AI Enablement Lead

Job Details Description About Canal Insurance Canal Insurance Company specializes in insurance for commercial trucking and specialty transportation operations. Canal was founded in 1939 and is located in Greenville, South Carolina. At Canal, we recognize that our success would not be possible without the hard work and dedication of our employees. We know that happiness and productivity go hand in hand, and to that end, we consciously cultivate a culture that enables us to recruit and retain the very best talent in the business. A Culture With YOU in Mind Located in beautiful downtown Greenville, SCCareer growth & advancement opportunitiesComprehensive benefits packageEmployee referral programCasual dress codeInnovation-focused & customer-centric80+ years of industry expertiseCommitted to giving back to our communityUnquestioned integrity and commitment Benefits At Canal Basic & Voluntary Life Insurance PlansMedical, Dental, & VisionShort Term & Long Term Disability401(k) plan with company match up to 6%Flexible Spending AccountsEmployee Assistance ProgramsGenerous PTO Plan AI Enablement Lead Location: Greenville, SC We are looking for an Lead AI Enablement to lead the enterprise's transition from isolated AI experiments to scaled, governed, business-embedded AI adoption. This is a hybrid strategic-and-tactical role: you'll set the vision for how AI creates value across the business, and you'll personally roll up your sleeves to make adoption happen — inside business functions and inside IT. You'll be the connective tissue between business stakeholders who need AI-powered solutions, IT teams who need to adopt AI to move faster and build better, and the governance function that keeps all of it safe, compliant, and trusted. You will own the strategy, but you will also own the outcomes. What You'll Do Strategy & Evangelism Define and own the enterprise AI adoption strategy, aligned to business priorities and operating model Act as the internal evangelist for AI across the organization — building executive buy-in, business unit champions, and grassroots enthusiasm Identify, prioritize, and build the business case for high-value AI use cases across functions (Sales, Finance, Ops, HR, Customer Service, etc.) Translate ambiguous business problems into concrete AI-enabled solutions, partnering with business leaders to scope and sequence initiatives Represent AI strategy and progress to executive leadership and the board as needed Business Use Case Delivery Partner directly with business unit leaders to identify workflows where AI can measurably improve speed, quality, or cost Own end-to-end delivery of prioritized use cases: from business case, to pilot, to scaled deployment, to measured value Build and manage a prioritized AI use case pipeline / roadmap with clear ROI tracking Establish repeatable frameworks for evaluating, piloting, and scaling AI solutions across the business IT & Engineering Adoption Drive AI adoption within IT and engineering to increase development speed, code quality, and delivery efficiency Own rollout, training, and adoption strategy for developer-facing AI tools (e.g., GitHub Copilot, coding assistants, internal AI platforms) Partner with engineering leadership to embed AI into the SDLC — code generation, testing, code review, documentation, incident response Define and track adoption and efficiency metrics for AI-assisted engineering (e.g., cycle time, defect rates, developer satisfaction) Microsoft Copilot & Enterprise Tool Adoption Own the enterprise-wide adoption strategy for Microsoft Copilot and other licensed AI productivity tools Design and run enablement programs: training, champions networks, use-case libraries, adoption campaigns Track and report adoption metrics beyond vanity numbers (licenses issued) — actual usage depth, workflow integration, and productivity impact Partner with procurement/vendor management on tool rationalization and licensing strategy AI Governance Build and own the enterprise AI governance framework: policies for responsible use, data handling, model risk, and human oversight Establish approval workflows and accountability models for deploying AI into business-critical processes Partner with Legal, Risk, Compliance, and Security to ensure alignment with regulatory requirements (e.g., EU AI Act, sector-specific regulation) Own the AI risk register and ensure a documented, auditable decision trail for AI systems in production What You'll Bring 8+ years in technology strategy, digital transformation, or enterprise IT, with recent (2+ years) hands-on AI/GenAI implementation experience Demonstrated experience translating business problems into deployed AI solutions — not just advisory or strategy-deck experience Experience driving AI/tooling adoption within an engineering or IT organization, with a grasp of SDLC and developer workflows Experience leading Copilot, ChatGPT Enterprise, or comparable enterprise AI tool rollouts at scale Working knowledge of AI governance frameworks and emerging regulation (EU AI Act, NIST AI RMF, ISO/IEC 42001, or equivalent) Strong stakeholder management skills — credible with both business executives and engineering leadership Experience building and running cross-functional programs involving Legal, Compliance, Security, and Data teams Comfortable operating at both strategic (roadmap, board reporting) and tactical (pilot design, adoption metrics, hands-on rollout) altitude Preferred Skills Prior experience in a Head of AI Enablement, AI Transformation, or similar hybrid role Consulting background implementing AI programs, or in-house experience scaling AI beyond pilot stage Familiarity with Microsoft's AI stack (Copilot, Azure AI, Power Platform) or equivalent enterprise AI ecosystem Experience standing up an AI Center of Excellence or governance council from scratch Success Metrics (First 12 Months) AI adoption strategy and governance framework defined, approved, and operational [X] business use cases moved from pilot to scaled production with measured ROI Copilot / enterprise tool adoption increased from [baseline]% to [target]% active usage Measurable improvement in engineering delivery metrics (cycle time, code quality, deployment frequency) attributable to AI tooling adoption AI governance council established and operating with clear escalation and sign-off processes Zero material AI-related compliance or risk incidents