Solution paths

Choose the transformation motion that matches the actual constraint.

Executive teams do not need more AI possibilities. They need a clear answer on where the bottleneck sits today, which migration path resolves it fastest, and what the owned output looks like once the work is complete.

4

Solution paths

2-12

Typical engagement weeks

Owned

Output and code

0

Lock-in required

How to choose

Start with the bottleneck, not the technology trend.

The right path becomes obvious when the team is clear on what is actually blocking value creation today. This section is meant to make that choice easier before a single line of migration work begins.

If the problem is

Your workflow is trapped inside a third-party operating platform.

Recommended path

Start with SaaS Migration.

When approvals, process logic, and data are all embedded in a vendor system, the path is to extract the operating model, not merely layer more automation on top.

If the problem is

Your systems are technically reachable, but agents cannot use them safely or coherently.

Recommended path

Start with API Transformation.

Most APIs were designed for developers, not autonomous systems. The work is to create a clearer tool layer, not to replace the application outright.

If the problem is

The constraint is fragmented or unusable data rather than application access.

Recommended path

Start with Data Migration.

If agents cannot retrieve governed, business-aware information, no amount of orchestration solves the decision-quality problem downstream.

If the problem is

Critical business logic is trapped inside custom software or manual operational workarounds.

Recommended path

Start with App Modernization.

The objective is to separate durable logic from brittle UI and rebuild it into maintainable, governed workflows the business can scale.

Core motions

Four routes into the same executive pipeline.

Each path feeds the same MigrateForce operating model: discovery, assessed readiness, ranked intervention planning, and governed execution. What changes is the system constraint being addressed first.

Complexity

Medium to high

Typical timeline

2-8 weeks

Break dependency on legacy operating systems of record

SaaS Migration

Extract data, workflow logic, permissions, and operating context from legacy SaaS platforms, then rebuild on AI-native or custom-owned infrastructure.

Full data extraction and transformation
Workflow logic preservation and redesign
Permission and approval mapping
Incremental migration paths when full cutover is not prudent

Common sources

SalesforceOracleSAPLegacy ERPHubSpotZendesk
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Complexity

Low to medium

Typical timeline

2-4 weeks

Make existing systems agent-ready without replacing them

API Transformation

Convert technical endpoints into semantically clear, governed tool interfaces that AI agents can use safely and productively.

Automated API discovery and validation
Integration code generation with auth and schema discipline
Semantic descriptions for AI consumption
Governance, observability, and operating controls

Common sources

REST APIsSOAPGraphQLLegacy APIsInternal services
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Complexity

Medium

Typical timeline

2-6 weeks

Create retrieval-ready and benchmark-ready data layers

Data Migration

Turn fragmented data sources into governed knowledge layers, RAG infrastructure, and agent-ready retrieval surfaces.

RAG pipeline creation and indexing
Vector embedding generation
Knowledge base structuring
Real-time sync connectors for live environments

Common sources

On-prem databasesWarehousesFile systemsData lakesMainframes
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Complexity

Variable

Typical timeline

4-12 weeks

Lift embedded business logic out of brittle interfaces

App Modernization

Analyze custom or legacy applications, isolate the business logic inside them, and rebuild it into governed agent workflows and modern system components.

Business logic extraction
Workflow decomposition and orchestration design
Incremental modernization instead of big-bang rebuilds
Delivery paths that preserve operations while architecture changes

Common sources

Desktop appsInternal toolsMobile appsLegacy systems
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Owned output

Every path produces reusable agent-ready building blocks.

The engagement does not end in a deck. It leaves behind working interfaces, governed data structures, and reusable execution assets your team can keep operating after the initial project closes.

01

Integration servers

Tool definitions, structured I/O, and authentication-aware execution surfaces.

02

AI-consumable APIs

Semantically clearer schemas, examples, and operating descriptions for agents.

03

Data connectors

RAG-ready indexes, knowledge bases, and benchmarkable evidence layers.

04

Workflow hooks

Trigger points, event handlers, and orchestration interfaces.

05

Skills and plugins

Reusable actions and delivery patterns that compound across projects.

06

Context managers

Memory, session state, and multi-step execution controls.

Choose The Right Starting Point

Run the assessment first if you want the path chosen with evidence.

A short assessment clarifies where the true bottleneck sits, what the economic upside looks like, and which migration motion is most defensible before the work begins.

Zero lock-in outputGoverned executionExecutive-ready recommendation