LGT Private Banking Shifts AI Strategy From Experiments to Production

Simon Gomez, head of data and innovation at LGT, notes that the firm has moved past experimental phases toward applications that support specific client processes. Recent deployments include tools that transcribe, summarize, and draft contact logs from client interactions. While the software handles the groundwork, the bank maintains a strict policy where staff must review, amend, and finalize all AI-generated outputs to ensure accountability.

Scaling Reliable Workflows

Transitioning from a successful pilot to daily production operations presents the steepest challenge. Gomez emphasizes that reliability remains a higher bar than mere functionality. Successful implementation requires robust data controls, clear access rights, and proactive training to help staff pivot away from established working methods. The bank tracks uptake and gathers feedback to identify friction points, finding that momentum builds most effectively when employees witness tangible time savings in their own administrative tasks.

By automating repetitive duties, relationship managers gain capacity to focus on high-value advisory work. LGT views this as a dual opportunity: reducing operational costs while simultaneously improving the quality of client service. The ultimate goal remains strengthening the human element of private banking, using technology to handle the routine so that judgment and personal relationships remain at the center of the firm's value proposition.