Explore areas where conversational experience can be brought in. Identify the automation scenarios and map the user journey to empathize with user and enhance the experience at each touchpoint. Once the user journey is mapped, how best intelligence can be infused in the chatbot to enhance user experience should be assessed. A good starting point is a chatbot with self-service capabilities helping users in processes such as onboarding, access management, FAQs etc.
Value in vision:
The enterprises should start small but should keep an eye on the future. Once the areas and business processes are identified, it is important to assess the tangible benefits and user value proposition. The transformation that the enterprise wishes to deliver must assess the ‘Should have', ‘Could have’ and ‘Shouldn’t have’. A strong roadmap needs to be built with a strategy to achieve it. Once this is created, a cost-benefit analysis of the investment should be performed and investment should be optimized.
Once you have established the use of a chatbot, and have a roadmap to get a conversational interface, it is very important to build a chatbot architecture which is robust, scalable, agile and designed while keeping in mind the cognitive requirements. Enterprises should build reference architecture using best-in-class platforms and products, which are best fit to solve the need while being cost effective. The other consideration while designing the solution is the run cost of the solution, KPIs and the analytics behind it.
The agile MVP:
With a strong roadmap, the aim should be to achieve the vision in small steps. Sprint planning for bot development should adhere to the vision and align with CI-CD ideology helping users to test fast, and eventually help the bot to evolve. Each sprint should end in adding value and target the next Minimum Viable Product (MVP). The Agile MVP enhances as the bot augments and evolves with new use-cases being added and the corresponding benefit it delivers.
Augment and evolve:
The critical component of any new technology adoption is dependent on change management. This begins with understanding the KPIs and effective communication on the rollout. KPIs for bots could be different depending on the purpose it serves like user adoption, cost reduction, enhanced experience etc. The bot needs to be measured on corresponding factors and new user stories can be added in the backlog as the bot progresses. Another key component is bot lifecycle management and monitoring user and bot behavior as the chatbot progresses in the lifecycle. As the adoption grows, more cognitive abilities should be added which can further enhance the value of the chatbot.
With the above framework, enterprises can achieve the best suited cognitive assistants for each use case. This could leave the enterprise with high-performing bots with multiple technology products and platforms.
Solving the multi-chatbot problem: Master Child Architecture
Chatbot products and platforms are a mixed bag, with products being ready for use cases, are faster to deploy, have trained NLP and are easy to integrate. The restriction is however scalability of the features; the scalability is limited to the service provider. The platforms are however tailored to specific needs and can be scalable to different features as needed.
Product vs Platform: