"Your business isn't generic, so your AI agents shouldn't be either."
With the buzz around AI agent use cases growing louder every day, many organizations find themselves overwhelmed by possibilities but unsure where to begin. Which AI agent use cases will deliver real value? How do you avoid investing in impressive-sounding agents that solve no actual business problems?
This guide cuts through the noise with five real AI agent use cases from different industries. Dataiku makes these powerful use cases possible by connecting your proprietary data sources with advanced AI capabilities. With The Universal AI Platform™, business and technical teams can collaborate to build agents that tap into trusted data, ML models, and existing systems — all in a single, governed platform.
These aren't theoretical concepts, they're real implementations helping organizations work smarter today. The below use cases aim to spark your imagination as you consider your own AI agent use cases.
The "Should I Build an Agent?" Quick Test
Before diving into AI agent use cases, here's a simple test to determine if you need an agent:
If your instructions look like: "Do A, then B, then C, if C equals 50, do D, otherwise do E" — you probably do not need an AI agent. You can achieve this with normal automation in Dataiku.
But if your instructions sound more like: "It depends on the circumstances. Here are some methods and tools. Use your best judgment..." — then an AI agent might be your perfect solution.
The best AI agent use cases involve judgment, adaptation to changing conditions, and the ability to retrieve and combine information from multiple sources to solve problems.
Getting Started: Choose Your First AI Agent Use Case Wisely
To identify the right AI agent use case for your first implementation:
- Start with business challenges, not technology
- What cumbersome processes eat up valuable time?
- Which specialized employees spend too much time on routine tasks?
- Where do decisions suffer from incomplete information?
- Evaluate technical readiness
- How accessible is your data?
- Are your internal processes well-documented?
- What governance requirements must you meet?
- Prioritize impact
- Which AI agent use case offers the highest ROI for your business?
- Which would be easiest to implement?
- Which use cases would face the least resistance from users?
5 AI Agent Use Cases That Deliver Real Business Impact
With countless potential AI agent applications, it's easy to get overwhelmed when deciding where to start. Should you focus on customer service, operations, finance, or another area entirely? The key is finding AI agent use cases where agents can handle complex decisions that traditional automation can't address.
The following AI agent use cases span different departments but share a common thread: They tackle knowledge-intensive processes, augment human expertise with data-driven insights, and integrate with existing workflows. As you explore these real-world implementations, consider how similar patterns might apply to your unique challenges.
- Support Ticket Manager
- Invoicing/Billing Processor
- Maintenance Scheduling Assistant
- Personalized Sales Assistant
- Clinical Trial Intelligence Assistant
1. Support Ticket Manager
Perfect for: IT departments, customer service centers, internal help desks
The Story: IT support teams typically handle dozens of tickets daily, many of which follow similar patterns but still require manual intervention. The support ticket manager agent transforms this workflow by continuously analyzing incoming requests, comparing them against previously resolved issues, and preparing draft responses. Each morning, IT staff arrive to find tickets already categorized by priority and complexity, with routine issues pre-populated with suggested responses and relevant documentation links. Complex tickets include enriched context that accelerates the resolution process.
Business Challenge: Teams drowning in hundreds of tickets daily, with vastly different complexity levels and constant pressure to reduce response times.
Agent Solution: A support ticket manager agent that works tirelessly in the background, scanning incoming tickets, automatically classifying them, and matching them against previously resolved issues to draft personalized responses.
Data Access Requirements: Ticketing system integration (e.g., Freshdesk, ServiceNow), historical ticket database, knowledge base/documentation and IT policy repository, user authentication and permissions data, IT asset inventory database
2. Invoicing/Billing Processor
Perfect for: Finance departments, accounts receivable teams, collection agencies
The Story: Accounts receivable teams struggle to effectively prioritize which unpaid invoices deserve immediate attention when dealing with thousands of outstanding payments. The invoicing/billing processor agent transforms this process by analyzing payment history, invoice amounts, and client relationships to create a prioritized dashboard. The system automatically generates tailored follow-up communications, from gentle reminders for typically reliable clients to more assertive language for chronically late payers, while keeping the CRM updated in real time.
Business Challenge: Invoice managers struggling to prioritize thousands of unpaid invoices with limited visibility into which ones deserve immediate attention.
Agent Solution: An intelligent invoicing/billing processor agent that analyzes unpaid invoices, prioritizes them based on amount, payment history, and client relationship, prepares customized follow-up emails, and automatically updates your CRM.
