Scaling AI in financial services: 7 steps to governance and architecture
Blog post from Elastic
Financial services companies face significant pressure to implement AI solutions, promising enhanced customer experience, reduced risk, and improved operational efficiency, with 42% planning to increase spending on AI agents in 2026. However, the main challenge in scaling AI lies in unifying fragmented data and enforcing governance, as poor data quality can undermine trust and regulatory compliance. Early AI adoption focused on customer-facing applications, but the emphasis has shifted to strengthening infrastructure, data, and governance. Organizations are now treating AI as an enterprise capability, requiring robust data foundations, pervasive governance, comprehensive observability, and cross-functional collaboration to drive business value and mitigate risks. Success in AI deployment depends on starting with small projects, ensuring secure agentic AI systems, and leveraging partnerships with technology providers to build a scalable, resilient AI architecture.