AI Agent Use Cases: 10 Real-World Examples for Business

AI agents are showing up in places where traditional automation has struggled: workflows that involve several systems, inputs that vary with the user and decisions that depend heavily on context. Instead of following a fixed sequence of rules, an agent can gather information, choose among available tools and determine what to do next as the task unfolds.

These capabilities make the range of potential AI agent use cases unusually broad, extending well beyond any single business function. An agent might investigate a customer support issue, analyze a revenue forecast, identify an equipment problem or help an employee find information buried across enterprise systems. In each case, the value comes from connecting reasoning with the data and tools required to complete the work.

The 10 examples below show how organizations are applying AI agents across business areas — and what separates a promising agent use case from a workflow that is better handled another way.

Traditional automation works best when the steps are known in advance: if this happens, do that. But many business processes are less predictable. The information needed might change from one request to the next, the relevant data may live in several systems and the appropriate next step might depend on what the workflow uncovers along the way.

Agentic artificial intelligence gives organizations a more effective way to handle this kind of work. An agent can interpret a request, retrieve relevant information, select among available tools and adjust its next step based on the results. Depending on the task, those tools might query a database, search documents, run code or interact with another application.

See how Snowflake CoWork and Cortex Agents turn governed enterprise data into agentic workflows that can answer questions, reason across information and take action:

Organizations use several types of AI agents, often combining multiple capabilities within the same workflow.

• Conversational agents: Interact with users through natural language to answer questions, retrieve information or guide people through processes.
• Workflow automation agents: Coordinate actions across applications, such as updating records, submitting requests or routing tasks.
• Analytics agents: Analyze enterprise data to generate insights, forecasts or recommendations that support business decisions.
• Voice agents: Provide conversational interfaces through spoken interactions.
• Data agents: Retrieve and analyze information from complex data environments, translating natural-language questions into queries or analytical workflows.
• Coding agents: Assist with software and data engineering tasks such as writing code, generating tests, troubleshooting errors, building pipelines or modifying existing applications.

In practice, these categories frequently overlap. A conversational agent used by a salesperson, for example, might also operate as a data agent when querying customer metrics and a workflow agent when updating a CRM system.

Organizations continuously monitor controls, investigate exceptions, collect evidence for audits, interpret policies and document how regulatory requirements are being met. Much of that work requires information from multiple systems, along with enough context to determine which records or requirements are relevant.

AI agents can help coordinate those workflows. An agent might gather supporting evidence for an audit, retrieve the policies associated with a control, identify missing documentation or assemble information required for a regulatory filing. Agents can also assist compliance teams as they investigate potential issues, bringing together transactions, communications and other records that would otherwise need to be reviewed across separate systems. Regulatory judgment and the responsibility of final approval remain with qualified professionals.

Compliance reporting is one example. Instead of manually collecting information from operational databases, financial systems and audit logs, teams could use an agent to gather the relevant records and organize them for review against reporting requirements.

Real-world example: FIS

FIS supports financial institutions with a Compliance Suite covering areas including anti-money laundering, trade and communications surveillance and regulatory reporting. After bringing multiple compliance solutions together on a platform built with Snowflake, FIS improved overall execution speeds 2.5x while reducing licensing, maintenance and overhead costs by 33%.

Read the full customer story >

Customer service involves a succession of decisions: What is the customer asking? What has already happened on the account? Which policy or troubleshooting guidance applies? Can the issue be resolved immediately, or does it need specialist attention?

Agents can assist with many of those questions. They might classify and prioritize incoming cases, retrieve account history, search product documentation, investigate technical issues or draft responses using the information gathered. When escalation is necessary, the agent can prepare the context for a human representative rather than requiring that person to reconstruct the case from the beginning.

Service agents might also support voice interactions, guide customers through multistep processes or coordinate actions across support and account systems.

Consider a recurring product issue. An agent can examine the customer’s description, search relevant documentation, review previous cases and account information, and use that context to determine the appropriate troubleshooting path. If the problem remains unresolved, the resulting case gets routed to a human with much of the initial investigation already completed.

Real-world example: Thomson Reuters

Thomson Reuters built an AI data analyst agent for its customer support organization. The agent analyzes support cases across structured and unstructured information, replacing a process in which employees manually read case descriptions to identify customer pain points and helping the team surface insights faster (as of Aug. 2025).

