One of the worldโs largest online retailers is looking to wring new efficiencies and cost savings out of greater reliance on the technology
Amazon.com is stepping up its use of generative artificial intelligence throughout its finance teams, as many companies look for ways to effectively move from testing and experimenting with the technology to prime time.
The e-commerce giant initially adopted rules-based systems, one of the earliest forms of AI that use a set of rules to solve problems and make decisions, in its finance organization, which it then augmented with machine learning. Generative AI now assists finance employees with more complex analyses.
Its finance teams are turning to generative AI in areas such as fraud detection, contract review, financial forecasting, personal productivity, interpretation of rules and regulations, and tax-related work, moves in part aimed at reducing costs, boosting efficiencies and increasing accuracy, company executives said. These use cases are in a mix of experimentation and implementation stages.
โWhile experimentation and getting to know the technology are things that we really want to speed up, actually deploying this into production and making sure that we are in a well-controlled situation is very, very important for us,โ said Dave George, vice president of finance technology at Amazon.
More broadly, Amazon is shifting its focus to AI innovations, with the capabilities of its cloud-computing unit Amazon Web Services driving a surge in companywide sales for the most recent quarter.
Amazon has said it expects cash capital expenditures to meaningfully increase this year as it makes investments in technology infrastructure, specifically generative AI efforts. The company booked $13.9 billion in cash capital expenditures during the first quarter, up 6% from the prior-year period. Amazon Web Services comprised 17.5% of Amazonโs $143.31 billion for the quarter, up from 16.8% of $127.36 billion the prior-year period.
Value creation from AI
The tone has changed in recent months across industries for finance executives navigating generative AI, from fascination with technological advances to a serious focus on its application and generating value from it. Some of the largest tech companies continue to be more aggressive and more willing to adopt generative AI.
Amazonโs in-house tax personnel make use of the technology as well. The tax compliance team created a tool that uses generative AI to help validate inbound invoices for value-added taxes, which businesses typically pay in the different stages of production of a good or service.
When the team receives an invoice, the tool automatically checks the invoiceโs validity before payment occurs, George said. Instead of manually validating these invoices, generative AI allows the team to automate the process, he said.
The many use cases
Generative AI also helps Amazon with the review of the many contracts it has with suppliers and customers. A tool takes into account Amazonโs standard reference data, policies and history of previous contracts to determine the changes needed to accommodate the contracts. AI then extracts key information for a more streamlined human review.
โThereโs a benefit there because humans are not wading through very, very dense contracts,โ said George, who leads the finance organizationโs two generative-AI working groups, one centered on innovation and the other on security.
The company trains models on vast data sets of financial transactions to detect and prevent financial fraud and anomalies. The models can identify patterns and irregularities that would be virtually impossible for humans to spot, said John Felton, chief financial officer of Amazon Web Services.
โThis enhanced fraud detection capability not only protects our bottom line but also helps us ensure compliance,โ said Felton, who, along with George, reports to the companyโs CFO, Brian Olsavsky, who has held the role since 2015. The company declined to comment on estimated cost savings.
These controls around transactions increasingly incorporate generative AI, and models that can interpret and explain the findings. Employees who were previously working with a random sample now focus on a list of the highest risk transactions enhanced by generative AI, George said.