AI’s Efficiency Dilemma in Banking
Agentic AI systems perform tasks autonomously, orchestrating data, models and workflows with minimal human supervision. For banks this promises faster analytics, cheaper execution and lower operational risk. The paradox is that when core tasks become inexpensive and near-instant, the traditional basis for advisory fees erodes and margin pools shrink.
The KPMG Precedent: A Glimpse into Fee Erosion
Industry coverage of KPMG has shown how leading firms are already using advanced AI to compress hours and automate analyses that once justified high fees. Reported examples make the point: when clients see identical outputs from machines, they push for lower pricing and shift commodity work to in-house or low-cost providers. That precedent illustrates how efficiency gains can translate into revenue pressure.
Reimagining Value: Beyond Automated Services
To preserve premium advisory margins, banks must shift from selling task-based outputs to selling scarce human judgment and outcomes few can replicate. That means reframing relationships, pricing and delivery.
Strategic Focus Areas for Premium Fees
- Proprietary insight and synthesis: Combine exclusive data assets with expert interpretation that AI cannot source or verify on its own.
- Risk governance and accountability: Offer legal and regulatory responsibility for decisions, backed by documented processes and indemnities.
- Complex structuring and negotiation: Lead deals requiring cross-disciplinary tradeoffs, bespoke structuring and real-time stakeholder arbitration.
- Implementation and change management: Deliver outcomes through project leadership, systems integration and behaviour change that machines cannot guarantee.
- Outcome-linked pricing: Move fees toward performance, alignment and shared savings rather than hours or reports.
Future-Proofing Profitability
Banks that win will treat agentic AI as a force multiplier for proprietary human strengths, not a substitute. Invest in scarce skills, reshape contracts to reflect accountability, and build bundled offerings that tie AI-driven efficiency to demonstrable client outcomes. Doing so preserves margin while leveraging the productivity gains AI delivers.
