patternestablishedmedium complexity

Workflow Automation

Workflow Automation with AI embeds models such as LLMs, OCR, and ML classifiers into orchestrated, multi-step business workflows. It uses triggers, AI-powered tasks, human-in-the-loop approvals, and system integrations to execute processes end-to-end with minimal manual effort. Traditional workflow or orchestration engines coordinate the sequence, while AI steps handle perception, understanding, and decision-making. Monitoring, governance, and exception handling ensure reliability, compliance, and auditability in production environments.

136implementations
28industries
Parent CategoryAutonomous Systems
01

When to Use

  • When you have repetitive, rule-heavy workflows that still require human judgment or document understanding.
  • When the process spans multiple systems (ERP, CRM, ticketing, email) and manual coordination is slow or error-prone.
  • When large volumes of semi-structured or unstructured data (emails, PDFs, forms) must be processed consistently.
  • When you want to reduce cycle time and manual effort while maintaining or improving quality and compliance.
  • When there is a clear, measurable business KPI (e.g., time-to-approve, cost-per-case, error rate) that automation can impact.
02

When NOT to Use

  • When the process is highly creative, exploratory, or non-repeatable, with no stable sequence of steps.
  • When decisions are extremely high-stakes (e.g., life-critical medical decisions, major legal judgments) and regulations require full human control.
  • When you lack reliable access to the necessary systems or data sources to complete the workflow end-to-end.
  • When the process volume is very low and the cost of building and maintaining automation outweighs the benefits.
  • When business rules and policies are unclear, constantly changing, or undocumented, making it hard to encode them in workflows.
03

Key Components

  • Event triggers (API calls, webhooks, message queues, scheduled jobs, UI actions)
  • Workflow or orchestration engine (BPMN, low-code workflow, DAG scheduler, agentic orchestrator)
  • AI task nodes (LLM calls, OCR, document classification, entity extraction, prediction models)
  • Business rules and decision logic (rule engines, policy checks, routing logic)
  • Human-in-the-loop steps (approvals, reviews, exception handling, escalation paths)
  • System integration connectors (ERP, CRM, ticketing, RPA bots, databases, SaaS APIs)
  • Data pre-processing and post-processing (validation, normalization, enrichment, formatting)
  • Context and memory management (state store, case file, conversation or workflow context)
  • Monitoring, logging, and tracing (observability for each step and AI call)
  • Governance and compliance controls (access control, PII handling, audit trails, retention)
04

Best Practices

  • Start with a narrowly scoped, high-volume workflow (e.g., one document type or one request type) before scaling to broader processes.
  • Explicitly model the workflow as a graph or BPMN/DAG with clear states, transitions, and timeouts instead of burying logic inside prompts.
  • Use AI for perception and judgment (classification, extraction, drafting) and keep final authority and control flow in deterministic code or workflow rules.
  • Design each AI step as a small, single-responsibility task (e.g., classify, extract, summarize) rather than one giant prompt that tries to do everything.
  • Standardize AI outputs with schemas (JSON, Pydantic models, function calling) and validate them before downstream steps consume them.
05

Common Pitfalls

  • Trying to fully automate complex, high-risk workflows without a human-in-the-loop or phased rollout.
  • Embedding critical business rules inside opaque prompts instead of explicit, testable logic.
  • Letting the LLM control the workflow path (e.g., deciding which API to call) without guardrails or validation of tool outputs.
  • Underestimating integration complexity with legacy systems (ERP, CRM, line-of-business apps) and RPA bots.
  • Not validating AI outputs, leading to malformed data, incorrect routing, or downstream system errors.
06

Learning Resources

07

Example Use Cases

01Invoice processing: ingest emailed invoices, extract key fields with OCR + LLM, validate against purchase orders, route exceptions to finance, and post approved invoices to ERP.
02Customer support triage: classify incoming emails or tickets, summarize the issue, suggest responses, auto-resolve simple cases, and route complex ones to the right support queue.
03Claims processing in insurance: parse claim forms and attachments, detect missing information, flag potential fraud, propose settlement amounts, and orchestrate approvals.
04Employee onboarding: generate personalized onboarding checklists, create accounts in HR and IT systems, schedule training, and track completion status.
05Contract review and approval: extract key clauses and risks from contracts, compare against policy, suggest redlines, and route to legal or business approvers.
08

