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AI in corporate finance delivers consistent efficiency and accuracy gains in measurable processes—especially record-to-report and internal audit—but evidence on strategic decision-support benefits is fragmented; successful transformation requires strong data foundations, workforce reskilling, process redesign and early AI governance.

PEMETAAN DOMAIN PROSES, OUTCOME, DAN TATA KELOLA AI-ENABLED FINANCE TRANSFORMATION PADA CORPORATE FINANCE FUNCTION: SYSTEMATIC LITERATURE REVIEW
Sigit Sukmono · February 02, 2026 · Jurnal Ekonomi dan Manajemen
openalex review_meta n/a evidence 7/10 relevance Summary only summary available; pdf_status=not_found DOI Source PDF

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The systematic review finds AI adoption in corporate finance is concentrated on measurable processes (record-to-report, internal audit) where it reliably improves efficiency and accuracy, while strategic outcomes like decision support remain patchily measured and dependent on data foundations, reskilling, process redesign, and governance.

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Penelitian ini menyusun sintesis terstruktur tentang AI-enabled finance transformation pada corporate finance function karena literatur masih terfragmentasi lintas FP&A, record-to-report, pelaporan, audit/assurance, dan governance, sehingga temuan sering heterogen. Metode yang digunakan adalah systematic literature review (SLR) terhadap artikel peer-reviewed; data dikumpulkan melalui penelusuran basis data akademik dan snowballing, lalu diseleksi dengan deduplikasi, screening judul–abstrak, serta evaluasi full-text. Analisis dilakukan secara deskriptif dan tematik untuk memetakan rantai teknologi, perubahan proses/kerja, outcome & metrik, governance. Hasil menunjukkan konsentrasi studi pada proses yang paling terukur, terutama record-to-report dan internal audit/compliance, dengan perhatian pada FP&A/forecasting dan sistem pelaporan. Outcome yang paling konsisten adalah efisiensi dan kualitas/akurasi, sedangkan outcome strategis (decision support, business partnering) masih memakai proksi yang kurang seragam. Keberhasilan transformasi dipengaruhi fondasi data, kapasitas SDM, dan redesign proses, serta membutuhkan governance kuat (auditability, readiness data/process mining, bias/model risk). Implikasinya, OCFO perlu implementasi bertahap: quick wins pada proses terukur, penguatan data dan reskilling, serta tata kelola AI sejak awal. Orisinalitas penelitian ini adalah kerangka lintas-silo yang menghubungkan pilihan teknologi, perubahan proses, metrik outcome, dan governance untuk menjelaskan heterogenitas dampak AI pada fungsi keuangan.

Summary

Main Finding

Penelitian menyintesis literatur tentang transformasi fungsi keuangan korporasi yang didorong AI dan menemukan bahwa dampak paling konsisten terjadi pada proses yang paling terukur (terutama record-to-report dan internal audit/compliance), dengan outcome utama efisiensi dan peningkatan kualitas/akurasi. Dampak strategis (decision support, business partnering) masih belum konsisten karena penggunaan proksi yang beragam. Keberhasilan transformasi bergantung pada fondasi data, kapasitas SDM, redesign proses, dan penerapan tata kelola AI yang kuat.

Key Points

  • Ruang lingkup literatur: studi tersebar lintas FP&A, record-to-report (R2R), pelaporan, audit/assurance, dan governance; hasil heterogen antar-silo.
  • Konsentrasi studi: proses yang mudah diukur (R2R, internal audit/compliance); juga ada perhatian pada FP&A/forecasting dan sistem pelaporan.
  • Outcome dominan: efisiensi (waktu, biaya) dan quality/akurasi data/hasil.
  • Outcome strategis: decision support dan business partnering sering diukur dengan proksi yang tidak seragam → interpretasi terbatas.
  • Determinan keberhasilan: kesiapan data (kualitas, integrasi), kapasitas SDM (skill AI/data), redesign proses kerja, dan tata kelola (auditability, manajemen bias/model risk, readiness untuk data/process mining).
  • Rekomendasi praktis untuk OCFO: pendekatan bertahap — raih quick wins pada proses terukur; simultan perkuat data & reskilling; integrasikan governance AI sejak awal.

Data & Methods

  • Metode: systematic literature review (SLR) terhadap artikel peer‑reviewed.
  • Strategi pengumpulan: pencarian pada basis data akademik + snowballing.
  • Proses seleksi: deduplikasi → screening judul & abstrak → evaluasi full‑text.
  • Analisis: deskriptif untuk peta distribusi studi; analisis tematik untuk memetakan:
    • rantai teknologi (tool/algoritma yang digunakan),
    • perubahan proses/pekerjaan,
    • outcome & metrik yang dilaporkan,
    • aspek governance (auditability, bias, readiness).
  • Sumbangan metodologis: kerangka lintas‑silo menghubungkan pilihan teknologi, perubahan proses, metrik outcome, dan governance untuk menjelaskan heterogenitas temuan.

