Teaching AI to Analyze a Legacy MS Access Application and Propose a Modernization Plan

Legacy Microsoft Access applications are the cockroaches of the enterprise world: resilient, everywhere, and very hard to kill.

They’ve quietly run critical workflows for 10, 15, sometimes 20+ years—managing orders, tracking inventory, calculating pricing, orchestrating approvals—often with zero formal documentation. Inside those .mdb and .accdb files lives decades of embedded business logic, fragile data constraints, and tribal knowledge.

And now those apps are in the way.

They don’t scale.
They’re hard to secure.
They don’t fit cloud-native architectures.
The one person who understands the VBA is retiring.

This is where AI changes the game.

Modern generative AI and agentic workflows can analyze a legacy Access application end-to-end: decode schemas, untangle relationships, interpret VBA, and propose a concrete modernization plan for a cloud-ready architecture. Done right, this doesn’t just speed up migration—it reduces risk, improves traceability, and creates an upgrade path instead of a rewrite gamble.

In this article, we’ll walk through how to:

Prepare a legacy Access app for AI analysis
Use AI to inventory structures, logic, and data risks
Turn the analysis into a modernization roadmap
Extend that roadmap with AI-driven enhancements
Navigate the limitations and human oversight requirements

We’ll assume you’re familiar with databases, basic ETL, and modern cloud concepts (e.g., Azure SQL, AWS RDS, microservices), but not necessarily an expert in AI-driven modernization.

2. Preparing the Legacy Access Application for AI Analysis

AI can’t reason about what it can’t see. The quality of your modernization plan depends heavily on the inputs you give it.

What AI Needs From Your Access App

To analyze a legacy Access application effectively, AI agents typically require:

Database schema and metadata
- Table definitions (names, columns, data types, constraints)
- Primary and foreign keys
- Indexes
- Relationships (one-to-many, many-to-many)
Queries and views
- Saved Access queries
- SQL statements (SELECT, INSERT, UPDATE, DELETE)
- Parameterized queries and macros
Code and business logic
- VBA modules and classes
- Form and report event handlers
- Macros and AutoExec routines
- Any embedded SQL inside VBA
Sample data
- Representative rows for each key table (typically 100–1000 rows)
- Edge cases (nulls, legacy values, “miscellaneous” codes)
- Enough volume to show real-world patterns, not synthetic noise
Structural diagrams (if available)
- Relationship diagrams exported from Access
- Any existing data model diagrams from previous projects

You don’t need all of this to start, but the more you provide, the better the AI’s analysis and recommendations.

Common Challenges With Legacy Access Apps

Legacy Access databases rarely come neatly packaged. Typical hurdles:

Incomplete documentation
No ERD, no functional specs, no data dictionary. The “documentation” lives in people’s heads and scattered emails.
Missing dependencies
- Linked tables pointing to external databases or other Access files
- References to COM components or external DLLs
- ODBC connections to long-forgotten SQL Servers
Inconsistent naming
- Tables like tbl1, tData, tblCustomer living side by side
- Columns like Code, Code2, NewCode, Code_New
- Queries named after end users instead of business processes
Tangled UI and logic
Forms that directly manipulate tables, with key business rules hidden inside button-click events.

AI can help untangle this—but only if you first extract the raw materials.

A Practical Workflow to Prepare Structured Inputs

Here’s a step-by-step approach you can standardize:

Inventory the database file(s)
- List all .mdb and .accdb files in scope
- Identify front-end vs. back-end files if split
- Note any linked tables (external sources)

Export the schema

From Access, you can:

Use the “Database Documenter” to generate an overview
Or use VBA / external tooling to script out structure

For example, using VBA in Access to export table definitions:

Public Sub ExportTableSchema()
    Dim db As DAO.Database
    Dim tdf As DAO.TableDef
    Dim fld As DAO.Field
    Dim f As Integer
    
    f = FreeFile
    Open "C:\exports\table_schema.txt" For Output As #f
    
    Set db = CurrentDb
    
    For Each tdf In db.TableDefs
        ' Skip system tables
        If Left(tdf.Name, 4) <> "MSys" Then
            Print #f, "TABLE: " & tdf.Name
            For Each fld In tdf.Fields
                Print #f, "  COLUMN: " & fld.Name & " (" & fld.Type & ")"
            Next fld
            Print #f, ""
        End If
    Next tdf
    
    Close #f
End Sub

AI can then ingest table_schema.txt as part of its analysis.

