Your enterprise AI framework determines your returns
Financial services leads every industry in AI adoption, yet most executives report underwhelming returns. Institutions adding AI onto unchanged workflows are unable to capture returns at scale.
Financial services leads every industry in AI adoption, yet most executives report underwhelming returns. Institutions adding AI onto unchanged workflows are unable to capture returns at scale.
Phase 1 of the AI framework is the exploration phase, where teams learn what works from pilots and institutional knowledge about AI capabilities accumulates. Without standardization across teams, AI sits alongside existing workflows and learnings don’t compound. Ultimately, the organization will have nothing to show for the investment except a collection of pilots that did not scale.
Moving from experimentation to enterprise implementation requires leadership to choose a platform that lets shared investment compound across teams. The choice signals that AI is no longer an experiment, but how the business operates.
Organizations routinely claim high AI usage while their metrics tell a different story. Knowing where time is actually being saved and where bottlenecks remain is needed because the flywheel cannot be built without the signals providing the knowledge of what baseline to build from.
The question is, “If we started with AI as a first-class citizen in the design, what would this process become?” The answer will almost always be structurally different, not just iteratively improved. When one part of a process becomes dramatically faster, the next constraint becomes visible, allowing teams to iterate over time.
In financial services, safeguarding trust is paramount. Governance is the mechanism that allows autonomous AI systems to operate without destroying that trust.
Financial services organizations have deployed AI from pilot to production more than any other industry. However, the same organizations who have deployed AI are also the most likely to report that returns have not matched expectations. Chun Schiros, Field CTO at AWS and former Chief Analytics Officer with over fifteen years in financial services, argues that this highlights challenges with the AI framework, not the technology.
Her framework maps three AI maturity phases:
The transition from phase one to phase two is marked by encoding institutional knowledge such as underwriting rules, claims adjudication logic, and compliance requirements into the AI layer. That expertise becomes the compounding asset competitors cannot replicate by leveraging the same foundation model.
In phase three, processes are redesigned with AI as a native element. Smaller teams become more strategic, asking “if we designed this process from scratch today, what would it look like?” The answer will almost always be structurally different, not just incrementally improved. When one step in a process becomes dramatically faster, it immediately exposes blockers in the next step, allowing teams to iterate over time in a continuous pressure-and-release cycle.
