Enterprise AI orchestration and control

Appian AI is best suited to complex enterprise workflows that are document-intensive, high-volume, and reliant on operational context. Appian AI streamlines end-to-end processes requiring data collection, evaluation, and automated decision-making.

A few primary enterprise use cases for Appian AI include:

• Claim intake and resolution
• Regulatory compliance and investigation
• Customer service triage
• Operational exception handling
• Account health monitoring

For a closer look at how Appian AI agents are applied in complex enterprise workflows, take a look at some of our most popular enterprise AI use cases here.

Appian process models serve as the orchestration engine, coordinating handoffs across AI agents, human tasks, business rules, RPA bots, and external enterprise systems. Data fabric grants AI agents secure, governed access to records across CRM, ERP, and legacy databases without moving data into a centralized repository. Organizations apply deterministic automation to repeatable work, cognitive AI skills to targeted tasks, and AI agents when execution paths must dynamically adapt based on real-time data.

Appian applies multi-layered governance across four critical dimensions:

• First-Class Agent Identities & Access Controls: Agents receive distinct identities separate from users and service accounts, operating strictly under provisioned permissions with row- and column-level data security.
• Environment-Wide Guardrails: Real-time input/output guardrails (using keyword, regex, and semantic matching) detect and automatically block or mask risks like prompt injections, PII, SSNs, and policy violations.
• Auditability & Traceability: Immutable audit logs record every agent run, input/output payload, tool invocation, reasoning step, and guardrail trigger.
• Human-in-the-Loop Safeguards: High-risk or low-confidence agent decisions are routed to human operators for validation.

Built on private AI principles, data remains strictly within the organization's Appian Cloud compliance boundary. Data is encrypted in transit and at rest using site-specific encryption keys, processed in-memory without persistent vendor storage, and maintained within designated geographic regions or specialized environments like AWS GovCloud. AI capabilities inherit object-level security, ensuring agents only access authorized records, while the platform maintains strict compliance certifications including SOC 2 Type II, SOC 3, PCI DSS, HIPAA, FedRAMP, and HITRUST.

Appian never uses customer data to train, retrain, or refine underlying Large Language Models (LLMs). LLM providers retain zero data retention rights, ensuring you maintain full ownership and deletion rights over your data.

Yes. Through the Appian Administration Console, administrators can enable, disable, or configure specific model providers (such as Anthropic Claude, AWS Bedrock, or OpenAI). Organizations can leverage Appian-managed models, connect custom cloud accounts, route calls through internal enterprise AI gateways via standard API protocols, or utilize automatic model selection to maintain application stability as underlying LLMs evolve.

Appian supports the Model Context Protocol (MCP) and Agent-to-Agent (A2A) orchestration. This allows external AI assistants to securely trigger Appian workflows, or allows your Appian AI agents to securely invoke external servers (like Snowflake or GitHub) and delegate tasks to sub-agents.

Organizations measure business impact using process intelligence to track cycle time reductions, automation rates, and labor savings against pre-AI baselines. To manage costs, Appian pairs the right automation method to each workflow phase—reserving AI agents for dynamic reasoning while keeping predictable tasks in low-cost business rules. Cost governance features include per-execution action metering, pre-deployment cost estimations, intelligent prompt caching to reduce token fees, and cost-center chargeback tagging.