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CX 2021: How will 5G impact the customer experience future

8 minutes read

5G, the most anticipated wireless network technology, is touted to alter the way people go about their daily lives at home and work. Its USP lies in being a lot faster with a capability to handle more connected devices than the existing 4G LTE network. The fastest 5G networks might be at least 10 times faster than 4G LTE, according to wireless industry trade group GSMA. 

5G signals run over new radio frequencies, needing radios and other equipment on cell towers to be updated. A 5G network can be built using three methods depending on the type of assets of the wireless carrier: low-band network that covers a wide area but is only about 20% faster than 4G; high-band network that boasts of superfast speeds but they don’t travel well, especially through hard surfaces; and mid-band network which balances both speed and coverage. 

Industry trade group GSMA estimates that by the year 2025, the number of 5G connections will reach 1.4 billion – 15 percent of the global total. Additionally, global IoT connections will triple to 25 billion by 2025, while global IoT revenue will quadruple to $1.1 trillion, according to this report published by GSMA. 

Image Source: www.speedtest.net 

How will 5G impact customer experience

Image Source: tmforum.org

The increased reliability, performance, and efficiency of the new spectrum will come as a boon while, at the same time, raise the bar for customer expectations. The intertwining of technology with our daily lives could also mean the proliferation of other technologies, including the Internet of Things (IoT), Augmented and Virtual Reality, Big Data, and Cloud Computing. 

Consumers have regularly cited reliability as their biggest gripe with 4G networks. Over 4 out of 10 (43%) consumers say the internet on their mobile device “cuts in and out sometimes/is not always strong,” according to a PwC survey titled, The Promise of 5G: Consumers Are Intrigued, But Will They Pay? 

According to Deloitte, India’s digital economy will exceed USD 1 trillion by 2025 as a result of increased smartphone usage, rapid internet penetration, and the advancement of mobile broadband and data connectivity. 5G, on the other hand, is likely to be the key catalyst of this expansion.

Video options, however, go beyond content consumption unto live support, too. For consumer-facing companies, live video support will open doors to better customer service, a crucial aspect of a good customer experience. A 5,000-person survey done by Oracle found that 75% of its respondents recognize the value and efficiency of voice and video chat. They also look forward to first-call resolutions.

Even for agents providing email support, a quick video explaining steps looks like a more efficient way to give a resolution instead of emails with a step-by-step guide, an aspect that companies can consider for seamless processes. 

The GSM Association, an industry organization representing mobile network operators around the world, says the number of IoT connections worldwide will grow manifold between 2019 and 2025, to over 25 billion.

AR/VR capabilities and 5G

5G’s advent is a likely measure to “revolutionize” tech, especially through AR and VR. The high speed and low latency of 5G might imply that processing power could be moved to the cloud thereby allowing for more widespread use of VR/AR technology.

AR/VR technologies powered by high-speed 5G could help boost interest in newer concepts like virtual stores and the use of AR to experience products in their homes, or makeup on one’s face, and more. The combination of high speed and minimal lag is perfect for both VR and AR, which has a lot in store for the gaming community too. According to Nielsen’s study Augmented Retail: The New Consumer Reality released in 2019, many people are willing to use VR/AR to check out products.

That said, true VR/AR growth from 5G is difficult to predict since it also depends on the pace of customer and brand adoption. Nevertheless, its use in customer experiences will be interesting to watch in the coming years.

Big data processing power and 5G 

AI and big data analytics are currently in use to identify customer patterns in order to personalize CX. 5G’s capabilities are likely to raise the bar on the volume of data companies collect and increase the pace at which AI can process it. 

Faster speeds and lower latency lend themselves to an influx as they prepare for the next wave of automation and AI-backed technologies. Businesses will begin relying on mobile networks more frequently than before while streamlining core operations.

5G latency is expected to be faster than human visual processing, thus making it possible to control devices remotely effectively, in (almost) real-time. 

Insurance and 5G

Image Source: www.capgemini.com 

Insurance agencies rely on network carriers to share data for selling policies. With larger mounds of data widely available through 5G, agencies will be at an advantage to leverage more data without having to host or own it themselves. This means greater efficiency to navigate through data in a simpler manner.

The Internet of Things (IoT) has seemingly benefitted the auto insurance industry the most. The data is easier to generate, which includes the policyholder’s car details, mileage, speed, and overall usage of the car depending on each drive. 

https://www.youtube.com/watch?v=n5wkY3gQYiU

With IoT, a policyholder’s car isn’t the only thing that could help generate data. In case of a home fire, an oven could be used to collect data for requisite claim information. Likewise, a drone could share accurate location data. 

Overall, agriculture, manufacturing, logistics, financial services will all benefit from lower latency, high speeds, thus ensuring an immersive experience for all. 

Dogan Kaleli, CEO at Stere.io, Founder at Nion, wrote in ‘Why 5G is a Major Game-changer for the #Insurance Industry?‘ that 5G along with revolutionary technologies will mark the beginning of the 4th industrial revolution or the flywheel effect.

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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.

Image Source

  • 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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