9 AI Agents Examples That Solve Real Enterprise Challenges

When ChatGPT hit headlines, many equated artificial intelligence with simple chatbots. Useful? Sure. But limited to isolated tasks and virtual assistants, they fell short of their full potential. 

That’s changing. 

Businesses are now entrusting AI agents with real decision-making power on complex tasks. These agents reason, adapt, and act autonomously—without waiting for human intervention. When they’re deployed directly into processes, they provide real value at enterprise scale. 

But where do they make the most impact? This post explores nine AI agent examples that can extend your team and transform operations.

Intelligent agents bolster productivity by handling a wide range of tasks that once required constant supervision. Advanced agents can decide and execute on complex decisions that older automation technologies couldn’t handle. 

Here are some high-level examples of what AI agents can do for you.

AI agents can analyze context and act accordingly. For example, they can:

• Interpret documents and policies to ensure compliance or enforce internal rules

• Classify documents, emails, and cases and route them correctly

• Recommend potential actions based on real-time process conditions

What used to take hours of manual effort now happens automatically—with human users stepping in only when needed.

For example, AI agents can:

• Execute subprocesses in workflows (e.g., managing revenue disputes with adaptive logic)

• Handle exceptions without relying on pre-defined rules

There are many types of agents in use today, from those handling simple requests to those tackling strategic decision-making.

Customer service teams are often drowning in a mass of tickets and queries. 

Intelligent agents lighten their load with: 

• Issue triage: Understanding request context and routing emails. 

• Email generation: Drafting email responses that employees review prior to sending. 

• Automating repetitive tasks: Resetting passwords, checking order statuses, or processing returns. 

These AI agents take repetitive tasks off the table so human agents can focus on customer care and personalized experiences, which improves response times and customer retention. 

Procurement issues aren’t one-off events—they cascade. Delayed shipments. Vendor failures. Price hikes. These add up, and minor hiccups can snowball into costly disruptions for all sorts of organizations, from manufacturers to government agencies. 

AI agents optimize procurement with:

• Vendor selection: Analyze delivery history, price trends, and fulfillment rates to flag top performers.
• Purchase automation: Trigger POs on a set schedule or when inventory dips below thresholds—escalating only exceptions for human approval.

The insurance industry is driven by risk and data. AI agents help insurers make faster, smarter decisions. Some use cases for underwriting include: 

• Risk assessments: Generate valuable insights like risk scores and pricing tiers by analyzing claims history, external data (such as geographic hazards), and market trends. 
• Document analysis: Extract key fields from applications, medical records, and third‑party reports in seconds.
• Recommendations: Propose the best coverage options based on underwriting rules and individual risk profiles.

AI agents give financial institutions the power to move fast without breaking things—they let organizations act quickly without risk. For example:

• Customer onboarding: Verify identities, cross-check watchlists, and flag anomalies. This simplifies know-your-customer (KYC) compliance without spiking liability. 

• Payments investigations: Fraud detection agents can identify suspicious behavior over time, draft reports, and escalate risks for rapid human review.

• Loan processing: Agents can scan income documents, pre-qualify applicants, or propose decisions.

Regulatory compliance can be relentless: Audits. Investigations. Policy enforcement. It’s hard to keep up. 

AI agents can deliver nonstop compliance oversight, flagging potential issues as they happen with:

• Policy enforcement: Monitor workflows and communications continuously, flagging data leaks, unauthorized transactions, and other violations before they escalate.

• Audit readiness: Log every action, approval, and data trail—surface missing pieces immediately and escalate when needed.

Autonomous agents eliminate blind spots. By catching risks early and automating routine oversight, AI agents let your team focus on growth—not cleanup and crisis control.

Human resources teams spend too much time buried in routine paperwork and answering common questions. AI agents step in to handle these tasks, freeing HR to focus on employee engagement and culture.

• Employee onboarding: Review new‑hire forms, assign training modules, dispatch handbooks, and guide benefit enrollments—automatically.

• Benefits guidance: Deliver personalized benefits information and recommend optimal plans based on individual profiles.

These AI agents use cases free HR from admin overload and elevate the employee experience.

Government agencies often struggle with paperwork and backlogs. AI agents take over the grunt work so your team can focus on serving the public.

• Citizen benefits processing: Extract applicant data, verify eligibility, and route cases for review.

• Records management: Classify, tag, and archive permit applications and citizen comments automatically. This speeds responses and preserves transparency.

AI agents help agencies serve the public faster and with greater consistency, without overburdening staff.

Clinical trials are slow, expensive, and often derailed by recruitment delays and inefficiencies. AI agents can help with:

• Smarter site selection: Analyze past performance, patient data, and geography to recommend high-yield sites—boosting enrollment and data quality.

• Faster patient matching: Scan medical records and trial criteria to flag eligible participants and support personalized treatment plans.

• Real-time compliance: Monitor trial activity, flag protocol deviations, and surface missing data—keeping teams audit-ready and on track.

By accelerating key steps, AI agents help get new treatments to market faster.

Legal work can be tedious. The stakes are high. And the margin for error? Almost none. AI agents, which can be powered by domain-specific language models, help legal teams move faster by: 

• Reviewing contracts: Extracting data, flagging risks, and summarizing findings
• Streamlining contract review: Ensuring no bottlenecks occur during contract review

AI agents once promised a revolution, but early pilots rarely delivered at scale. Today, when embedded in processes, these agents pair safety and governance with high-value output. They scale across organizations and provide measurable impact over time. 

For example, Century Fire Protection used Appian’s AI-based intelligent document processing (IDP) to reduce their invoice operating time by 36%. Embedding AI‑based IDP into your own workflows can unlock similar efficiency gains across your organization.

Goal-based agents can work for nearly any objective. AI agents can slash costs, boost customer satisfaction, and accelerate compliance. And when deployed into the right processes, agents don’t just support your digital transformation strategy—they scale it.

Before scaling AI agents, assess your data readiness and AI maturity. Clean, accessible, well-governed data is the backbone of any successful deployment. Start in one domain—ideally where a custom agent can solve a defined process and deliver measurable impact. Early wins build momentum and generate user feedback to refine your approach.

As you expand, safety and oversight become critical. Use governance frameworks to ensure transparency and control across both internal processes and external systems. Run goal-based agents inside an orchestration layer so every action is auditable, tied to user behavior, and aligned to your business goals.