Implications for AI Economics

  • Pengukuran dan comparability:
    • Butuh standardisasi metrik untuk outcome strategis (decision quality, business partnering value) agar studi dapat dibandingkan dan digabungkan.
  • Riset desain & identifikasi kausal:
    • Perlu studi kuantitatif yang lebih kuat (eksperimen, quasi‑eksperimen, longitudinal) untuk menilai efek kausal AI pada kinerja keuangan dan keputusan manajerial.
  • Ekonomi organisasi & tenaga kerja:
    • Teliti dampak reskilling, perubahan tugas, dan substitusi/tambah nilai tenaga kerja dalam fungsi keuangan.
  • Governance & pasar:
    • Analisis biaya‑manfaat dari tata kelola AI (auditability, mitigasi bias, model risk) dan implikasi regulasi bagi pelaporan korporasi.
  • Data readiness & infrastruktur:
    • Kuantifikasi nilai investasi pada data/infrastruktur dan titik balik (thresholds) di mana investasi tersebut mengubah efektivitas adopsi AI.
  • Kebijakan praktis untuk OCFO:
    • Prioritaskan investasi pada proses terukur untuk quick wins sambil membangun kapasitas data dan tata kelola untuk mendukung inisiatif strategis jangka panjang.

Assessment

Paper Typereview_meta Evidence Strengthn/a — This is a systematic literature review synthesizing published studies rather than producing primary causal estimates; the underlying literature is heterogeneous and largely descriptive, so the paper does not itself provide new causal identification. Methods Rigormedium — The study follows standard SLR steps (database searches, snowballing, deduplication, title–abstract screening, full-text evaluation, descriptive and thematic analysis) and produces a cross-silo framework, but the description lacks mention of pre-registered protocol/PRISMA compliance, explicit inclusion/exclusion criteria, formal quality/risk-of-bias appraisal, or quantitative meta-analysis. SamplePeer-reviewed academic articles identified via academic database searches and snowballing, covering AI applications across corporate finance subfunctions (FP&A/forecasting, record-to-report, reporting, internal audit/assurance, governance); exact number of articles and time span not specified in summary. Themesproductivity governance skills_training org_design adoption human_ai_collab GeneralizabilityExcludes gray literature and practitioner reports, which may bias findings toward academic topics., Concentration of studies on highly measurable processes (e.g., record-to-report, audit) limits applicability to less-measured strategic functions., Heterogeneous study designs and outcome measures reduce ability to generalize effect sizes or causal claims., Likely skew toward contexts studied in available literature (e.g., developed-country firms, larger organizations) though geographic/sample coverage not specified.

Claims (8)

ClaimDirectionOutcomeConfidence & EvidenceDetails
The literature on AI-enabled finance transformation is fragmented across FP&A, record-to-report, reporting, audit/assurance, and governance, producing heterogeneous findings. Research Productivity mixed fragmentation of literature and heterogeneity of findings
Reading fidelity high
Study strength medium
not reported
0.24
This study used a systematic literature review (SLR) method: academic database searches plus snowballing, deduplication, title–abstract screening, full-text evaluation, and descriptive plus thematic analysis. Research Productivity null_result research method (SLR) implementation
Reading fidelity high
Study strength high
not reported
0.4
Existing studies concentrate on the most measurable processes, especially record-to-report and internal audit/compliance, with notable attention to FP&A/forecasting and reporting systems. Adoption Rate positive concentration of research across finance processes
Reading fidelity high
Study strength medium
not reported
0.24
The most consistent outcomes reported across studies are efficiency gains and improved quality/accuracy. Organizational Efficiency positive efficiency and quality/accuracy of finance processes
Reading fidelity high
Study strength medium
not reported
0.24
Strategic outcomes (e.g., decision support, business partnering) are still measured by studies using heterogeneous and non‑uniform proxy metrics. Decision Quality mixed measurement of strategic outcomes (decision support, business partnering)
Reading fidelity high
Study strength medium
not reported
0.24
Successful AI-enabled finance transformation depends on data foundations, human resource capacity (reskilling), and process redesign, and it requires strong governance (auditability, data/process mining readiness, management of bias and model risk). Governance And Regulation positive factors influencing success of transformation
Reading fidelity high
Study strength medium
not reported
0.24
Practical implication: Chief Financial Officers (OCFOs) should pursue a phased implementation—target quick wins on highly measurable processes, strengthen data and reskilling, and embed AI governance from the outset. Governance And Regulation positive recommended implementation strategy for OCFOs
Reading fidelity high
Study strength speculative
not reported
0.04
Original contribution: the paper provides a cross‑silo framework linking technology choices, process changes, outcome metrics, and governance to explain heterogeneity of AI impacts on the corporate finance function. Innovation Output positive novel framework linking technology, processes, metrics, and governance
Reading fidelity high
Study strength speculative
not reported
0.04

Notes