Use Cases

AI agents automate complex, multi-step work that requires reasoning and dynamic decision-making. This page explains the business value of AI agents, provides real-world examples, and helps you identify opportunities for AI agents in your own applications.

The strongest AI agent use cases share a common pattern: the AI agent must research, discover, and synthesize information across multiple sources where the path to the answer isn't predetermined. The AI agent adapts its approach based on what it finds — replacing the investigative work that humans do when they gather information, interpret results, and decide what to do next.

For step-by-step guidance on building AI agents, see Create and Configure an AI Agent. For a hands-on walkthrough, see Tutorial: Build a Triage Agent.

Understanding business value

AI agents deliver value by automating complex work that was previously impossible to automate with rules-based systems alone. Understanding the business outcomes helps you identify high-impact opportunities and justify AI agent projects.

Value drivers

AI agents create business value through:

• Unlocking new automation frontiers: Automate processes that require reasoning, interpretation, and dynamic decision-making. These are tasks that were previously too complex for traditional automation.
• Reducing manual effort: Eliminate repetitive cognitive work like reviewing documents, synthesizing data from multiple systems, and making routine decisions.
• Improving consistency: Apply the same reasoning and decision criteria across all cases to reduce variability in outcomes.
• Accelerating processes: Complete multi-step workflows in seconds or minutes instead of hours or days.
• Scaling expertise: Codify decision-making logic and best practices so they can be applied consistently across high volumes of work.

Measuring success

Track these metrics to demonstrate AI agent value:

• Time savings: Hours of manual work eliminated per week or month.
• Process acceleration: Reduction in average case resolution time.
• Volume handled: Number of cases, requests, or transactions processed automatically.
• Accuracy improvement: Reduction in errors or rework compared to manual processes.
• Cost per transaction: Total cost (development and execution) divided by volume processed.
• Exception rate: Percentage of cases requiring human intervention.

Building the business case

When proposing an AI agent project:

1. Identify the manual process: Document current time, effort, and pain points.
2. Quantify the opportunity: Calculate volume, frequency, and cost of manual work.
3. Define success criteria: Set clear targets for automation rate, accuracy, and time savings.
4. Start small: Build a focused agent for one segment of the process, measure results, then expand.
5. Plan for iteration: Budget time for prompt refinement, tool optimization, and monitoring.

These examples show how you can use AI agents to solve real-world business challenges:

• Research and retrieval use case.
• Sales and marketing use case.
• Account management use case.
• Customer service use case.
• Internal workflow automation use case.
• Document management and extraction use case.

Research and retrieval use case

Scenario: Compliance investigation and evidence gathering

Your compliance team investigates potential policy violations across the organization. Each investigation requires gathering evidence from multiple systems — HR records, communication logs, access audit trails, prior case history, and relevant policy documents. Analysts spend hours collecting and cross-referencing this information before they can assess risk and recommend action.

Traditional process models can retrieve data from individual systems, but they cannot adapt their search strategy based on what they find. An analyst investigating a data access violation might discover a pattern that leads them to check additional systems or expand the time window of their search. This adaptive, discovery-driven workflow is where AI agents provide the most value.

Solution with AI agents

Using an AI agent, you can build a Compliance Investigation AI Agent that gathers evidence across multiple systems, cross-references findings, and produces a structured risk summary for human review.

The AI agent receives an investigation request, determines which systems to query based on the violation type, retrieves relevant records, identifies patterns and connections across the data, and produces a comprehensive summary with risk assessment and recommended next steps.

How it works

1. Receives investigation request: The AI agent is triggered when a new investigation case is created. It receives the case details including the violation type, subject, time period, and any initial evidence.
2. Determines search strategy: Based on the violation type, the AI agent decides which systems to query and in what order. A data access violation triggers queries to access logs and permission records. A policy violation triggers queries to HR records and communication logs.
3. Gathers and cross-references evidence: The AI agent queries each relevant system, reviews the results, and decides whether additional searches are needed. If access logs reveal unusual patterns, the AI agent expands its search to related systems or time periods.
4. Synthesizes findings: The AI agent compiles all evidence into a structured summary including a timeline of events, identified risk factors, relevant policy references, and connections to prior cases.
5. Produces risk assessment: The AI agent assigns a risk level based on the evidence and recommends next steps (close with no action, escalate to legal, or request additional information from the subject).
6. Routes for human review: The completed investigation summary routes to a compliance officer for final judgment. The human reviewer has all evidence organized and contextualized, reducing review time from hours to minutes.

