The Orchestration Moat And Its Allies
Why durable enterprise AI defensibility in financial services lies above the model layer
Foundation models continue to advance, and their costs are trending downward as efficiency improves. Recent API price cuts and performance convergence across leading providers suggest that models are increasingly interchangeable. Yet despite this, enterprises show limited willingness to switch vendors. What explains this paradox? Differentiation and defensibility in enterprise AI are shifting upward in the stack: into orchestration, data, and industry vertical specialization.
🎼 The orchestration moat. Orchestration is the layer where models, tools, and workflows combine to deliver outcomes. Each refined prompt, integrated tool chain, and feedback loop embeds itself in the day-to-day cadence of a company’s operations. Over time, this creates a form of reflexive dependence: every cycle of use adds conventions, context, and trust patterns that become difficult to unwind.
Switching providers may be technically simple, but the organizational cost of retraining teams, rebuilding workflows, and re-establishing behavioral consistency makes it prohibitive. As a result, orchestration grows stickier the more it is used.
🔐 The data moat. When orchestration becomes central to operations, it also generates proprietary, domain-specific data. This includes workflow telemetry, compliance metadata, and customer interaction histories that improve automation over time.
This dynamic creates a powerful flywheel: orchestrated processes generate unique data, that data improves automation, which drives more usage, producing even richer data. Competitors without access to this loop cannot easily replicate its depth or performance.
This is where the shift from services to software comes into focus. By codifying high-friction, regulated processes into repeatable, automated workflows, companies move beyond one-off services and toward service-as-software. Each cycle strengthens their position by generating data and compounding trust.
🏦 The vertical moat. Generic AI tools often struggle in industries where workflows are complex and regulation is strict (like financial services). In these environments, success depends on embedding domain expertise directly into orchestration: compliance checks, audit trails, industry-specific context windows, and other specialized rules.
👉🏼 We believe the strongest opportunities in applied AI come from domain-specific agent orchestration platforms. These companies don’t just layer intelligence on top of existing services; they embed deeply within customer operations, replacing manual processes with productized, software-driven workflows that deliver measurable outcomes.
In that configuration, models themselves, whether open or closed, become components in a higher-value system. The moat lives above the model, in the compounding advantage of workflows, data loops, and domain expertise.
