By Hugh O. Stewart 

If you’re preparing for an accounting software transition, you’ve probably seen a growing number of AI-powered tools that promise to simplify data migration. 

Many of these tools are genuinely impressive. They can move and manipulate enormous volumes of data in sophisticated ways, completing tasks in minutes that would take days or weeks to do manually.  

But before you evaluate what a tool can do, you need to understand what it was designed to do. 

Every data migration tool is built around a set of assumptions about what a successful migration looks like, what types of data are valuable, and what tradeoffs between efficiency and transparency are acceptable.  

If those assumptions don’t align with your implementation strategy, you can end up with a migration that technically succeeds while failing to deliver the reporting capabilities, historical continuity, or user confidence your organization needs. 

Before selecting any AI-powered migration tool, ask three questions: 

1. What outcome is this tool optimized to achieve? 
2. Will this tool help us build the accounting system we actually want? 
3. How does this tool maintain trust in the data throughout the migration? 

The answers to these questions will help you determine whether the tool is right for your migration. 

What Outcome Is This Tool Optimized to Achieve?

The outcome that a particular software tool is optimized for reflects its definition of success.  

Many tools optimize for implementation efficiency by reducing manual effort, accelerating go-live, and minimizing consulting time. Those outcomes sound great, in theory, since a faster implementation usually means less business disruption, but implementation efficiency isn’t necessarily the same thing as maximizing long-term value from your accounting system. 

A data migration tool that is optimized primarily for speed may not be able to handle all of the data structures and relationships you need to set up critical reports in the target system. It might limit your ability to: 

• preserve detailed historical transactions, 
• enrich legacy transactions with Sage Intacct dimensions, 
• consolidate duplicate master data, or 
• support future reporting requirements. 

If a tool is built around a definition of success that doesn’t align with your migration goals, it doesn’t matter what technical capabilities it has. You’ll find yourself fighting with the tool throughout the migration, and you may have to compromise your migration strategy to use the tool. 

Will This Tool Help Us Build the Accounting System We Actually Want?

There are two fundamentally different approaches to data migration. 

The first views data migration as a technical copy-and-paste task. Tools built for this approach are designed to copy historical data from the legacy system and reformat it to satisfy the target system’s import requirements as quickly as possible. 

However, if you try to recreate your legacy data architecture in the target system, you’ll also end up recreating the legacy system’s reporting limitations. 

The second approach sees data migration as an opportunity to transform and enrich historical data so it behaves as though it was originally created in the target system, with all of its upgraded future-oriented configuration settings. Tools built to support this approach need to offer much more customization.  

A tool that is designed to enrich your historical data needs to be able to handle challenges like: 

• dimensional enrichment 
• duplicate master data suppression  
• transaction enhancements (e.g. Inter-entity transaction balancing) 

Instead of asking whether a tool can migrate the types of data you have in the legacy system (like invoices or bank transfers), ask whether it can effectively support the accounting system you actually want to operate after go-live.

How Does This Tool Maintain Confidence in the Data Throughout the Migration?

The real currency of every ERP implementation is confidence. 

If finance teams don’t trust the migrated data, they’ll continue relying on spreadsheets, legacy systems, and manual reconciliations long after go-live, and the implementation will be a failure.  

The keys to maintaining confidence in data migration are transparency, validation, and recoverability. 

Transparency 

Finance teams should understand what changes are being made to their data and why at every point throughout the migration. Many AI-powered workflows provide little-to-no transparency into their reasoning or processes, which can erode trust in the output of those workflows.  

Validation 

Every migration should begin by establishing a validation standard that everyone agrees represents an accurate view of the business. And that validation standard should be used throughout the migration, because mistakes are bound to happen when working with data at scale. After every extraction, transformation, or load step, the migrated data should be compared against that standard before work continues to pinpoint the source of the error.  

If a data manipulation tool combines multiple steps into one, it can make it difficult to validate the data after each transformation or relocation, making it harder to isolate the source of any errors that appear.  

Recoverability 

While it’s possible to execute a data migration perfectly on the first try, it’s risky to count on an automated tool getting everything perfect. When you’re working with data at scale, your data mapping and configuration decisions can have unexpected consequences, and legacy data can contain forgotten structures that trigger upload errors.  

However, some data migration tools are designed to upload data like invoices and their payments directly into a live environment, without including a step for human validation of the data. Those transactions can’t be easily deleted, meaning you’ve got one shot for both the AI and the humans to make all the right mapping, configuration, and transformation decisions. For most organizations, that’s an unacceptable level of risk.  

A much safer approach is to load data into an implementation environment, validate the results, and then move the data into the production environment. Uploading into an implementation environment gives you the ability to recover from mapping or configuration errors by deleting everything and restarting the migration — an option you don’t have when uploading directly to the live environment.  

Align Your Data Migration Tools With Your Migration Strategy  

AI-powered tools have the potential to dramatically accelerate extraction, mapping, transformation, and validation of data during migrations. 

But a tool can’t define what a successful migration looks like for your organization. 

Before selecting any AI-powered migration tool, make sure you understand what outcome it was designed to achieve, whether it’s capable of supporting the accounting architecture you’re trying to build, and how it will help your team maintain confidence in the migrated data from beginning to end. 

At Platform Transition, we believe that AI and automation can be powerful tools in the hands of experts who are following proven processes — and a dangerous trap for novices who replace strategic decisions with a tool’s built-in assumptions. We evaluate all the tools we incorporate into our data migration process using this same framework. If it helps us improve quality, reduce implementation time, or lower cost without compromising our migration methodology, we embrace it.  

Schedule a meeting or request a quote to discuss how we can support your next accounting data migration project.