Finding Your AI Agent Opportunities: Operational Discovery
The biggest barrier to implementing AI agents in enterprises isn't technology – it's discovery. Organizations often struggle to identify where AI can have the most impact. The key question isn't "Where can we use AI?" but rather "Where are we struggling to scale?"
The Scale Problem: Your Best Discovery Tool
When operations strain under growth, that's where AI opportunities hide. The symptoms are clear:
Teams constantly adding headcount but still falling behind
Increasing backlogs of requests and tickets
Growing complaints about response times
Employees spending more time on process than value-add work
Multiple tools and systems attempting to solve the same problems
Breaking Down Workflows: The JOBS Framework
Here's a practical framework for discovering AI agent opportunities in your organization:
J - Journey Mapping
Start by mapping the complete journey of a workflow. For example, take employee onboarding:
Request initiation
Approval flows
System access provisioning
Equipment requests
Training assignments
Documentation collection
O - Operational Bottlenecks
Identify where the process slows down:
Manual approvals
Data entry
Information verification
Cross-department coordination
Status updates and follow-ups
B - Break into Atomic Tasks
Decompose each step into its smallest components:
What specific information is being collected?
What decisions are being made?
What systems are being accessed?
What communications are happening?
S - Solve with AI Agents
Analyze which components an AI agent could handle:
Information gathering and validation
Routine decision-making
System interactions
Status tracking and updates
Pattern recognition and prediction
Case Study: IT Support Workflow Transformation
Let's see this framework in action with a common enterprise workflow:
Before: Traditional IT Support
Employee submits ticket
Support agent reviews and categorizes
Routes to appropriate team
Agent investigates issue
Implements solution
Updates ticket and follows up
After: Breaking it Down
Journey Mapping:
Initial contact points (email, chat, portal)
Issue description and categorization
Knowledge base searching
Solution implementation
Follow-up and verification
Operational Bottlenecks:
Time spent on initial triage
Repeated questions for clarification
Manual knowledge base searches
Multiple hand-offs between teams
Status update requests
Atomic Tasks:
Understanding user intent
Extracting relevant details
Matching issues to solutions
Accessing system information
Updating tickets
Communicating progress
AI Agent Solutions:
Natural language understanding for issue categorization
Automated knowledge base searching
Pattern matching for common problems
System access and basic troubleshooting
Proactive status updates
Escalation to human agents when needed
The Discovery Process: 5 Key Questions
When evaluating any operational area, ask:
Volume Question "What tasks are we repeating most often?"
High-volume activities are prime candidates for AI automation
Look for patterns in support tickets, requests, or approvals
Scalability Question "Where are we adding headcount fastest?"
Areas requiring constant staffing increases signal scaling problems
Consider if AI could handle the baseline work
Time Question "What tasks consume the most human time?"
Focus on activities that take time but don't require complex decision-making
Look for opportunities to free up human expertise
Error Question "Where do we see the most mistakes or rework?"
Inconsistent processes often benefit from AI standardization
Consider where human error creates the most issues
Integration Question "Which processes span multiple systems or departments?"
Cross-functional workflows often have the most friction
AI agents can serve as intelligent coordinators
Starting Your Discovery Journey
Begin with these practical steps:
Audit Your Tickets
Review support tickets across departments
Look for common patterns and requests
Identify time-consuming but routine tasks
Track Time Allocation
Have teams log their daily activities for a week
Identify where time goes to routine vs. strategic work
Note activities that interrupt focused work
Map Dependencies
Document cross-department workflows
Identify approval chains and bottlenecks
Note where processes frequently stall
Measure Impact
Calculate time spent on common requests
Estimate costs of delays and bottlenecks
Quantify the impact of errors and rework
The Path Forward
The key to successful AI agent implementation lies in thorough discovery. By systematically breaking down workflows and identifying opportunities for AI augmentation, organizations can:
Target the highest-impact areas first
Design more efficient processes
Scale operations without linear headcount growth
Improve both employee and customer experience
Remember: Start with where you need to scale, break it down systematically, and look for opportunities where AI agents can remove friction and automate routine work.
Ready to start your AI agent discovery journey? Learn more about how ai.work can help transform your operations.