Most companies struggle to pick the right AI agent use cases. They either chase flashy demos or waste time on low-value projects. In our recent webinar, my colleague Christian Capdeville and I shared a simple five-step framework. This framework helps organizations choose AI agent use cases that deliver real results and ROI.
The Cost of Getting AI Agent Use Cases Wrong
You need to deliver the right use cases so that you can build credibility for your future efforts.
— Christian Capdeville, Senior Director of Content and Product Marketing at Dataiku
We opened the webinar by explaining what's at stake when organizations get AI agent use cases wrong. The consequences extend far beyond a single failed project.
The cost of poor AI agent use case selection includes:
- Killing future AI initiatives before they start
- Wasting time and resources on low-impact projects
- Missing the six to 12 month competitive advantage window
- Losing stakeholder trust that's hard to rebuild
Building trust with leaders becomes critical when selecting AI agent use cases because leaders need to see measurable KPIs and clear business value, not just impressive technology demonstrations.
The market advantage opportunities exist for organizations that choose AI agent use cases strategically. There's a 6-12 month window where early movers can establish meaningful differentiation. Companies that nail their AI agent use case selection and implementation can transform business processes faster than competitors who are still experimenting.
Do You Need an AI Agent? The 3-Question Test
Before diving into specific AI agent use cases, we outlined three key criteria that separate genuine opportunities from traditional automation projects. This evaluation prevents organizations from building AI agents where simpler solutions would work better.
1. Complexity Level
Skip AI agents for: Simple if-then rules, basic automation, problems that traditional software can solve
Use AI agents for: Tasks requiring reasoning across different inputs, adapting to new information based on context, or making judgment calls that consider multiple variables simultaneously
2. Data Source Diversity
Skip AI agents for: Single, clean data sources with well-structured inputs
Use AI agents for: Multiple data sources needing integration, mixing structured and unstructured data, or information requiring contextual understanding
3. Process Type
Skip AI agents for: Static processes that rarely change and happen the same way every time
Use AI agents for: Processes that benefit from learning over time, complex branching logic, or workflows that need to adapt to different situations
Our 5-Step Framework for Choosing AI Agent Use Cases
Based on many customer implementations, we've developed a systematic methodology that reduces the risk of choosing poor AI agent use cases while maximizing business impact.
Step 1: Start With Business Problems (Not Technology)
Always root your use cases in business challenges, not necessarily a trending technology.
— Marissa Creatore, Product Marketing Manager at Dataiku
The first step requires organizations to identify genuine business challenges rather than getting distracted by flashy new technologies. It's easy to want to build a proof-of-concept just because the tech is exciting, but successful AI agent use cases must solve real business problems, not showcase impressive capabilities. When thinking about business problems, we focus on three key areas:
Process Pain Points: Where are workflows cumbersome or time-consuming? Which teams face information overload or access barriers? What critical decisions get delayed by manual information gathering? These pain points often represent ideal AI agent use cases because they involve repetitive work that requires some level of organizational intelligence or cumbersome documentation.
Knowledge Worker Constraints: Look for specialized employees spending significant time on repetitive tasks, high-value workers dedicating energy to low-value activities, and situations where accessing multiple systems slows people down. These scenarios benefit from AI agent use cases that handle routine work while humans focus on complex decisions.
Decision Support Gaps: Identify where employees lack information needed for good decisions, which areas pose the highest risk due to incomplete data, and what processes suffer from information silos. AI agent use cases can bridge these gaps by aggregating and analyzing information from multiple sources.
Step 2: Map AI Agent Use Cases to 3 Value Types
Different AI agent use cases deliver value in distinct ways. Understanding these three categories helps set appropriate expectations and choose implementation approaches that match your goals.
Process Automation AI Agent Use Cases streamline processes with control and adaptability. These implementations automate multi-step processes across systems. You can start with human-in-the-loop approaches to build trust, then move to full automation.
Worker Augmentation AI Agent Use Cases empower professionals with intelligent assistants. The human remains in control of complex decisions while AI handles heavy lifting. I like to think of this one as putting a supersuit on your employees.
Intelligent Business Chains represent the most transformative AI agent use cases. These implementations reimagine entire processes from an AI-first perspective, often enabling new business models and approaches.
Step 3: Check Your Technical Readiness
Many AI agent use cases fail because of weak foundations rather than poor use case selection. We recommend evaluating three critical areas:
Data Accessibility represents the foundation for successful AI agent use cases. Organizations need to ensure relevant data is digitized and accessible, that teams have permission to access necessary systems, and that data can be integrated without major structural changes. As Marissa emphasized during our session, if getting data access will take six months or more, that particular AI agent use case probably shouldn't be your first implementation.
Warning Sign: If getting data access will take six months or more, that AI agent use case probably shouldn't be your first implementation.
Process Documentation becomes uniquely important for AI agent use cases. Current processes need to be well-documented with clear success metrics. You must identify subject matter experts who can guide implementation and validate that the AI agent performs correctly.