Solutions Using Workflow Automation

21 FOUND
automotive4 use cases

Automotive Smart Supplier Selection

This AI solution analyzes cost, quality, sustainability, and risk data to help automotive manufacturers identify and select the optimal mix of suppliers. By continuously optimizing procurement and supply chain decisions, it improves resilience, reduces material and logistics costs, and supports sustainability and compliance targets.

automotive4 use cases

Automotive Supply Chain Resilience AI

This AI solution analyzes complex automotive supply networks using graph-based LLMs to detect vulnerabilities, forecast disruptions, and simulate risk scenarios such as pandemics or geopolitical shocks. It recommends optimized sourcing, inventory, and logistics strategies that strengthen resilience, reduce downtime, and protect revenue across the end-to-end automotive supply chain.

fashion3 use cases

Fashion Alliance Strategy Intelligence

This AI suite analyzes digital transformation, blockchain adoption, and AI risk management across the fashion ecosystem to guide strategic industry alliances. It synthesizes market signals, partner capabilities, and regulatory trends to help brands, suppliers, and tech providers form high-value collaborations that accelerate innovation. By quantifying benefits and risks of prospective partnerships, it enables more resilient, sustainable, and future‑proof fashion value chains.

healthcare10 use cases

Healthcare Resource Orchestration AI

This AI solution coordinates beds, staff, operating rooms, transport, and patient flow in real time across hospitals and clinics. By continuously optimizing scheduling, triage, and capacity allocation, it reduces wait times and bottlenecks, cuts operational costs, and improves patient outcomes and staff satisfaction.

hr24 use cases

AI Talent Assessment Orchestration

This AI solution covers AI systems that design, deliver, and interpret candidate assessments across the hiring funnel, turning resumes, tests, simulations, and behavioral signals into standardized, comparable skills profiles. By automating assessment workflows and surfacing decision-ready insights for recruiters and HR leaders, these tools improve quality of hire, reduce time‑to‑fill, and cut manual screening effort while enhancing fairness and consistency in selection decisions.

sports11 use cases

AI Sports Fan Engagement Media

This AI solution uses AI to power interactive sports broadcasts, personalized content discovery, and real-time fan engagement across streaming, social, and in-venue channels. It blends live data, athlete avatars, and automated highlight creation with ad and content optimization to keep fans watching longer and interacting more deeply. The result is higher audience retention, new digital revenue streams, and more effective media monetization for sports leagues and broadcasters.

architecture and interior design13 use cases

AI Spatial Layout Designer

AI Spatial Layout Designer automatically generates and optimizes floor plans and interior layouts from constraints like dimensions, use cases, and style preferences. It converts sketches, photos, and brief requirements into 2D/3D room configurations and visualizations, enabling rapid iteration and side‑by‑side option comparison. This shortens design cycles, improves space utilization, and lets architects and interior designers focus on higher‑value creative and client-facing work.

hr11 use cases

AI Workforce Planning & Allocation

This AI solution covers AI systems that forecast staffing needs, match people to roles, and automate scheduling across HR functions. By continuously optimizing workforce allocation, these tools reduce labor costs, minimize understaffing and overtime, and free HR teams from manual planning so they can focus on strategic talent initiatives.

mining11 use cases

AI-Powered Mining Loading Automation

Suite of AI systems that automate and optimize loading operations across open-pit and underground mines, from shovels and loaders to autonomous haul trucks and cargo drones. These tools use real-time data to improve loading accuracy, reduce cycle times, and cut fuel and energy use while enhancing safety in high‑risk zones. The result is higher throughput, lower operating costs, and more predictable, resilient mining operations.

automotive4 use cases

AI Automotive Process Optimization

This AI solution uses AI and machine learning to continuously monitor automotive production lines, detect bottlenecks, and recommend optimal process adjustments in real time. By improving line balance, reducing scrap and rework, and increasing overall equipment effectiveness (OEE), it boosts throughput and lowers manufacturing costs while maintaining consistent quality.

ecommerce10 use cases

Ecommerce Understock Prevention AI

Ecommerce Understock Prevention AI predicts future product demand and continuously monitors inventory levels across channels to prevent stockouts without overstocking. It dynamically adjusts purchasing, replenishment, and allocation decisions for every SKU and warehouse. This reduces lost sales, rush shipping costs, and working capital tied up in excess stock while keeping high-demand items consistently available.

automotive9 use cases

Automotive Predictive Scheduling

This AI solution uses AI to predict equipment failures, optimize production schedules, and dynamically adjust factory operations across automotive manufacturing. By combining predictive maintenance with multi-objective optimization, it minimizes downtime, stabilizes throughput, and improves energy and resource utilization, resulting in higher plant productivity and lower operating costs.

