EXCLUSIVE: “Not-so-Secret Agents” – Fernando Cea and Antoni Vidiella, Globant in ‘Discover Money20/20’
Fintech Finance.
For Globant, AI is more than a function on a user interface– AI is developing the firm’s software application from front to back. The Argentine-hatched company has been invested in AI for more than a decade, operating a decentralized ‘studio’ model, where teams specialize in verticals such as airlines, finance, energy, and media, or on particular customer platforms, such as Salesforce and Google Cloud. The rules held within the COBOL code– a language launched in 1959– were drawn out by Globant’s AI accelerator platform GeneXus Enterprise AI, and its findings were then verified by the bank’s IT team.
The proof-of-concept project resulted in 11,600 lines of code being translated into 4,921 lines of Java microservices code in 105 hours– compared to the estimated 560 hours required for a similar project carried out using more traditional methods.
With that work done, Globant was able to forecast a three-year timeframe for the bank’s entire IT modernization project, rather than the 10 years that such a project typically takes. In the last 6 months, Globant has entered strategic partnerships with Faros AI (Globant engineers use Faros to analyze software application performance) and Google Cloud– a tie-up that aims to enhance the use of Google Cloud AI across the industry.
Cea says such integrations are the ‘traditional way of introducing AI’, while investment in agents for the automation of workflows is a modern feature of the Age of AI.
For Globant, AI is more than a function on a user interface– AI is developing the firm’s software application from front to back. The Argentine-hatched organization has been invested in AI for more than a decade, operating a decentralized ‘studio’ model, where teams specialize in verticals such as airlines, finance, energy, and media, or on specific client platforms, such as Salesforce and Google Cloud. The bank’s legacy system, with around 60 million lines of code, was years old, presenting security risks, and hindering the bank’s ability to introduce modern products.
During a four-week pilot project, the Globant team used AI to document the essential knowledge held within the legacy source code, then developed Java microservices aligned with the bank’s target architecture to run a parallel system that performed the same functions, but more efficiently. The rules held within the COBOL code– a language released in 1959– were extracted by Globant’s AI accelerator platform GeneXus Enterprise AI, and its findings were then confirmed by the bank’s IT team.
The proof-of-concept project resulted in 11,600 lines of code being translated into 4,921 lines of Java microservices code in 105 hours– compared to the estimated 560 hours required for a similar project carried out using more traditional methods.
With that work done, Globant was able to predict a three-year timeframe for the bank’s entire IT modernization project, rather than the 10 years that such a project typically takes. Antoni Vidiella, Managing Director for Financial Solutions at Globant, argues that while most attention on agentic AI has been focused on using it as an individual productivity tool, including for banking services, it’s just as– if not more– useful in upgrading legacy IT. In the last 6 months, Globant has entered strategic partnerships with Faros AI (Globant engineers use Faros to assess software productivity) and Google Cloud– a tie-up that aims to improve the use of Google Cloud AI across the industry.
Cea says such integrations are the ‘standard way of introducing AI’, while investment in agents for the automation of workflows is a modern feature of the Age of AI.
