Today’s enterprise relies on Agile processes, lean methodologies and unique avenues to automate, lowering the overhead costs without compromising on the quality.
With the advent of the emerging technology advancements such as CNN, RNN and other Deep Learning libraries, it is possible to make a machine to think like human beings, to interact like human beings and to also take decisions like human beings.
In a classic scenario of today’s manufacturing industry, ecosystems lack connected ness, not every factory has smart devices or infrastructure in place, to start getting data out of machines, etc.
This can lead to unplanned outages, non-replenished stock inventories, delays in procurement, leading to assembly line delays and other relevant burning avenues, that automatically leads to a lowered Customer Satisfaction.
“We live in the era of Alexa, Siri, & Google Voice Assistant. We can look to automate a lot of business processes using Conversational AI”
All of this due to lack of data transparency, below par customer service and leads to loss of revenue. Also, with growing man power costs and other cost overheads, most enterprises are looking to automate some of their process workflows thro’ the incumbency and convergence of IoT and AI.
Imagine creating a service agent that can interact thro’ speech with customers, and is a connected system overall which is always “live data” ready? It can easily take count of live stock inventories thro’ IoT Systems and translate the data into a voice output making customer experience human like. Similarly, a Service Request registered can be automatically pushed into Replenishment Cycle for timely action and faster turnarounds, etc.
Some of the fast-growing areas of AI adoption comes in the area of automating Customer Service, having a Digital Agent creating meeting requests with Potential Prospects, discovering leads for Augmenting Inside Sales Teams, Etc.
Let’s look at how to create an interactive Agent by creating a Conversational AI mechanism, registering Customer Request for a Service meeting. The section would include tech components and stack details and how it enables a greater Customer Satisfaction thro’ a seamless ‘Human-Machine’ interoperability. We use Microsoft Azure as an example.
To set up a service agent on the Microsoft Azure stack, we can go to the Microsoft Azure console, and select the AI and Machine Learning web service to create a service agent which can look to interact with prospects and generate leads for an inside sales organization within an enterprise.
We can incorporate and set the Q&A backend Engine Libraries to pre-empt and prompt pervasive responses related to the services and offerings you have to offer, in sync with input queries coming from potential prospects trying to view your website/services page.
For pre-emption and predictability engine that the service agent would use to display relevant output upon receiving queries, can be tuned and customized based on the ML algorithm libraries available for consumption in the Microsoft Azure Console.
Once the engine model is tuned and trained to respond to queries, the same can be integrated with various social media platforms like FB, Twitter, Slack and others.
Q&A service requests can be authenticated and authorized, to ensure the same engine can be connected to Social Media plug ins, such as Facebook Messenger using API’s that can be consumed as a service.
The overall process is basically comprising of NLP based queries coded on Python and the set up consumes ML and AI based available libraries which is ready to be consumed under Microsoft Azure Console to get the service agent operational.
This is a scenario where the input comes as a Text and the output is also displayed as a Text. The same scenario can be enabled thro’ voice commands, where initially there will be a ‘Speech to Text’ Conversion where the machine understands the input, consumes the libraries in the backend to retrieve results against the queries requested.
And the final output can also be rendered as a voice output involving a ‘Text to Speech’ conversion to render the output.
Typical areas the Conversational AI based Service Agents tend to benefit includes avenues of Contact Center Management Automation, Lead Generation for Inside Sales, HR Automation, Service Request IR Management, etc.
Specifically in the Usecase above we looked at garnering a front end Conversational AI service agent that can interact with prospects visiting a website, and converse on the messenger window effectively, pre-empting the right value propositions and directing the prospects over to the Inside Sales Leads team for further Qualification and profiling.
We looked at using Microsoft Based Azure libraries for Machine Learning and AI, consuming and training the data models to come up with customized responses to specific queries that got keyed in.
Typical technology stacks involved with this included AI and ML libraries, Coding development on Python, NLP based frameworks for Speech to Text and Text To Speech conversions.
Essentially the core benefits with our Conversational AI based service agent includes:
– Improved Response Rate with potential Prospects
– Personalise Communication
– Automation of Repetitive Tasks
– Round the Clock Availability
– Reduction in Delays to Attend to Prospects
Likewise, we can look to extend similar use cases using other available platforms such as Google Dialogflow, IBM Watson, and AWS Lex churning automated insights for areas of Incident Record Management Automation, Customer Behaviour Churns, etc.