Read the full customer story >

A significant portion of sales work happens around the conversation with the customer. Representatives research accounts, review previous interactions, look for changes in buying signals, prepare for meetings and update systems afterward. Marketing and revenue teams perform similar work when identifying audiences, analyzing engagement or deciding how to follow up with prospects.

AI agents can pull more of that activity into a connected workflow. Before a meeting, for example, an agent might assemble account history, recent product usage, support activity and relevant market information into a briefing. During prospecting, it could research contacts and identify information that helps a representative personalize outreach. After an interaction, the agent might summarize what happened and prepare recommended follow-up actions for review.

Other examples extend across the go-to-market organization, including lead and account prioritization, campaign analysis, proposal support, competitive research and customer expansion.

Account research is a particularly good fit because the information sellers need often exists already but is scattered across CRM records, usage data, call transcripts and other sources. An agent can gather that context much faster while leaving the seller responsible for the relationship and final communication.

Real-world example: Intercom

Intercom uses an AI agent-driven workflow to research sales contacts before representatives review the results. Research that previously took roughly 40 minutes now takes about 30 seconds, while customer insight decks that once required three to four hours are produced in less than 10 minutes. Across these workflows, Intercom reports saving more than 2,000 hours of sales-team time each month.

Read the full customer story >

Forecasting and predictive analytics are used across nearly every business function. Finance teams forecast revenue and cash flow, sales organizations model pipeline performance, operations teams anticipate demand and inventory requirements and customer teams look for signals such as churn or changing engagement.

Agents can help coordinate the work surrounding those predictions. An agent can retrieve the appropriate historical data, invoke an analytical or predictive model, compare results with previous periods and explain which factors contributed to a change. Users can then continue the investigation with follow-up questions instead of treating each forecast as a separate analytical exercise.

Other predictive use cases include identifying customers at risk of leaving, estimating product demand, anticipating capacity requirements or assessing operational and financial risk. The underlying predictive models remain central — the agent provides an interface and workflow around them, connecting the prediction with the data and analysis needed to understand it.

Revenue forecasting is one example. An agent can retrieve current bookings, pipeline, renewals and historical performance data, prepare the inputs required by the forecasting model and invoke the appropriate analysis. Once the forecast is generated, it can compare the results with previous projections, surface the accounts or assumptions driving the largest changes, and gather supporting data for further investigation. Sales and finance teams get a more current view of expected revenue, while analysts remain responsible for validating assumptions, interpreting unusual results and determining whether the forecast warrants action.

Real-world example: WHOOP

WHOOP developed an AI/ML financial forecasting model that made its forecasting process three times faster. Centralized access to the underlying data also reduced the complexity involved in preparing information for modeling and analysis (as of Nov. 2024).

Read the full customer story >

Manufacturing environments generate continuous streams of information about equipment, production, quality and supply, creating opportunities for agents across operations — from helping engineers investigate production problems to monitoring equipment performance and coordinating maintenance.

For example, an agent can examine production metrics when throughput declines, correlate the change with sensor readings or process conditions, and retrieve documentation relevant to the affected equipment. Quality teams can use agents to investigate patterns in defects, while operators might ask natural-language questions about current performance across a line or facility.

Predictive maintenance is one of the clearest applications. Equipment telemetry often provides signals before a failure occurs, but identifying those signals and determining what action they warrant requires analysis. An agent can monitor operating data, surface an anomaly and gather the surrounding information technicians need to investigate it. With appropriate controls, the workflow might continue into scheduling an inspection or creating a maintenance request.

Real-world example: O3sigma 

O3sigma analyzes hundreds of parameters across industrial equipment to provide predictive and prescriptive maintenance guidance. Its system identifies potential root causes and recommends changes before issues affect production. In one deployment, a suggested motor adjustment increased printing speed by 15%; another manufacturer improved overall equipment effectiveness by 30% across four production lines.

Read the full customer story >

Data products give people access to curated data, metrics and analytical capabilities without requiring them to understand every system underneath. Agents create another interface for those products: instead of navigating a fixed set of dashboards or reports, users can ask questions and explore the data conversationally.

The applications range from internal analytics products used by employees to customer-facing experiences embedded directly into commercial products. A customer might ask about activity in their account, while an operations leader explores performance metrics or a product manager investigates how users interact with a feature.

