Implementer’s Guide to AI: Finance Leaders Transition from Caution to Customer-Centric Scale
ResearchNXT, in partnership with Salesforce, has released the latest edition of the Implementer’s Guide to AI, focused on Financial Institutions, including banking, insurance, and fintech enterprises. Drawing from 300+ survey responses and 6 expert interviews, the report unveils how AI adoption has moved into the mainstream — yet enterprise-wide execution remains constrained by compliance complexity and risk sensitivity.
AI Is Widely Adopted — But Scaling Requires Trust
AI usage is now standard across financial institutions. However, most initiatives remain limited to pilots, with only selective deployments reaching enterprise-scale impact. While leaders have sharpened AI vision and can articulate specific value areas, execution lags due to regulatory caution and difficulty justifying ROI.
From Efficiency to Experience
Early adoption focused on cost optimisation and process efficiency. This focus is now shifting toward customer engagement, retention, proactive advisory, and building long-term trust — signalling AI’s evolution from a support enabler to a strategic growth lever.
Key Insights from the Report
- 69% of respondents have already adopted AI, yet only a fraction have reached full-scale implementation.
- AI maturity is shallow — experimentation dominates due to high costs, unclear ROI, and regulatory hesitation.
- Vendor non-compliance and regulatory uncertainty are the top scaling barriers, followed by data quality gaps and talent shortages.
- Customer retention, engagement, and revenue impact are overtaking cost as priorities.
- Fraud detection and chatbot-driven service automation are the most mature use cases, reflecting sector-specific focus on risk and efficiency.
- AI governance maturity remains limited, despite strong IT and data infrastructure.
From Automation to Agentic AI: The Next Leap
The report highlights Agentic AI as the future state of AI maturity in retail. Built on Salesforce Agentforce, these autonomous AI agents can process signals across systems, act intelligently in real time, and orchestrate workflows without constant human involvement.
Agentic AI represents a shift from isolated automation to multi-agent orchestration — enabling proactive customer engagement, end-to-end workflow execution, and predictive decision-making at scale.
