AI Solutions and Adoption Lead

** Job Summary*
* The  
** AI Solutions & Adoption Lead
** will help the Print Supplies move AI from experimentation into business value. This is a hands-on role for someone that has already worked with AI, workflow, automation, analytics, or business systems and is ready to grow into broader AI leadership.

This person will partner with Print Supplies teams to identify practical AI opportunities, design AI-enabled solutions, support agentic workflows, test output quality, train users, and drive adoption. The role requires someone who can work close to the work: interviewing users, mapping processes, reviewing data, testing AI outputs, coordinating with technical teams, and helping business teams use AI in daily operations.

The person will not need to arrive with every answer. He or she should bring strong hands-on experience, good judgment, curiosity, and the ability to learn quickly. Over time, this role is expected to help define standards, playbooks, training methods, and the operating model for AI across the Print Supplies.

The ideal candidate has personally helped launch AI, automation, analytics, digital, or workflow solutions with users. They understand that AI adoption depends on more than tools. It requires business context, user trust, data readiness, quality checks, training, process fit, and measurable outcomes.

You do not need to hold a formal 'AI Lead' title to apply for this role. You should have enough hands-on experience to lead AI use cases, work with technical partners, support business teams, and learn into broader ownership.

** Responsibilities*
* ** 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,…