AI Agent Use Cases

AI agents embedded in business capabilities, value streams, and core processes

AI agents are most effective when they are designed as part of how work gets done—not as isolated tools.

The use cases below represent AI agents we have implemented to support ideation, knowledge access, operational workflows, and governance across the enterprise.

Each use case is designed to integrate with existing systems, workflows, and controls.

1

Product Ideation through AI Visualisation

Capability focus

Product ideation, innovation management, early-stage design

What the AI agent does

  • Translates ideas, requirements, or problem statements into visual concepts
  • Generates and iterates on product concepts, flows, and variations
  • Supports rapid exploration of alternatives and trade-offs

How it fits

  • Embedded into early product discovery and ideation workflows
  • Augments human creativity with faster exploration and comparison

Business outcome

  • Faster ideation cycles
  • Better alignment between business, design, and technology
  • Reduced friction in early-stage decision-making
2

Knowledge Base Search & Information Discovery

Capability focus

Knowledge management, self-service enablement, internal support

What the AI agent does

  • Interprets natural language questions from users
  • Searches across structured and unstructured knowledge sources
  • Surfaces relevant, context-aware answers with references

How it fits

  • Embedded into portals, chat interfaces, or collaboration tools
  • Reduces reliance on manual searches or expert dependency

Business outcome

  • Faster access to information
  • Reduced operational interruptions
  • Improved user productivity and consistency
3

Service Agent for Issue Support & Follow-Up

Capability focus

Service management, incident handling, user support

What the AI agent does

  • Helps users describe issues and identify potential workarounds
  • Assists in creating and categorizing service or issue records
  • Tracks progress and proactively follows up with users

How it fits

  • Integrated into service workflows and issue management processes
  • Acts as a first-line assistant before escalation

Business outcome

  • Reduced support effort
  • Improved issue quality and resolution time
  • Better user experience with less manual interaction
4

Automated Employee Onboarding

Capability focus

Employee lifecycle management, onboarding processes

What the AI agent does

  • Guides new employees through onboarding steps
  • Coordinates tasks across systems and teams
  • Answers onboarding-related questions contextually

How it fits

  • Orchestrates onboarding activities across L1 onboarding processes
  • Provides consistent guidance while allowing human oversight

Business outcome

  • Faster time-to-productivity
  • Reduced manual coordination
  • More consistent onboarding experience
5

Accounting Assistance & Invoice Processing

Capability focus

Financial operations, accounting processes

What the AI agent does

  • Assists in running invoice-related jobs and checks
  • Identifies anomalies, missing data, or inconsistencies
  • Supports exception handling and processing steps

How it fits

  • Embedded into accounting and financial workflows
  • Supports decision points rather than replacing controls

Business outcome

  • Reduced manual effort
  • Improved accuracy and timeliness
  • Better visibility into exceptions and issues
6

Architecture Assistance for Design & Code Compliance

Capability focus

Architecture governance, design assurance

What the AI agent does

  • Reviews architecture designs against defined principles and standards
  • Analyzes code and design artifacts for alignment with target architecture
  • Flags deviations, risks, and improvement opportunities

How it fits

  • Integrated into design review and development workflows
  • Acts as a continuous architecture assurance layer

Business outcome

  • Faster, more consistent architecture reviews
  • Reduced rework during delivery
  • Stronger alignment to architectural intent
7

Security Agent for Design & Code Compliance

Capability focus

Security governance, risk management

What the AI agent does

  • Reviews designs and code for security compliance
  • Identifies potential vulnerabilities and control gaps
  • Supports security reviews with contextual recommendations

How it fits

  • Embedded into development and review pipelines
  • Supports security teams with early and continuous feedback

Business outcome

  • Earlier detection of security risks
  • Reduced compliance overhead
  • Improved security posture without slowing delivery

How These Use Cases Are Implemented

Across all use cases, AI agents are:

  • ✓ Aligned to business capabilities, value streams, or L1 processes
  • ✓ Designed with clear decision boundaries and escalation paths
  • ✓ Integrated with existing systems and workflows
  • ✓ Governed with security, auditability, and monitoring in mind

AI agents are treated as long-lived architectural components, not experiments.

When AI Agents Add the Most Value

AI agents are most effective when:

  • ✓ Processes involve repeated decisions or information retrieval
  • ✓ Manual effort creates delays or inconsistency
  • ✓ Clear process ownership and outcomes exist
  • ✓ Human judgment remains essential but can be augmented

Explore AI agents aligned to your business architecture

If you are exploring how AI agents could support your capabilities or value streams, we can help assess opportunities, define designs, and embed them into execution.