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CX Trends 2021: Here’s how businesses are winning Customer Experience moments

5 minutes read

In the pandemic era and the subsequent lockdowns around the country, in-person customer experience seems like a far-fetched dream for most of us who have made our homes into our offices, our beds, or living rooms into our conference rooms, and vice versa.  

While brands across the globe are building processes for the new normal, where the virtual world continues to gain popularity, even as the second wave of the COVID-19 pandemic rages on, with a third wave expected to hit soon enough. 

The last 18 months since the pandemic struck have taught a crucial lesson to every business and marketeer by disrupting set notions and practices. The key to a thriving business in questionable times like these is to understand the importance of customer experience and travel up the graph from a good to a great one, in order to sustain oneself. Simultaneously, it’s important to workaround forecasts in a volatile setting for every business type to ensure preparedness.  

What began as a global healthcare crisis also led to a significant transition into a digital-friendly world. From work-from-home setups to e-commerce, getting food home delivered, and more, customers are more online than ever before, leading to a rise in digitally-savvy professionals driving and engaging in better CX.

If you are investing in CX, where do you begin?

According to research conducted by Gartner, companies that successfully implement customer experience projects begin by focusing on how they collect and analyze customer feedback.

Despite these turbulent times for people and businesses, customer expectations have seen an upward graph and so providing a top-notch customer experience is a challenge everyone is trying to meet in order to retain their loyal customer base. 

PwC, through their future of CX report, surveyed 15,000 consumers and found that 1 in 3 customers will leave a brand they love after just one bad experience, while 92% would completely abandon a company after two or three negative interactions. 

Whether you use surveys, web forms, or Net Promoter Score (NPS) programs, read through customer comments, suggestions, and opinions to see what they expect from you. Then, invest in those projects to meet their expectations.

Read on for trends we are seeing and expect to see in 2021: 

CX Trend 1: Going digital for customer interaction in the pandemic era: 

The shift to digital that has been aided manifold amid the pandemic, has seen consumer behavior move on for all services including e-commerce, finance, healthcare, wellness, and more. Forrester has predicted that 2021 will see digital customer service interactions increase by 40%.

According to Gartner, the new normal makes it mandatory for the service industry especially to transition to a “digital-first” strategy, thereby enabling improved customer interactions via proactive engagements on messaging platforms. By the year 2025, 80% of customer service organizations are expected to abandon native mobile apps in favor of messaging platforms for a more seamless customer experience.

Even at the workplace, a digital transition means conferences and seminars move to Zoom conferences (and other related apps) and webinars. The year 2020 also made way for a paradigm shift in the Ed-Tech space when educational models have moved online and full-time courses too are being held on the web. 

All Images Courtesy: zendesk.co.uk/CX Trends report 

CX Trend 2: The rise and stay of contactless service in the new normal: 

As the COVID-19 pandemic continues to impact both customers and organizations, it has instead generated a shift to a contactless approach as the best alternative of providing a service without person-to-person contact. According to a survey by IDC, over 36% of manufacturers said that their service or product installation will now have a contactless approach. The survey also predicts that by 2021, 65% of organizations will have shifted to a digital-first approach through automated ‎operations and ‎contactless experiences. However, it is also imperative that technicians, as well as customers, are safe amid this transition which might also see an increased implementation of the latest technologies and capabilities including artificial intelligence and augmented reality, and mixed reality for optimized service. 

This prediction by IDC also aligns with Forrester’s 2021 prediction that says that consumers will continue to prefer digital interactions and customer service, to keep themselves safe.

CX Trend 3: Emotive technology and why there’s a noticeable rise:  

The pandemic and the subsequent lockdown also led to an all-time high of reported mental health problems, which were largely said to have been triggered by social media. It is thus a need-based search for a solution to overcome issues such as these which also benefit consumers and businesses. 

According to Harvard Business Review, “When companies connect with customers’ emotions, the payoff can be huge.” The ability to generate positive emotions in a customer and leaving a good lasting impression is called brand intimacy which helps brands drive conversions and customer loyalty. 

In the present day, companies are dealing with a lot more data amassed from their customers which helps them figure out what their customers are feeling through the use of facial recognition, movement data, health data like heart rate and blood pressure, social media behavior, and more. 

One of the ways that this immense power on a customer’s lifestyle choices can be used for a good cause like being able to tackle mental health struggles including anxiety and depression, emotional health crisis, and more. 

Microsoft now plans to embed Teams with a series of “wellness” tools to address these crisis situations that will help monitor emotional health, mental health and provide necessary tips and tricks. Other apps including Wysa, Headspace, Calm and more help with a chat to help you feel at ease, extend therapist support when needed, and also provide guided meditation sessions to help keep your mind calm. High-stress levels and anxiety are also known to reduce immunity levels, which in turn might increase vulnerability to other health issues, and open up the unfortunate possibilities for other lifestyle disorders including hypertension and diabetes. 

CX Trend 4: Empathy, a core element in CX: 

Empathy has emerged as a core organizational capability in the year 2020 and so empathetic customer support is now imperative for customer service in 2021. According to Forrester, organizations must recognize the needs of their customers both physically and emotionally, to provide better empathetic customer support and experience. This metric has skyrocketed as consumers around the world have been adjusting to the pandemic, lockdown, and the new normal. 

A recent report published by Gartner predicts that by 2025, customers will engage a freelance customer service expert to address 75% of their customer service needs. Steven Petruk, President, Global Outsourcing Division at CGS, shares, “Amid the challenges of the pandemic, customer care centers have all but done away with any metrics around call duration and are actively encouraging agents to spend more time on the phone with clients. While empathy has not been an operational performance metric in the past, it absolutely is the prime area of focus now and will continue to be. In an effort to measure empathy, many companies are adding empathy-specific questions to their post-call surveys.” 

With an ever-changing business landscape, more so amid the second wave of the pandemic and a probable third wave expected soon, companies globally have an opportunity to re-strategize and plan their roadmap as a short-term goal depending on what might work best for them in the present situation, with the flexibility to rehash their MO every few months or annually. 

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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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