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Are you geared up for the one-two punch of Cloud and AI?

Posted by Ajay Pandey
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With consumerization on the rise, digital is becoming all-pervasive and is number one on the agendas of CXOs across the globe. So what is driving organization strategies for digital transformation across industries? Amongst the usual suspects, cloud is noticeably picking up steam and seems to be paving the way to the next-generation digital enterprise. Adopting a cloud first strategy offers multiple benefits, such as:

  • Increase in Quality of Service ( QoS)
  • Improved Business Agility
  • Shifting from a CAPEX to an OPEX model

Cloud offers an enterprise a variety of advantages, the most critical one being able to increase their infrastructure capacity whilst also being able to deploy new services as and when they are needed, with ease. However, this flexibility comes at a price. How does one ensure that there is complete visibility and control over respective data at all times, coupled with top-notch quality of cloud dependent services being offered? The answer - A robust Cloud Quality Engineering and Assurance strategy.

Quality Engineering for Cloud and Emerging Technologies

Cloud Quality Engineering is a key cog in the Digital Transformation Assurance wheel, with a key focus on emerging technologies and innovations related to SaaS, ensuring that organization’s business outcomes are assured. The key Quality aspects include functionality, Customization/Configuration Validations and Automation for accelerated delivery and time to market.

What is unique about Quality Engineering is that it proactively bakes in quality into the entire software development life-cycle, and not just at the end, during a dedicated “Testing” phase. This ensures Continuous Quality, accelerating the continuous integration-continuous delivery life-cycle, and integrating seamlessly in a DevOps environment.

Impact of Emerging Technologies on Cloud

From an assurance perspective, Cloud Quality Engineering bears the onus of ensuring cloud technologies seamlessly integrate and synergize with emerging technologies, namely Artificial Intelligence and Chatbots. Customer Relationship Management is a space where most of these technologies are disrupting the traditional business models and ways of working. I will elaborate on this further using Salesforce CRM as an example.

  • Artificial Intelligence - Salesforce Einstein
  • Salesforce Einstein is the first comprehensive AI for CRM, designed to help every business be smarter and more predictive about their customers. Machine learning, deep learning, predictive analytics, natural language processing, and data mining - you name it and Einstein has it.

    What all does Einstein do?

    • Predictive Lead Scoring & Segmentation
    • Case Prioritization
    • Root Cause Analysis
    • Product Personalization
    • Image-to-Text conversion

    There are 35+ Einstein features built across Sales, Service, Marketing, Community, Analytics, Apps, Commerce and IoT Cloud.

    Quality Engineering and Testing of an AI embedded product such as Einstein would significantly vary from traditional testing in the following ways:

    Focus Areas

    Quality Engineering Approach


    Data Intensive Testing

    Assuring the Data consistency, Storage and transformation would be some of the Quality checks to be done

    Artificial Intelligence systems are best suited when there is enormous volumes of Data

    Testing Algorithms

    Creating Training & Test datasets which can be used to train

    Datasets themselves can be

    machine learning models accurately for the expected output

    created using programming languages like Haksell

    Testing Intelligent Virtual/Digital Assistants deployed on Desktop/Mobile

    QE Approach would involve the below

    • Integration testing of the different layers of technology involved
    • Voice generated input data creates infinite test cases
    • Test Automation for Voice

    Example: Salesforce’s LiveMessage Bot

    Testing Natural Language Processing ( NLP) Features

    Validating the NLP again would result in testing different data combinations with Boundary values

    Automated tests would be needed

  • Chatbots
  • Chatbots are complementary to Salesforce Einstein, providing a conversational interface to the Salesforce’s Platform, Knowledge and CRM data.

    Salesforce currently features the following bots:

    • Live Message - a bot that lets enterprises users engage with customers through messaging apps such as Facebook Messenger, SMS or MMS directly within its Service Cloud.
    • Service Cloud Bots - which automate data gathering and connect to relevant account records and service cases in its Service Cloud.

    Quality Engineering and Assurance of bots would be carried out in the following way:

    Bot Components

    Quality Engineering Approach


    Back end components like

    • Natural Language Processor
    • Context Analyzer
    • Decision Maker
    • Response Generator

    Validation of each of these would involve

    • Testing different data combinations
    • Linguistic tests
    • API Tests to check the request and response

    Automated tests would be needed

    Chatbot Ecosystem

    A Chatbot ecosystem Assurance would lead to multiple validations as it would involve:

    • Interaction Channels (Chat, SMS, Mobile app, Virtual agent etc.)
    • Human/Machine UX
    • Cloud Services ( Open API’s, databases, apps, devices etc)

    A disruptive way of testing a bot would involve validating the bot by building an Assurance Bot which would perform End to End Validations

    Key things to be kept in mind while developing an Assurance Bot would be:

    • Simulate and test various user scenarios ( All permutation & Combinations)
    • Validate the enhanced functionality
    • Validate the self-learning ability of the BUT ( Bot under Test) over a period of time

    In conclusion, Artificial Intelligence and Chatbots are complementary emerging technologies which when used collaboratively with Cloud can disruptively and exponentially increase benefits, especially in the CRM space. These technologies and their applications in business scenarios will keep increasing and improvised upon at a break neck speed. Ensuring that organizations are able to successfully implement and reap the promised benefits at a swift space means that their Quality Engineering strategy should continuously evolve and adapt. This will ensure that the organization is future ready.

    About Author

    Ajay Pandey- Principle Consultant, Business Application Services, Wipro, Ltd.

    Ajay Pandey is part of Digital Assurance practice. He has 17 years of diverse experience across Test management, Consulting, Pre-sales and Delivery. Ajay currently leads Cloud-SaaS assurance sub practice and is responsible for Salesforce, Workday and MS CRM. He is passionate about emerging technologies.

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