Not All “Enterprises” Are Equivalent
In discussions about AI in financial services, the term “enterprise” is often used without sufficient precision. In reality, enterprise scale — especially in the banking sector — carries specific operational, technological, and regulatory implications that fundamentally change how AI must be developed and deployed.
Quantifying Enterprise Scale in Banking
The largest U.S. banks operate at a scale that rivals or exceeds many sovereign economies in both capital flow and organizational complexity. Let’s take a quick look at some 2024 data on U.S. Fortune 500 banks.
Using $1B in annual revenue as the benchmark for “enterprise” categorization.
Using 1000 employees as the benchmark for “enterprise” categorization.
A few highlights from the above figures:
JPMorgan Chase reported $178 billion in revenue, with over 300,000 employees.
Bank of America, Wells Fargo, and Citigroup each employed over 200,000 people and reported revenues exceeding $80 billion.
Fifth Third Bancorp and KeyCorp each manage multi-billion-dollar operations with tens of thousands of employees.
They also highlight two important realities:
Even the smallest F500 banks are vast, integrated systems by software industry standards.
At this scale, small operational changes can produce outsized financial and regulatory effects.
Scaling Risk Like the USS Enterprise
To visualize how scale changes operational design, consider the evolution of the USS Enterprise in Star Trek. Each generation of the starship grew not only in size but also in complexity, purpose, and risk tolerance.
Various manifestations of USS Enterprise.
Not all starships named Enterprise are the same. You don’t pilot a starship designed for deep space exploration with the tools of a shuttlecraft. The same applies to AI in financial enterprises. When tech startups talk about selling to enterprises, they often imply vastly different capabilities.
NCC-1701 (Original) ~289 meters
Regional bank — clear scope, centralized command, limited mission types.
NCC-1701-D (TNG) ~642 meters
Diversified global bank — larger crew, multi-mission operations, higher resilience needs.
NCC-1701-E (Films) ~685 meters
Modern financial institution — sleek, integrated systems, designed for adaptive response.
NCC-1701 (Kelvin) ~725 meters
Universal hybrid-fintech bank — extreme performance, high autonomy, global risk exposures.
Implications for AI System Design
1. Risk Magnitude
Automation failures in a 50-person SaaS company cause churn. In a 200,000-employee financial institution, they can trigger systemic regulatory or reputational impact. Risk categorization of business processes may be dynamic depending on market conditions and current capital allocations.
2. Workflow Evolution
Bank workflows evolve with jurisdiction, product innovation, and economic cycles. AI systems must adapt easily, maintaining memory, context, and auditability throughout.
3. Governance Requirements
Agents must be flexible, observable, and controllable under risk-based policy. Regulatory-class governance isn’t an optional add-on — it is a baseline capability. Data lineage requirements for GSIB processes that impact enterprise risk valuation are non-negotiable.
4. Systems Integration
Financial enterprises operate on complex, distributed legacy and modern systems. AI agents must be composable and interoperable across diverse APIs, data stores, data formats, and entitlements schemes.
5. Memory and Continuity
Multi-agent systems must retain and pass context across time and deployment units. Stateless automation fails in stateful institutions.
Designing for Scale: The Agent-Based Approach
At Artian, our multi-agent AI systems are designed with these enterprise-scale realities in mind. We emphasize:
Contextual memory and continuity across workflows
Governed autonomy with embedded compliance logic
Audit trails and observability by default
Composable architecture that works with existing enterprise systems
Highly productive agent development and deployment experiences
As financial services institutions scale, their complexity and operational demands do not just increase — they change in nature, sometimes drastically. Like the USS Enterprise evolving from a light cruiser to a galaxy-class flagship, the systems supporting these organizations must evolve to match.
Enterprise-grade AI cannot be built for agility alone — it must also be built for accountability, continuity, and resilience.
And that’s where real innovation begins.
