AI Solutions and Adoption Lead

1. Identify Practical 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 and maintain an 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 using real business examples before rollout.  

• 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.  

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

5. Create Evaluation and Quality Standards 

• Define what good AI 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 to identify strong 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 company’s AI adoption matures, this role will expand into broader ownership of: 

• AI Adoption 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 should assist, where it can act, and where human approval is required.