Up to 50% of customer interactions are about questions and concerns about service quality and issues experienced. What if all interactions can be turned into an increase in loyalty (NPS), and triggers for new revenues, while at the same time reducing the technical care costs up to 70%? The CSP industry as a whole has a challenge: growth is not at the level expected by the industry, and cost pressure is increasing. Gen AI has significant potential to bring efficiency gains (up to 40% according to industry analysts such as McKinsey), and while doing that also has the potential to drive revenue increase via smart suggestions for upsell as part of the care experience. In real life, the customer care end-to-end chain involves more than end-user chatbots: the experience needs to drive efficiency to Care teams (for assisted care) as well as for wholesale customers who are reselling CSP services. Managing this total landscape is critical for capturing the full business potential of Gen AI driven customer care. A key element for success in applying GenAI to complex process automation involving expert subject matter expertise in operational environments is the combination of Predictive AI (dealing with expert data analysis & operational decision making) and GenAI technology (dealing with customer engagement). Avoiding hallucinations and being trustworthy are essential for an effective digital transformation on complex operational processes, and to enable a multi-channel automated Customer Care experience. In the proposed solution, Gen AI + AI/ML-driven solutions are leveraged to form end-to-end AI-driven Care Experience. Covering both wholesale business, Assisted Care as well as end-user Care channels. Thus dramatically reducing time and cost needed to solve customer care issues. AI engines are used to harness network data to drive issue detection and resolution, as well as creating varieting communication channel specific response style to fit the needs of the specific channel. This is integrated to BSS platform for harnessing in variety of customer care situations (end-user experience as well as Care team experience using Gen AI driven Copilot and Case management for structured followup using SLA levels and KPI monitoring).