ERP data migrations often take far longer than expected. Why? The underlying workflows are typically manual, fragmented, and slow to validate at scale. At Doyen we aim to reduce the time for migrations by building a wholistic framework for data migrations powered by AI.
Every ERP data migration is typically composed of three core workflows: value mapping, schema mapping and data loading, and reconciliation. When these workflows are well-structured and automated (often with the help of new AI technology), migrations move faster and with fewer iterations.
We'll take a look at each workflow, but before we do it's useful to consider what we mean by a workflow and why historically data migration workflows were painful.
What is a Workflow?
In an ERP migration, a workflow is a repeatable set of steps involved in moving financial data from a source system to a destination system while preserving meaning, accuracy, and auditability. These workflows usually span finance, accounting, and technical teams and evolve over multiple iterations as edge cases are discovered.
Historically, much of this work lived in spreadsheets, scripts, and one-off fixes, requiring heavy involvement from technical teams to build and maintain custom transformations. AI and automation are now enabling new ways to increase both the speed and accuracy of data migrations, while reducing reliance on bespoke scripting and engineering effort. Let’s take a look at the three key workflows and how they are changing.
Value Mapping: Preserving Financial Meaning
Value mapping is the process of defining how financial concepts should exist in the destination system. While this includes ensuring continuity for core concepts like the chart of accounts, vendors and customers, and dimensions such as departments or locations, migrations are often an opportunity to intentionally redesign these structures.
In practice, value mapping is rarely a one-to-one translation. Large organizations can have hundreds or even thousands of accounts, vendors, and dimensional values to manage. During a migration, finance teams often use the opportunity to clean up a chart of accounts, consolidate or reclassify vendors, standardize dimensions, or restructure how multiple entities roll up for reporting. Managing this volume of change in spreadsheets is tedious and error‑prone, and much of the work is repetitive.
At Doyen, we integrate AI directly into an accessible UI designed for finance professionals. AI assists value mapping by proposing candidate mappings, highlighting ambiguity early, and allowing validated mappings to be reused across iterations. Finance teams can also soon add natural-language instructions to guide how mappings should be structured. This is an area of ongoing investment for Doyen, with the goal of making complex mapping work as easy and intuitive as possible.
Schema Mapping and Data Loading: Moving the Data Correctly
Once values are mapped, the data still needs to conform to the destination ERP’s structure. This includes aligning object schemas, required and optional fields, data types, and load sequencing rules.
In practice, schema mapping and data loading are time-consuming because they require deep knowledge of the destination system and technical effort. Source schemas vary widely across ERPs, billing systems, and custom exports, forcing teams to write one-off scripts and transformations to handle edge cases and inconsistencies. Much of this work is repetitive and brittle, and it often requires ongoing involvement from technical teams to translate business requirements into executable logic.
Doyen addresses this by integrating AI into an accessible UI built for finance professionals. AI generates schema mappings directly from the source data, regardless of structure, and automates much of the transformation logic that previously required custom scripts. Finance teams can add natural-language instructions to encode business rules that are unique to their organization, while remaining fully in control of the outcome. Data loading is managed through the same UI, making it easy to load subsets of data, validate what loaded and what did not, and re-load quickly as mappings evolve. This reduces reliance on technical teams and shortens each iteration of the migration process.
Reconciliation: Proving the Numbers Are Right
Finance teams need confidence that trial balances, account-level balances, AR and AP totals, and revenue figures match between the source and destination systems.
Traditionally, reconciliation is one of the more time-consuming parts of a migration. Teams export trial balances from both systems, manually align account IDs that no longer match, and stitch data together in spreadsheets. They reconcile across multiple time periods, re-run comparisons after each data load, and track discrepancies by hand. Each iteration introduces more files, more formulas, and more room for error.
Automation changes this by making reconciliation continuous rather than episodic. Doyen allows finance teams to define reconciliation logic using natural-language instructions, automatically generating reports that compare source and destination systems across periods and accounts. AI helps surface discrepancies and patterns, while finance professionals remain in control of how results are interpreted and resolved.
Final Thoughts
Every ERP migration relies on the same three workflows. Historically, most teams have managed them manually. The result? Long timelines and (often) missed deadlines.
Doyen exists to systematize and continuously improve these workflows. As automation and AI evolve, we will keep iterating to make ERP data migrations faster, easier, and less painful.