An agent can interpret the question, identify the relevant data and metrics, and return an answer using the definitions built into the data product. Follow-up questions allow the investigation to continue without requiring users to formulate queries or locate another dashboard.

For organizations offering data as part of their own product or service, this also opens up new experiences. Rather than exposing customers only to predefined reports, an agent can provide personalized access to the portion of the data each customer is permitted to see.

Real-world example: Fireblocks

Fireblocks developed Fire Genie, a customer-facing AI agent that gives users natural-language access to information about their own digital assets and wallets. The company also built an internal agent for employee analytics. That internal agent now handles more than 2,000 analytical queries per month — roughly 40–50% of all queries — and saves capacity equivalent to two full-time analysts.

Read the full customer story >

Data engineering includes a large amount of work that’s systematic but rarely identical from one project to the next. Engineers build and maintain pipelines, map schemas, write transformation logic, troubleshoot failures, test data and document how information moves between systems.

Agents can assist across that lifecycle. An engineer might describe the desired pipeline in natural language and have an agent generate a starting implementation, or use an agent to investigate why an existing job failed. During migration or integration projects, agents could compare source and target schemas, identify likely mappings and help generate transformations for engineers to validate.

Mergers and acquisitions provide an especially demanding version of the problem. A newly acquired business introduces unfamiliar applications, data models and pipelines that teams must understand before the information can be integrated. An agent could inspect those environments and help identify corresponding fields, dependencies and transformation requirements.

The goal isn’t to remove engineering judgment. Agents are particularly useful for accelerating repetitive implementation and investigation so engineers can spend more time on architecture, data quality and the decisions that require deeper knowledge of the environment.

Real-world example: Matillion

Matillion’s Maia uses a team of agentic data engineers to design, build, optimize and maintain data pipelines. Users describe what they need in natural language, and Maia handles work across ingestion, transformation, orchestration, testing and deployment. Matillion says the approach can save weeks of engineering effort on data projects (as of Sept. 2025).

Read the full customer story >

Organizations often have far more data than people who know how to work with it. Business users may understand the question they need answered but lack knowledge of the underlying schemas, query languages or systems required to find the answer.

Analytics agents can assist non-technical users by turning natural-language questions into analytical workflows. Someone might ask why sales declined in a particular market, which products are gaining adoption or how a customer segment has changed over time. The agent determines which data is relevant, performs the required analysis and returns an answer that the user can investigate further.

The use case extends beyond simple question answering. Agents can help people discover available data, compare metrics across periods, break results down by additional dimensions and investigate unexpected findings through a sequence of follow-up questions.

Reliable semantic context is especially important here. Business concepts such as revenue, active customer or conversion rate often have specific organizational definitions. An analytics agent needs access to those definitions so that a natural-language question maps to the intended metrics and relationships rather than merely producing a plausible query.

Real-world example: Snyk

Snyk gives employees natural-language access to information used across customer support, sales, development and product workflows. Its AI applications answer approximately 2,500 questions each month and save an estimated 1,250 hours that employees would otherwise spend searching for information across different sources (as of Aug. 2025).

Read the full customer story >

Finance teams oversee workflows that combine structured financial records, documents and approvals across several systems. Accounts payable and receivable, reconciliation, transaction investigation, expense review, financial close and reporting all include steps that require information to be gathered and checked before someone decides what happens next.

Agents can take on portions of that coordination. During reconciliation, for example, an agent might compare records from different sources, identify mismatches and gather supporting information for the exceptions that require investigation. In accounts payable, it could extract information from invoices, compare it with purchase and payment records, and route unusual cases for review.

Agents can also support financial analysis around these processes. A finance team investigating an unexpected variance can use an agent to retrieve the relevant transactions, examine changes by business unit or category, and continue drilling into the result through follow-up questions.

Because financial actions carry real consequences, approval boundaries are especially important. An agent might prepare or recommend an action while authorization for payments, adjustments or other sensitive transactions remains with the appropriate person.

Real-world example: Emirates Insurance

Emirates Insurance has automated reconciliation and other document-heavy financial workflows. Its reconciliation automation saved 380 hours of work in three months, while AI-assisted document processing has also accelerated activities including claims registration and identity verification.