Benefits

• Faster investigations: Compress hours of manual research into minutes of automated evidence gathering.
• Comprehensive coverage: The AI agent checks all relevant systems systematically, reducing the risk of missed evidence.
• Adaptive search: The AI agent expands or narrows its investigation based on what it discovers, mimicking how experienced analysts work.
• Consistent documentation: Every investigation produces a structured summary with the same format and level of detail.
• Human judgment preserved: The AI agent gathers and organizes evidence, but a human makes the final determination.

Why this is a strong AI agent use case

This use case demonstrates the core pattern where AI agents outperform traditional automation:

• The path isn't predetermined. The AI agent decides where to look based on what it finds.
• Multiple systems must be queried and cross-referenced.
• The AI agent course-corrects during execution (expanding searches, following leads).
• A human makes the final decision, but the AI agent eliminates the manual research burden.

Sales and marketing use case

Scenario: Lead enrichment and qualification

Your sales team receives hundreds of inbound leads daily from web forms, events, and marketing campaigns. Each lead requires research to determine company size, industry, and fit with your ideal customer profile before routing to the right sales representative. Manual qualification creates bottlenecks, delays follow-up, and results in inconsistent scoring.

Traditional process models can route leads based on simple rules, but they struggle to synthesize data from multiple sources or adapt to incomplete information. An AI agent researches each lead across multiple systems, interprets what it finds, and makes a qualification decision — following the same adaptive pattern that a human sales analyst would use.

Solution with AI agents

Using an AI agent, you can build a Lead Qualification AI Agent that automatically enriches, scores, and routes inbound leads based on your ideal customer profile.

The AI agent receives new lead data, enriches it by querying external data sources and your CRM, applies your qualification criteria, and updates the lead record with a priority score and recommended next action. It can handle incomplete data gracefully and escalate high-priority leads immediately.

How it works

1. Receives new lead data: The AI agent is triggered when a new lead record is created. It retrieves the lead's basic information such as name, company, email, and initial form responses.
2. Enriches lead information: The AI agent queries external data sources to gather company size, industry, revenue, and technology stack. It checks your CRM to determine if the company is an existing customer or has prior engagement history.
3. Applies qualification criteria: Using your ideal customer profile rules (provided as a knowledge tool or embedded in the prompt), the AI agent evaluates the lead against criteria such as company size, industry fit, budget indicators, and engagement signals. It assigns a qualification score and tier (for example, Tier 1 - High Priority, Tier 2 - Standard, Tier 3 - Nurture).
4. Routes and updates: The AI agent updates the lead record in your CRM with the enriched data, qualification score, and tier. For high-priority leads, it immediately assigns the lead to a senior sales representative. For standard leads, it routes to the appropriate territory owner.

Benefits

• Faster response times: High-priority leads are identified and routed within minutes instead of hours or days.
• Consistent qualification: Every lead is evaluated using the same criteria, eliminating subjective scoring variations.
• Improved conversion rates: Sales representatives receive better-qualified leads with complete context, enabling more effective conversations.
• Reduced manual research: Eliminates hours of manual data gathering and CRM lookups per day.

Account management use case

Scenario: Proactive churn risk identification

Your account management team manages hundreds of accounts, but identifying at-risk customers before they churn requires investigating multiple signals across support tickets, product usage, billing history, and engagement metrics. By the time a customer reaches out to cancel, it is often too late to intervene effectively.

Manual monitoring does not scale, and simple threshold alerts miss the nuanced patterns that indicate genuine risk. An AI agent investigates each account by gathering signals from multiple systems, interpreting patterns across the data, and synthesizing a risk assessment — adapting its analysis based on what it discovers about each account's unique situation.

Solution with AI agents

Using an AI agent, you can build a Churn Risk AI Agent that continuously monitors customer health signals and identifies accounts requiring intervention.

