In this series, we explore what businesses are telling us about AI adoption in lending, examining the key barriers and challenges shaping progress.
When ambition meets friction
Artificial intelligence (AI) in lending now spans far more than predictive models. It includes Machine Learning (ML) systems that power credit and fraud analytics, Generative AI (GenAI) that automates insights and content creation, and increasingly, Agentic AI – autonomous systems that can reason, act, and adapt across workflows.
Yet, as these forms of AI evolve, one reality remains constant: integration complexity can be the hidden cost of AI adoption.
Experian’s research on the perceptions of AI in lending shows that 84% of lenders plan to prioritise AI in the next two years, and 89% believe it will play a critical role across the lending lifecycle. However, despite this ambition, only 38% report achieving meaningful ROI from existing implementations. 15% of respondents also said that integration complexity is the top barrier to adopting AI in lending, ahead of uncertain ROI and lack of expertise, revealing a widening gap between strategic ambition and operational execution.
The disconnect isn’t about belief in AI’s value; it’s about how to make it work at scale. Many lenders have discovered that their biggest challenge isn’t model performance, but rather the web of integrations that connect everything together.
Why is integration so complex?
Deploying AI in lending involves connecting a complex network of systems, including data repositories, decision engines, compliance frameworks, and vendor tools. Each must interact flawlessly while meeting stringent standards for fairness, transparency, and governance.
This complexity arises from several reinforcing factors:
- Legacy infrastructure
Core banking and credit systems were never designed to host AI workloads. Integrating ML and GenAI into these legacy environments creates costly dependencies and slows deployment. - Data silos
Customer, credit, and fraud data often reside in disconnected environments, resulting in inconsistent insights and less explainable AI outcomes. - Fragmented vendor ecosystems
Each vendor brings its own APIs and integration standards. The result is duplicated work, escalating costs, and a patchwork of partial connections. - Compliance and governance demands
Every AI model must be auditable and explainable. Integrating these requirements into multiple systems compounds complexity. - Talent and expertise gaps
Few lenders have teams skilled across data engineering, data science, ML Ops, GenAI, and integration design, leaving critical bridges incomplete.
The result is an invisible drag on innovation. AI projects stall not because the models fail, but because the ecosystem around them cannot connect.
The hidden cost of AI adoption
Integration complexity is the silent tax on every AI initiative. It doesn’t appear in budgets, yet it inflates costs, extends timelines, and erodes returns.
For data science teams, it means less time innovating and more time fixing pipelines. For IT, it means managing brittle integrations that break with every system change. For business leaders, it means projects that take longer and deliver less than promised. And for the organisation, it creates a trust issue in both the technology and its outcomes. When AI decisions can’t be traced or reproduced because data or model integrations are opaque, confidence collapses.
Overcoming Integration Complexity
The path forward isn’t about replacing everything old with something new. It’s about connecting intelligently, building infrastructure that unites data, analytics, and decisioning under a common, interoperable framework.
- Build AI-ready infrastructure
An AI-ready infrastructure is modular and connected. It utilises APIs, shared data models, and orchestration layers to enable ML, GenAI, and Agentic systems to collaborate effectively. This allows lenders to scale responsibly without disrupting legacy operations. - Embed governance and transparency
Regulation and responsibility must be built into the architecture, not added later. AI-ready systems ensure models are explainable, decisions are auditable, and governance is consistent across all integrations. - Collaborate with trusted ecosystem partners
No lender can solve integration complexity alone. Collaboration with trusted data and analytics partners provides proven frameworks, shared standards, and interoperability that accelerate transformation.
Experian’s research highlights this dynamic. 72% of lenders trust Experian to deliver reliable AI, and 66% already view it as a strategic AI partner, reinforcing that trust is the foundation of scalable innovation.
From fragmented to unified
Lenders that overcome integration complexity move from fragmented systems to connected ecosystems, and the benefits are transformative:
- Speed to Value – Unified data and model pipelines cut deployment times dramatically.
- Consistency and Explainability – Decisions are made on complete, traceable data.
- Scalability – One integration foundation supports multiple AI use cases across credit, fraud, marketing, and collections.
Each integration solved accelerates future value, turning AI adoption into a flywheel for enterprise-wide agility.
Futureproofing the AI Ecosystem
As AI matures from model to ecosystem, integration becomes an ongoing capability, not a one-time challenge. Future-ready lenders will:
- Adopt open standards for interoperability across ML, GenAI, and Agentic tools.
- Invest in modularity, allowing teams to innovate without destabilising core systems.
- Rethink workflows, embedding AI into decisioning processes, not just data pipelines.
The ability to integrate rather than replace will define a competitive advantage, protect past investments, and enable continuous learning and adaptation.
The industry leaders in AI will not be those with the most advanced models but those with the most connected ecosystems, where ML, GenAI, and AI Agents operate in concert, powered by simplicity beneath the surface.
