For many large companies, artificial intelligence still lives in side projects. Small teams test tools, run pilots, and present results that struggle to spread beyond a few departments. Citi has taken a different path, where instead of keeping AI limited to specialists, the bank has spent the past two years pushing the technology into daily work in the organisation.
That effort has resulted in an internal AI workforce of roughly 4,000 employees, drawn from roles that range from technology and operations to risk and customer support. The figure was first reported by Business Insider, which detailed how Citi built its “AI Champions” and “AI Accelerators” programmes to encourage participation not central control.
The scale of integration is notable, as Citi employs around 182,000 people globally, and more than 70% of them now use firm-approved AI tools in some form, according to the same report. That level of use places Citi ahead of many peers that still restrict AI access to technical teams or innovation labs.
From central pilots to team-level adoption
Rather than start with tools, Citi focused on people. The bank invited employees to volunteer as AI Champions, giving them access to training, internal resources, and early versions of approved AI systems. The employees then supported colleagues in their own teams, acting as local points of contact not formal trainers.
The approach reflects a practical view of adoption. New tools often fail not because they lack features, but because staff do not know when or how to use them. By embedding support inside teams, Citi reduced the gap between experimentation and routine work.
Training played a central role. Employees could earn internal badges by completing courses or demonstrating how they used AI to improve their own tasks. The badges did not come with promotions or pay rises, but they helped create visibility and credibility in the organisation. According to Business Insider, this peer-driven model helped AI spread faster than top-down mandates.
Everyday use, with guardrails
Citi’s leadership has framed the effort as a response to scale not novelty. With operations spanning retail banking, investment services, compliance, and customer support, small efficiency gains can add up quickly. AI tools are being used to summarise documents, draft internal notes, analyse data sets, and assist with software development. None of these uses are new on their own, but the difference lies in how they are applied.
The focus on everyday tasks also shapes Citi’s risk posture. The bank has limited employees to firm-approved tools, with guardrails around what data can be used and how outputs are handled. That constraint has slowed some experiments, but it has also made managers more comfortable allowing broader access. In regulated industries, trust often matters more than speed.
What Citi’s approach shows about scaling AI
The structure of Citi’s programme suggests a lesson for other large enterprises. AI adoption does not require every employee to become an expert. It requires enough people to understand the tools well enough to apply them responsibly and explain them to others. By training thousands instead of dozens, Citi reduced its reliance on a small group of specialists.
There is also a cultural signal at play. Encouraging employees from non-technical roles to participate sends a message that AI is not only for engineers or data scientists. It becomes part of how work gets done, similar to spreadsheets or presentation software in earlier decades.
That shift aligns with broader industry trends. Surveys from firms like McKinsey have shown that many companies struggle to move AI projects into production, often citing talent gaps and unclear ownership. Citi’s model sidesteps some of those issues by distributing ownership in teams, while keeping governance centralised.
Still, the approach is not without limits. Peer-led adoption depends on sustained interest, and not all teams move at the same pace. There is also the risk that informal support networks become uneven, with some groups benefiting more than others. Citi has tried to address this by rotating Champions and updating training content as tools change.
What stands out is the bank’s willingness to treat AI as infrastructure not innovation. Instead of asking whether AI could transform the business, Citi asked where it could remove friction from existing work. That framing makes progress easier to measure and reduces pressure to produce dramatic results.
The experience also challenges a common assumption that AI adoption must start at the top. Citi’s senior leadership supported the effort, but much of the momentum came from employees who volunteered time to learn and teach. In large organisations, that bottom-up energy can be hard to generate, yet it often determines whether new technology sticks.
As more companies move from pilots to production, Citi’s experiment offers a useful case study. It shows that scale does not come from buying more tools, but from helping people feel confident using the ones they already have. For enterprises wondering why AI progress feels slow, the answer may lie less in strategy decks and more in how work actually gets done, one team at a time.
(Photo by Declan Sun)
See also: JPMorgan Chase treats AI spending as core infrastructure
Want to learn more about AI and big data from industry leaders? Check outAI & Big Data Expo taking place in Amsterdam, California, and London. This comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
