AI Solutions and Adoption Lead

1. IdentifyPractical AI Use Cases

• Work with business leaders and frontline teams to identify AI opportunities with measurable value.

• Translate business pain points into specific AI use cases with users inputs outputs and success metrics.

• Prioritize use cases based on business value feasibility data readiness user adoption and risk.

• Separate useful AI opportunities from low-value demos or tool-led experiments.

• Create andmaintainan AI opportunity backlog with business owners expected outcomes dependencies and adoption needs.

2. Design and Deploy AI-Enabled Solutions

• Design practical AI solutions including assistants workflow automation agentic workflows document analysis summarization classification knowledge tools task routing and decision support.

• Define the right level of AI involvement: assist draft summarize classify recommend route execute escalate or require approval.

• Create requirements process maps user stories acceptance criteria test cases and launch checklists.

• Work with engineering data security and systems partners to connect AI solutions to business tools and data.

• Test AI outputs usingreal businessexamples beforerollout.

• Support launch collect feedback measure results and improve the solution after real usage begins.

3. Support Agentic AI Workflows

• Help design multi-step AI workflows that can use tools follow instructions route tasks summarize information update systems or escalate to humans.

• Define where human review manual takeover fallback retry and escalation are needed.

• Work with technical teams to support integrations with CRM ERP ticketing systems knowledge bases documents Slack email data platforms or internal tools.

• Help test agentic workflows for accuracy reliability user trust and business safety.

• Monitor where agentic workflows fail and help improve the process prompts tools data or user handoff.

4. Drive AI Adoption

• Work directly with users to understand where AI helps and where it adds friction.

• Create training materials user guides SOPs FAQs demos office hours and practical examples.

• Run workshops and working sessions to help teams use AI safely and effectively.

• Help business leaders make AI part of daily operations instead of a side experiment.

• Measure adoption through usage user acceptance edits rejections escalations and feedback.

• Identifywhy users ignore distrust or misuse AI output then improve the solution and rollout plan.

5. Create Evaluation and Quality Standards

• Define whatgoodAI output looks like for each use case.

• Create simple evaluation criteria for accuracy completeness usefulness consistency safety and user acceptance.

• Test AI outputs against real examples and failure cases.

• Partner with legal compliance security and risk teams when use cases involve sensitive data customers regulated processes or business-critical decisions.

• Track quality after launch through user feedback corrections error rates escalation rates cost latency and business outcomes.

6. Coach and Enable Others

• Coach junior team members and business partners on AI use-case discovery process mapping solution design testing rollout and adoption.

• Review project plans and give feedback on scope user needs metrics risks and launch readiness.

• Create reusable templates checklists playbooks training materials and examples.

• Help teams learn how toidentifystrong AI opportunities and avoid weak ones.

• Over time help develop an internal bench of AI-capable operators analysts product managers and transformation partners.

7. Grow Into AI Operating Leadership

• As the companys AI adoption matures this role will expand into broader ownership of:

• AIAdoption playbooks.

• Launch readiness checklists.

• Evaluation and quality frameworks.

• User training programs.

• Business impact reporting.

• AI governance partnership with legal security compliance and data teams.

• Recommendations on which AI initiatives to scale pause redesign or retire.

• 5-10 years of experience across AI solutions automation analytics product business systems operations consulting enterprise technology or digital transformation.

• Hands-on experience helping launch AI automation analytics workflow or business-facing technology solutions.

• Experience working with business users to understand needs map processes test solutions and support adoption.

• Experience partnering with technical teams such as engineering data security or enterprise systems.

• Experience creating practical delivery materials such as requirements user guides workflow maps test cases training materials SOPs dashboards or project updates.

• Ability to communicate with both technical and non-technical teams.

• Strong interest in AI and growing into AI adoption leadership.

• Practical understanding of how LLMs AI assistants automation RAG agents prompts tool use and structured outputs can support business work.

• Ability to understand the limits of AI output and design review steps where needed.

• Ability to work with technical teams on data access integrations APIs permissions system dependencies and production readiness.

• Ability to test AI outputs using real examples and explain quality issues in business language.

• Familiarity with evaluation monitoring tracing logging failure review cost and reliability is helpful.

• Ability to move from discovery to launch without waiting for perfect conditions.

• Ability to create requirements workflow maps test plans launch plans guides training materials SOPs and impact updates.

• Ability to map a process across users systems handoffs approvals exceptions and failure points.

• Strong understanding of user adoption training trust incentives and workflow friction.

• Ability to define success through metrics such as time saved cycle time reduction error reduction adoption rate output acceptance escalation reduction customer response time or employee experience.

• Good judgment on where AI shouldassist where it can act and where human approval is.