Across many global enterprises, artificial intelligence remains confined to experimentation. Innovation labs test new tools, small technical teams run pilots, and progress is reported in presentations that rarely translate into day-to-day impact. Despite widespread interest, AI adoption often stalls before it reaches the broader workforce.
Citi has taken a markedly different approach. Instead of treating AI as a specialist capability or a future-facing experiment, the global bank has spent the last two years embedding AI into everyday work across the organisation. The result is a quiet but significant internal transformation: nearly 4,000 employees now actively participate in Citi’s internal AI ecosystem, and more than 70% of its global workforce uses firm-approved AI tools in some form.
This is not a story of flashy product launches or radical reinvention. It is a case study in how large, regulated enterprises can scale AI responsibly—by focusing on people, workflows, and trust rather than hype.
Moving Beyond AI as a Side Project
In many large organisations, AI lives on the margins. A few departments test tools, while the rest of the company watches from a distance. The reasons are familiar: regulatory concerns, unclear ownership, lack of skills, and fear of unintended consequences.
Citi’s leadership recognized early that this model would limit long-term value. With a workforce of approximately 182,000 employees spread across banking, operations, risk, compliance, and customer support, the bank understood that meaningful AI impact would only come if the technology reached frontline teams.
Rather than centralizing AI experimentation within a single innovation unit, Citi aimed to decentralize adoption while keeping governance firmly in place. The goal was not to turn everyone into an AI expert, but to make AI a practical tool that employees could use confidently in their daily roles.
Building an Internal AI Community, Not a Command Center
Citi’s AI journey did not begin with technology selection. It began with people.
The bank launched internal initiatives designed to encourage voluntary participation, most notably its AI Champions and AI Accelerators programs. Employees from across the organization—technology, operations, risk management, customer service, and beyond—were invited to step forward.
Those who joined gained access to training, internal documentation, and early exposure to approved AI tools. More importantly, they became informal points of contact within their teams. Rather than acting as top-down trainers, AI Champions supported colleagues by answering questions, sharing use cases, and demonstrating how AI could improve everyday tasks.
This peer-led model reduced one of the most common barriers to adoption: uncertainty. Employees are often hesitant to use new tools not because they lack interest, but because they are unsure how to apply them safely and effectively. Having support embedded within teams helped bridge that gap.
Training That Encourages Participation, Not Pressure
Training was a cornerstone of Citi’s internal AI rollout, but it was designed to motivate rather than mandate.
Employees could earn internal recognition—such as digital badges—by completing learning modules or demonstrating practical AI use in their roles. These badges were not tied to promotions or compensation. Instead, they served as signals of credibility and initiative, helping employees gain visibility within the organization.
This approach avoided the resentment that can come from compulsory training programs. Instead, it fostered curiosity and peer recognition. Employees learned not because they were required to, but because they saw tangible benefits in their work.
According to reports, this organic, peer-driven learning model helped AI adoption spread faster than traditional top-down directives often do in large enterprises.
AI in Everyday Workflows, Not Experimental Silos
One of the defining features of Citi’s AI strategy is its focus on everyday use cases.
Employees use AI tools to:
- Summarize lengthy documents and reports
- Draft internal communications and meeting notes
- Analyze structured and unstructured data
- Support software development and testing
- Improve customer service workflows
None of these applications are groundbreaking on their own. What makes them impactful is scale. When thousands of employees save minutes or hours on routine tasks, the cumulative effect is significant.
This focus on incremental efficiency also makes AI less intimidating. Instead of positioning AI as a transformative force that might disrupt jobs, Citi frames it as a productivity aid that removes friction from existing processes.
Guardrails First: Trust Over Speed
Operating in a heavily regulated industry means Citi cannot afford experimentation without safeguards. From the beginning, AI access has been limited to firm-approved tools with clear rules around data usage, privacy, and output handling.
Employees are restricted in what data they can input and how AI-generated content can be used. While this has slowed certain experiments, it has also built trust across the organization. Managers and compliance teams are more willing to support broader AI access when they know guardrails are in place.
