37 live
Physical World AI
AI systems operating against cameras, sensors, and field capture where uptime, evidence handling, and safety workflows matter.
Who this atlas is for
For teams evaluating which AI workflows are turning into products
Built for AI product teams, founders, infrastructure buyers, investors, and enterprise evaluators who want to understand which AI use cases are moving into production — and what backend systems they require.
- AI product teams
- Founders
- Infrastructure buyers
- Investors
- Enterprise evaluators
Use the atlas to compare where buyer demand, production complexity, and backend control requirements are beginning to converge.
200 production AI workflows mapped to the backend systems they require.
Every useful AI workflow eventually becomes a backend workflow.
Workflow mapping
Bring a real AI workflow into view before you buy or build around it.
Map the operational path, review points, and infrastructure edges that will decide whether an AI feature actually runs in production.
Category map
Where AI is landing first in production
The atlas groups AI use cases by the operating environment they inherit: physical capture, enterprise workflow, operational coordination, regulated review, or frontier product creation.
- 45 live
Enterprise AI
AI systems embedded in customer or employee workflows that need identity, tool access, and reliable operating controls.
- 41 live
Operational AI
AI systems that coordinate work across finance, security, supply chain, DevOps, procurement, and internal operations where durable workflow state, approvals, escalation, and audit history matter.
- 63 live
Regulated AI
AI workflows where auditability, reviewability, retention, policy controls, and legal accountability are central to rollout.
- 14 live
Frontier AI
AI-native software creation and deployment categories where autonomy increases the need for runtime, environment, and rollback controls.
Use case grid
A living map of AI systems and their backend requirements
Each entry tracks the buyer, capability layer, production constraints, company examples, and why the backend model matters once the workflow moves beyond a demo.
Start with a workflow category, then compare the buyers, lifecycle, companies, risks, and backend requirements behind each use case.
Showing Enterprise AI
- Enterprise AIEstablished
AI Customer Support Agents
Customer-facing AI agents that answer questions, resolve issues, take actions across systems, and escalate to humans when confidence or policy requires it.
Buyers
4 buyer profiles
Lifecycle
7 production steps
- Retrieval over product documentation, policies, and prior interactions
- Tool use across CRM, ticketing, billing, identity, and order systems
- Multilingual conversation handling with tone and policy controls
Open atlas entry - Enterprise AIScaling
AI Field Service Operations
AI systems that help schedule work, guide technicians, surface service knowledge, and improve first-time fix rates across distributed service organizations.
Buyers
4 buyer profiles
Lifecycle
7 production steps
- Technician guidance and service knowledge retrieval
- Scheduling, prioritization, and resource optimization support
- Issue summarization across service history and asset context
Open atlas entry - Enterprise AIEstablished
AI Workplace Knowledge Agents
AI assistants and agents that help employees search, synthesize, and act across internal knowledge, workflows, and enterprise systems without losing permissions context.
Buyers
4 buyer profiles
Lifecycle
7 production steps
- Permissions-aware search across internal tools and knowledge bases
- Synthesis over documents, messages, tickets, and organizational context
- Task execution across enterprise apps and internal workflows
Open atlas entry - Enterprise AIEstablished
AI Sales Development and Revenue Agents
AI systems that identify prospects, enrich accounts, generate personalized outreach, classify replies, coordinate follow-up, and book meetings while staying inside sales policy and CRM governance.
Buyers
5 buyer profiles
Lifecycle
7 production steps
- Lead sourcing and enrichment
- ICP matching
- Personalized outbound generation
Open atlas entry - Enterprise AIEstablished
AI Data Analyst and BI Agents
AI systems that answer business questions, generate analyses, explain metrics, query governed data, and turn insights into operational follow-up across teams.
Buyers
6 buyer profiles
Lifecycle
7 production steps
- Natural-language querying
- Metric explanation
- Dashboard and report generation
Open atlas entry - Enterprise AIEmerging
AI Customer Success Renewal and Expansion Agents
AI systems that help customer success teams monitor account health, detect renewal risk, prepare QBRs, recommend expansion plays, and coordinate customer follow-up across CRM, usage, support, and billing systems.