Read the full customer story >

Employees encounter the same friction internally that customers do externally: finding the right policy, resolving an access problem, troubleshooting software or figuring out which process to follow. IT, HR and operations teams spend substantial time answering those recurring requests.

Internal service agents give employees a conversational starting point. An agent might answer a policy question, retrieve technical documentation, gather details about an IT issue or guide someone through an onboarding process. When the request requires action, the workflow can extend into ticketing, identity or other enterprise systems, subject to the organization’s permissions and approval rules.

For service teams, agents can also triage incoming requests and gather information before handing a case to a specialist. That reduces the repetitive back-and-forth involved in establishing basic context and gives the person handling an escalation a clearer starting point.

Help-desk automation is one example, but the applications are much broader than IT. Similar agents can support HR inquiries, procurement questions, employee onboarding, benefits navigation or other internal services where information is distributed across policies, documentation and operational systems.

Real-world example: Under Armour

Under Armour’s first agentic workflow is an AI-powered internal help desk. The company reports that the workflow is already delivering operational financial benefits while improving self-service for employees, with plans to extend agents into additional business functions.

Read the full customer story >

The strongest use cases require enough interpretation or coordination that traditional rules-based automation would be cumbersome, but they still have a defined outcome.

Good candidates often share several characteristics:

• The workflow involves multiple steps or systems: Completing the task requires retrieving information, applying business logic and taking one or more subsequent actions.
• Inputs vary: Requests don’t always arrive in a predictable format that can be handled with a fixed sequence of rules.
• The agent has access to useful context and tools: Enterprise data, documentation, semantic definitions and APIs give the agent the information and capabilities needed to complete the task.
• The outcome can be evaluated: Teams can define what a correct result looks like and where human review should remain part of the process.
• The workflow has meaningful business value: Automating or accelerating the process saves time, improves access to information, reduces operational friction or helps teams respond more quickly.

Starting with a bounded workflow also gives teams a clearer environment for testing agent behavior before expanding into additional systems and responsibilities.

Choosing the use case is only the beginning. Once an agent moves into an operational workflow, teams also need to define what information it should use, which tools it’s allowed to invoke and how its behavior will be evaluated.

Several foundational elements become especially important:

• Governed data access: An agent should operate within existing roles, privileges and data policies rather than receiving unrestricted access to enterprise information.
• Business and semantic context: Metrics, entities, relationships and other definitions help agents interpret questions consistently and select the appropriate data.
• Controlled tool access: Agents should receive only the tools required for their assigned workflows, with human approval retained where the consequences of an action warrant it.
• Evaluation and observability: Teams need visibility into how an agent interpreted a goal, which tools it selected and whether its outputs met expected quality thresholds.
• Ongoing iteration: Changes to data, tools, instructions and models can affect agent performance, making evaluation an ongoing part of operating the system.

Snowflake Cortex Agents provides the orchestration layer for building and running agents across structured and unstructured data. Agents can use tools such as Cortex Analyst and semantic views for structured analysis, Cortex Search for unstructured information and custom tools or external systems when a workflow requires action beyond Snowflake.

Snowflake Horizon Catalog provides governance, lineage and access controls across the underlying data, while Horizon Context supplies the business semantics agents need to interpret metrics, entities and relationships consistently. AI Guardrails add runtime protections for agent interactions.

Cortex Agent Evaluations then gives teams a way to measure whether agents are behaving as intended, including answer correctness, logical consistency and tool selection and execution. Organizations can expose those agents through their own applications or give knowledge workers access through Snowflake CoWork.

Together, these capabilities give teams a governed foundation for moving from a bounded AI agent use case to broader production workflows without rebuilding context, controls and evaluation for every new agent.

AI agents can fit into a growing number of business processes, but not every possible use case deserves the same priority. The strongest candidates have a clear outcome, access to the right enterprise context and enough structure to define what the agent should — and should not — do.

Early use-case selection is especially important. A narrowly scoped workflow gives teams a practical way to test how well an agent interprets business context, selects tools and handles exceptions before expanding its responsibilities.

Over time, the bigger opportunity isn’t simply to accumulate more agents, but to build the shared data, semantic context, governance and evaluation practices that allow new use cases to be added without starting from scratch each time. Organizations that establish that foundation can build out agentic workflows that operate across more of the business while remaining grounded in the same definitions, permissions and controls.