Building a Voice AI Proof of Concept for HSBC
Turned HSBC’s voice data into action. James led an AI Voice Insights POC that transcribed calls, surfaced themes, and auto‑summarised meetings with compliant, scalable pipelines. Outcome: clear dashboards and significant time saved from automated notes and categorisation.
Across HSBC’s global operations, millions of hours of human interaction,calls, meetings, and voice recordings,were being captured but never analysed effectively. Critical insights about customer needs, operational inefficiencies, and risk signals were buried within unstructured voice data. Manual note-taking and administrative follow-up were consuming significant staff time, creating both productivity loss and missed intelligence opportunities. The challenge was to unlock the value of this vast voice dataset while ensuring privacy, accuracy, and scalability across multiple business lines.
James was asked to join the Innovation Portfolio to lead the development of an AI-led Voice Insights Platform—a proof of concept designed to automatically transcribe, analyse, and extract meaning from enterprise voice data. The goal was to transform hours of unstructured conversation into actionable intelligence, enabling faster decision-making and reducing administrative workload across the organisation.
James collaborated with data scientists, engineers, and compliance experts to define the end-to-end product vision and delivery roadmap. His role focused on translating business problems into deliverable AI capabilities, combining natural language processing (NLP), sentiment analysis, and entity recognition to surface patterns and insights from voice recordings.
We built prototypes that demonstrated how the system could automatically generate meeting summaries, detect emerging customer or operational themes, and provide dashboards for real-time insight. Alongside the technical delivery, James coordinated stakeholder engagement across innovation, operations, and risk functions to ensure alignment and adoption readiness.
The proof of concept demonstrated the potential to save over 1.5 million hours of manual administration annually through automated note-taking and categorisation of conversations. It also showed that voice data could become a powerful new source of business intelligence, surfacing trends, risk indicators, and customer sentiment that were previously invisible. The initiative positioned HSBC to leverage AI as a core enabler of operational efficiency and decision intelligence, influencing the strategic direction of future AI-driven platforms within the bank.
Across HSBC’s global operations, millions of hours of human interaction,calls, meetings, and voice recordings,were being captured but never analysed effectively. Critical insights about customer needs, operational inefficiencies, and risk signals were buried within unstructured voice data. Manual note-taking and administrative follow-up were consuming significant staff time, creating both productivity loss and missed intelligence opportunities. The challenge was to unlock the value of this vast voice dataset while ensuring privacy, accuracy, and scalability across multiple business lines.
James was asked to join the Innovation Portfolio to lead the development of an AI-led Voice Insights Platform—a proof of concept designed to automatically transcribe, analyse, and extract meaning from enterprise voice data. The goal was to transform hours of unstructured conversation into actionable intelligence, enabling faster decision-making and reducing administrative workload across the organisation.
James collaborated with data scientists, engineers, and compliance experts to define the end-to-end product vision and delivery roadmap. His role focused on translating business problems into deliverable AI capabilities, combining natural language processing (NLP), sentiment analysis, and entity recognition to surface patterns and insights from voice recordings.
We built prototypes that demonstrated how the system could automatically generate meeting summaries, detect emerging customer or operational themes, and provide dashboards for real-time insight. Alongside the technical delivery, James coordinated stakeholder engagement across innovation, operations, and risk functions to ensure alignment and adoption readiness.
The proof of concept demonstrated the potential to save over 1.5 million hours of manual administration annually through automated note-taking and categorisation of conversations. It also showed that voice data could become a powerful new source of business intelligence, surfacing trends, risk indicators, and customer sentiment that were previously invisible. The initiative positioned HSBC to leverage AI as a core enabler of operational efficiency and decision intelligence, influencing the strategic direction of future AI-driven platforms within the bank.
Across HSBC’s global operations, millions of hours of human interaction,calls, meetings, and voice recordings,were being captured but never analysed effectively. Critical insights about customer needs, operational inefficiencies, and risk signals were buried within unstructured voice data. Manual note-taking and administrative follow-up were consuming significant staff time, creating both productivity loss and missed intelligence opportunities. The challenge was to unlock the value of this vast voice dataset while ensuring privacy, accuracy, and scalability across multiple business lines.
James was asked to join the Innovation Portfolio to lead the development of an AI-led Voice Insights Platform—a proof of concept designed to automatically transcribe, analyse, and extract meaning from enterprise voice data. The goal was to transform hours of unstructured conversation into actionable intelligence, enabling faster decision-making and reducing administrative workload across the organisation.
James collaborated with data scientists, engineers, and compliance experts to define the end-to-end product vision and delivery roadmap. His role focused on translating business problems into deliverable AI capabilities, combining natural language processing (NLP), sentiment analysis, and entity recognition to surface patterns and insights from voice recordings.
We built prototypes that demonstrated how the system could automatically generate meeting summaries, detect emerging customer or operational themes, and provide dashboards for real-time insight. Alongside the technical delivery, James coordinated stakeholder engagement across innovation, operations, and risk functions to ensure alignment and adoption readiness.
The proof of concept demonstrated the potential to save over 1.5 million hours of manual administration annually through automated note-taking and categorisation of conversations. It also showed that voice data could become a powerful new source of business intelligence, surfacing trends, risk indicators, and customer sentiment that were previously invisible. The initiative positioned HSBC to leverage AI as a core enabler of operational efficiency and decision intelligence, influencing the strategic direction of future AI-driven platforms within the bank.
