Manual checks across systems bottlenecked the financial close. Now, AI automates ledger entries while analysts manage exceptions.
A multinational technology company with over 270,000 employees and USD 60 billion in annual revenue processes complex financial close cycles globally.
The journal entry process required collecting data from multiple systems and performing extensive manual checks to minimize errors during peak...
Enterprise technology and consulting for cloud computing, AI, and business solutions.
Note — IBM is also the vendor behind this implementation.
IBM's Journal entry processing is part of this use case:
Related implementations across industries and use cases
Finance teams managed 1.5M transactions via scattered spreadsheets. Now, AI agents extract data and route journal entries in seconds.
Renewal quotes built by hand, scattered across regional systems—quarter-end always a scramble. Watsonx now generates them six months early.
9,000 POs handled through fragmented manual steps. End-to-end AI agents cut quote comparison from 30 minutes to 3.
Manual spot-checks missed duplicates. AI now audits 100% of spend, catching $20k in errors and reclaiming 160 hours for analysis.
Fraud signals buried in unstructured bank data. Generative AI now categorizes each account before models score 500 transactions per second.
Selling AI transformation required proving it internally first. A company-wide rollout wired Copilot into daily work across every function.
Agents looked healthy — 200s, no errors — while decisions quietly deteriorated. Unified tracing across every layer now surfaces the why.
90% of invoices had exceptions; AP analysts corrected them line by line. An agent now resolves mismatches before they reach finance.
Bond selection took days of manual review. Custom AI agents now surface risks, empowering analysts to execute trades in hours.
Manual checks across systems bottlenecked the financial close. Now, AI automates ledger entries while analysts manage exceptions.
A multinational technology company with over 270,000 employees and USD 60 billion in annual revenue processes complex financial close cycles globally.
The journal entry process required collecting data from multiple systems and performing extensive manual checks to minimize errors during peak...
Enterprise technology and consulting for cloud computing, AI, and business solutions.
Note — IBM is also the vendor behind this implementation.
IBM's Journal entry processing is part of this use case:
Related implementations across industries and use cases
Finance teams managed 1.5M transactions via scattered spreadsheets. Now, AI agents extract data and route journal entries in seconds.
Renewal quotes built by hand, scattered across regional systems—quarter-end always a scramble. Watsonx now generates them six months early.
9,000 POs handled through fragmented manual steps. End-to-end AI agents cut quote comparison from 30 minutes to 3.
Manual spot-checks missed duplicates. AI now audits 100% of spend, catching $20k in errors and reclaiming 160 hours for analysis.
Fraud signals buried in unstructured bank data. Generative AI now categorizes each account before models score 500 transactions per second.
Selling AI transformation required proving it internally first. A company-wide rollout wired Copilot into daily work across every function.
Agents looked healthy — 200s, no errors — while decisions quietly deteriorated. Unified tracing across every layer now surfaces the why.
90% of invoices had exceptions; AP analysts corrected them line by line. An agent now resolves mismatches before they reach finance.
Bond selection took days of manual review. Custom AI agents now surface risks, empowering analysts to execute trades in hours.