By Cien Solon, CEO and Founder of LaunchLemonade
Artificial intelligence has long been viewed in financial services as a productivity tool – a faster way to process documents, detect fraud or automate customer support. That no longer reflects what is happening inside financial institutions. AI is becoming part of the operating model itself.
Across banks, fintechs, insurers and asset managers, AI agents are moving beyond isolated tasks. They are being used to monitor transactions, generate reports, interpret regulation, assess risk and coordinate workflows across departments. Above them, orchestration layers assign tasks, route data and decide when human intervention is needed.
Firms are beginning to build a new operating system for work. That creates major upside, but also a new category of operational risk.
From automation to infrastructure
For years, firms treated AI as an add-on. A chatbot improved service. A model improved underwriting. A predictive engine strengthened portfolio analysis.
Now AI is being embedded into business processes. Rather than simply supporting employees, AI systems are coordinating with one another to complete tasks end to end. One agent may gather customer data, another may assess compliance requirements and a third may produce reporting – all governed by an orchestration layer.
This shifts software from being a tool employees use to a system that increasingly directs how work happens.
For finance leaders, AI is no longer only a technology strategy. It is becoming an organisational design decision.
Efficiency creates dependency
The benefits are clear. AI-native operations can reduce manual work, accelerate decisions and lower costs.
Processes that once took days can happen in minutes. Teams can scale without matching headcount. Compliance can become continuous rather than periodic.
But these gains create a hidden trade-off: dependency.
As firms rely more heavily on interconnected AI systems, they become dependent not just on models, but on data quality, orchestration logic and the infrastructure linking everything together.
In a traditional environment, a failure might cause a delayed payment or reporting error. In an AI-native environment, a failure in one system can spread across multiple functions at once.
The rise of coordination risk
In traditional organisations, coordination happens through managers and process controls. In AI-enabled organisations, coordination increasingly happens through software logic. Systems decide who acts, when they act and what information they receive.
That can improve speed, but it also creates vulnerabilities.
Poorly designed orchestration rules may cause an AI agent to escalate unnecessary issues, miss anomalies or generate conflicting outputs across teams. Small technical misalignments can create larger organisational consequences.
For financial firms, where trust and accuracy are critical, these risks are not theoretical. When firms automate coordination, they also automate the possibility of systemic error.
Regulation is getting harder
At the same time, regulation is becoming more complex.
In the United States, oversight remains fragmented across federal agencies and state rules. The European Union’s AI Act introduces a stricter risk-based framework. In the UK, regulators are focusing more closely on operational resilience, accountability and model risk.
For fintech founders, the challenge is no longer simply building innovative products. It is building systems that can withstand scrutiny across multiple jurisdictions.
The regulatory question is shifting from whether firms use AI to whether they can explain how it works.
Rethinking the organisation
AI adoption is also forcing firms to rethink how work gets done. Reporting lines may change as AI takes over tasks once handled by middle management. Teams may need to be built around AI-enabled workflows rather than traditional silos.
For leadership teams, the questions are no longer just technical: which decisions should remain human, which can be delegated and where accountability should sit when AI becomes part of the workforce.
Governance must move earlier
Many organisations still treat AI governance as something to address after deployment. That is becoming unsustainable.
When AI influences operational decisions, governance must be built in from the start. Firms need clear accountability for who owns AI-driven decisions, how outputs are validated, when humans intervene and how audit trails are maintained.
Without that, businesses risk creating systems that look efficient on paper but are fragile in practice.
Finance is being re-risked
The financial industry has always evolved with technology. But AI is different because it changes not just the tools firms use, but the structure of work itself.
AI agents and orchestration layers are becoming foundational infrastructure in financial services. They promise gains in speed and efficiency, but also introduce new dependencies and governance demands.
The conversation can no longer focus only on optimisation. As AI becomes the operating system of work, finance is not just becoming more efficient, it is becoming fundamentally re-risked.
About the author
Cien Solon is the CEO and Founder of LaunchLemonade, a secure governed platform of AI agents for regulated industries. An experienced AI transformation leader, Cien has been building AI-powered solutions since 2018 and working with generative AI since 2022. Her mission is to ensure that smaller businesses in regulated sectors can adopt AI confidently, without the enterprise price tag. LaunchLemonade was included in Entrepreneur UK’s Top 100 Startups to Watch, and Cien was shortlisted in the Technology category of the 2025 Investec Early-Stage Entrepreneur of the Year Awards.
