AI Solutions Specialist

CSIPaducah, KY

About The Position

This is a business analysis and product operations role, not a data science or modeling role. You will not be building models. You will be the person who turns "we think AI could help with this" into a scoped, prioritized, execution-ready piece of work. CSI is standing up an internal AI capability that will work across the entire organization in a regulated financial services environment. We've deployed enterprise AI tooling org-wide, and the demand for AI-enabled workflows is growing. The AI Solutions Specialist role is key in closing the current gap. You'll sit at the front door of that demand — running intake, interviewing the people doing the work, documenting how their process functions, and shaping requests into specifications a build team can act on. You'll own the backlog that comes out of it, and you'll follow the work through to whether anyone has actually used it.

Requirements

  • 3–5 years in business analysis, product operations, process improvement, or transformation.
  • Bachelor's degree in Business, Operations, Information Systems, or a related field, or equivalent experience.
  • Experience with running requirements gathering with non-technical stakeholders, owned a backlog, and written documentation someone else could actually build from.
  • Comfortable being the person who says "walk me through what you do on a Tuesday" and then turning that into a specification.

Nice To Haves

  • Financial services, banking, or another regulated industry — you've worked inside compliance constraints rather than around them
  • Hands-on user of AI tools (Claude, ChatGPT, Copilot) as a working habit, not a curiosity
  • Experience in Jira, Confluence, or comparable work management tooling
  • Exposure to governance or architecture review processes

Responsibilities

  • Run AI use case intake
  • Serve as the intake point for AI requests from across the business
  • Interview stakeholders and document current-state workflows — including the undocumented steps and workarounds
  • Write the use case spec: problem, scope, data classification, success measure, effort estimate
  • Assess feasibility and business value; recommend prioritization or decline with a reason
  • Own the backlog
  • Maintain a groomed, execution-ready backlog with clear requirements, dependencies, and acceptance criteria
  • Run refinement with build partners; hand off work that doesn't require a second discovery pass
  • Maintain documentation for delivered capabilities so they can be reused rather than rebuilt
  • Drive adoption
  • Partner with the AI Adoption Manager and department AI Champions on rollout and onboarding for delivered capabilities
  • Track usage and adoption; identify where solutions stalled and why
  • Feed friction points and expansion opportunities back into the intake pipeline

Benefits

  • Eligibility for incentive awards based on both individual and business performance.
  • Comprehensive range of benefits.
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