CCC Strategy & Transformation Lead
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Description -
Strategy & Transformation Lead – Customer Support (AI-Driven Transformation)
Role Overview
The Strategy & Transformation Lead – Customer Support is a senior practitioner responsible for shaping and executing the end-to-end transformation of customer support through AI, digital capabilities, and operating model innovation.
This role provides delivery oversight and execution leadership across the full lifecycle of transformation initiatives—from ideation and business case development through piloting, scaled deployment, operational sustainment, and eventual decommissioning or reinvention.
The individual will act as a bridge between strategy, operations, technology, and customer experience, ensuring AI-driven initiatives deliver measurable improvements in customer outcomes, operational efficiency, and business value.
Key Accountabilities
1. Contributing to Strategic Leadership & Transformation Vision
Help define and continuously evolve the customer support transformation strategy, anchored in AI enablement and digital-first experiences.
Translate enterprise priorities into an actionable multi-year transformation roadmap (e.g., virtual agents, agent assist, automation, predictive support).
Identify opportunities to shift from reactive support to proactive, predictive, and outcome-based service models.
Provide thought leadership on emerging AI capabilities and their application to customer support.
2. End-to-End Lifecycle Ownership (Idea → End-of-Life)
a. Ideation & Opportunity Identification
Take part in structured discovery to identify customer pain points, inefficiencies, and growth opportunities.
Evaluate ideas using value-based frameworks (customer impact, cost reduction, revenue potential, scalability).
Build compelling business cases and investment proposals aligned with strategic goals.
b. Design & Experimentation
Define/Refine solution concepts, operating model implications, and success metrics.
Oversee proofs of concept (POCs) and pilots leveraging agile methodologies.
Ensure strong alignment between business, data science, and technology teams.
c. Testing & Validation
Assist with establishing robust testing frameworks (customer experience, operational impact, model effectiveness).
Validate AI model performance, adoption, and ethical considerations (bias, transparency, trust).
Make data-driven go/no-go decisions for scaling.
d. Deployment & Scaling
Contribute to enterprise rollout of validated solutions across regions, channels, and customer segments.
Partner with operations to embed solutions into workflows (e.g., agent tools, omnichannel platforms).
Drive change management, training, and adoption strategies at scale.
e. Sustainment & Optimization
Establish governance for ongoing performance monitoring and continuous improvement.
Ensure solutions evolve with changing customer needs, data inputs, and business priorities.
Optimize cost-to-serve while maintaining or improving CX outcomes.
f. End-of-Life / Reinvention
Define criteria for sunsetting, replacing, or modernizing solutions.
Transition planning to minimize business disruption and maximize value extraction.
Capture lessons learned to inform future initiatives.
3. AI-Driven Customer Support Transformation
Drive adoption and success of AI capabilities such as:
Conversational AI (chat/voice virtual agents)
Agent Assist and real-time guidance
Predictive analytics and intent recognition
Workflow automation and case routing
Ensure AI solutions are integrated across the customer journey and support ecosystem.
Balance customer experience, operational efficiency, and cost optimization.
4. Operating Model & Governance
Establish a transformation governance model including prioritization, funding, risk management, and performance tracking.
Define clear roles across product, operations, data, and technology teams.
Ensure disciplined execution through structured program management and reporting.
5.
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