Export queries and SQL

Export saved queries to a text file or use VBA to loop through QueryDefs
Capture both names and SQL bodies

Public Sub ExportQueries()
    Dim db As DAO.Database
    Dim qdf As DAO.QueryDef
    Dim f As Integer
    
    f = FreeFile
    Open "C:\exports\queries.sql" For Output As #f
    Set db = CurrentDb
    
    For Each qdf In db.QueryDefs
        Print #f, "-- Query: " & qdf.Name
        Print #f, qdf.SQL
        Print #f, ""
    Next qdf
    
    Close #f
End Sub

Export VBA and macros
- Use the VBA editor to export modules
- Or use tools / scripts to bulk-export all modules to .bas and .cls files
- Export macros to XML if possible
Extract sample data
- For each key table, export a CSV with:
  - Representative sample
  - Edge-case records (e.g., unusual statuses, max/min dates)
- Mask or anonymize data where required (more on that below)
Document known business context

Even a one-page summary helps AI:
- What the application does (e.g., “order management for B2B customers”)
- Critical workflows
- Known pain points (“report X runs for 20 minutes every Monday”)
- Future-state goals (“needs to integrate with Salesforce and Power BI”)

This preparation can often be semi-automated and reused across multiple legacy databases.

Why Data Quality Matters for AI Accuracy

AI models are pattern engines. Garbage in, garbage out still applies.

Complete metadata helps AI:
- Detect hidden relationships
- Infer business meaning from naming patterns
- Distinguish core entities (Customer, Order, Product) from helper tables
Representative sample data helps AI:
- Identify PII and sensitive fields by content, not just names
- Understand cardinality and usage patterns
- Suggest realistic partitioning and indexing strategies

If you only give AI table names and a vague description, you’ll get generic advice. Feed it rich metadata and sample data, and you get a tailored, actionable modernization plan.

Sensitive Data and Compliance Considerations

Before uploading any schema or sample data to an AI platform:

Classify the data
- PII (names, emails, phone numbers, addresses)
- PHI (medical details)
- Financial data (credit cards, account numbers)
- Confidential internal notes
Mask or anonymize where possible
- Replace names with synthetic values
- Remove or hash identifiers
- Redact free-text notes if you can’t safely process them
Check your AI provider’s policies
- Data residency and storage
- Training usage (is your data reused to train models?)
- Encryption in transit and at rest

A good compromise model: share full structure and partial, masked samples that still preserve field types and typical patterns (e.g., email-like values, date ranges) without exposing real identities.

3. Core AI Capabilities for Analyzing Legacy Access Systems

Once you’ve prepared inputs, AI agents can do surprisingly deep analysis of your legacy Access application.

3.1 Inventorying Structures and Dependencies

Generative AI can parse your exported schema, queries, and VBA to build a holistic map of your application:

Tables and relationships
Joins and query patterns
Lookup and reference tables
Derived or computed fields
Code paths and event-driven logic

Instead of a human manually clicking through the Relationships window and hunting for dependencies, you can let an AI agent:

Generate a dependency graph
Identify tables never used (candidates for deprecation)
Trace where a particular field is read and written
Spot circular dependencies in queries and code

Example: Detecting Circular Dependencies and Hidden Lookup Tables

Given a set of exported queries, an AI agent might produce insights like:

Query qryCalculateDiscounts reads from tblOrders and writes to tblOrderDiscounts.
Query qryUpdateOrders subsequently reads tblOrderDiscounts and updates tblOrders.Total.
Forms use both queries in a sequence, effectively creating a circular calculation path.

It might also detect that:

tblCode1 and tblCode2 are both small tables with Id and Description
Used only as joins in several queries
Likely “lookup tables” that should become dimension tables in a star schema.

AI can then recommend:

Breaking circular update flows into idempotent operations
Normalizing lookup tables into shared dimensions (e.g., DimStatus, DimReasonCode)
Introducing clear primary/foreign key relationships where implicit joins are currently used.

