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FinSense Africa is recruiting for several key positions as part of its transformation into an AI-augmented organization. Discover the opportunities below.
AI Adoption & Enablement Lead
Contract Type: Full-time
Qualification: BA/BSc/HND, MBA/MSc/MA
Location: Not specified
Field: Data, Business Analysis and AI, ICT/Computing
Job Description:
As the AI Adoption & Enablement Lead, this role is the primary change agent driving human adoption of AI across the organization – transforming AI platform capabilities into real, daily productivity gains as part of the multi-year AI workforce transformation. The role bridges the AI engineering team and the wider business, translating what is technically possible into what is practical and valuable for teams.
Required Skills:
The position requires a blend of technology, communication, training, and change management skills. It exists to accelerate safe and responsible AI adoption – by cultivating a network of AI champions, sourcing and guiding high-impact use cases, and embedding AI copilots into daily workflows – so that the organization becomes truly AI-augmented and human-led.
Desired Profile:
Technical Skills:
Adoption Strategy and Planning: Develop and own the AI adoption and deployment roadmap aligned with the transformation plan, with clear objectives for the AI augmentation index.
Training Program Delivery: Design and deliver training programs, workshops, demos, and onboarding for AI copilots and tools across business units.
Deployment Content: Produce guides, quick-start guides, prompt libraries, FAQs, and success stories that make AI easy to adopt and reuse.
Champion Network: Build and coordinate a cross-functional network of AI champions and a community of practice; equip champions to drive adoption locally.
Use Case Pipeline: Source, qualify, and prioritize AI use cases with business owners and the AI engineering team; track them from idea to adoption.
Adoption Measurement: Define adoption KPIs, instrument usage tracking with the engineering team, and report progress and impact to management and the AI steering committee.
Responsible AI Deployment: Embed human-in-the-loop, transparency, and responsible AI principles into all deployments; help users understand controls and escalation paths.
Stakeholder Engagement: Collaborate with business unit heads, HR/training, risk and compliance, and internal communications to ensure the success of adoption initiatives.
Feedback Loop: Gather feedback from
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