Most financial services institutions have proven AI works. Few have made it work at enterprise scale – or turned it into new revenue.
91% of leaders plan to increase AI investment in 2026, yet only 39% track ROI. That gap is the problem.
The gap isn’t budget. It’s execution. AI is being applied to tasks, not systems – embedded in workflows instead of transforming how financial products and services operate.
Legacy infrastructure, fragmented data, and regulatory complexity slow progress. The result is a productivity plateau across the industry.
Why this matters now for financial services
- Rising customer expectations for real-time, personalised financial experiences
- Pressure to turn AI from cost savings into revenue generation
- Legacy systems and regulation slowing the shift from pilot to production
Financial services institutions aren’t just experimenting with AI. They’re being pushed to make it deliver measurable, enterprise-wide impact.
The shift
Agentic AI – systems that plan, reason, and act across workflows – is how FS leaders are breaking through.
It moves AI from task automation to end-to-end orchestration, unlocking new capabilities and revenue streams. Here’s what that looks like in production:
90% reduction in customer response times
Jaja Finance
5,000%+ ROI On AI-driven prospecting
TP ICAP
50% reduction in call wait times
Genie
What’s inside?
- Why FS institutions get stuck at the productivity plateau
- What’s structurally different about agentic AI in regulated environments
- The three enablers for scaling AI:
- Scalable cloud foundations
- Human-centred design
- AI-ready, governed data
- Real-world FS case studies with measurable outcomes
- How Slalom and AWS help organisations move from pilot to production
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