Transform Pre-Post Call Through AI
· Lead the TA strategy, roadmap, and delivery of AI-powered pre-call and post-call capabilities that enhance field planning, execution, and follow-up.
· Translate business priorities, field needs, and customer insights into scalable AI solutions that improve field effectiveness and customer engagement.
· Continuously optimize AI recommendations and workflows using user feedback, performance analytics, and business outcomes.
Drive Pre-Post Call AI in Partnership with Stakeholders
· Serve as the primary liaison between business stakeholders and technical teams, ensuring solutions meet user, business, and commercial objectives.
· Facilitate prioritization decisions by balancing business value, user impact, technical feasibility, and resource constraints.
· Partner with IT and vendor teams to deliver high-quality solutions against agreed milestones, priorities, and success criteria.
Lead GPro TA Strategy, Adoption, and Optimization
· Serve as the TA business lead for GPro, ensuring alignment with TA and enterprise priorities.
· Partner with Sales, Marketing, Sales Analytics, IT, Commercial Learning & Development, and vendor teams to define requirements and deliver capabilities that improve field effectiveness.
· Lead stakeholder engagement, adoption, training, and change management efforts to maximize business value and user adoption.
· Drive continuous improvement by identifying opportunities to enhance workflows, solve business challenges, and evolve GPro to meet changing business needs.
Drive Field Productivity and Workflow Efficiency
· Embed AI-powered insights and recommendations into field workflows to enhance planning, execution, and decision-making.
· Identify and eliminate process inefficiencies through automation and workflow optimization, increasing productivity and reducing administrative burden.
· Design scalable user experiences that simplify decision-making and enable more effective customer engagement.
Measure Success and Drive Performance
· Establish and manage outcome-based KPIs to evaluate adoption, engagement, performance, and business impact.
· Monitor results, identify performance gaps, and implement corrective actions to improve user adoption and commercial outcomes.
· Drive accountability through regular performance reviews and stakeholder alignment.
Champion Data Quality and Responsible AI
· Ensure AI-generated outputs are trusted by enforcing strong data quality standards, governance, and regulatory requirements.
· Champion responsible AI principles, including fairness, transparency, accountability, and explainability across the product lifecycle.