AI powered business process automation with Snowflake

Project highlights

• Built first-of-its-kind AI-powered insurance compliance and ESG classification capabilities natively on Snowflake.
• Leveraged over 20 Snowflake capabilities in production to automate compliance, reporting and operational workflows.
• Processes 2.5 million daily sales records, more than 157,000 ESG transactions and 2.3 million searchable business records through AI-powered automation.
• Created repeatable patterns for compliance process improvement, governed self-service and AI-powered reporting to support use across industries.
• Used Cortex Code to accelerate delivery, reducing development timelines from months to weeks.
• Seven-year ongoing client relationship evolving from enterprise data warehousing to AI-powered operational platforms and intelligent automation.
  

About the client

The client is one of Australia's largest property groups, managing a diverse portfolio of retail, office and logistics assets.

Over several years, the organisation invested heavily in Snowflake as its enterprise data platform. As business expectations evolved, the challenge shifted from simply storing and reporting data to using AI and automation to improve day-to-day operations, reduce manual effort and enable faster business decisions.

Client request

The client wanted to move beyond traditional reporting and use Snowflake to automate operational processes, improve regulatory compliance and give business users faster access to trusted information.

They needed a partner that understood both enterprise data architecture and complex operational workflows, capable of applying AI safely within a governed environment while creating reusable capabilities that could scale across the business.

Challenge

The organisation needed more than better reporting. It wanted to use AI to automate operational processes that had traditionally relied on manual document review, spreadsheet reconciliation and specialist knowledge.

The solution had to solve four connected challenges:

1. Automate complex document-driven compliance processes while maintaining complete auditability.
2. Deliver trusted self-service analytics across multiple business domains without compromising governance.
3. Process millions of operational records through scalable, automated data pipelines.
4. Create reusable AI and data patterns that could support future business use cases rather than solving individual problems in isolation.

Solution

AI-powered operational workflows

We transformed manual, document-intensive business processes into governed AI-powered workflows. For example, AI now reads insurance certificates, extracts key policy information, matches it against lease records, identifies exceptions and routes work through review workflows with a complete audit trail. The same approach is used to classify more than 157,000 financial transactions for ESG reporting, improving consistency while significantly reducing manual effort.

Governed self-service across the business

Rather than building separate reporting solutions for each function, we established governed data products across procurement, finance, sales, customer and technology domains. Business users can securely search, discover and explore trusted data using AI-powered search and natural language, while IT retains control through embedded governance, security policies and continuous data quality monitoring.

Automation at enterprise scale

We replaced a manual monthly sales reporting process with an automated workflow that ingests 2.5 million daily and 9.8 million hourly retail sales records through live data sharing. A Streamlit-based management application enables teams to review exceptions, while AI-powered search and automated notifications distribute retailer performance insights without manual reconciliation or reporting. The same delivery pattern can be extended to automate other recurring operational processes across the business.

Results

The project significantly influenced how the organisation uses Snowflake.

Instead of acting primarily as a data warehouse, Snowflake now supports operational decision-making, compliance automation and governed self-service across the enterprise.

Key outcomes include:

• More than 157,000 ESG transactions automatically classified for regulatory reporting.
• 11 production Streamlit applications supporting operational business processes.
• 376 automated Snowflake Tasks orchestrating enterprise data pipelines.
• Over 30 source systems integrated into a single-governed platform.
• 2.3 million invoice records searchable using AI-powered semantic search.
• 2.5 million retail sales records processed daily through automated Snowflake pipelines.
• Six business domains supported through governed self-service data products. The project changed how the organisation works with data by giving business teams direct access to trusted, reusable information.

Lessons learned

The greatest opportunity for AI often sits inside operational processes rather than traditional analytics.

By combining AI, automation and governed data products within the same platform, organisations can eliminate repetitive manual work while improving consistency, auditability and business confidence.

The project also demonstrated how Snowflake, when architected thoughtfully, evolves from a reporting platform into a dynamic operational environment — enabling document intelligence, automated decision-making, self-service and continuous business improvement.