Role Summary
We are seeking an experienced Enterprise AI Implementation Business Analyst to support the design and implementation of enterprise-scale Generative AI and intelligent automation solutions.
This role will serve as the bridge between business stakeholders, Product Owners, AI/ML teams, data engineering teams, architects, application teams, and QA teams. The Business Analyst will identify and define high-value AI use cases, translate business needs into actionable requirements, support solution design and implementation, and ensure AI capabilities deliver measurable business value.
The ideal candidate combines strong business analysis and process-design experience with practical knowledge of Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), enterprise search, AI agents, and enterprise data integration.
Key Responsibilities
Business Analysis & AI Use Case Discovery
* Partner with business stakeholders and subject-matter experts to identify opportunities for AI-enabled transformation.
* Conduct discovery workshops and stakeholder interviews.
* Understand current business processes, pain points, manual activities, and information gaps.
* Document current-state and future-state business processes.
* Identify and prioritize AI use cases based on business value, feasibility, data readiness, complexity, and risk.
* Develop clear problem statements, business outcomes, and measurable success criteria.
* Help determine whether business problems should be addressed through Generative AI, traditional automation, analytics, search, or existing application capabilities.
Requirements Management
* Translate business needs into detailed functional and non-functional requirements.
* Develop and maintain:
* Epics and features
* User stories
* Acceptance criteria
* Business requirements
* Process flows
* Business rules
* Data requirements
* Integration requirements
* Partner with Product Owners to maintain and prioritize the product backlog.
* Ensure requirements are sufficiently detailed and testable for AI, engineering, data, and QA teams.
* Maintain traceability between business objectives, requirements, implementation, and testing.
Generative AI & LLM Requirements
* Define business requirements for AI capabilities such as:
* AI assistants and copilots
* Enterprise knowledge search
* Question-and-answer experiences
* Document summarization
* Information extraction
* Content generation
* Intelligent recommendations
* Workflow automation
* AI agents
* Define representative business questions, prompts, user scenarios, and expected outcomes.
* Work with AI engineers to define expected model behavior.
* Establish business expectations for:
* Accuracy
* Relevance
* Grounding
* Source attribution
* Response completeness
* Context awareness
* User permissions
* Identify scenarios where AI responses may require clarification, escalation, human review, or additional validation.
Enterprise Knowledge & RAG
* Work with business and technical teams to identify authoritative enterprise data and knowledge sources.
* Define requirements for connecting AI solutions to structured and unstructured enterprise information.
* Support requirements for Retrieval-Augmented Generation (RAG) solutions.
* Define business rules around what information should be searchable and retrievable.
* Partner with data teams to assess source-data quality, completeness, ownership, and freshness.
* Help define document metadata, taxonomy, categorization, and retrieval requirements.
* Validate whether AI responses are grounded in appropriate enterprise information.
AI Testing & Evaluation
* Develop business test scenarios for AI capabilities.
* Define expected results and acceptance criteria for AI-generated responses.
* Coordinate User Acceptance Testing (UAT) with business stakeholders.
* Evaluate AI responses for:
* Accuracy
* Relevance
* Completeness
* Grounding
* Source correctness
* Consistency
* Business usefulness
* Document incorrect, incomplete, or potentially misleading AI responses.
* Partner with AI engineering teams to analyze failure patterns and improve solution performance.
* Support regression testing as models, prompts, knowledge sources, and retrieval configurations change.
AI Governance & Responsible AI
* Support implementation of enterprise AI governance requirements.
* Identify business requirements related to:
* Data privacy
* Sensitive information
* Role-based access
* Security
* Regulatory requirements
* AI transparency
* Human oversight
* Ensure AI solutions respect existing enterprise data-access permissions.
* Help identify business risks associated with inaccurate or inappropriate AI responses.
* Partner with security, legal, compliance, and governance teams as required.
Integration & Implementation Support
* Work with architects and engineering teams to define integration requirements between AI capabilities and enterprise systems.
* Support integration with enterprise applications, APIs, databases, document repositories, and knowledge platforms.
* Clarify business rules and requirements throughout development.
* Participate in design reviews, sprint planning, demos, testing, and release-readiness activities.
* Track requirements, decisions, dependencies, risks, and open questions through implementation.
Business Readiness & Adoption
* Partner with business teams to prepare users for new AI-enabled capabilities.
* Document changes to existing business processes and user workflows.
* Support development of user documentation and training materials.
* Gather feedback from pilot users and early adopters.
* Identify usability and adoption barriers.
* Track whether implemented capabilities are achieving expected business outcomes.
* Recommend improvements based on user feedback and production performance.
Required Qualifications
* 5+ years of experience as a Business Analyst, Product Analyst, Systems Analyst, or similar technology delivery role.
* Experience supporting complex enterprise application, data, automation, or digital-transformation programs.
* Strong requirements gathering and stakeholder-management experience.
* Experience developing user stories and acceptance criteria in Agile environments.
* Strong process-analysis and process-mapping capabilities.
* Experience coordinating requirements across business and technical teams.
* Strong analytical and problem-solving skills.
* Experience with Jira and Confluence or similar Agile delivery tools.
* Excellent written and verbal communication skills.
* Ability to translate technical concepts into business language and business requirements into actionable technical requirements.
Preferred Qualifications
* Experience implementing Generative AI or LLM-based enterprise solutions.
* Understanding of Large Language Models (LLMs) and Generative AI concepts.
* Familiarity with Retrieval-Augmented Generation (RAG).
* Understanding of semantic/vector search and enterprise knowledge retrieval.
* Familiarity with prompt design and prompt evaluation.
* Exposure to AI agents and agentic workflows.
* Experience working with structured and unstructured enterprise data.
* Experience integrating AI capabilities with enterprise applications and APIs.
* Familiarity with cloud-based AI platforms and services.
* Experience with AI testing, evaluation, or quality measurement.
* Understanding of Responsible AI, privacy, security, and AI governance principles.
Key Competencies
The successful candidate should demonstrate:
* Ability to translate ambiguous AI ideas into clearly defined business use cases and requirements.
* Strong understanding of business processes and enterprise workflows.
* Ability to work effectively with both senior business stakeholders and highly technical engineering teams.
* Ability to challenge use cases where AI does not provide sufficient business value.
* Strong attention to data quality and information accuracy.
* Ability to evaluate AI output critically rather than treating generated responses as inherently correct.
* Strong ownership from initial discovery through production implementation.
* Ability to manage multiple use cases, stakeholders, and dependencies simultaneously.
* Curiosity and willingness to learn rapidly as enterprise AI technologies evolve.
Success Measures
Success in this role will be measured by:
* Quality and clarity of AI business requirements.
* Successful conversion of business problems into implementable AI use cases.
* Accuracy and business usefulness of implemented AI capabilities.
* Successful UAT and business acceptance.
* User adoption and satisfaction.
* Reduction in manual effort or process cycle time.
* Improvements in information discovery and employee productivity.
* Effective identification and mitigation of AI-related business risks.
* Delivery of measurable business value from enterprise AI investments.
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