Leadership Resource Center
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Key topics for CX Leadership
This blog post explores whether B2B customer experience (CX) teams should build AI solutions in-house or invest in external providers. It highlights the challenges CX leaders face, including resource allocation, ongoing maintenance, and high development costs. The post features insights from a Head of AI at a major SaaS platform, who notes that while engineers can create AI demos, maintaining and improving an internal AI system over time would require significant investment—over $1M USD in 2–3 years. The article helps decision-makers assess whether an in-house AI initiative is a viable long-term strategy.
The article explores why chatbots are less commonly used in B2B customer experience (CX) compared to consumer businesses. It highlights key differences in customer profiles, organizational structures, and support needs, emphasizing that B2B interactions are more complex, relationship-driven, and high-stakes. As a result, B2B leaders prefer AI tools as internal support systems rather than direct customer-facing chatbots.
This blog discusses whether ChatGPT Enterprise can replace Twig as a Customer Experience (CX) CoPilot. It provides a detailed comparison of both tools, highlighting their capabilities, strengths, and suitability for customer-facing teams. CX leaders will gain insights into how each platform supports complex customer interactions and which might be the better fit for their organization.
This blog outlines a detailed onboarding plan for implementing an AI Brain within a Customer Experience (CX) team. It covers key stages, including platform setup, data ingestion, beta testing, and enterprise-wide rollout. The blog emphasizes the importance of quick value delivery, continuous feedback loops, and leadership involvement to ensure successful adoption across the organization.
This blog provides an in-depth review of Twig's architecture and AI model, focusing on how it handles private data for enterprise CX teams. It covers key features like automated data refreshes, PII filtering, and Retrieval Augmented Generation (RAG) to reduce AI hallucinations and ensure traceable, accurate responses. The blog also highlights Twig's human-centric controls, allowing teams to customize AI behavior, refine responses, and maintain data security without engineering support.
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This blog outlines key factors CX leaders should consider when evaluating AI solutions for their teams. It emphasizes the importance of deterministic AI for consistent, fact-based, and traceable responses, along with the need for human control to edit, update, and personalize AI outputs. The blog also addresses data limitations in enterprise environments, highlighting challenges like low-density data, gaps in adjacent product knowledge, and the need for domain-specific insights to improve AI effectiveness.
