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Author: Saloni Walimbe

  • How DevOps Tools Are Contributing to Automation Initiatives Across the IT Landscape

    How DevOps Tools Are Contributing to Automation Initiatives Across the IT Landscape

    Prolific advancements in IT, specifically in agile methods and culture over the years, has led to burgeoning needs for a more holistic end-to-end software delivery approach. Agile development is an umbrella term that encompasses numerous incremental and iterative software development practices — including Kanban, SAFe (Scaled Agile Framework), Scrum, Extreme Programming (XP), Lean Development, among others.

    Each methodology works on its own unique approach. However, the core commonalities they share lie mainly in their values and vision. The methodologies integrate continuous feedback from iteration to refine and deliver a successful software system. They include various forms of continuous evolution, such as continuous integration, planning and testing, of both software and project. Furthermore, one of the most significant aspects of agile methodologies is their keen focus on empowering workforces to work together and collaborate more efficiently, thus simplifying and speeding up decision-making processes.

    Advancement in agile development is what has birthed the DevOps culture. The technology is born from the rising need to adapt to increasing software velocity and throughputs generated through agile methods.

    Understanding DevOps

    The DevOps market consists of a set of practices, designed to integrate and automate processes between IT and software development teams, enabling them to create, test and deploy software in a quicker and more efficient manner.

    The term DevOps, which was coined in 2009 by Andrew Clay Shafer and Patrick Debois, is a combination of the words “development” and “operations.” It is also indicative of a cultural shift in the IT sphere, which bridges the gap between historically siloed teams, such as operations and development.

    The DevOps framework essentially adopts a culture where the critical focus is on facilitating collaboration between the business, operations and development teams. Although tools are considered as pillars of the technology, DevOps is designed to deliver continuous value for customers, empower organizations to provide solutions of the highest quality at swift rates, and bring about the automation of the entire business process spectrum, from building to deployment of a product or application.

    Shifting Trends Toward Automation in Software Development Processes and the Subsequent Impact on DevOps Adoption in the IT Space

    The software development process consists of several integral parts, among which testing is a crucial task. It allows developers to monitor the functionality of the software, monitor and register performance, and detect any aspects that require remediation. However, with software projects becoming increasingly more complex as the years progress, and with project development life cycles accelerating, conventional methods of manual QA (quality assurance) testing fall short in meeting testing objectives within the desired time frames.

    Consequently, software developers are shifting their focus toward automated workflows and testing tools, in order to expedite testing routines without compromising on consistency and completeness in QA processes.

    DevOps practices are significantly dependent on automation to ensure frequent and timely deliveries across platforms. Studies have shown that nearly 85% of IT leaders claim that automation is a critical aspect of their DevOps strategies. This is attributed largely to the fact that automation empowers infrastructures to successfully field constant changes in codes that are associated with DevOps tools. 

    Furthermore, automation allows the effortless scaling of environments and unburdens skilled workforces from mundane and repetitive tasks, enabling them to focus on more integral aspects.

    Considering a process perspective, automation in testing involves the addition of test automation actions and tools to regular software development regimens. For instance, new builds delivered to a repository can be automatically subjected to automated testing procedures using a number of prescribed tools, with little to no need of developer intervention. The results are recorded with care, then compared to previous test runs and eventually submitted for review to developers. 

    Depending on the outcome, the software can then be cycled back to the developers for further remediation or approved for deployment. This is especially relevant in terms of the DevOps framework, which relies on continuous integration/continuous delivery (CI/CD) pipelines.

    CI/CD pipelines are a prominent outcome of developer operations, allowing developers to validate the quality of the software with minimal intervention, and deploy apps and software to customers in a quicker manner.

    CI/CD platform CircleCI, for example, has recently raised over $100 million in Series E funding, in an effort to boost the development of their DevOps product suite. This move is indicative of CircleCI’s proficiency in delivering an automated approach to DevOps practices, including tools for companies to create, test and deliver software.

    How Is the Current Pandemic Impacting Digital Development?

    The COVID-19 pandemic has asserted a massive influence on the industrial landscape, driving workforces across the globe into remote-only work set-ups. With millions of people working from home for a seemingly indefinite period, enterprises across the globe are rallying to accelerate automation efforts in order to more efficiently deliver digital business applications to engage their employees from remote workspaces.

    According to various studies, nearly 73% of IT operations and DevOps teams are working toward maintaining and expediting initiatives for digital transformation throughout the current crisis, holding the belief that the value of digital offerings is set to witness a positive growth trajectory in the social distancing era.

    DevOps industry players such as JFrog are working toward providing enterprises and teams with access to cloud-based DevOps technologies, free of cost, particularly those which are undertaking active research and response efforts to combat the COVID-19 pandemic. These DevOps technologies include Pipelines, Xray and Artifactory, among others.

    While the exact impact of the global crisis on the DevOps environment in particular and the IT space in general is yet unclear, it is becoming more prominent that the strong focus on the prevention of such crises in the future — both in and out of the IT realm — is likely to set a great precedent for the growth of the DevOps industry in the years ahead.

