A catalog of agentic AI automation opportunities, organized as a grid: business functions (which repeat across industries) crossed with domains (which give each function its industry-specific shape). Built to answer, concretely: where in our business is agentic AI actually a good fit, and how far can automation realistically go there?
FUNCTION (the common pattern) × DOMAIN (the industry-specific variant) → USE CASE
See 00-navigation-and-methodology/knowledge-map.md for the full grid model and a worked example.
New to this repository? Read 00-navigation-and-methodology/how-to-use-this-repository.md, which routes you by function-first, domain-first, or "show me everything."
- What Is This Repository
- Principles
- The Agentic Automation Spectrum (L1–L4)
- Capabilities & Outcomes: the capability taxonomy and outcome categories every use case is tagged against
- Discovering Use Cases. Process analysis, workshops, shadowing, and other discovery methods, aligned to OASIS Phase 1
- Use-Case Anatomy, covering how to turn an idea into a bounded workflow
- Evaluation & Rollout, test layers, launch gates, and autonomy progression
- Purchasing & Procurement
- Sales
- Customer Relations & Support
- Marketing
- Data & Analytics
- HR & People
- Finance & Accounting
- IT & Security
- Legal & Compliance
- Risk & Internal Audit
- Supply Chain & Operations
- Quality Management
- Operations & Resource Scheduling
- Claims Management
- Research & Development
- Financial Services
- Healthcare & Life Sciences
- Retail & CPG
- Manufacturing & Industrial
- Insurance
- Telecom & Media
- Public Sector & Government
- Technology & SaaS
- Energy & Utilities
- Education & Higher Education
- Travel & Hospitality
- Industrial & Heavy Operations
- Scientific Research & Life Sciences R&D
- Capital Markets & Investment Banking
- Advertising & Media
- The Opportunity Grid: the full function × domain matrix
- Prioritization Framework
46 detailed entries spanning the common function patterns plus domain-exclusive workflows. See 05-use-cases/ for the full list. Domain-exclusive entries are intentionally not forced into the general grid. Newer entries also carry a "Who This Is For" section (plain-language / business / technical summaries) and a KPIs Influenced section; see the use case template.
Not sure where to look? See INDEX.md for every file, or 00-navigation-and-methodology/how-to-use-this-repository.md.
- Read Principles and the Agentic Automation Spectrum.
- Scan the Opportunity Grid for your domain, or browse a function that interests you.
- Read the full use case entries for your top candidates, checking the workflow boundary and exception paths.
- Use the Prioritization Framework to sequence candidates.
- Apply Use-Case Anatomy and Evaluation & Rollout to prepare a discovery brief.
- Move your top candidate into OASIS Phase 1, formalize its value case with oasis-fulcrum, and assess its risk with Responsible-AI.
Nexus is the cross-cutting opportunity catalog feeding OASIS Chapter 5 (Opportunity Portfolio and Transformation Horizons). It is not itself a Part III chapter companion, the way Axiom is a background reference rather than a chapter companion. See the Companion Repository Index for the full map of which repository backs which chapter.
Nexus answers "where should we look?" Once a use case is selected here, it moves into the chapter-specific companions that answer "how do we build, evaluate, secure, cost, and govern it":
- Ageis (Chapter 14): agentic coding delivery, once the use case reaches implementation.
- Forge (Chapter 15): data and knowledge engineering, where a use case's data dependencies are scoped.
- Loom (Chapter 16): human-AI workflow, where a use case's human-in-the-loop shape is decided.
- Helm (Chapters 17/18/19/21/26): deployment, operations, and AgentOps once the use case ships.
- Verity (Chapter 18). The deep evaluation and reliability methodology.
- Compass (Chapters 19–20): responsible AI, security, and regulatory compliance.
- Fulcrum (Chapter 22), cost and value tracking once the use case is in production.
This is a living catalog: see CONTRIBUTING.md. New use cases, domain-specific variants, and grid corrections are especially welcome.
Every use case's Value Signal is a plausible hypothesis, not a committed business case (see Principles). Every use case's Key Risks section is a starting flag, not a complete risk assessment; see Responsible-AI for full treatment before deployment.
Licensed under CC BY-SA 4.0. Reuse and adaptation are welcome with credit to KnowledgeTrails-OASIS, a link to the license, an indication of changes, and release of adaptations under the same license.
Shripadraj Mujumdar is an Agentic AI & Automation Strategist, Advisor, and Responsible AI Expert with 28+ years of experience in enterprise architecture and AI-driven transformation, including deep hands-on work in Agentic AI, Generative AI, and enterprise data and knowledge platforms. His practice spans designing multi-agent systems, knowledge-graph and RAG architectures, accelerated delivery capabilities, and Responsible AI governance frameworks aligned to global regulatory standards. This methodology ecosystem distills that practitioner experience. Architecture, delivery, evaluation, governance, and economics, into a single, reusable body of work.
Ankit Mirajkar is a Data & AI Architect and technology consultant specializing in modern data platforms, enterprise data architecture, and Agentic AI. His expertise spans scalable data engineering, AI-ready data platforms, Generative AI, and cloud technologies, with a strong focus on turning complex data challenges into practical, production-ready solutions. He also works at the intersection of architecture, technology strategy, and innovation to help organizations build intelligent, scalable data ecosystems.