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How is AI extending customer support during COVID-19 pandemic

4 minutes, 14 seconds read

With over 3 million confirmed cases of COVID-19 throughout the world and more than 200,000 deaths to date since the first report; coronavirus has spread wreaking havoc on any back-office operation, and more intensely on call centers throughout the globe.

For a couple of years now, organizations have only been theorizing the possibility of AI to enhance customer support. It was always a thing that could wait. However, now AI is proving to be a pressing matter over other priorities, and organizations are ready for widespread development than perhaps assumed.

Improved Customer Satisfaction

From banking to travel to finance; given reduced staffing and limited work-from-home options, the call center agents are overwhelmed by the influx of calls; for which the consumers are facing long latencies. These circumstances can, in turn, lead to a huge strain on the workforce and the industry as well. As businesses struggle to cover an increase in call volume, according to an old adage “necessity is the mother of invention.”, AI-enabled customer support has come to rescue. 

“People want what’s best for them, and they can switch on a dime because there’s always a new disruptor disrupting the last disruptor. So companies should just strive to keep changing and adapting to their customers’ needs.”

Ben Chestnut, Co-founder & CEO of MailChimp

AI has the capability of revolutionizing the relationship between a company and it’s clients. 64% of consumers and 80% of business buyers said that they want companies to interact with them in real-time. AI in customer support today can provide significant cost saving, triage calls on priority, volume elasticity, and meet customer expectation; that will eventually benefit the business in the long term.

Primary Concerns

Due to the pandemic outbreak and prolonged lockdown periods in several countries, businesses are forced to transition to work from home models. However, companies are not in favour of giving access to sensitive data to its employees outside the office premises. Along with privacy concerns, there are mobility concerns with the call center operations. Theoretically, technology can simplify mobility solutions. In a developing country like India, where only 2-3% of people use wired broadband and the majority of users rely on mobile data, uninterrupted internet connection is a real struggle.

“Now more than ever, customers need fast responses and AI and Automation can help”

Gadi Shamia, CEO of Replicant.

AI in Customer Support

Artificial intelligence in customer service is extremely useful to answer FAQs and resolve common customer support issues without the presence of a live agent. It can classify calls on the basis of options, business priorities and suggest solutions to the consumer according to their specific needs. Unlike the generation-old IVRs, the AI-enabled customer service, powered by NLP, shall understand the customer’s needs and allow him to converse as if he was speaking with a live agent. 

With the rising number of COVID-19 cases, customer queries at hospitals are increasing exponentially caused by high demand in consultation. To adapt to the situation, hospitals are turning to chatbots and virtual assistants. Here are some interesting use cases of AI in customer support bots.

Lili

Vozy’s Lili, is a conversational AI platform that provides customer assistance by alleviating pressure due to high call volume.

WHO Health Alert chatbot

The World Health Organization (WHO) has launched a dedicated messaging service, the WHO Health Alert chatbot to provide the latest news and information on COVID -19.

Read: How is technology helping to combat coronavirus pandemic?

Illinois

In partnership with Google AI, Quantiphi and Carahsoft created a 24/7 AI-enabled customer service bot, Illinois to provide immediate assistance to the filers with the FAQs.

Hitee

Hitee is the world’s first insurance specific chatbot solution. It allows integrating document processing workflows, ticket management systems, etc. to further simplify and automate customer support. Apart from 10x increasing customer interaction, Hitee also brought in new business leads and renewals for an eminent insurance company, Religare.

The crux

One fit for all is a myth now, even in customer support. AI-powered bots are proving to be revolutionary in customer support when it comes to customization of User Experience. Companies like Amazon, Starbucks and Netflix are implementing AI to track and analyse customer data and provide quick and easy resolutions to the customer problems. It also provides companies with deeper insights into the product based on demographic gender and various other factors.

AI-powered bots are capable of providing 24 X 7 customer support, more importantly after working hours and holidays. They prove to be not only cost-effective but also scalable throughout the enterprise. 

Customer support is the mainstay of any business. In these testing times, every call centre is under intense pressure due to the pandemic outbreak. Since customer expectations are higher than ever businesses are looking for advanced technological capabilities to bridge the gap. By adding AI-powered tools in customer support operations, businesses can not only improve customer experience but also have numerous business implications such as lower customer churn, higher revenues, less staff turnover and increased growth. If you need interfacing software for your specific business needs, please feel free to write to us at hello@mantralabsglobal.com.

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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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