Objective: Transform current 27-person structure to AI-leveraged teams focused on senior expertise + AI execution.
- Scope: Technical leadership across all 4 teams
- Focus: Architecture alignment, engineering standards, AI adoption strategy
- Reports to: VP Engineering
- Scope: Commercial strategy and market alignment across teams
- Focus: Market requirements, commercial partnerships, regional compliance
- Reports to: Commercial Leadership
- Size: 6 people
- Composition: 1 Senior PM, 1 Senior EM, 2 Senior Engineers, 1 Senior Analytics Engineer, 1 Senior Product Designer
- Focus: Payment links, Gateway, Digital UPI, Web/Mobile UX
- AI-Native Model: Senior professionals design strategy/architecture → AI executes implementation
- Owns: End-to-end digital payment flows, experimentation, performance, quality
- Size: 6 people
- Composition: 1 Senior PM, 1 Senior EM, 2 Senior Engineers, 1 Senior Analytics Engineer, 1 Senior Product Designer
- Focus: TCR, Tap2Pay, Hardware integration, Mobile apps
- AI-Native Model: Hardware integration strategy → AI-generated mobile/backend code
- Owns: Physical payment acceptance, device management, monitoring, quality
- Size: 6 people
- Composition: 1 Senior PM, 1 Senior EM, 3 Senior Engineers, 1 Senior Operations Engineer
- Focus: Merchant onboarding, PSP integration (Adyen), Core platform services
- AI-Native Model: Platform architecture design → AI-automated service development
- Owns: Platform services, vendor integrations, quality, performance, monitoring, operational excellence
- Size: 3 people
- Composition: 1 Senior PM, 2 Senior Engineers
- Focus: Fraud monitoring, Chargeback management, Compliance oversight
- AI-Native Model: Risk system design → AI-automated compliance monitoring
- Owns: Risk systems, compliance automation, fraud detection, regulatory monitoring
- Current: 27 people
- Traditional Growth Path: Would require +6 people (33 total) over next 12 months
- AI-Native Target: 23 people (21 team members + 2 centralised leaders)
- Total Efficiency Gain: 30% fewer people than traditional scaling (10 people fewer)
- Focus: Senior expertise that can leverage AI for 2-3x output
- Each team owns quality, performance, monitoring, experimentation
- AI handles implementation, testing, deployment automation
- Teams own complete end-to-end delivery
- Senior people focus on strategy/architecture, AI handles execution
- Cross-stream functions absorbed into teams
- Embedded leadership within each team
- Minimal handoffs between product, design, engineering
Senior People Do:
- High-level thinking and strategy
- System design and architecture
- Business logic and requirements
- Risk assessment and compliance strategy
- Strategic analytics and experimental design
- Complex operations and incident response
- Design strategy and user research
- Data science insights and business intelligence
AI Handles:
- Code implementation and generation
- Automated testing and quality assurance
- Deployment and infrastructure automation
- Performance monitoring and optimisation
- ETL pipelines and data processing
- Standard reports and dashboard automation
- Design system implementation and responsive layouts
- Basic operational monitoring and alerting
- Teams 1 & 2: Embedded senior analytics engineers for product analytics and experimentation
- Team 3: Platform metrics and operational analytics handled by operations engineer
- AI: Automates data pipelines, standard reporting, dashboard generation
- Team 3: Dedicated senior operations engineer leads operational strategy
- All Teams: Senior engineers own operational excellence for their domains
- AI: Handles deployment automation, monitoring, basic incident response
- Teams 1 & 2: Embedded senior product designers for customer-facing experiences
- Cross-team: Design system coherence maintained through collaboration
- AI: Implements design systems, creates responsive layouts, generates basic prototypes
Target state for Acquiring proposition across global markets