Many large companies continue to relegate artificial intelligence (AI) to side projects, confining its use to small teams for testing and pilots with limited organizational spread. Citi, however, has adopted a markedly different strategy over the past two years, proactively integrating AI into the daily operations of its vast workforce rather than restricting it to a specialist domain. This ambitious initiative has cultivated an internal AI workforce of approximately 4,000 employees, spanning diverse roles from technology and operations to risk management and customer support.

This extensive integration was initially reported by Business Insider, which highlighted Citi's innovative “AI Champions” and “AI Accelerators” programs. These initiatives were designed to foster broad participation rather than enforce central control. The scale of adoption is particularly noteworthy, considering Citi employs roughly 182,000 individuals globally; more than 70% of these employees now utilize firm-approved AI tools in some capacity. This level of engagement significantly positions Citi ahead of many industry peers who typically limit AI access to specialized technical teams or dedicated innovation labs.

Citi's foundational approach prioritized people over tools. The bank invited employees to volunteer as AI Champions, providing them with essential training, access to internal resources, and early versions of approved AI systems. These champions, in turn, offered crucial support to their colleagues within their respective teams, functioning as accessible local contacts rather than formal trainers. This practical, embedded support model proved effective in bridging the common gap between experimental technology adoption and its seamless integration into routine work, where new tools often fail due to a lack of understanding regarding their appropriate application.

Training was a cornerstone of the program. Employees could earn internal badges by successfully completing courses or by demonstrating tangible improvements in their tasks through AI utilization. While these badges did not correlate with promotions or salary increases, they significantly contributed to enhancing visibility and credibility within the organization. This peer-driven dissemination model, as detailed by Business Insider, proved more effective in accelerating AI adoption compared to traditional top-down mandates.

Citi's leadership framed the widespread AI effort as a response to the need for scale and efficiency, rather than a pursuit of novelty. With extensive operations encompassing retail banking, investment services, compliance, and customer support, even marginal efficiency gains achieved through AI can yield substantial cumulative benefits. AI tools are currently deployed for various everyday tasks, including summarizing documents, drafting internal notes, analyzing complex datasets, and assisting with software development. While these individual applications are not inherently novel, their widespread and integrated application across the organization represents a significant departure from conventional approaches.

The emphasis on everyday tasks also shapes Citi's careful risk posture. The bank strictly limits employees to firm-approved tools, implementing robust guardrails around data usage and output handling. Although these constraints may have incrementally slowed certain experimental initiatives, they have simultaneously fostered greater managerial confidence, enabling broader access to AI tools. In highly regulated industries such as banking, maintaining trust and compliance frequently takes precedence over optimizing for speed alone.

Citi's programmatic structure offers valuable insights for other large enterprises contemplating AI integration. Successful AI adoption does not necessitate transforming every employee into an expert. Instead, it requires a sufficient number of individuals to comprehend and apply the tools responsibly, and critically, to effectively explain their use to others. By training thousands rather than a select few, Citi diminished its reliance on a small cadre of specialists. Furthermore, encouraging participation from non-technical roles sends a potent cultural signal: AI is not exclusively the domain of engineers or data scientists, but rather an integral component of how work is accomplished, akin to spreadsheets or presentation software in previous technological eras.

This paradigm shift aligns with broader industry trends, where many companies struggle to transition AI projects from pilot phases to full production, often citing talent deficiencies and ambiguous ownership. Citi’s model circumvents some of these challenges by distributing ownership within teams while maintaining centralized governance. However, this peer-led approach is not without its limitations; it relies on sustained employee interest, and adoption rates can vary across teams. There is also the inherent risk of informal support networks becoming uneven, potentially leading to disparate benefits across groups. Citi actively addresses these concerns by rotating Champions and continuously updating training content as AI tools evolve.

What truly distinguishes Citi’s strategy is its pragmatic decision to treat AI as fundamental infrastructure rather than a mere innovation. Instead of pondering AI's potential for transformative business disruption, Citi focused on identifying specific areas where AI could effectively reduce friction in existing workflows. This practical framing facilitates measurable progress and alleviates pressure to achieve dramatic, headline-grabbing results. Moreover, Citi’s experience challenges the common assumption that AI adoption must originate from the top. While senior leadership provided essential support, much of the initiative's momentum was generated by employees who voluntarily invested time in learning and teaching. In large, complex organizations, cultivating such bottom-up energy can be challenging, yet it is often the decisive factor in the long-term sustainability of new technologies. As more companies endeavor to move beyond initial pilots into full-scale AI production, Citi’s robust case study offers invaluable lessons. It demonstrates that true scale is achieved not by acquiring more tools, but by empowering people with the confidence to effectively utilize the tools they already possess. For enterprises grappling with slow AI progress, the solution may lie less in strategic blueprints and more in understanding and facilitating how work is genuinely performed, one team at a time.