Diag ‐ delete - magicplatforms/ai-workflows GitHub Wiki

Below is a ready‑to‑paste Azure DevOps Wiki page. It includes a table of contents, Azure DevOps note‑callouts, and nine Mermaid sequence diagrams comparing a “typical day” before and after AI across major business functions.

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  2. Copy everything between the horizontal rules into a new Wiki page.

AI‑Driven Workflow Transformation

[[TOC]]

[!NOTE] Mermaid diagrams render automatically once the page is saved or previewed (no additional extensions required).

Sales – Before vs After AI

sequenceDiagram
    title Sales – Before vs After AI
    participant SDR
    participant AE
    participant LinkedIn
    participant Prospect
    participant CRM
    participant AI as AI_Agent
    participant Calendar
    participant Coach as Win_Coach
    alt **Before AI**
        SDR->>LinkedIn: Scrape leads
        SDR->>Prospect: Draft & send emails
        SDR->>CRM: Log calls
        AE->>CRM: Update opps nightly
    else **After AI**
        AI->>IntentDB: Mine intent signals
        AI->>Prospect: Auto‑personalise outreach
        AI->>Calendar: Schedule call
        AE->>Coach: Realtime win‑probability tips
        CRM-->>AE: Forecast auto‑roll‑up
    end

Human Resources – Before vs After AI

sequenceDiagram
    title HR – Before vs After AI
    participant Recruiter
    participant Candidate
    participant HRBP
    participant ResumeDB
    participant Calendar
    participant AI as GenAI_Ranker
    participant Chatbot
    participant Analytics as HR_Analytics
    alt **Before AI**
        Recruiter->>ResumeDB: Screen 200 résumés
        Recruiter->>Candidate: Phone screens
        Recruiter->>Calendar: Block interview slots
        HRBP->>HR_Analytics: Build turnover deck monthly
    else **After AI**
        AI->>ResumeDB: Rank résumés
        Chatbot->>Candidate: Run 1st interview
        Calendar->>Panel: Auto‑book interviews
        Analytics-->>HRBP: Live attrition heat‑map
        HRBP->>Analytics: Push‑button stay‑bonus scenario
    end

Legal – Before vs After AI

sequenceDiagram
    title Legal – Before vs After AI
    participant Associate
    participant Paralegal
    participant Westlaw
    participant Client
    participant LLM as Research_LLM
    participant ContractAI
    alt **Before AI**
        Associate->>Westlaw: Hunt precedent
        Associate->>Client: Draft memo & redline contract
        Paralegal->>Docs: Bates‑stamp discovery
    else **After AI**
        LLM->>Westlaw: Search & summarise precedent
        ContractAI->>Client: Mark risk clauses & draft NDA
        Paralegal->>Docs: Validate AI‑coded discovery
        Paralegal->>Team: Prep strategy brief
    end

Finance / Banking – Before vs After AI

sequenceDiagram
    title Finance / Banking – Before vs After AI
    participant Analyst
    participant Excel
    participant Compliance
    participant AI as MarketAI
    participant Surveillance as Surveillance_AI
    alt **Before AI**
        Analyst->>DataFeeds: Copy data to Excel
        Analyst->>Excel: Build valuation model
        Analyst->>Note: Write 8‑page report
        Compliance->>Trades: Hand‑check overnight
    else **After AI**
        AI->>FilingsDB: Ingest filings & generate model
        AI-->>Note: Draft equity research note
        Analyst->>AI: Stress‑test assumptions
        Surveillance-->>Trades: Pre‑clear in seconds
    end

Education – Before vs After AI

sequenceDiagram
    title Education – Before vs After AI
    participant Teacher
    participant Student
    participant Parent
    participant Grader
    participant PlanAI as Lesson_Generator
    participant TutorAI
    participant Analytics
    alt **Before AI**
        Teacher->>Plan: Craft lesson plan
        Teacher->>Grader: Manually grade papers
        Teacher->>Parent: Send emails
    else **After AI**
        PlanAI-->>Teacher: Differentiated lesson plan
        TutorAI->>Student: Instant feedback
        Analytics-->>Teacher: Performance dashboards
        Teacher->>Class: Socratic discussion
        Teacher-->>Parent: 30‑sec video update
    end

Technology / Software Dev – Before vs After AI

sequenceDiagram
    title Software Dev – Before vs After AI
    participant Engineer
    participant Repo
    participant CI
    participant Copilot
    participant LLM as BuildHelp_LLM
    alt **Before AI**
        Engineer->>Repo: Design API & write boilerplate
        Repo->>CI: Wait for build & tests
        CI-->>Engineer: Code review feedback
    else **After AI**
        Copilot-->>Repo: 60–80 % scaffolding & autotests
        Engineer->>Code: Focus on edge cases & architecture
        CI->>LLM: Build errors
        LLM-->>Engineer: Explain fixes in chat
        CI-->>Prod: Release cadence halves
    end

Healthcare – Before vs After AI

sequenceDiagram
    title Healthcare – Before vs After AI
    participant Physician
    participant Patient
    participant Guidelines
    participant Coder
    participant ScribeAI as Ambient_Scribe
    participant ClinicalLLM
    alt **Before AI**
        Physician->>EHR: Type notes
        Physician->>Patient: Dictate letters
        Physician->>Guidelines: Search care guidelines
        Coder->>Claim: Assign CPT codes
    else **After AI**
        ScribeAI-->>EHR: Auto‑generate note
        ClinicalLLM->>Physician: Diagnostic suggestions & orders
        Coder->>Claim: Audit AI‑assigned codes
        Physician->>Patient: Spend extra time counselling
    end

Supply‑Chain / Ops – Before vs After AI

sequenceDiagram
    title Supply‑Chain / Ops – Before vs After AI
    participant Planner
    participant ERP
    participant Supplier
    participant ML as Demand_Model
    participant GenAI
    participant Twin as Digital_Twin
    participant Router as Routing_Engine
    alt **Before AI**
        Planner->>ERP: Export data & tweak forecast
        Planner->>Supplier: Call for updates
        Dispatcher->>Routes: Build route sheet
    else **After AI**
        ML-->>Planner: Demand forecast
        GenAI->>Supplier: Draft PO & negotiation email
        Twin-->>Planner: Network simulation
        Router-->>Carrier: Issue loads autonomously
        Planner->>Exceptions: Handle only anomalies
    end

Insurance (P&C / Life) – Before vs After AI

sequenceDiagram
    title Insurance – Before vs After AI
    participant Adjuster
    participant Underwriter
    participant Claim
    participant CV as CV_Model
    participant GenAI
    participant FraudAI
    alt **Before AI**
        Adjuster->>Claim: Read claim & request photos
        Adjuster->>PartsDB: Price parts
        Adjuster->>Report: Write assessment
        Underwriter->>Tables: Crunch risk tables
    else **After AI**
        CV->>ClaimPhotos: Score damage from images
        GenAI-->>Claim: Draft settlement
        Underwriter->>RiskAI: Review AI risk score
        Underwriter->>RiskAI: Tune appetite
        FraudAI-->>Claim: Flag anomalies in real‑time
    end

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