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
Neurons Lab helps financial institutions move from AI-curious to AI-enabled. We deliver AI training programs and custom AI agents designed for regulated environments — from pilot to production, at scale.
Key team members
Oliver Low
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