Objective Identify and validate high-value AI opportunities, rapidly prototype solutions, and ensure implementations deliver measurable business outcomes and tangible ROI. KPI Revenue/cost impact of implemented AI solutions Stakeholders' satisfaction with business case clarity and realization Areas of Responsibility Lead Discovery Process and Validate AI Opportunities Facilitate stakeholder workshops to identify and prioritize high-impact AI use cases Develop business cases with clear ROI models and success metrics Build consensus among stakeholders on solution direction Map and Redesign Business Processes Document current workflows and pain points Design workflows with appropriate boundaries and controls for financial environments and Agentic AI systems Quantify expected business improvements Define AI Solution Requirements Translate business needs into clear technical requirements Create user stories and acceptance criteria Establish validation approaches for measuring success Create Solution Prototypes Build functional demonstrations using no-code/low-code tools Gather user feedback to refine concepts Develop prompt templates for financial use cases Implement AI Governance and Compliance Develop testing methodologies for AI systems in regulated environments Ensure alignment with financial regulations (e.g US SR 11-7, EU AI Act) Create documentation standards for model risk management Leverage Financial Services Industry (FSI) Ontologies Apply industry-standard financial ontologies (e.g. FIBO) to structure and ground data Design knowledge graph integrations to improve AI accuracy and compliance Validate ontology completeness and accuracy for financial applications Ensure Ongoing Business Alignment Manage stakeholder expectations throughout delivery Lead business review sessions Mitigate risks to value realization Stay Current on AI Capabilities Actively test and experiment with emerging AI technologies firsthand Evaluate new tools and platforms for business value Share relevant insights with stakeholders Skills Stakeholder management and workshop facilitation Business process analysis, requirements gathering, and documentation Project scoping, ROI modeling, and business case development LLM frameworks and prompt engineering for financial applications Hands-on AI tool experience, including no-code/low-code platforms Financial ontologies and graph databases (Neo4j, RDF/OWL) AI governance, testing, and validation in regulated environments Traits Strategic business thinker who can identify valuable AI application opportunities Detail-oriented process analyst who can map complex workflows Technically curious with practical, hands-on AI knowledge Exceptional communicator who can translate between technical and business stakeholders Creative problem-solver able to rapidly prototype solutions Client-focused consultant who builds trust and drives business outcomes Experience 3-5+ years experience in business analysis, process improvement, or consulting Practical implementation experience with LLMs and generative AI Hands-on experience with one or more no-code/low-code platforms Hands-on experience with orchestration frameworks (LangChain, AutoGen) or knowledge graphs Demonstrated success in requirements gathering and stakeholder management Demonstrated success moving AI projects from proof-of-concept to production in regulated environments Experience with digital transformation or technology implementation projects Background in financial services with understanding of regulatory requirement Terms & conditions B2B contract, full remote, can be part-time or full-time engagement. Time zone: Europe