TAS: A community is born to scale AI
TAS is focusing on an internal community to transform skills, processes, and operating models. From compliance to the knowledge base, through to application development, AI is becoming a widespread and structured workstream.
An internal community spanning across the organization is driving seven development workstreams in the field of artificial intelligence. The objective is twofold: to spread technological culture and skills within the organization and, at the same time, to rapidly bring concrete AI initiatives into execution. “The initiative represents the evolution of TAS Lab, which started in 2023 as an experimental space and has now been transformed into a more structured and widespread community model,” says Fabrizio Annatelli, AI Program Lead, alongside Gabriele Paoleschi, from the TAS AI Community. “Adopting AI on a large scale is a journey that involves structure, processes, and above all, the organization. Therefore, we need to bring people on board, support them over time in understanding the technology’s potential and risks, and develop new mindsets and skills.”
Involving the company’s workforce
Hence the explicit focus on reskilling, considered a key lever for managing the transformation. “Not riding the AI wave today means risking falling behind. But it’s not about replacing people: the scope of skills is shifting, as is the way they are utilized,” underlines Annatelli. “TAS’s response was to directly involve the company’s workforce: the community, open to everyone, has seen widespread participation from employees across the company.”
The TAS Community
The profiles involved are diverse: from developers to business unit and department managers. Everyone participates in the seven active AI workstreams, directly contributing to project definition and execution. “In this initial phase, we are focused on internal transformation and process efficiency,” notes Annatelli. “We have structured pathways, organized into different sessions, to align the community on objectives and build a concrete execution plan. AI is not just an initiative confined to IT; it represents a cross-functional lever that requires organizational coordination and a clear structuring of processes.”
The three key workstreams
Among the ongoing projects, three main directions emerge. The first concerns regulations and is among the most complex. “Working with banks and insurance companies,” Annatelli premises, “a priority is to strengthen internal compliance safeguards and build a framework that clarifies what can and cannot be done with AI, in light of a continuously evolving regulatory landscape.” Alongside compliance, the application development workstream leverages AI to support coding, in compliance with the principles of explainability and ethics. This is a more mature area, partly thanks to the widespread use of AI in software development, where the human-in-the-loop model remains central. However, the most strategic project is the corporate knowledge base. “AI models are generic by nature,” highlights Annatelli. “Value is created when they are tailored to the specific context of the company. For this reason, we are building a structured knowledge base that includes regulatory, application, and business content. The knowledge base does not only serve to train models but enables a plurality of use cases: from proactive ticket management to generating analyses, all the way to supporting operational activities. AI agents specialize in specific tasks but do not make autonomous decisions: the responsibility always remains with humans.”
From experimentation to industrialization
The community has already brought several workstreams into the execution phase and is preparing for an advanced training phase, particularly on the knowledge base. “We are structuring the system to ensure localized data governance for critical content. For the most sensitive use cases, a dedicated LLM infrastructure based on open-weight models is being trialed, with an initial summary expected by May,” says Gabriele Paoleschi. “This allows us to securely manage large volumes of critical documentation. Another key element is the evolution of the operating model: no more rigid technological constraints, but adaptive processes, because AI evolves too quickly to be locked into static architectures. We need a dynamic approach capable of continuously updating itself, also in line with regulatory developments.”
The first Use Cases: Between Electronic Payments and T+1 Settlement
Alongside the main workstreams, TAS is developing vertical applications, particularly in the electronic payments sector. Predictive artificial intelligence is already being used for transaction monitoring in fraud prevention using mature techniques. Generative AI enables a broader scope of applications: from the corporate knowledge base to decision support for T+1 settlement, from an advanced GUI design framework for banking applications to advanced reporting systems based on natural language. “A concrete case concerns the transition to the T+1 regulation, which reduces financial transaction settlement times to one day,” explains Paoleschi. “We have developed proofs of concept to support matching and reconciliation activities, which are increasingly critical today due to shortened timelines, and here AI provides decision support by intercepting anomalies and suggesting possible corrective actions.”
A look at the market
Finally, TAS continues to closely monitor market developments to integrate AI into its offerings in a way that aligns with customer needs. “We are in a phase of continuous scouting,” concludes Annatelli. “AI is evolving rapidly and requires a pragmatic approach to be integrated into the business at scale. It is not a vertical technology, but a cross-functional capability that must be structured and governed.”
English version by TAS
Original article from AziendaBanca by G.C. : https://www.aziendabanca.it/notizie/tecno/tas-nasce-la-community-per-scalare-ai