<![CDATA[This research explores how the integration of big data and artificial intelligence can optimize corporate tax compliance through improved reporting accuracy and effective administrative controls. Employing a mixed methods approach with convergent parallel design, this study collected quantitative data from 187 medium to large enterprises in Indonesia and qualitative data from 28 in-depth interviews with tax practitioners. PLS-SEM analysis revealed significant causal relationships between big data system quality, AI effectiveness, and system integration level on corporate tax compliance. Research findings demonstrate an increase in tax compliance rate from 82.4% to 94.7%, a reduction in reporting errors by 75.9%, and data processing time efficiency up to 66.4%. Implementation of machine learning and natural language processing achieved risk detection accuracy of 87.3% with a false positive rate of only 8.6%. Qualitative findings revealed fundamental transformation of tax teams' roles from operational focus to strategic, with 70% of time now allocated to tax analysis and planning. Critical success factors include data quality, top management support, human resource competency in data analytics, and integrated technology architecture. Main challenges encompass ERP system fragmentation, organizational change resistance, and substantial investment requirements. This research contributes to developing a technology integration framework in taxation and provides practical guidance for companies as well as policy recommendations for tax authorities to support Indonesia's digital transformation of tax administration]]>
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