AI Innovation Analyst
Publish Date 07.02.2026
Roofing Corp of America
Job Description - AI Innovation Analyst
Roofing Corp of America (RCA) is a subsidiary of FirstService Corporation (NASDAQ and TSX: FSV), a North American leader in the essential outsourced property services sector. Since inception, RCA has added 16 roofing services companies to its growing portfolio with 29 branch locations across the United States, each differentiated by a unique and localized commercial strategy. RCA has united some of the most respected regional roofing companies into a single, scalable platform - allowing our companies to maintain their local identity while gaining access to resources, technology, and operational excellence. RCA's seasoned and growing Field Support Office drives strategic development, enhances operational efficiencies, and leverages the organization's expanding scale. This central team also facilitates extensive best-practice sharing across our high-performing network of local contractors, ensuring exceptional service and continuous improvement across the organization.
RCA is committed to innovation, safety, and sustainability, empowering our partners to deliver high-quality results with exceptional customer service. For additional information on Roofing Corp of America, please visit www.roofingcorp.com.
Position Details:
The AI Innovation Analyst is a hybrid business, technology, and project execution role responsible for helping RCA identify, evaluate, prototype, implement, and communicate practical AI use / use case development / education across the organization. This role will work directly with executives, business unit leaders, functional managers, frontline staff, IT, Security, Data, and external vendors to turn AI ideas into well-defined, secure, measurable solutions. This role is part business analyst, part project manager, part AI technical resource, and part AI innovation catalyst. The ideal candidate is comfortable facilitating conversations with C-level executives about strategic priorities, while also sitting with field, operations, sales, finance, service, HR, marketing, or IT users to understand how work actually gets done and where AI can remove friction. This includes capturing use cases, documenting requirements, assessing feasibility, coordinating pilots, supporting technical configuration, testing AI tools, measuring outcomes, and helping users adopt approved AI capabilities safely. This is not a pure data scientist, machine learning engineer, or research role. It is a practical enterprise AI enablement role focused on business value, secure adoption, project execution, and hands-on solution facilitation.
Positioning Statement
The AI Innovation Analyst will help RCA turn AI from an emerging technology into a practical, secure, and measurable business capability. This role will work across all levels of the organization - from C-level leaders to frontline staff - to identify opportunities, shape use cases, coordinate pilots, support technical enablement, promote responsible AI use, and help RCA safely scale AI-driven innovation across the enterprise.
Role Purpose
The AI Innovation Analyst will help RCA:
Identify high-value AI use cases with business users
Translate business needs into AI solution requirements
Facilitate AI workshops with executives, managers, and staff-level employees
Support development and configuration of AI prototypes and pilots
Coordinate AI projects from intake through pilot and production readiness
Help ensure AI solutions meet RCA security, governance, and data access requirements
Build prompt templates, AI playbooks, and user enablement materials
Track AI adoption, outcomes, risks, issues, and lessons learned
Serve as a practical translator between business teams, IT, Security, Data, vendors, and leadership
Required Qualifications
Bachelor's degree in Business, Information Technology, Computer Science, Data Analytics, Engineering, Cybersecurity, or a related field; equivalent experience may be considered
4+ years of experience in business analysis, project management / coordination, technology implementation, data/analytics, automation, innovation, or digital transformation
Demonstrated interest in and working knowledge of generative AI, automation, analytics, or enterprise AI tools
Experience gathering requirements, documenting workflows, facilitating stakeholder discussions, and translating business needs into solution requirements
Experience coordinating cross-functional projects with business, technical, and leadership stakeholders
Ability to work with both senior executives and staff-level users in a clear, credible, and practical manner
Strong analytical skills with the ability to assess business value, feasibility, risk, and adoption considerations
Excellent written and verbal communication skills
Strong organizational skills and ability to manage multiple pilots, tasks, stakeholders, and deadlines
Working knowledge of Microsoft 365 tools, SharePoint, Teams, Excel, PowerPoint, and collaboration platforms
Ability to learn new tools quickly and explain them to others in simple, practical terms
Preferred Qualifications
Experience with multiple AI or automation platforms, such as:
ChatGPT Enterprise or Business
Microsoft Copilot
Claude / Claude Code
Azure AI
Power Automate / Power Apps
AI-enabled SaaS tools
Experience creating prompts, GPTs, AI assistants, automations, dashboards, or workflow prototypes
Familiarity with AI concepts such as LLMs, RAG, AI agents, prompt engineering, embeddings, APIs, and human-in-the-loop workflows
Familiarity with AI security concepts such as data oversharing, prompt injection, hallucination, DLP, least privilege, audit logging, and approved tool governance
Experience working in construction, field services, commercial services, industrial operations, or multi-business-unit organizations
Experience supporting M&A integration, operational standardization, or enterprise transformation programs
Familiarity with business systems such as ERP, CRM, field service, estimating, service management, or ticketing platforms
Core Competencies:
Business Analysis
Understands business processes quickly
Asks strong discovery questions
Documents workflows and requirements clearly
Identifies root causes, not just symptoms
Converts business needs into actionable use cases
Project Management
Organizes work into plans, tasks, owners, deadlines, and risks
Keeps stakeholders aligned
Tracks issues and follows through
Communicates status clearly
Escalates blockers early
AI Technical Curiosity
Experiments safely with AI tools
Understands practical AI capabilities and limitations
Can build simple prototypes or demos
Learns emerging tools quickly
Knows when to involve deeper technical resources
AI Security & Governance Awareness
Understands why data access, privacy, identity, logging, and approval controls matter
Recognizes high-risk use cases
Supports responsible AI practices
Helps users avoid unsafe or unapproved AI usage
Stakeholder Communication
Can speak credibly with executives about business value
Can speak practically with staff about daily workflow pain points
Can translate between business, IT, Security, Data, and vendor teams
Builds trust with users who may be unfamiliar with AI
Innovation Mindset
Looks for practical ways to improve how work gets done
Balances creativity with control
Focuses on measurable outcomes
Challenges assumptions constructively
Turns ideas into structured pilots
Success Metrics:
Metric
How It Is Measured
Metric
How It Is Measured
Use Case Pipeline
Number and quality of AI use cases captured, documented, and advanced
Stakeholder Engagement
Participation from executives, managers, and staff-level users in AI discovery and pilots
Pilot Execution
Pilots delivered with clear scope, timeline, success metrics, and lessons learned
Business Value
Measured productivity, cycle-time, quality, revenue, margin, or risk improvement
AI Adoption
Growth in use of approved AI tools, templates, and workflows
Governance Compliance
Use cases documented, reviewed, approved, and inventoried according to RCA standards
Security Awareness
Reduction in unapproved AI usage and improved understanding of responsible AI practices
Enablement Impact
Training participation, prompt library usage, and user satisfaction
Portfolio Visibility
Accurate tracking of AI ideas, pilots, tools, owners, costs, risks, and outcomes
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