How to Detect Redundant Workflows in Legacy Systems Using AI Video Analysis

The most expensive software in your stack isn’t the one you just bought; it’s the one your team has been using for fifteen years. Legacy systems are often "black boxes" of productivity loss, where users navigate through dozens of unnecessary clicks, redundant data entry screens, and circular navigation paths that no one remembers building. For decades, the only way to audit these systems was through manual observation or invasive "process mining" agents that required months of integration.

However, a shift in Computer Vision (CV) and Large Language Models (LLMs) has introduced a more efficient method: using AI to analyze video recordings of user sessions to detect redundant workflows legacy applications are hiding. By treating the UI as a visual data source, modern tools like Replay can now reverse-engineer these workflows, document them, and even convert them into modern React components.

TL;DR: Can AI Detect Redundant Workflows?#

Yes. By using Computer Vision (CV) and Optical Character Recognition (OCR) to analyze screen recordings, AI can identify repetitive UI patterns, circular navigation, and "dead-end" workflows. Tools like Replay take this further by converting these visual recordings into documented React code and Design Systems, effectively automating the first 80% of a legacy modernization project.

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The Invisible Burden: Why Legacy Workflows Become Redundant#

Legacy software rarely starts out inefficient. Redundancy is an emergent property of "feature creep" and "organizational silos." When a new business requirement arises, developers often add a new screen or button rather than refactoring the existing architecture, fearing that touching the original code will break mission-critical dependencies.

Over a decade, this results in:

1. •Input Duplication: Users entering the same customer ID across three different legacy modules.
2. •Circular Navigation: Forcing a user to return to a "Home" screen to access a sub-menu that should be contextually available.
3. •Shadow Workflows: Users taking screenshots or using Excel to bridge gaps between two disconnected legacy screens.

To detect redundant workflows legacy systems harbor, you must look at how the user interacts with the interface, not just the underlying database logs.

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How AI Video Analysis Works: From Pixels to Logic#

Traditional process mining looks at log files (SIEM, ERP logs, etc.). The problem? Legacy systems often don’t log UI interactions—they only log database commits. If a user spends ten minutes struggling with a redundant form but never hits "Save," that inefficiency is invisible to traditional logs.

AI video analysis changes the paradigm. By recording a user’s screen, AI can "see" the struggle. Here is the technical breakdown of how AI can detect redundant workflows legacy software contains:

1. Frame-by-Frame Semantic Analysis#

AI models (specifically Vision-Language Models or VLMs) analyze the video stream to identify UI elements: buttons, text fields, modals, and dropdowns. It maps the "Visual State" of the application at every second.

2. Action Sequence Mapping#

The AI tracks the mouse cursor and keyboard events. It builds a "Directed Acyclic Graph" (DAG) of the user's journey. If the graph shows the user visiting Screen A -> Screen B -> Screen A repeatedly before reaching Screen C, the AI flags this as a "Circular Redundancy."

3. Heuristic Pattern Matching#

By comparing thousands of user sessions, the AI identifies "pockets of friction." If 90% of users click "Cancel" on a specific legacy pop-up, the AI identifies that workflow step as redundant or obsolete.

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Comparison: Manual Auditing vs. AI Video Analysis#

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Using Replay to Detect Redundant Workflows Legacy Systems Hide#

At Replay, we’ve pioneered a process called Visual Reverse Engineering. Instead of just telling you that a workflow is redundant, we show you what the optimized version should look like in modern code.

Step 1: Record the Legacy Session#

The process begins by recording a standard user performing a task in the legacy environment. Because Replay is platform-agnostic, it works on Mainframe emulators, Delphi apps, old Java Swing UIs, or Silverlight applications.

Step 2: AI Decomposition#

Replay’s engine analyzes the video and extracts the "Atomic Components." It identifies that the "Customer Search" bar on Screen 1 is the same as the one on Screen 5, identifying a redundant UI pattern.

Step 3: Code Generation#

Once the redundancies are identified, Replay generates a clean, documented React component library and a Design System based on the intended workflow, stripping away the legacy friction.

Example: Converting a Redundant Legacy Form to React#

Imagine a legacy workflow where a user has to enter a "Project Code" across three different screens. Replay identifies this redundancy and suggests a single-state React component.

typescript
// Replay-Generated Component: Optimized Project Entry
// Purpose: Consolidates 3 redundant legacy screens into a single stateful form.

import React, { useState } from 'react';
import { Card, Input, Button, Stepper } from '@/components/ui';

const OptimizedProjectWorkflow: React.FC = () => {
  const [step, setStep] = useState(0);
  const [formData, setFormData] = useState({
    projectId: '',
    clientName: '',
    allocation: 0
  });

  // Replay identified that 'projectId' was redundantly asked for 
  // on legacy screens 0x44 and 0x89. Consolidating here.
  const handleNext = () => setStep((prev) => prev + 1);

  return (
    <Card className="p-6 max-w-2xl mx-auto">
      <Stepper activeStep={step} steps={['Identity', 'Allocation', 'Review']} />
      
      {step === 0 && (
        <div className="space-y-4">
          <Input 
            label="Project ID" 
            value={formData.projectId}
            onChange={(e) => setFormData({...formData, projectId: e.target.value})}
          />
          <Input 
            label="Client Name" 
            value={formData.clientName}
            onChange={(e) => setFormData({...formData, clientName: e.target.value})}
          />
          <Button onClick={handleNext}>Continue</Button>
        </div>
      )}

      {/* Steps 2 and 3 follow, stripping redundant 'Back' navigation found in legacy */}
    </Card>
  );
};

export default OptimizedProjectWorkflow;

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The Technical Challenge: Solving the "Semantic Gap"#

The hardest part of using AI to detect redundant workflows legacy systems contain is the "Semantic Gap." This is the difference between what a user does (clicks a blue pixel) and what they intend (authorizing a purchase order).