Governance Considerations vary significantly across different AI agent use cases. Organizations need built-in governance capabilities like our trace explorer functionality to maintain control and visibility as implementations scale.
Step 4: Rank AI Agent Use Cases by Impact
Use our simple 2x2 prioritization matrix to evaluate AI agent use cases across three dimensions:
ROI Potential Questions:
- How much time will this save?
- What revenue or cost impact might result?
- Will this reduce risk or improve quality?
Implementation Complexity Assessment:
- Are all required data sources accessible?
- How much customization is needed?
- What integration challenges might arise?
User Readiness Evaluation:
- Are end users comfortable with AI solutions?
- How much change management is required?
- Do executives support this initiative?
The sweet spot combines high value with easier implementation. However, don't ignore high-value, harder implementations — plan these as your second wave projects.
Step 5: Pick One AI Agent Use Case and Scale Fast
We hear leaders asking 'where are our agents, why weren't they here yesterday?' It's easy to say 'here's a list of 10 agents my team is working on,' but you must focus on only one use case to start.
— Marissa Creatore, Product Marketing Manager at Dataiku
Why starting with one works:
- Learn from the first project and apply lessons to subsequent implementations
- Build relationships with key stakeholders who ensure success
- Generate leadership buy-in through demonstrated results
- Avoid spreading resources too thin across multiple experiments
Scaling after success becomes much faster. You can leverage proven patterns, existing data connections, established governance structures, and stakeholder trust to target higher-value, more complex AI agent use cases.
5 Real AI Agent Use Cases in Production
During our webinar, we walked through actual implementations that demonstrate how the framework translates into business value across industries. These production-ready examples show the specific tools and components within Dataiku that enable AI agent use cases spanning from process automation to intelligent business chains.
The five use cases we overviewed include:
- Ticket Support Agent (Process Automation): Cross-industry IT support automation
- Maintenance Scheduling Assistant (Worker Augmentation): Manufacturing multi-agent coordination
- Dynamic Selling Assistant (Worker Augmentation): Retail pricing and inventory optimization
- AML Investigation Assistant (Worker Augmentation): Financial services compliance workflow
- Clinical Trial Intelligence Assistant (Intelligent Business Chains): Life sciences site selection scaler
These examples show how organizations move beyond proof-of-concepts to production deployments that solve real business problems. Check out our comprehensive guide to 5 AI Agent Use Cases to Kickstart Your Team's Transformation for deep dives into more AI agent use case examples.
Technical Foundation That Enables Success
During our Q&A session, Christian highlighted key technical capabilities that make these AI agent use cases possible:
LLM Flexibility: The Dataiku LLM Mesh architecture connects to any language model — OpenAI, Anthropic, locally hosted models, or others. Different models excel at different tasks, enabling optimization across multiple AI agent use cases.
Built-in Governance: Trace Explorer provides complete visibility into agent actions, creating audit trails for compliance. Visual debugging helps teams quickly identify and fix issues when AI agent use cases don't perform as expected.
Development Options: Visual agents enable business users to create AI agent use cases without coding. Code agents provide full customization for technical teams. Both types work together in the same workflow.
Expert Tips
Based on our customer implementations, several factors consistently determine AI agent use case success:
1. Start With Clear Business Value
Choose AI agent use cases with measurable ROI that justify implementation costs. Focus on real problems rather than impressive technology to ensure solutions address genuine business needs.
2. Build Strong Foundations First
- Ensure data access and quality before starting development
- Document processes thoroughly with clear success metrics
- Set up governance and monitoring capabilities from day one
3. Include Subject Matter Experts Throughout
Work with people who understand the processes best. Use visual tools that non-technical experts can understand. Get their input on agent instructions and expected outcomes to prevent technical solutions that miss business requirements.
4. Plan for Transparency
Use tools that show what agents are doing to build stakeholder trust. Be able to explain agent decisions for regulatory compliance and organizational governance. Monitor performance and iterate as business conditions change.
What's Next for Your AI Agent Use Cases?
There is a window of opportunity to drive real differentiated value in the next six to 12 months by nailing this.
— Christian Capdeville, Senior Director of Content and Product Marketing at Dataiku
The competitive advantage window won't remain open indefinitely. Poll results from our session revealed that organizations are at different stages with AI agent use cases:
- Some have structured cross-functional processes for AI agent use case selection
- Others use departmental approaches or central team decisions
- Many are still figuring out their use case selection methodology
The question isn't whether you'll implement AI agent use cases — it's whether you'll choose them strategically enough to maintain competitive advantages. Dataiku provides all the tools you need to execute this systematic approach, from the LLM mesh and built-in governance for enterprise-grade deployments to visual development environments that enable collaboration between business and technical teams.
Organizations that follow systematic approaches like our 5-step framework will build capabilities for long-term success while competitors struggle with failed experiments. The key is disciplined execution of proven methodology rather than hoping individual AI agent use cases will succeed despite poor selection processes.