3.2 Mapping Access Structures to Modern SQL/Cloud Equivalents

Legacy Access data types and patterns don’t always translate cleanly to cloud databases. AI can help by learning and applying mapping patterns:

Text / Memo to VARCHAR / NVARCHAR(MAX)
Yes/No to BIT
OLE Object or Attachment to VARBINARY(MAX) or blob storage references
Date/Time with mixed semantics (date-only, timestamp, “magic” values like 1/1/1900) to appropriate SQL types and constraints

Instead of manually deciding the mapping for every column, AI can:

Apply a default mapping strategy
Flag edge cases (e.g., Text(255) used for JSON-like data)
Suggest modern alternatives (e.g., move large free text to a separate table or document store)

Example: Star-Schema-Friendly Models

AI can analyze query patterns and infer:

tblOrders frequently joined to tblCustomers, tblProducts, tblSalesReps
Several “code” tables used purely for descriptive labels

From this, it can propose:

FactOrder table with metrics (quantity, price, discount, tax)
Dimension tables: DimCustomer, DimProduct, DimSalesRep, DimOrderStatus
Surrogate keys and proper foreign keys

And it can auto-generate a proposed DDL script for a cloud database:

CREATE TABLE DimCustomer (
    CustomerKey INT IDENTITY(1,1) PRIMARY KEY,
    LegacyCustomerId INT NOT NULL,
    CustomerName NVARCHAR(200),
    Region NVARCHAR(100),
    IsActive BIT,
    CreatedDate DATETIME2,
    ModifiedDate DATETIME2
);

CREATE TABLE FactOrder (
    OrderKey INT IDENTITY(1,1) PRIMARY KEY,
    LegacyOrderId INT NOT NULL,
    CustomerKey INT NOT NULL,
    ProductKey INT NOT NULL,
    OrderDate DATE,
    Quantity INT,
    UnitPrice DECIMAL(18,2),
    DiscountAmount DECIMAL(18,2),
    TotalAmount AS (Quantity * UnitPrice - DiscountAmount) PERSISTED,
    CONSTRAINT FK_FactOrder_DimCustomer FOREIGN KEY (CustomerKey)
        REFERENCES DimCustomer(CustomerKey)
);

You still review and refine, but the heavy lifting of initial mapping is automated.

3.3 Automatically Identifying Sensitive and Compliance-Relevant Data

AI is particularly good at identifying sensitive data based on:

Field names (SSN, DOB, CardNumber, MRN, Diagnosis)
Value patterns (credit card format, national ID patterns, email formats)
Context (“notes” fields attached to customer or patient records)

It can classify fields as:

PII (e.g., CustomerEmail, PhoneNumber)
PHI (e.g., Diagnosis, TreatmentPlan)
Financial (e.g., BankAccountNumber, IBAN)
High-risk free text (e.g., CustomerNotes that might contain unstructured personal details)

Example: Highlighting Sensitive Notes in Memo Fields

Consider a tblCustomerNotes table with:

CustomerId
NoteDate
NoteText (Memo / Long Text)

AI can:

Sample anonymized values from NoteText
Detect that notes often contain:
- Health-related details (“diagnosed with…”)
- Financial hardships (“cannot pay this month due to…”)
- Legal or compliance-sensitive information

The AI can then recommend:

Treating NoteText as PHI or sensitive data
Encrypting this column in the target database
Restricting access via role-based controls
Including it in audit logging and data retention policies

This early classification is critical for GDPR, HIPAA, PCI, and internal audit requirements.

3.4 Analyzing Embedded VBA and Proposing Modern Logic

Access apps often bury essential business logic in:

Form events (OnLoad, OnClick, BeforeUpdate)
Macros triggered at startup
VBA modules that orchestrate multi-step workflows

AI can process exported VBA and:

Identify distinct workflows (e.g., order approval, invoice generation)
Distinguish business rules (discount calculations, validation rules) from UI logic
Detect anti-patterns (direct table updates from UI, tight coupling across forms)

From there, it can propose:

Modular services (e.g., OrderService, BillingService)
API endpoints (/orders/{id}/approve)
Event-driven flows using queues or pub/sub systems

Example: Translating Complex VBA Workflow to Event-Driven Architecture

Suppose you have a VBA procedure like:

Public Sub ApproveOrder(orderId As Long)
    ' Validate order
    If Not IsOrderValid(orderId) Then
        MsgBox "Order is not valid."
        Exit Sub
    End If
    
    ' Update status
    CurrentDb.Execute "UPDATE tblOrders SET Status = 'Approved' WHERE OrderId = " & orderId
    
    ' Recalculate discounts
    Call RecalculateOrderDiscount(orderId)
    
    ' Generate invoice
    Call GenerateInvoice(orderId)
    
    ' Email customer
    Call SendCustomerEmail(orderId, "Your order has been approved.")
    
