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• Phase 2: Development and Integration (Months 7-18, Q3 2026-Q2 2027): » Focus: Engineering builds core platform (UDI, dashboards, compliance engine); Data Science develops AI agents and analytics; Technology enables pipelines and integrations. » Critical Paths: API stability (Engineering), data access (Data Science), security certificates (Technology). » Collaboration: Agile sprints with joint reviews, e.g., Engineering/Data Science on model APIs. » Milestones: Prototypes, code repos, models. Metrics: System uptime >99%, AI accuracy >90%, etc. • Phase 3: Testing and Pilot (Months 19-24, Q3-Q4 2027): » Focus: Envisioning Laboratory for Beta testing (e.g. VAT auto-assess pilots, no-stop border simulations). Refine based on feedback. » Collaboration: Cross-team hackathons for issue resolution. » Milestones: Evaluation reports, dashboards. Metrics: Reduced non-compliance detections, user satisfaction >85%, etc • Phase 4: Rollout & Optimization (Months 25+, Q1 2028 onward): » Focus: Full deployment, monitoring, and scaling (e.g. add minor taxes). Annual reviews for enhancements. » Collaboration: Ongoing via shared DevOps; external audits for ethics. » Milestones: Live operations, performance audits. Metrics: Cost < current % of revenue, voluntary compliance rates >95%, zero breaches. Proposed Budget allocation: 30% digital infrastructure, 40% development/AI, 20% testing, 10% optimization. Risks (e.g., data integration delays) mitigated via contingency planning. Success tracked holistically: Voluntary compliance via on-time filings and evasion reductions; efficiency through effort metrics and resolution times. This statement empowers teams to deliver a transformative platform, ensuring SARS leads in digital tax administration while upholding fairness and innovation.
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Intelligent Case Management System: » Automate routine tasks with Agentic AI (AI bots) - (autonomous for scripted tasks like query resolution, with human escalation for exceptions and high risks), drawing from examples such as the ATO’s AI governance for transparency and bias mitigation. Integrate compliance improvement for prioritising cases based on risk matrices. » Architecture: Workflow orchestration via tools, with AI “hooks” (e.g., via APIs to models trained on current and historical data) for proactive notifications, intuitive resolution, and evidence-based audits.
Design principles prioritise user-centricity, scalability, and security: 1. Intuitive Interfaces: Taxpayer dashboards with AI-guided chatbots (e.g., for clarity and certainty augmentation and tools), reducing compliance burden/effort to irreducible lows, inspired by examples like the IRAS’s seamless digital services. 2. Proactive and Responsible Enforcement: AI anticipates needs (e.g. a pre-populated assessments) and ensures timely and equitable responses, with metrics tracking rations such as compliance yield and unit cost/revenue ratios. 3. Modular and Interoperable: Use open standards (e.g., RESTful APIs, like IRAS’s Digital Integration Incentive) for extensibility to future taxes or partners. 4. Resilience and Performance: Design for low latency (<1s for queries) and zero major breaches, with hybrid setups for data sovereignty. 5. Cross-Team Alignment: Emphasise DevOps pipelines, shared repositories, and ethical guidelines to integrate engineering builds, data science models, and technology infrastructure - also address the culture and change management aspects for collaboration and inclusivity. 6.3.3 INTEGRATED PROGRAMME: PHASED IMPLEMENTATION, COLLABORATION, AND METRICS The programme follows a 3–5-year agile framework, using “Gantt” charts for visualisation, critical path analysis for dependencies (e.g., infrastructure before AI deployment), and quarterly milestones. It aligns with project management best practices (e.g., PMBOK), with cross-team collaboration via joint sprints, hackathons, and shared tools like Jira / Confluence. • Phase 1: Planning and Design (Months 1-6, Q1-Q2 2026): » Focus: Requirements gathering, architecture blueprints, legal reviews (e.g. Data exchange protocols and MOU’s). Technology team leads foundational infrastructure (e.g., cloud setup, security certs). » Collaboration: All teams in workshops; Data Science prototypes AI models. » Milestones: Infra blueprints, initial UDI designs. Metrics: On-time delivery (100%).
4. Partnership Integrations and Border Modernisation: » Expand SARS payment platform to link with South African Reserve Bank (SARB) for instant payments, enhancing financial inclusion and integrity through real- time analytics, similar to IRAS’s digital payment options. » For Customs & Excise, design a path to a “no-stop” single-window system (inspired by examples from Singapore TradeNet and EU Smart Borders). IoT sensors,
AI cameras, and biometric checks for low-risk flows (~1.5% intervention rate), sharing data with Border Management Authority (BMA) for seamless goods/ people profiling. The idea is to reduce the visibility and involvement of Customs Officers, except for service facilitation by exception. » Architecture: Secure data exchanges via APIs and “blockchain” type technology for immutable records, ensuring POPIA-compliant privacy. Ethical AI and Data Governance: » Balance autonomy (high for routines, low for high stakes)
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with human oversight, per examples from the ATO’s transparency statements and IMF analytics guidelines. Include bias audits, data quality pipelines, and helpful monitoring tools. 6.3.2 DESIGN GUIDANCE: PRINCIPLES AND BEST PRACTICES
SARS Modernisation White Paper 2025/26 – 2029/30
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