construction10 use cases

AI-Powered Construction Site Assessment

This AI solution uses AI, computer vision, and generative design to analyze construction sites, assess environmental and safety conditions, and optimize civil and structural designs. By automating site analysis, project planning, and sustainability evaluations, it reduces rework, accelerates project delivery, and improves compliance with environmental and safety standards.

mining7 use cases

Autonomous Mining Haulage

Autonomous Mining Haulage refers to the use of self-driving trucks, loaders, drills, and aerial vehicles to move ore, waste, and supplies across mine sites with minimal human intervention. These systems use onboard perception, mapping, and planning to navigate complex open-pit and underground environments, coordinate routes, and operate continuously across shifts. The focus is on automating repetitive, heavy mobile equipment tasks such as hauling, loading, and short-range logistics that are traditionally labor-intensive and exposed to high safety risks. This application matters because haulage and material movement are among the largest cost and bottleneck drivers in mining operations, and they are also a major source of accidents and downtime. By automating haul trucks, underground loaders, and cargo drones, mining companies can reduce dependence on scarce skilled operators, improve safety by removing people from hazardous zones, and achieve more consistent, predictable production. The result is lower cost per ton, higher equipment utilization, and more stable throughput from pit or stope to processing plant.

finance10 use cases

AI Transaction Compliance Monitoring

This AI solution uses AI to automatically monitor financial transactions, detect suspicious patterns, and streamline AML/KYC reviews across banks, wealth managers, and other financial institutions. It replaces manual investigations with intelligent agents and APIs that continuously flag, prioritize, and explain risk events, improving regulatory compliance while cutting review times and false positives. The result is stronger AML controls, lower compliance costs, and reduced risk of regulatory penalties and financial crime exposure.

architecture and interior design6 use cases

AI Preliminary Floor Plan Design

AI Preliminary Floor Plan Design tools automatically generate, analyze, and refine early-stage layouts for residential and commercial spaces based on requirements, constraints, and design preferences. They help architects and interior designers explore multiple options in minutes, improve space utilization, and accelerate client approvals, reducing both design cycle time and rework costs.

insurance17 use cases

AI Claims Liability Engine

AI Claims Liability Engine automates assessment of insurance claims by analyzing documents, images, and historical data to estimate fault, coverage applicability, and likely payout ranges. It streamlines claims handling, reduces leakage and fraud risk, and enables more consistent, data-driven liability decisions that accelerate settlement and improve loss ratios.

architecture and interior design13 use cases

AI Spatial Design & Planning

AI Spatial Design & Planning tools automatically generate, evaluate, and visualize floor plans and interior layouts in 2D and 3D from high-level requirements, sketches, or existing spaces. They combine layout optimization, style generation, and spatial data platforms to accelerate design iterations, reduce manual drafting time, and improve space utilization. This enables architects and interior designers to deliver better concepts faster, win more projects, and lower design production costs.

mining3 use cases

Autonomous Mining Operations

Autonomous Mining Operations refers to the use of intelligent, automated and remotely operated equipment to perform core mining activities such as drilling, hauling, loading, and fleet coordination with minimal human presence on site. These systems leverage data from sensors, control systems, and mine-planning tools to execute tasks, adapt to changing conditions, and coordinate equipment in real time across the mine lifecycle. This application matters because it directly addresses several structural challenges in mining: hazardous working environments, high labor dependency in remote locations, variable productivity, and high fuel and maintenance costs. By shifting from manual to autonomous and semi-autonomous operations, miners can increase ore recovery, improve equipment utilization and uptime, reduce safety incidents, and stabilize production. AI techniques are used to perceive the environment, optimize routes and dispatching, adjust operating parameters, and continuously improve performance of fleets and processes over time.

insurance15 use cases

AI Insurance Claims Orchestration

This AI solution uses AI to triage, validate, and process insurance claims end-to-end across property, casualty, and medical lines. By automating document intake, fraud checks, coverage validation, and payment decisions, it accelerates claim resolution, reduces manual effort, and improves payout accuracy and customer experience.

advertising4 use cases

AI Ad Creative Design

This AI solution uses AI to generate, adapt, and animate advertising creatives across formats, channels, and audiences. It accelerates creative production, enables large-scale testing of variations, and improves campaign performance by continuously learning which designs drive higher engagement and conversions.