The AI agent analyzes support ticket volume and sentiment, product usage trends, billing issues, and engagement patterns. It synthesizes these signals to identify at-risk customers and creates prioritized tasks for your account management team with context about why the account is flagged.

How it works

1. Monitors customer accounts: The AI agent runs on a schedule or is triggered by specific events like support ticket spikes or usage drops. It receives a list of customer accounts to analyze.
2. Gathers health signals: The AI agent queries your support system to retrieve ticket volume, resolution time, and sentiment. It also queries your product analytics platform to check for usage declines, feature adoption, and login frequency.
3. Synthesizes risk indicators: The AI agent evaluates the combined signals to identify concerning patterns. It assigns a risk score and category (for example, High Risk - Usage Decline).
4. Creates intervention tasks: For at-risk accounts, the AI agent updates the customer record with the risk assessment and creates a task for the assigned account manager. The task includes a summary of risk factors and recommended actions.

Benefits

• Early intervention: Identify at-risk customers weeks before they would typically surface through manual monitoring.
• Comprehensive analysis: Synthesize signals from multiple systems that would be time-consuming to correlate manually.
• Prioritized outreach: Account managers receive a prioritized list of accounts with context, enabling focused intervention efforts.
• Reduced churn: Proactive outreach based on data-driven risk assessment improves retention rates.
• Scalable monitoring: Continuously monitor hundreds or thousands of accounts without adding headcount.

Customer service use case

Scenario: Service request acknowledgment and triage

Imagine your public works department receives hundreds of online service requests every day. While most submissions are straightforward, some include complex or unclear descriptions that require human review. This manual triage can delay response times and create operational bottlenecks.

While traditional process models can automate simple acknowledgments, they often struggle to interpret free-text descriptions or classify ambiguous issues. An AI agent combines intelligent text understanding with dynamic response generation to provide instant, meaningful communication.

Solution with AI agents

Using an AI agent, you can build a Service Acknowledgment and Triage AI Agent that automatically reads, classifies, and acknowledges incoming service requests.

The AI agent monitors new requests, interprets their content, and assigns them to the right department. It can then generate a tailored acknowledgment message by referencing a knowledge base of approved response templates, adjusting the tone and routing logic based on the request type and urgency.

How it works

1. Monitors new requests: The AI agent is triggered when a new request record is created. It uses the request description and metadata to retrieve relevant context, such as location or category.
2. Analyzes and classifies: The AI agent uses natural language understanding to interpret the free-text request. It determines the issue category and priority level.
3. Generates an acknowledgment message: Drawing from a knowledge base of response templates, the AI agent composes a personalized acknowledgment. For example, it might reassure a user about an urgent repair or thank them for reporting a non-critical issue.
4. Sends and updates: The AI agent sends the acknowledgment (via email or SMS) and updates the service request record with the classification details and a timestamp for reporting.

Benefits

• Timely communication: Customers receive personalized messages acknowledging their requests, which improves transparency.
• Automated triage: The AI agent automatically interprets and categorizes incoming requests to reduce repetitive review work.
• Consistent messaging: Every acknowledgment follows an approved tone and format, with language dynamically adjusted to suit the context.
• Improved routing: Structured outputs from the AI triage feed directly into departmental queues, improving routing accuracy.

Internal workflow automation use case

Scenario: Budget allocation enforcement

A company's finance department manages multiple departmental budgets. Employees submit purchase or expense requests throughout the month, but finance teams often discover exceeded spending limits after the fact. This manual tracking leads to overspending, delayed approvals, and compliance issues.

Solution with AI agents

You can create a single Budget Control AI agent to automatically enforce budget limits and spending rules. Unlike traditional rule-based systems, the AI agent can interpret complex, natural language spending policies, analyze request descriptions for compliance with nuanced business rules (like "emergency repairs are exempt from standard approval limits"), and make contextual decisions based on historical spending patterns and seasonal budget adjustments. The AI agent validates every incoming purchase or expense request against current budget allocations stored in record types, applying intelligent policy interpretation that would require extensive custom logic in traditional process models.

