Executive Briefing: A multichannel chatbot strategy

Description

Powered by a number of machine learning (aka cognitive) services and comprised of building blocks such as engagement channels, channel devices, bot frameworks, and backend APIs, chatbots provide natural language conversational interfaces to customers beyond the traditional web and mobile interfaces. A number of customer service use cases can be enabled through chatbots to increase customer engagement and divert calls from high-cost channels, such as customer service. In the healthcare industry, for example, chatbots can be used to field health benefits inquiries and claims inquiries and to help customers find a doctor.

However, there are a number of key factors to consider when planning an investment in AI-based chatbots. Technology executives must think of chatbots as strategic investments that follow the “think strategically and act tactically” mindset. Strategic investment in chatbots requires an architecture strategy and implementation roadmap. Sharad Gupta shares a framework to ensure long-term strategic investment in chatbots.

The key factors that make up the decision-making framework for leaders include:

• Customer engagement channels: Multiple channels in which chatbots can be deployed, including engagement devices (e.g., Amazon Echo and Google Home devices), messaging platforms (e.g., Facebook Messenger), the web, mobile, SMS, and email. Channel integration ensures customer journeys are connected and tracked.
• Chatbot technology building blocks: Key considerations include choosing technology building blocks that maximize reuse of chatbot solutions across customer engagement channels while minimizing the long-term vendor lock-in. Investment in backend APIs and microservices to access core business data and capabilities is highly desirable as organizations go down this path.
• Build versus buy: Making informed “build versus buy” decisions requires an understanding of the vendor landscape. Key considerations include understanding the types of the vendor and their core competencies and focus areas. Vendor landscape includes technology platform vendors (e.g., Amazon, Microsoft, IBM, and Google), vertical solutions vendors (e.g., 24/7 and IPSoft), device platform vendors (e.g., Amazon Echo and Google Home), and professional services vendors (e.g., Deloitte and Infosys).
• Data: Data is truly becoming an asset for organizations and a key competitive differentiator. Most vendors have the state-of-the-art machine learning algorithms and technical talent but lack access to core business data. Understanding data availability, data ownership, data security, and data sharing issues are critical in approaching vendor relationships.
• Data science competency: Cultivation of a data science skill set internally should be part of the talent management strategy of every organization that seeks to advance in the AI-powered solutions. Competencies include experimenting, building, and maintaining chatbots. In addition to the technical data science skills, understanding of the business and the ability to identify business use cases for chatbots are key.