Buyers
5 buyer profiles
Lifecycle
7 production steps
- Account health analysis
- Renewal risk detection
- Expansion opportunity identification
Open atlas entry - Enterprise AIScaling
AI Revenue Forecasting and Pipeline Inspection
AI systems that help revenue teams inspect pipeline quality, forecast bookings, detect deal risk, and coordinate next-best actions across CRM, calls, emails, usage, and billing data.
Buyers
5 buyer profiles
Lifecycle
7 production steps
- Forecast explanation
- Pipeline risk detection
- Deal inspection
Backend needs
- Identity
- Event routing
- Approval workflow
Open atlas entry - Enterprise AIEmerging
AI Product Feedback and Roadmap Intelligence
AI systems that synthesize customer feedback, support tickets, sales notes, usage data, and market signals into product themes, roadmap inputs, and prioritized opportunities.
Buyers
5 buyer profiles
Lifecycle
7 production steps
- Feedback clustering
- Feature request extraction
- Sentiment and urgency scoring
Backend needs
- Tenant boundaries
- Evidence storage
- Review workflow
Open atlas entry - Enterprise AIEstablished
AI Enterprise Search and Knowledge Governance
AI systems that help employees search, synthesize, and act on company knowledge while respecting permissions, freshness, source authority, and governance policies.
Buyers
5 buyer profiles
Lifecycle
7 production steps
- Permission-aware search
- Source-grounded answers
- Knowledge freshness detection
Backend needs
- Identity
- Scoped access
- Tenant boundaries
Open atlas entry - Enterprise AIEmerging
AI Account-Based Marketing Campaign Agents
AI systems that help marketing and sales teams identify target accounts, generate account-specific messaging, coordinate campaigns, and track engagement across channels.
Buyers
5 buyer profiles
Lifecycle
7 production steps
- Account research
- Persona-specific messaging
- Campaign orchestration
Backend needs
- Identity
- Review workflow
- Event routing
Open atlas entry - Enterprise AIEmerging
AI Partner Operations and Channel Enablement
AI systems that help companies onboard partners, answer partner questions, route deal registrations, generate enablement materials, and coordinate channel workflows.
Buyers
5 buyer profiles
Lifecycle
7 production steps
- Partner onboarding
- Deal registration triage
- Partner Q&A
Backend needs
- Identity
- Tenant boundaries
- Approval workflow
Open atlas entry - Enterprise AIScaling
AI Customer Onboarding Agents
AI systems that guide new customers through setup, implementation, training, data import, configuration, and first-value workflows.
Buyers
5 buyer profiles
Lifecycle
7 production steps
- Guided setup
- Account configuration support
- Training personalization
Backend needs
- Customer identity
- Scoped access
- Workflow state
Open atlas entry
ScaleMule production layer
The backend control layer that keeps recurring across AI products
Across AI use cases, teams keep rebuilding the same operational backend: access control, event routing, evidence handling, review workflows, and auditability.
Scoped access and identities
AI products need reviewer roles, service identities, environment boundaries, and customer-scoped permissions before they can act safely.
Event-driven workflow control
Agents, reviewers, files, webhooks, and downstream systems need a durable operational path instead of ad hoc background glue.
Auditability and review history
High-stakes AI systems need traceable decisions, reviewer overrides, policy changes, and incident reconstruction.
Tenant-aware storage and data boundaries
Customer records, evidence, transcripts, and generated assets need clear separation across teams, tenants, programs, and environments.
Usage, billing, and operational telemetry
As AI products commercialize, teams need metering, rate controls, service visibility, and clearer cost attribution.
Integration-safe backend model
Production AI products depend on APIs, files, events, and operational review surfaces that stay coherent as the product grows.
Connect the market map to the platform layer
Explore the public architecture model and the hosted Cloud path behind ScaleMule’s positioning as backend infrastructure for AI and API products.