  • Intelligent Virtual Assistants and Their Role in Helping Brands Transform Customer Experiences

    Intelligent Virtual Assistants and Their Role in Helping Brands Transform Customer Experiences

    The relationship between computers and humans has undergone a significant paradigm shift over several decades. From the emergence of the web in 1995 to applications or apps in 2005, technology has grown by leaps and bounds as the years progressed. This growth has brought technology to the modern era, where artificial intelligence (AI) has ultimately begun to come of age.

    Engineers and scientists have invested a great deal of effort, in terms of both energy and time, over the past few decades, which has allowed AI to be adapted into nearly every technological innovation in recent years. One such innovation is the intelligent virtual assistant (IVA).

    An IVA is a conversational AI system that has the capability to emulate human interaction in order to carry out and optimize tasks—customer service, for example. Digital assistants are gaining widespread attention across the industrial landscape, owing to a plethora of technological advancements, including machine learning and deep neural networks, among others. What makes these virtual assistants intelligent is their ability to emulate three key elements that enable customer service agents to solve problems—learning, reasoning and understanding.

    How did these conversational AI technologies achieve these capabilities? To answer this question, it is essential to delve into the evolution of the IVA market.

    Evolution of IVAs

    Although the terms “chatbots” and “intelligent virtual assistant” are often used interchangeably, and despite the fact that these technologies share a core commonality—i.e., their ability to facilitate consumer-enterprise interaction—these conversational AI technologies have some distinct differences. These differences lie mainly in complexity, scope and abilities, which IVAs’ possess more of than chatbots.

    In simple words, IVAs are the result of a decade-long chatbot evolution.

    Chatbots were first introduced in the late 60s and early 70s. These technologies were programmed with rudimentary capabilities, including scripted responses based on user inputs in the form of specific phrases and keywords.

    However, with conversational flows becoming increasingly more complex over time, these chatbots began facing limitations in their scope, mainly owing to their lack of learning process and pre-programmed responses. With no memory or context retention capabilities, the bots were unable to comprehend relevant user information between sessions or even during the conversation.

    However, conversational AI evolution reached a turning point with the emergence of two novel technologies, machine learning (ML) and natural language understanding (NLU).

    NLU gives the digital assistant the ability to comprehend and interpret inputs from users in natural language, which can enable the bot to converse with a customer naturally, as a customer service agent would.

    Meanwhile, ML allows the bots to self-train based on data such as previous customer service interaction transcripts or by observing live customer-agent interactions. This helps the IVAs to cater to customer needs in a more efficient and seamless manner.

    The technological advantages provided by these technologies, therefore, facilitated the gradual evolution of simple chatbots into IVAs, which possess in-depth domain knowledge that they can leverage to understand customer preferences and intents, fulfill their needs and enhance performance over time by gaining new insights from each interaction.

    As a result of these advanced capabilities, automation and customer satisfaction are assured outcomes of IVA adoption. Thus, brands that make use of this technology can make sure that they refocus on their primary objective: ROI.

    The Gradual Shift of IVA Technology from a Personal to a Commercial Landscape

    Starting out as an ideal solution for virtual personal assistants, such as Apple’s Siri which allows users to use voice-technology to perform myriad tasks like texting, scheduling meetings or making phone calls, IVAs are now gaining prominence as crucial tools for brands to implement effective customer engagement.

    AI assistants are rapidly making their way out of personal applications and entering a more business-oriented landscape, for instance, Oracle’s Digital Assistant which is designed to be an ideal platform for sales, marketing and other industry verticals by interfacing with a vast software app portfolio.

    IVA technology is used across a range of industrial applications, for example, taking up the role of sales agents or customer service executives, which makes it a scalable and appealing option for enterprises. Businesses predominantly adopt digital assistants to drive efficiency, enhance ROI, optimize processes and help reduce workloads.

    As the industrial space grows increasingly more competitive, customer engagement has emerged as one of the most significant assets for a successful brand. In fact, studies have shown that almost 96% of customers across the globe believe that customer service is a significant factor contributing to their loyalty to a brand.

    IVAs help brands deliver positive customer experiences by covering a number of different aspects. For instance, AI assistants can quickly solve customer problems through customers’ preferred channels, ensuring speed and convenience. They can also integrate with various systems and databases of brands, thus offering a robust knowledge base to provide tailored solutions to customers. Additionally, they are able to adapt their tone and language to context, which helps them emulate human conversations. All these factors help digital assistants play the role of an efficient and helpful customer service representative.

    How Brands Are Leveraging Conversational AI Assistants to Drive Customer Engagement

    Given the strong capacity of conversational AI technology to engage actively with customers and optimize customer service, more and more companies are adopting the technology as an integral part of their customer experience activities.

    To illustrate, Volvo has plans to develop conversational assistance to guide drivers in the use of various in-car apps to deliver a more effortless connected car experience.

    Likewise, Spotify has also earned positive reviews for its AI-based technology to track user patterns and make accurate recommendations and predictions based on preferences of individual customers, in turn elevating the overall user experience.

    The IVA industry has witnessed several prolific advancements over the years. The future outlook for the technology seems positive, with even more exciting avenues as the years progress, and AI researchers investigating the prospect of enhancing the emotional intelligence of assistants. The ability to comprehend not just the user’s preferences but also their emotions would revolutionize the way customer service is delivered, thus positioning AI assistants as a pivotal component of the future customer experience landscape.

    — Saloni Walimbe