Common Workflows ‐ All Industries - magicplatforms/ai-workflows GitHub Wiki

End-to-End “AI-Powered SaaS” Workflow (What They All Have in Common)

  1. Multi-Source Data Capture

    • Each platform begins by ingesting raw operational data from its domain: • Contracts, filings, and emails (Legal) • ERP, telematics, and forecast files (Supply Chain) • HR records, résumés, payroll data (HR) • Call recordings, CRM events, emails (Sales) • Web-tracking, ad metrics, intent signals (Marketing)

  2. Secure Cloud Ingestion & Normalisation

    • Data streams are loaded into a cloud data layer (AWS, Azure, GCP, or a vendor-run cloud).
    • ETL/ELT jobs and APIs validate, de-duplicate, tag PII, and map everything to a common schema.
    • Encryption, role-based access, and region-specific storage satisfy compliance (GDPR, SOC 2, etc.).

  3. Enrichment & Context Building

    • Reference data is fused in (e.g., industry benchmarks, carrier tables, skills taxonomies).
    • Domain ontologies or vector embeddings add semantic context so downstream models “understand” the records (e.g., clause types, product SKUs, job skills, buyer intent).

  4. AI / ML Processing Layer

    • Predictive ML: forecasting, anomaly detection, scoring (lead score, attrition risk, ETA, demand).
    • Generative LLMs: drafting language (contracts, job ads, emails), summarising calls, natural-language Q&A.
    • Agent Orchestration: rule or RL-driven agents that decide whether to escalate, auto-reply, adjust plans, etc.
    • All vendors wrap this in guard-rails (private weights, no training on customer data, human-in-the-loop).

  5. Insight Delivery & Collaboration

    • Dashboards, chat assistants, and in-record side-panels surface: – Risk flags (non-standard clause, delayed shipment) – Recommendations (next best action, schedule change, comp offer) – Auto-generated content ready for review.
    • Notifications flow to email, Slack/Teams, or mobile.

  6. Human Review & Approval

    • End-users (lawyers, planners, HRBPs, sellers, marketers) validate AI suggestions, tweak drafts, and click “apply” or “send.”
    • Approval hierarchies and e-signature steps log accountability.

  7. Automated Execution & Write-Back

    • Upon approval, the platform triggers downstream actions: e-sign a contract, release a purchase order, update payroll, send a personalised email, launch an ad set.
    • APIs or embedded RPA write results back to core systems (ERP, CRM, HCM) so the source of truth stays updated.

  8. Feedback & Continuous Learning Loop

    • Every executed action and user correction is captured as new training data.
    • Models are retrained or fine-tuned regularly; performance metrics (accuracy, cycle time, $$$ saved) feed KPI dashboards.
    • Admins can tweak policies, add new data sources, or expand use cases without uprooting the stack.

Optional Visual (Mermaid Sequence)

sequenceDiagram
    participant User
    participant SaaS_UI as SaaS App UI
    participant Data_Lake as Cloud Data Layer
    participant AI_Engine as AI / ML Engine
    participant External as External Systems

    User->>SaaS_UI: Upload / connect data\n(or trigger event)
    SaaS_UI->>Data_Lake: Secure ingest & normalise
    Data_Lake->>AI_Engine: Contextual data feed
    AI_Engine->>AI_Engine: Predict • Generate • Decide
    AI_Engine-->>SaaS_UI: Insights • Drafts • Alerts
    SaaS_UI-->>User: Review & approve
    User->>SaaS_UI: Confirm action
    SaaS_UI->>External: Execute (sign, update, send)
    External-->>SaaS_UI: Status / result
    SaaS_UI->>Data_Lake: Log outcome
    Data_Lake->>AI_Engine: Feedback for retraining

This pattern underpins DocuSign, Harvey, Kinaxis, Project44, Workday, Personio, Salesforce, Gong, HubSpot, and 6sense alike. What changes from product to product is which data is ingested and which decisions or documents are generated—but the architectural loop of ingest → enrich → AI reasoning → human-validated action → feedback stays remarkably consistent.