Millions Saved (and Earned) with a Custom AI Tech Strategy

With a partnership of nearly thirty years, Concentrix already had a consulting team embedded in the client’s business that was able to conduct a deep dive into AI implementation sticking points in its AI tech strategy. Our approach involved a holistic and extensive application of qualitative and quantitative methods, including:

• Over 100 hours of live observation sessions and demos to understand and analyze AI agent-assisted processes
• Focus groups and subject matter expert interviews spanning multiple geographic regions to identify opportunities
• Advisor and leadership surveys to understand technology and tool challenges
• Due diligence review and comparisons of available AI resources
• Exploration of AI landscape and uses across other Concentrix clients

We examined advisor personas based on tenure, geographic location, and line of business (LOB) to discover similar processes and needs across these dimensions. As a result, our strategy was directed towards optimizing the process efficiencies universally, rather than tailoring it to specific personas.

Based on this deep dive, we identified 12 unique use cases for Al to enhance advisor efficiency and utilization, using successful examples from other Al implementations across similar clients to provide a benchmark for success—and quickly helped the client avoid common pitfalls.

We classified the following AI tech strategy recommendations as likely wins—high-value and highly feasible:

• Voice-enabled interactive knowledge base: An interactive search tool that would allow advisors to query the knowledge base verbally and receive written responses.
• Real-time article recommendations: Deliver relevant article links via a “find article button” that initiated an as-needed review of the advisor’s recent conversation to power results.
• Non-voice content correction: Instantly rewrite content to reflect customer sentiment and correct all grammatical errors with a single click.
• Uplift assistant: Deliver real-time personalized and contextual sales pitch language and objection rebuttals, equipping advisors with elite sales language skills.

Our second tier of recommendations, which we classified as calculated risks that had high potential value, but were more challenging to develop, included:

• Non-voice suggested conversation bot: Allow AI to continuously monitor conversations and script potential conversational and troubleshooting step responses in real time.
• Continuously improving knowledge base: Respond to/learn from troubleshooting effectiveness and resolution data from recent interactions to self-refine over time.
• GenAI voice bot or contact deflection: GenAI-driven bot to deflect voice traffic by conversationally resolving customer issues during off-hours (and eventually for all contacts).
• Customer account consolidation: Collapse duplicate accounts within the CRM to allow advisors to more easily select the correct account.

These recommendations included a validation of which technology to use based on the client’s overall AI tech strategy and existing investments (including Microsoft Copilot, Microsoft Azure, and Concentrix iX Hello™), ways to address deficiencies in the client’s content and data structure (via content refactoring, conversation flow, and AI integration), and change management recommendations to ensure the utilization of available solutions once deployed.