Petrobras saves $120M in tax filings within three weeks using AI-driven automation

Petrobras, South America's largest energy company with over 45,000 employees and 70 years of deepwater oil and gas experience, faced persistent inaccuracies in its tax filings under Brazil's notoriously complex regulatory regime. Brazilian tax code is among the most intricate in the world, and Petrobras's exposure across upstream, downstream, and administrative operations made compliance particularly demanding. While the company had invested heavily in AI for exploration and production, its corporate functions — tax, procurement, HR, and finance — remained largely manual. Each tax season required employees to work through weekends to meet filing deadlines, a burden that had persisted for 15 years, with no guarantee of accuracy.

Petrobras deployed Automation Anywhere's Automation Co-Pilot for Business Users within its tax department, leveraging generative AI to process unstructured regulatory documents at scale. The team ingested hundreds of pages of Brazilian tax code alongside months of historical internal tax data into a generative AI model, enabling natural language-driven automation of filing preparation and processing. Rather than a lengthy phased rollout, the implementation moved quickly from deployment to operational use within the tax function. The system allowed tax staff to interact with complex regulatory content without requiring deep technical expertise, bridging the longstanding gap between Petrobras's advanced AI capabilities in engineering and its minimal automation in back-office functions.

Within three weeks of deployment, Petrobras identified $120 million in tax savings — surfaced through AI analysis of filings that manual review had consistently missed. The filing process itself was fully automated, enabling the team to complete submissions within three days, compared to multi-week efforts requiring weekend overtime. It was the first tax season in 15 years where employees did not work weekends during the filing period. Key outcomes include:

• $120M in savings identified within 3 weeks
• 3-day filing completion (vs. prior multi-week, overtime-dependent process)
• >$1B projected annual savings as the model scales across HR, procurement, and finance
• Savings earmarked in part for Petrobras's renewable energy transition strategy

• Feeding large volumes of domain-specific regulatory documents into a generative AI model — combined with historical operational data — can surface compliance gaps and financial recoveries that manual review routinely misses.
• The gap between AI maturity in technical operations and administrative functions is common in large industrials; closing it quickly is achievable with the right automation tooling.
• A focused departmental pilot (tax) with measurable outcomes creates organizational proof points that accelerate enterprise-wide AI adoption across HR, finance, and procurement.
• Operational burden reduction (eliminating 15 years of weekend work) is as significant a success metric as cost savings when building internal support for AI programs.