Data Access Requirements: Invoice database (including PDF/document-based invoices), banking transaction data, customer payment history, CRM system data (e.g., Salesforce), client relationship information, email templates and communication history
3. Maintenance Scheduling Assistant
Perfect for: Manufacturing facilities, utility companies, property management firms
The Story: Maintenance managers face the constant challenge of balancing equipment uptime, technician availability, and production schedules. When unexpected issues arise, the traditional process involves frantic calls, spreadsheet manipulations, and inevitable compromises. The Maintenance Scheduling Assistant transforms this chaos by instantly recalculating optimal schedules based on equipment health data, production priorities, and resource availability. It presents multiple scenarios with clear trade-offs between production impact, maintenance costs, and long-term equipment reliability.
Business Challenge: Maintenance managers juggling equipment reliability, technician availability, production schedules, and unexpected breakdowns — often with competing priorities.
Agent Solution: A Maintenance Scheduling Assistant that integrates with predictive maintenance systems to create optimal maintenance schedules, adjusts in real-time to unexpected events, and communicates clearly with all stakeholders.
Data Access Requirements: Equipment sensor data and health metrics, maintenance history records, production scheduling system, technician availability and scheduling data, spare parts inventory database, maintenance procedure documentation, equipment dependencies mapping
4. Personalized Sales Assistant
Perfect for: Retail chains, regional sales teams, e-commerce operations
The Story: Regional managers responsible for dozens of stores typically rely on monthly reports to identify performance issues, making it difficult to respond quickly to emerging problems. The Personalized Sales Assistant transforms this delay into real-time insights through conversational analysis of sales data, inventory levels, and customer trends. When agents identify performance issues, they automatically analyze root causes such as inventory stockouts, pricing misalignment, or shifting customer segments, and recommend targeted interventions based on predictive demand models and customer segmentation.
Business Challenge: Regional sales managers struggling to optimize pricing, inventory, and promotions across dozens of stores, each with unique customer demographics and buying patterns.
Agent Solution: A Personalized Sales Assistant that conducts real-time analysis across sales data, inventory levels, and demand forecasts to identify improvement opportunities and recommend targeted actions.
Data Access Requirements: Store-level sales transaction data, inventory management system integration, customer purchase history, product catalog and specifications, pricing history database, promotional campaign records, competitor pricing information, logistics and supply chain data
5. Clinical Trial Intelligence Assistant
Perfect for: Pharmaceutical companies, contract research organizations, biotech firms
The Story: Launching clinical trials involves months of manual work to identify suitable research sites and investigators who can meet enrollment targets. The Clinical Trial Intelligence Assistant transforms this process by connecting diverse data sources — from internal trial databases to public research records — to quickly identify optimal sites based on past performance, investigator qualifications, and patient demographics. What once required weeks of manual research and analysis is now accomplished in minutes, with comprehensive reports that include competitive analysis of key investigators.
Business Challenge: Clinical Operations teams spending months manually screening and selecting clinical sites and investigators for trials, often with incomplete information and little visibility into past performance.
Agent Solution: A Clinical Trial Intelligence Assistant that connects diverse data sources including trial databases, socioeconomic factors, and research grant records to identify optimal sites and investigators that meet complex protocol requirements.
Data Access Requirements: Clinical trial history database, www.clinicaltrials.gov public registry, principal investigator profiles and performance, research grant records, Social Determinants of Health datasets, census demographic data, patient enrollment statistics, scientific publication databases
Why These AI Agent Use Cases Deliver Real Business Value
What makes these AI agent implementations successful when many AI initiatives fail to gain traction?
- They're grounded in your proprietary data - leveraging your organization's unique data and assets
- They integrate with your existing systems - connecting to databases, analytics pipelines, and ML models
- They provide full visibility and control - offering clear insight into every data source and decision path
- They can be created by both technical and business teams - expanding who can contribute to AI innovation
It's Time to Transform Your Organization With AI Agents
The competitive advantage today belongs to organizations creating differentiated AI agents tailored to their unique business needs and data ecosystems — not those deploying generic AI solutions.
These AI agents don't replace your workforce; they amplify human capabilities by eliminating repetitive tasks. They free your IT team to solve complex problems instead of resetting passwords. They give your sales managers instant insights instead of waiting for monthly reports. They help your clinical researchers make data-driven decisions in minutes instead of weeks.
Ask yourself: Which knowledge workers in your organization are spending valuable time on repetitive tasks that could be handled by AI agents? Where are decisions being delayed by information that's difficult to access? How could your proprietary data become a competitive advantage through intelligent agents?
Your business isn't generic. Your AI agents shouldn't be either.