Job Description

Roles & Responsibilities

• The role is responsible for delivering small, iterative AI-enabled automations with rapid turnaround, typically within days to weeks, while preparing larger opportunities for formal DAAI engagement. The ideal candidate combines strong business analysis capability with hands-on experience using tools such as Microsoft Copilot Studio, Claude code, Appian, prompt-driven workflows, and agentic AI concepts. The role will support the GCOO team backlog by assessing demand, clarifying scope, preparing use cases, building proofs of concept, and helping business stakeholders adopt practical AI solutions safely and effectively.

  
  

  Key Responsibilities:

  • Rapidly build small-scale AI automations, prototypes, and proof-of-concept solutions using Microsoft Copilot Studio, Claude code, Appian, prompt-driven workflows, and other approved enterprise tools.

  • Design and refine prompts, agent instructions, workflow logic, validation checks, and human-in-the-loop controls to improve solution reliability and business usability.

  • Partner with GCOO business teams to identify, assess, and prioritize lightweight AI automation opportunities that improve productivity, turnaround time, control, and service quality.

  • Translate business problems into clear use cases, process maps, user stories, acceptance criteria, data needs, risks, and delivery-ready requirements for DAAI and technology teams.

  • Bridge business stakeholders, DAAI specialists, platform teams, risk, compliance, and operations teams to accelerate use case discovery, readiness, and delivery alignment.

  • Manage the GCOO AI automation backlog, including intake, triage, impact assessment, prioritization, dependency tracking, status reporting, and escalation of delivery risks.

  • Assess solution feasibility, expected benefits, operational readiness, data availability, information security considerations, and responsible AI implications before progressing use cases.

  • Support adoption by preparing guidance, demos, stakeholder communications, operating procedures, and handover material for business users and support teams.

  • Monitor delivered automations for usage, effectiveness, issues, feedback, and opportunities for continuous improvement or scale-up through formal DAAI delivery channels.

Desired Candidate Profile

• Bachelor’s degree in business, Computer Science, Information Technology, Engineering, Data Analytics, or a related discipline.

• 2-3 years of hands-on experience with Microsoft Copilot Studio, Claude code, Appian, workflow automation, low-code/no-code platforms, or similar enterprise automation tools.

• Working knowledge of generative AI, agentic AI concepts, prompt engineering, retrieval-based solutions, human-in-the-loop review, and AI-assisted business workflows.

• Ability to rapidly prototype practical automations, test outputs, refine prompts/workflows, document limitations, and convert learnings into scalable delivery requirements.

• 5–10 years of experience across business analysis, process automation, digital transformation, technology delivery, product ownership, or AI-enabled operations.

• Strong business analysis skills, including stakeholder engagement, process discovery, requirements elicitation, user story writing, acceptance criteria definition, and benefits articulation.

• Good understanding of data handling, access controls, auditability, operational risk, information security, and responsible AI considerations in a regulated banking environment.

• Strong communication skills with the ability to explain AI-enabled solutions, trade-offs, risks, and business value to both technical and non-technical stakeholders.

• Experience working in Agile delivery environments and managing small backlogs, prioritization forums, demos, and iterative delivery cycles is preferred.

Attributes:

Change Advocate and Delivery Accelerator:

• Identifies high-value opportunities for AI-enabled improvement, challenges manual ways of working, and helps teams adopt practical automation in a controlled, iterative, and measurable manner.

Analytical and Innovation Mindset:

• Connects business needs, process pain points, AI capabilities, data constraints, and governance requirements to shape pragmatic solutions that can be tested quickly and scaled responsibly.