    MsgBox "Order approved and customer notified."
End Sub

An AI modernization assistant might propose:

Domain services:
- OrderValidationService
- PricingService
- InvoicingService
- NotificationService
API design:
```
POST /api/orders/{orderId}/approve
```
Event-driven flow (pseudo-architecture):
- OrderApproved event published to a message bus
- pricing-service subscribes to recalculate discounts
- billing-service subscribes to generate invoice
- notification-service subscribes to send customer email

Example scaffold (C# / .NET style):

[HttpPost("orders/{orderId}/approve")]
public async Task<IActionResult> ApproveOrder(int orderId)
{
    if (!await _orderValidationService.IsValidAsync(orderId))
        return BadRequest("Order is not valid.");

    await _orderService.UpdateStatusAsync(orderId, "Approved");

    await _eventBus.PublishAsync(new OrderApprovedEvent(orderId));

    return Ok("Order approved.");
}

The AI doesn’t just translate line-by-line—it suggests a modern architectural pattern aligned with microservices or domain-driven design.

4. Turning Analysis into a Modernization Plan

Raw analysis only gets you halfway. The real value comes when AI synthesizes findings into a concrete, prioritized modernization roadmap.

4.1 Identifying Risks and Technical Debt

Based on schema, code, and usage patterns, AI can flag:

Inconsistent data types
Same concept represented differently across tables:
- CustomerId as TEXT in one table and LONG in another
- Date stored as text in some places
Duplicate or missing keys
- Tables without primary keys
- Composite keys implemented via convention, not constraints
- Duplicate records due to lack of uniqueness constraints
Performance hotspots
- Queries with multiple nested joins and no indexes
- Full table scans on large tables
- Access reports that time out or hang
Hidden business logic
- Calculations performed in form controls only
- Business rules embedded in macros instead of reusable procedures

AI can then categorize these into:

Must-fix before migration
Can-be-fixed-during-migration
Post-migration optimization opportunities

4.2 Estimating Effort, Impact, and Likely Issues

Using patterns from similar migrations, AI can:

Estimate effort ranges (e.g., story points, person-days) for key tasks
Predict risk levels (e.g., high-risk workflows due to complexity and lack of tests)
Highlight dependencies between tasks (e.g., you must standardize customer IDs before moving to a new data warehouse)

Organizations that apply AI-driven planning often see:

Up to 40% reduction in error rates
by catching inconsistent mappings and missing logic earlier.
Around 30% reduction in migration timelines
by automating assessment, mapping, and initial scaffolding.

These aren’t magic numbers—they depend on quality inputs and human oversight—but they’re realistic targets seen in practice.

4.3 Validating Functional Equivalence

One of the biggest fears in modernization: “Will the new system actually behave the same for critical workflows?”

AI can help by:

Comparing legacy workflows (from VBA, macros, and usage logs) with proposed modern flows
Identifying functional gaps
Generating test cases to validate behavior

Example: Approval Workflow Validation

For an order approval process, an AI agent can:

Map the Access workflow steps:
- Validate order totals and mandatory fields
- Check credit limit
- Require supervisor approval above a threshold
- Update order status, generate invoice, send email
Compare with the proposed microservice/API workflow and highlight:
- Missing credit limit check in the API draft
- Supervisor approval logic not handled in the new UI spec
Generate test scenarios:
- “Order below credit limit, no supervisor required”
- “Order above credit limit, supervisor approves”
- “Order with missing mandatory field should be rejected”

This ensures the modern system preserves mission-critical functionality before legacy Access is retired.

4.4 Optimizing for Cloud Databases

AI can analyze Access query patterns to suggest:

Indexing strategies
Based on WHERE clauses and join conditions
Partitioning approaches
By date (e.g., OrderDate) or tenant (e.g., CustomerRegion, OrgId)
Caching strategies
For reference data heavily reused (e.g., product catalogs, code tables)

Example: Auto-Generating Optimal Cloud Indexes

From analyzing queries:

80% of queries filter on OrderDate and CustomerId
Joins frequently use CustomerId and ProductId
Reports group by Region and OrderDate

AI might propose:

CREATE INDEX IX_FactOrder_OrderDate_CustomerKey
ON FactOrder (OrderDate, CustomerKey);

CREATE INDEX IX_FactOrder_ProductKey
ON FactOrder (ProductKey);

CREATE INDEX IX_DimCustomer_Region
ON DimCustomer (Region);

These recommendations can dramatically improve performance in Azure SQL, AWS RDS, or similar platforms, especially under concurrent loads.

4.5 Drafting Security, Roles, and Data Governance Models

AI can also draft:

Role-based access models
- Roles like OrderEntry, Approver, Finance, Admin
- Permissions mapped to entities and actions
Audit and logging strategies
- Which actions to log
- Where to store logs (e.g., Log Analytics, CloudWatch)
- Retention policies
Data lineage requirements
- Tracking how data moves from Access to cloud
- Mapping fields to new data warehouse schemas
- Annotations for GDPR subject access requests

Example: Recommending Metadata Governance Tools

If your target environment is Azure, AI might recommend:

Azure Purview / Microsoft Purview for:
- Cataloging schemas and data assets
- Classifying PII and sensitive data
- Managing lineage from Access → staging → curated zones

Similar recommendations can be made for AWS (e.g., AWS Glue Data Catalog) or GCP (e.g., Data Catalog), giving you a governance path aligned with your chosen platform.

5. Extending Modernization with AI-Driven Enhancements

Once you’ve lifted your Access app into a modern architecture, AI opens the door to capabilities that simply weren’t possible before.

5.1 Real-Time Dashboards and Streaming Analytics

Access is fundamentally a desktop client database. It’s not built for:

Real-time metrics
Stream processing
Large-scale analytical workloads

Modern architectures let you:

Capture events as they happen (orders placed, approvals completed, invoices paid)
Stream data into platforms like Kafka, Event Hubs, or Kinesis
Feed real-time dashboards in Power BI, Tableau, or custom web apps

You don’t need “Netflix-scale” streaming, but the same patterns apply at smaller scale:

Event producers (APIs, services)
Event hub or queue
Stream processors for analytics or ML
Real-time visualization

AI can help design this pipeline, including which events to emit and how to structure them.

5.2 Replacing Manual Data Entry with Document Intelligence

Access apps often rely on manual data entry from:

Invoices
Purchase orders
Paper forms
Emails and PDFs

AI-powered document intelligence can:

Extract key fields (vendor, amount, date, line items)
Validate values against master data (e.g., known suppliers, product catalogs)
Feed structured data directly into modern SQL or NoSQL databases

Example: Invoice Scanning to Modern Database

A typical flow:

Supplier emails a PDF invoice
Document AI service extracts:
- InvoiceNumber
- InvoiceDate
- SupplierName
- Line items (Product, Quantity, Unit Price, Total)
AI validates:
- Supplier exists in DimSupplier
- Total matches sum of line items
- Tax calculations are reasonable
Data is inserted into:
- FactInvoice (for analytics)
- AP_Transactions (for operational processing)

Access could never gracefully handle this at scale; modern architectures plus AI can.

5.3 Enriching Data With Sentiment, Key Phrases, and Forecasts

Once your data lives in cloud databases and data lakes, you can apply AI to:

Sentiment analysis on customer feedback fields
Key phrase extraction from notes and comments
Forecasting on demand, revenue, or support volumes

This turns legacy Access data into a foundation for:

Predictive dashboards (“forecasted demand by region”)
Alerting (“sudden spike in negative sentiment for product X”)
Automated decision frameworks (“flag high-risk orders for review”)

5.4 Automating Migration Scripts and API Scaffolding

AI can further accelerate the build phase by:

Generating SQL migration scripts:
- CREATE TABLE statements
- Data transformation logic
- Incremental load pipelines
Translating stored procedures and complex queries into:
- Equivalent SQL for the target platform
- Or service-level methods in your chosen language (C#, Java, Python)
Generating API scaffolding:
- Controller/endpoints definitions
- DTOs and models
- Basic validation

Used carefully, this can improve productivity by 35–45% across coding and testing phases, especially for boilerplate and repetitive tasks.

You still need engineers to:

Review the generated code
Harden it for security and performance
Integrate it into CI/CD pipelines

But AI takes care of much of the plumbing.

6. Challenges, Limitations, and Human Oversight Requirements

AI is powerful, but it’s not a silver bullet. A realistic modernization plan acknowledges the limits.

6.1 AI Can’t Magically Recreate Missing Knowledge

If:

Documentation is nonexistent
Key business rules exist only in people’s heads
The AI sees only partial code and data

Then its recommendations will have gaps. You still need:

Business SMEs to explain critical workflows
Data stewards to clarify edge cases and exceptions
Architects to align changes with enterprise standards

AI can surface questions you need to ask; it can’t invent accurate answers for missing logic.

6.2 Need for Strong Data Stewardship and Architecture

AI may suggest a reasonable data model or service decomposition, but:

It won’t know your long-term domain boundaries
It may not fully reflect your org’s naming standards or reference architecture
It can’t decide your tolerance for complexity vs. simplicity

You need human architects to:

Validate and refine proposed schemas and APIs
Decide where to use microservices vs. modular monolith patterns
Integrate with existing enterprise systems and contracts

6.3 Security and Compliance Require Human Validation

Even if AI:

Identifies PII/PHI
Recommends encryption and RBAC
Suggests tools like Purview

You still must:

Validate classifications with legal and compliance teams
Ensure encryption and key management align with policies
Conduct manual security reviews and penetration testing

Regulators and auditors won’t accept “the AI said it was secure” as evidence.

6.4 Organizational Change and User Adoption

Modernizing technology is only half the battle. The other half is:

Changing how people work
Replacing familiar Access forms with web UIs
Introducing new workflows and processes

AI doesn’t solve:

Training needs for end users
Resistance to change
Governance for who can build what in the new environment

You’ll need:

Clear change management plans
Pilot groups and champions
Phased cutovers with rollback options

6.5 Hybrid AI-Human Modernization Teams

The most successful projects use AI as a force multiplier, not a replacement:

AI:
- Automates assessment, mapping, scaffolding
- Suggests architectures and optimizations
- Generates documentation and tests
Humans:
- Provide domain knowledge and constraints
- Make design decisions and tradeoffs
- Validate, correct, and extend AI outputs

Think of AI as a highly productive junior analyst/engineer that works 24/7 but always needs expert review.

7. Conclusion: Building Repeatable AI-Driven Modernization Workflows

Modernizing legacy Access applications used to be a painful, high-risk manual process. Generative AI and agentic workflows fundamentally change that equation.

With the right preparation and oversight, AI can:

Inventory and understand complex legacy schemas and VBA logic
Detect risks, hidden dependencies, and sensitive data early
Propose modern, cloud-ready architectures and data models
Generate migration scripts, API scaffolding, and test cases
Extend your modernization with analytics, document intelligence, and ML

But there are non-negotiables:

High-quality inputs: schema, queries, VBA, and representative sample data
Expert oversight: architects, data stewards, and SMEs guiding and validating
Strong governance: security, compliance, and change management baked in

A Reusable Blueprint You Can Apply

You can formalize this into a repeatable workflow:

Prepare Inputs
- Export schema, queries, VBA, and sample data
- Mask sensitive fields where needed
- Document high-level business context
Run AI Analysis
- Inventory tables, relationships, and dependencies
- Identify sensitive data and compliance needs
- Analyze query patterns and embedded logic
Validate Findings
- Review AI outputs with SMEs and architects
- Correct misinterpretations
- Prioritize risks and technical debt
Generate a Modernization Plan
- Target architecture and data model
- Migration strategy and phases
- Performance, security, and governance plans
- Effort and risk estimates
Extend With Advanced Analytics
- Real-time dashboards and streaming events
- Document intelligence for ingestion
- Predictive models on the modernized data platform

Next Steps

If you want to start applying this approach:

Pick a contained Access application (not the most critical one) as a pilot.
Build a small AI-human modernization squad: architect, data engineer, SME, and AI practitioner.
Run a proof of concept:
- 2–4 weeks to prepare inputs, run AI analysis, and draft a modernization plan
- Validate whether the AI-generated outputs match your expectations

From there, you can refine your playbook into a repeatable pattern for the rest of your Access estate—and more broadly, for other legacy platforms as well.

AI won’t modernize your legacy systems for you. But with the right workflow, it will transform how fast, how safely, and how intelligently you can do it.