In financial services, confidence and control often matter more than rapid innovation. Citi’s approach reflects this reality, prioritizing responsible deployment over unchecked experimentation.
What Citi’s AI Rollout Reveals About Scaling AI
Citi’s experience offers several insights for other large enterprises struggling to move AI from pilots to production.
1. AI Adoption Is a People Problem Before It Is a Technology Problem
Tools alone do not drive change. Employees need confidence, context, and support to integrate AI into their workflows.
2. Not Everyone Needs to Be an Expert
Citi did not aim to train a small elite group of specialists. By empowering thousands of employees with practical knowledge, the bank reduced dependence on a narrow talent pool.
3. Distributed Ownership Accelerates Adoption
When AI knowledge is embedded within teams, adoption becomes more resilient. Employees learn from peers who understand their specific challenges.
4. Central Governance Still Matters
While adoption is decentralized, governance remains centralized. This balance allows innovation without sacrificing compliance or control.
Cultural Impact: Making AI Everyone’s Tool
Beyond productivity gains, Citi’s AI initiatives have sent a powerful cultural signal. Encouraging participation from non-technical roles reinforces the idea that AI is not just for engineers or data scientists.
Over time, AI begins to feel like a standard workplace tool—similar to spreadsheets, email, or presentation software. This normalization is critical for long-term adoption.
Industry research consistently shows that many AI initiatives fail to scale due to unclear ownership and talent shortages. Citi’s model addresses both by distributing responsibility while maintaining a shared framework.
Challenges and Limitations of Peer-Led Adoption
Despite its strengths, Citi’s approach is not without challenges.
Peer-led models depend on sustained engagement. Not all teams adopt new tools at the same pace, and some Champions may be more active than others. There is also the risk of uneven support, where certain departments advance faster due to stronger local advocates.
Citi has attempted to mitigate these risks by rotating Champions, refreshing training content, and continuously updating internal resources as tools evolve. This helps prevent stagnation and ensures knowledge remains current.
Treating AI as Infrastructure, Not Innovation Theater
Perhaps the most important takeaway from Citi’s experience is its mindset shift. The bank has largely avoided framing AI as a revolutionary force that must transform the business overnight.
Instead, it treats AI as infrastructure—a capability that quietly improves how work gets done. This framing reduces pressure to deliver dramatic results and makes progress easier to measure.
Rather than asking, “How will AI change everything?”, Citi has focused on “Where can AI remove friction today?” That question leads to practical, sustainable gains.
Bottom-Up Momentum in a Top-Down World
While Citi’s senior leadership supported the AI initiative, much of its momentum came from employees who volunteered time to learn, experiment, and help others.
In large organizations, bottom-up energy is difficult to cultivate, yet it often determines whether new technology truly takes hold. Citi’s experience challenges the assumption that AI adoption must be driven exclusively from the executive suite.
Empowering employees to lead adoption within their teams created ownership, trust, and continuity that formal mandates often struggle to achieve.
Lessons for Enterprises Moving from AI Pilots to Production
As more companies seek to scale AI beyond experimentation, Citi’s internal rollout offers a valuable blueprint:
- Focus on adoption, not announcements
- Invest in training that empowers rather than enforces
- Embed AI support within teams
- Balance access with strong governance
- Measure progress through daily impact, not hype
Ultimately, Citi’s success highlights a simple truth: AI does not scale because it is powerful—it scales because people feel comfortable using it.
Conclusion: The Quiet Path to Enterprise AI Success
Citi’s 4,000-person internal AI rollout is notable not for its spectacle, but for its restraint. By emphasizing people, everyday use cases, and responsible guardrails, the bank has quietly achieved what many organizations still struggle with: turning AI from a side project into part of how work gets done.
For enterprises wondering why AI progress feels slow, Citi’s experience suggests the answer may not lie in acquiring more tools or rewriting strategy decks. Instead, it lies in empowering employees—one team at a time—to use AI confidently, responsibly, and consistently.
In the long run, that quiet work may prove to be the most scalable AI strategy of all.