Reinvention of the customer interaction pattern, access to knowledge, internal service-talent developments, and Artificial Intelligence (AI) machine learning (ML) advancements are creating an urgent imperative for service organizations to reconfigure their service operations – improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills – for the future. In a digital age, live voice-face (audio and video recognition) contacts with language model (LM), founded with reasoning and inferencing model engine or ensemble (RIME), that matter even more in providing personalized and secured knowledge and a high-quality customer experience.
What does Secai do?
AI-enabled Digital SmartCard Device for Secured Connections, Interactions and Transactions on Service-Assist Capabilities: designed to facilitate knowledge, identities, recognition and language models with RIME in ways to retain privacy, anonymity, digital authenticity, does not require internet, and cannot be held by two entities simultaneously
Voice-Video-Gesture-Text Interaction With Knowledge Center: designed as decentralized peer-to-peer technology to facilitate interactions with knowledge providers and access devices
Interoperability and Multimodal With Switching Mechanism: high velocity transactions in network and in peer-to-peer (for example, patient and personal doctor) – fostering ubiquitous access across in-and-out networks
The following are examples of the operational improvements Intelligent (virtual) “Service Assist” – or SECAI – can have for specific use cases:
Customer self-service. SECAI–fueled Intelligent “Service-Assist” can give immediate and personalized responses to complex customer inquiries regardless of the language or location of the customer. By improving the quality and effectiveness of interactions via automated channels, AI/ML could automate responses to a higher percentage of customer inquiries, accessing internal and external service-related knowledge, enabling customer care teams to take on inquiries that can only be resolved by a human agent. Our research found that roughly half of customer contacts made by banking, citizen/resident services, airport, retail and utilities companies are already handled by card or card-related machines, including but not exclusively AI/ML. We estimate that Intelligent (Virtual) “Service Assist” AI would further reduce the volume of human-serviced contacts by up to 50-65 percent, depending on a company’s existing level of automation.
Efficient and effective content creation: SECAI is designed to produce a vast variety of content for interactions to significantly reduce the time required for ideation and content drafting, saving valuable time and effort. This facilitates diffusion of consistent knowledge across different pieces of content, ensuring a uniform corporate voice, communication style, and format. Team members can collaborate and leverage (AI) “Service Assist” – and AI/ML models – to scale and integrate their ideas into a single cohesive piece. This would allow teams to significantly enhance personalization of marketing messages aimed at different customer segments, geographies, and demographics. Mass email emails, alerts, social media posts, proposals, campaigns, etc. can be instantly translated into as many languages as needed, with different imagery and messaging depending on the location and audience. SECAI’s ability to produce content with varying specifications could increase customer value, attraction, conversion, and retention over a lifetime and at a scale beyond what is currently possible through traditional techniques.
Product/service discovery and recommender personalization: With SECAI, product and service discovery and recommender of choices, options and rewards can be personalized with multimodal inputs from text, images, and speech, and a deep reasoning and inferences (RIME) of customer buying of products and utilization of various services. For example, SECAI technology can leverage individual user preferences, behavior, and purchase history to help customers discover the most relevant products and generate personalized product descriptions. This would allow CPG, travel, and retail companies to improve their e-commerce sales by achieving higher conversion rates (read more on our Encore).
Virtual expert to augment employee performance: SECAI trained on proprietary historical knowledge and carefully designed reasoning and inferences (RIME) including policies, research, and customer interaction could provide always-on, deep technical and knowledge support. Today, frontline spending is dedicated mostly to validating services and humans interacting with customers, but giving frontline workers access to information and knowledge as well could improve the customer experience. The SECAI technology would also build declarative knowledge on related industries (e.g., services related to airlines, security and airport functions); procedural knowledge on related operations that are flowing from one section to another services (e.g., services related to health-care, lab-test, diagnosis and pharmacy functions); and episodic knowledge on automating information management tasks related to collecting and processing data with natural-language capabilities (e.g., services related to retail, inventory, supply chain traceability, manufacturing, etc.) increase the automation potential because its capabilities are fundamentally engineered to do cognitive tasks for customers and send alerts on semantic knowledge queries from public sources. For example, a financial institution is building an AI assistant with the aim of helping tens of thousands of managers quickly find and synthesize answers from a massive internal knowledge base. The model combines search and content creation so managers can derive and recommend choices to any client at any moment.
Building digital SmartCard with AI/ML-enabled and Intelligent (virtual) ’Service-Assist’ platform has at least four major benefits:
First, and most directly, security in design: simplest way to counter-train and defend deep-fakes, fallback on global outages, resilient to the fragility of the single-point systems, which otherwise, a recent research of MIT found that, too much (18x -20x) compute will be needed to expend each counter-train with strongest defense on finding attacks compared to and for learning to defend
Second, personalized secured advisory: advanced personalization and rendering help companies and their customers predict potential problems even before they occur. That allows them to take action to prevent or address the problem in an efficient and personal way, reducing costs and risks for the company
Third, proactive knowledge: by identifying problems before customers do, agencies can reach out proactively with potential solutions, encouraging the customer to use the fastest, most cost-effective resolution channel
Finally, retroactive learning: where the customer learns without initiating contact, they receive responsive, white-glove service, regardless of the channel they choose. Service-response systems with reasoning and inferences make good use of available knowledge to understand the customer’s situation, derive the context, and recommend the choices
