Senior AI Solutions Analyst - Finance

The core responsibilities for the job include the following:

Drive AI Adoption in Daily Work:

Support the adoption of AI-enabled ways of working across CAO Finance teams.

Identify opportunities to embed AI into activities such as reconciliations, reporting, issue analysis, drafting, research, and documentation.

Translate finance process challenges into practical AI use cases.

Develop and maintain reusable use cases, prompts, playbooks, templates, and best practices.

Support workshops, demonstrations, office hours, and capability-building sessions.

Gather user feedback and identify opportunities to improve adoption and usability.

Track adoption and help teams integrate successful solutions into their day-to-day workflows.

Deliver High-Impact AI Use Cases:

Analyze finance processes to identify manual, repetitive, time-consuming, and error-prone activities that could benefit from AI or automation.

Assess opportunities based on potential value, feasibility, risk, and scalability.

Work with finance teams and technology partners to design and pilot AI-enabled solutions, including copilots, agents, and workflow automation.

Perform testing and coordinate user acceptance testing with finance teams.

Document requirements, workflows, expected outputs, validation criteria, and process changes.

Gather feedback from pilots and iterate solutions before broader implementation.

Help standardize successful solutions so they can be reused across teams and processes.

Measure and report impact, including time saved, capacity created, cycle-time reduction, adoption, and quality improvements.

Ensure Governance and Control Alignment:

Ensure AI use cases follow established SOX, audit, data security, privacy, and responsible AI requirements.

Maintain appropriate documentation for implemented AI solutions.

Support the definition of validation procedures and human-in-the-loop review steps.

Identify potential process or control risks and escalate them to the appropriate process owner, risk, compliance, or technology partner.

Work with relevant stakeholders to ensure solutions operate within approved AI and finance governance frameworks.

Lead Change Across Teams:

Partner with finance teams to understand pain points and identify opportunities where AI can improve existing ways of working.

Support implementation and change-management activities for new AI-enabled workflows.

Create practical training materials, user guides, demonstrations, and adoption resources.

Gather feedback from users and translate it into improvements or additional use cases.

Coordinate across Finance, Transformation, FinTech, Product, Engineering, and Risk/Compliance stakeholders as required.

Prepare clear updates on use-case progress, adoption, benefits, issues, and risks for stakeholders.

Requirements:

4-6+ years of relevant experience in finance operations, accounting, finance transformation, FinTech enablement, process excellence, or a related field.

Strong understanding of finance and CAO processes such as close/RTR, reconciliations, intercompany reporting, and controls.

Experience improving finance processes through technology, automation, standardization, or process redesign.

Strong analytical and problem-solving skills, with the ability to translate business problems into structured solutions.

Ability to work effectively with finance and cross-functional stakeholders.

Strong written and verbal communication skills.

Curiosity and willingness to experiment with emerging AI technologies and new ways of working.

Preferred:

Hands-on experience implementing AI/automation solutions.

Familiarity with SOX and control environments.

Experience building business cases tied to productivity and risk reduction.

Lean/Six Sigma, Agile, or program management experience.

What Success Looks Like:

Deliver and support AI use cases that reduce manual effort and improve finance processes.

Help finance teams incorporate AI effectively into their daily workflows.

Build reusable playbooks, prompts, templates, and solutions that can be adopted by multiple teams.

Demonstrate measurable improvements in productivity, cycle time, quality, and/or controls.

Maintain appropriate documentation, validation, and human oversight for implemented solutions.

Build strong working relationships across finance and technology teams.

Develop a growing pipeline of practical AI opportunities based on real finance pain points.