Long Form

State of AI and data pipeline automation in financial services 2026

Only 18.6% of financial institutions have enterprise-wide orchestration — the foundation for AI readiness and scalability

Standout findings:

  • 91.4% say automation improves resilience and compliance, yet 65.1% say legacy platforms limit modernization
  • >50% report high maintenance and infrastructure costs tied to current automation environments
  • 61.4% say siloed environments constrain AI readiness
  • The biggest automation challenges are data and coordination gaps
  • Redwood Software customers are 20% less likely to report high labor costs from manual oversight

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Why read this report?

AI in financial services is accelerating, from real-time risk modeling to always-on compliance and intelligent portfolio management. Financial institutions are investing in intelligent automation, AI-driven technologies and modern data infrastructure to improve operational efficiency, streamline workflows, reduce operational costs and support better decision-making.

Yet most aren’t ready to scale AI. Automation solutions remain fragmented, data pipelines are inconsistent and coordination across systems is limited. Many organizations still rely on manual processes and disconnected workflows, which increases risk, limits scalability and reduces the cost savings potential of AI initiatives. This research shows where automation is delivering value today, why progress stalls at the process level and what it takes to build orchestrated, AI-ready operations.

What’s inside:

  • Benchmarks on automation and orchestration maturity across financial services companies
  • Where automation delivers results today and where fragmentation limits impact
  • How manual tasks and disconnected processes limit operational efficiency
  • The operational and cost consequences of tool sprawl and disconnected environments
  • How data pipelines move across systems and how coordination breaks down across legacy systems, cloud and SaaS environments
  • The high-value AI use cases organizations are prioritizing — and the infrastructure required to support them

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