1. Change Strategy & Stakeholder Enablement:
- Develop and implement comprehensive change management plans for AI product and platform rollouts, focusing on minimizing resistance and maximizing engagement.
- Identify, analyze, and prepare key stakeholders across the organization, crafting targeted communication and training programs for technical and non-technical audiences.
- Build a network of AI champions and super-users to foster a culture of data-driven experimentation and advocacy.
2. Usage & Adoption Monitoring:
- Define, track, and report on a suite of adoption and utilization KPIs (e.g., active users, feature usage frequency, session depth, user proficiency scores).
- Establish and monitor dashboards for real-time visibility into AI tool adoption across different business units and user personas.
- Conduct regular pulse checks, surveys, and interviews to gather qualitative feedback on user experience, barriers, and enablers.
3. Value Itemization & Realization:
- Partner with Finance, Product, and Business leads to define the value hypothesis for each major AI initiative (e.g., cost savings, revenue growth, risk reduction, productivity gains).
- Create and maintain a "Value Realization Roadmap," linking specific AI capabilities to business processes and quantifying expected vs. achieved outcomes.
- Lead post-implementation reviews to measure ROI, document case studies, and identify opportunities for value optimization and scaling.
4. Continuous Improvement & Sustainment:
- Analyze adoption data and user feedback to identify gaps, friction points, and training needs.
- Develop targeted interventions (micro-trainings, guides, workflow tweaks).
- Iterate on change strategies based on empirical evidence to improve the adoption curve for subsequent AI rollouts.
- Institutionalize best practices and learnings into the company鈥檚 AI deployment playbook.