SME Consultant – AI Use Case Discovery

Location & Duration

• Location: Singapore
• Duration: 6 months (with potential for extension based on project scope)

Role Overview

The SME Consultant will lead the discovery, validation, and refinement of AI use cases across a major international banking group – Corporate Banking Division. This role is responsible for engaging stakeholders, assessing business impact, mapping user journeys, and evaluating technical feasibility to ensure successful AI adoption and measurable outcomes.

Key Responsibilities

1. Stakeholder Engagement & Change Management

• Identify and map the stakeholder ecosystem: end-users, decision-makers, process owners, and technical teams.
• Assess stakeholder maturity, readiness for change, and commitment to participate in workshops, feedback sessions, and user acceptance testing (UAT).
• Develop and execute a change management and communication plan to drive user adoption and process re-engineering.

2. AI Use Case Discovery & Validation

• Work with solution architect to conduct deep-dive workshops and interviews with business and technical stakeholders to clarify pain points, process inefficiencies, and strategic priorities.
• Work with solution architect to analyze and validate the AI use cases listed in the Client Use Case Analysis, including documentation verification, trade flow optimization, performance management, predictive marketing, onboarding automation, compliance, and legal analytics.
• Quantify current process metrics (volumes, error rates, manual touch points, cost of delay/error) and define measurable success criteria for each use case.

3. Technical Assessment & Integration Planning

• Work with solution architect to evaluate source systems, data availability, and integration requirements for each use case (e.g., core banking systems, trade booking platforms, BI dashboards).
• Work with solution architect to assess API readiness, data quality, and system constraints; collaborate with technical teams to define integration specs and support testing.
• Review compliance, security, and data residency requirements relevant to AI model deployment.

4. Data Readiness & AI Feasibility

• Assess the availability, quality, and structure of data required for AI model training and inference (documents, trades, emails, chat history).
• Identify gaps in data access, privacy concerns, and requirements for data anonymization or cleansing.
• Evaluate build-vs-buy options for AI solutions (off-the-shelf vs. custom), cloud provider preferences, and internal SME support for domain-specific model optimization.

5. User Journey Mapping & Opportunity Identification

• Map current-state user journeys for key personas (e.g., Operations Analyst, Trader, Relationship Manager).
• Identify pain points, risks, and AI opportunity zones within each journey.
• Articulate future-state journeys enabled by AI, highlighting process improvements and business value.

6. Reporting & Recommendations

• Deliver comprehensive findings, including business case justification, technical feasibility, stakeholder readiness, and prioritized recommendations.
• Present actionable insights to senior management and project sponsors.

Required Qualifications

• Proven experience (8+ years) in financial services, consulting, or technology roles focused on AI, automation, or digital transformation.
• Deep understanding of capital markets, transaction banking, compliance, and operational processes.
• Strong analytical skills with experience in process mapping, business case development, and quantitative analysis.
• Familiarity with AI/ML technologies, data integration, and cloud platforms (AWS, Azure, GCP).
• Excellent stakeholder management, facilitation, and communication skills.
• Experience with change management and user adoption strategies.
• Knowledge of regulatory requirements (MAS, HKMA, JFSA) and data governance in financial services.

Preferred Skills

• Experience with OCR/IDP, LLMs, BI tools (Tableau, QuickSight), and workflow automation.
• Ability to work cross-functionally with business, IT, and compliance teams.
• Prior exposure to large-scale AI discovery or implementation projects in banking or capital markets.

Deliverables

• Validated AI use case inventory with business impact and feasibility assessment.
• Stakeholder map and change management plan.
• Technical integration and data readiness report.
• Current-state and future-state user journey maps.
• Final recommendations and executive presentation.