How it works

1. Monitor new requests: The AI agent watches for new purchase or expense submissions. Each request triggers the AI agent automatically.
2. Validate available budget: The AI agent retrieves the current budget balance for the relevant cost center from record types. It compares the request amount against the available funds.
3. Apply policy rules: If sufficient funds exist and the request complies with spending limits, the AI agent updates the budget balance and marks the request as approved. If the request violates policy, the AI agent rejects it automatically and logs the reason.
4. Tracking and reporting: Every decision is captured for transparency. Finance teams can view summarized reports of spending activity and policy violations in a dashboard.

Benefits

• Enforced financial governance: Prevent overspending and policy violations before they occur.
• Real-time validation: Instantly assess budget availability during request submission.
• No manual review: Automate approvals and rejections with consistent rule enforcement.
• Audit-ready visibility: Maintain complete traceability for compliance and reporting.

Document management and extraction use case

Scenario: Automated loan application review

A financial institution reviews thousands of loan applications every month. Each application includes structured data and unstructured documents, such as pay stubs, bank statements, and tax forms. The current review process is manual and time-consuming, leading to delays and inconsistent decisions.

Solution with DocCenter and AI agents

Use DocCenter and AI agents together to automate document review, data extraction, and decision orchestration.

• DocCenter manages document AI models that classify and extract data from application documents.
• Multiple AI agents coordinate to use those models to perform review, validation, and decision support tasks.

How it works

1. Application intake: Applicants upload loan documents through a portal. The process automatically sends all files to AI DocCenter.
2. Document classification: A classification model in AI DocCenter identifies the document type. A Document Intake AI Agent uses the model to classify the document and route it appropriately.
3. Data extraction: Each classified document is processed by an extraction model to pull structured information, such as salary amount or tax paid.
4. Reconciliation and validation: Loan officers can review extracted data in reconciliation tasks. A Validation AI Agent can use the reconciled data to ensure completeness and accuracy.
5. Automated decisions: A Decision AI Agent evaluates loan eligibility using Appian rules and record data. Approved applications move directly to underwriting.
6. Monitoring and continuous improvement: DocCenter provides accuracy metrics. AI agents track interactions, and human workers use this insight to refine prompts or retrain models.

Benefits

• End-to-end automation: Combine AI DocCenter and multi-agent orchestration for faster loan processing.
• Reduced manual effort: Automate classification, extraction, and validation of key loan data.
• Improved accuracy: Use reconciliation data from AI DocCenter to continuously improve AI performance.
• Scalable and transparent: Easily manage large document volumes while maintaining visibility into every model version.

Identify AI agent opportunities

You can find strong AI agent opportunities by examining your existing applications and processes. Look for areas where humans perform investigative, interpretive, or synthesis work that can't be fully scripted in advance.

Look at human task nodes

Examine the human task nodes in your process models. These tasks exist because someone must investigate, interpret, research, or synthesize information before making a decision. These are exactly the areas where AI agents can help.

Common patterns that indicate an AI agent opportunity:

• A human reviews data from multiple systems before making a routing decision.
• A human reads unstructured text (emails, documents, free-text fields) and decides what action to take.
• A human researches context across several records before approving or rejecting a request.
• A human triages incoming items by interpreting their content and applying judgment.

The AI agent replaces the reasoning loop

The AI agent replaces the dynamic reasoning portion of a workflow — not the entire process. The workflow continues after the AI agent completes its work. For example:

• A process model receives a new case, triggers an AI agent to research and classify it, then routes the classified case through deterministic approval steps.
• A scheduled process identifies accounts to review, triggers an AI agent to analyze each account's health signals, then creates tasks for account managers based on the AI agent's findings.

Evaluate fit before building

Before building an AI agent for an identified opportunity, confirm the use case meets these criteria:

• The path to the solution varies based on what the AI agent discovers during execution.
• The task requires reasoning across multiple data points or systems.
• The work cannot be fully encoded as a fixed sequence of steps.
• The value of automation justifies the AI action consumption.

If the task follows a predictable path or involves a single cognitive operation (classify, extract, summarize), use a process model with AI skills instead. See About AI Agents for detailed guidance on when AI agents are not the right choice.

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