in the bud today—not two years down the road?