Replay bridges this gap by using LLMs to interpret the OCR text within the video. If the AI sees the text "Auth Code" in a legacy Windows 95 app and then sees "Verification Number" in a web-portal, it understands these are semantically identical. It flags the second data entry as a redundant workflow.

Code Block: Prompting for Workflow Logic Extraction#

If you are building your own internal analysis tool, you might use a prompt structure similar to what we use at Replay to analyze frame data:

typescript
type UIFrameData = {
  timestamp: number;
  detectedText: string[];
  activeElements: { type: string; label: string }[];
};

/**
 * Analyzes frame sequences to identify redundancy.
 * This logic powers the detection of redundant workflows in legacy apps.
 */
async function analyzeWorkflowRedundancy(frames: UIFrameData[]) {
  const prompt = `
    Analyze the following sequence of UI frames from a legacy application.
    Identify:
    1. Repetitive data entry (entering the same info twice).
    2. Circular navigation (returning to start to reach a sub-page).
    3. Unnecessary confirmation dialogs.
    
    Data: ${JSON.stringify(frames)}
  `;

  // Call to an LLM like GPT-4o or Claude 3.5 Sonnet
  const redundancyReport = await llm.analyze(prompt);
  return redundancyReport;
}

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5 Signs AI Has Detected a Redundant Workflow#

When you run your legacy recordings through an AI analysis platform, look for these five "Red Flags" that indicate a workflow is ripe for removal:

1. •The "Yo-Yo" Pattern: The user moves back and forth between two screens more than three times in a single session.
2. •Clipboard Overuse: The user is frequently copying text from one legacy screen and pasting it into another (AI detects this via OCR and focus changes).
3. •Idle Logic Gaps: Long pauses (over 10 seconds) where the user is looking at a screen but not interacting. This often indicates the user is looking for information that should be on that screen but isn't.
4. •The "Esc" Escape: High frequency of users hitting the "Escape" key or "Cancel" buttons in specific modules.
5. •Multi-App Context Switching: The user frequently minimizes the legacy app to check an Excel sheet or a modern web app, indicating the legacy workflow is missing a critical data integration.

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Why "Visual First" is the Only Way for Legacy Modernization#

When developers try to detect redundant workflows legacy systems have by reading the source code (COBOL, RPG, VB6), they get lost in the "How." They see how the data moves, but they don't see the "Why."

Visual analysis focuses on the "User Intent." By recording the video, you capture the definitive truth of the business process. Replay then takes that truth and converts it into a modern technical stack. This is why visual reverse engineering is 10x faster than manual documentation. You aren't just guessing what the legacy code does; you are observing what the business actually does and rebuilding it correctly in React.

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The Future: From Detection to Autonomous Refactoring#

We are approaching a point where AI won't just detect redundant workflows legacy software possesses—it will fix them automatically.

Imagine a pipeline where:

1. •Replay records 100 hours of legacy usage.
2. •The AI identifies that 30% of the steps are redundant.
3. •The AI generates a new, streamlined React application.
4. •The AI writes the "Glue Code" (APIs/Adapters) to connect the new UI to the old database.

This isn't science fiction. This is the core mission of Replay. We are turning the "Visual Debt" of legacy systems into "Component Assets" for the modern enterprise.

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Frequently Asked Questions (FAQ)#

1. How does AI detect redundant workflows in legacy software without access to the source code?#

AI uses Computer Vision and OCR to analyze the screen recordings (video) of a user session. It identifies patterns like repetitive data entry, excessive navigation steps, and "dead-end" screens by mapping the user's visual journey. By comparing these journeys across multiple users, it can statistically determine which steps are unnecessary for the final outcome, effectively mapping the workflow without ever needing to read a single line of legacy COBOL or Java code.

2. Is video analysis secure for sensitive legacy data?#

Security is a top priority for platforms like Replay. During the analysis phase, sensitive data can be PII-masked (Personally Identifiable Information) using automated blurring or text-replacement algorithms. Furthermore, the analysis can often be performed on-premise or within a private cloud environment, ensuring that the visual data never leaves the corporate perimeter while the AI extracts the structural "logic" of the workflow.

3. Can AI-detected workflows be automatically converted into React code?#

Yes. This is the primary function of Replay. Once the AI identifies the core components and the logical flow of a legacy application, it uses Large Language Models to map those visual patterns to modern React components, complete with state management (like TanStack Query or Redux) and a clean Design System. This effectively automates the "UI Discovery" and "Component Drafting" phases of modernization.

4. What is the difference between Process Mining and AI Video Analysis?#

Process Mining typically relies on backend log files (event logs) to reconstruct workflows. It is excellent for understanding data flow but fails to capture the "User Experience" or "UI Friction." AI Video Analysis looks at the actual pixels the user sees. This allows it to detect redundancies that don't produce a log entry—such as a user struggling with a confusing menu or manually re-typing data from a PDF into a legacy form.

5. How many recordings are needed to detect redundant workflows legacy systems contain?#

While a single recording can reveal obvious redundancies, the AI becomes significantly more accurate with 10–20 recordings of the same task performed by different users. This "ensemble" approach allows the AI to distinguish between a single user's habit and a systemic redundancy built into the software itself.

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Modernize Your Legacy UI with Replay#

Stop guessing what your legacy software is doing and start seeing it. Replay converts your legacy screen recordings into documented React code, Design Systems, and optimized workflows. Whether you're looking to detect redundant workflows legacy systems are hiding or you're ready to migrate to a modern web stack, Replay provides the definitive visual path forward.

Ready to turn your video recordings into a modern Component Library?

Visit Replay.build and start your visual reverse engineering journey today.