Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(153)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

Tips for Designers To Tackle Layoffs 

The Layoff Season…

SAP Lab Laid off 300 employees. Ericsson to fire 8500 people. Headlines like these have become very common these days. Over 340 organizations have laid off more than 1.10 lakh people so far across the world. Not only humans but even robots were fired by Google recently. Meta, Amazon, Twitter, Zoom, and Microsoft are some of the major companies to join the layoff bandwagon. These big tech companies have large teams with multiple people with the same skill set. Over the past few weeks, dozens of them have frozen hiring and made significant cutbacks to eliminate redundant positions in departments like HR, marketing, and design. This has led to high competition for jobs in these areas and skill sets and a decrease in job opportunities.

The value of a designer is not always acknowledged, especially in these times of economic uncertainty where cost-cutting becomes a priority. This has also resulted in a cut in the budget for design departments, which leaves designers with fewer employment options. The assumption that design is a luxury rather than a necessity also hinders people from appreciating the worth of designers. This article discusses some tips for designers to tackle layoffs and AI-based solutions that can assist them to stay relevant in UI/UX design.

Automation and technology advancements have led to an increase in the use of design software and tools, making it possible for non-designers to create designs and perform tasks that human designers previously did. This has led to a decrease in the need for human designers in specific industries such as graphic design and website design, where the use of templates and pre-designed elements has become more prevalent.

Who benefits during the recession?

Contract or Freelance Designers

Companies may have less money to spend on design work, which can lead to fewer opportunities for designers. However, designers who are willing to work on a contract or freelance basis may still be able to find jobs, as companies may look to save money by hiring contractors or freelancers rather than full-time employees.

Additionally, designers with a diverse set of skills and the ability to adapt to changing market conditions may be more likely to find work during a recession.

User Experience Designers & Researchers 

UX designers and researchers may still be able to find work, as companies may be looking to improve their online presence and user experience to stay competitive which is why improving their digital products and services may become their key focus area. This can lead to an increase in demand for UX designers and researchers.

Business leaders may also cut costs by streamlining their products and services during these tough times, which can increase demand for user research to understand customer needs and preferences.

It’s crucial to remember that the job market is extremely dynamic and subject to rapid change during a recession, making it challenging to forecast how the demand for designers will change.

Essential Product Companies

Companies that build business-essential products may see an increase in demand, as companies and organizations look to cut costs by investing in more efficient and cost-effective products. These types of products may include items such as software, hardware, and equipment that help companies streamline operations and improve productivity. Additionally, companies that specialize in cost-cutting solutions, such as supply chain optimization or cost-saving consulting, may also see an uptick in business during a recession. It’s important to note that not all product-based companies will benefit during a recession, it will depend on the type of product they produce and the industry they operate in.

How to stay relevant in designing amidst this economic uncertainty?

# Focussing on Soft Skills

While technical expertise is crucial, employers don’t hire only people who possess those skills. The workforce continues to place a high priority on soft skills, also known as employability or transferable skills, which are frequently influenced more by personality than by education or training. The soft skills that may be most important in an uncertain job market for 2023 and beyond, include:

1. Critical thinking skills

Critical thinking involves being able to think creatively and strategically, identify problems, and come up with innovative solutions. These skills are highly transferable across different industries and roles, making them an asset for any team.

2. Communication skills

Effective communication helps in building positive relationships, resolving conflicts, and promoting understanding. Employers value individuals with strong communication skills as they can work effectively with others, and can represent the company positively. It involves both verbal and written communication, and the ability to listen actively.

3. Mental Flexibility

Mental flexibility, also known as cognitive flexibility, is the ability to adapt to new situations, to think outside the box, and consider different perspectives. Employers value individuals with this type of cognitive ability that allows people to adjust their thinking and behavior to changing circumstances. 

4. Teamwork Ability

Teamwork is important in any profession, regardless of the industry or role. Employers value individuals with strong teamwork skills as they can work effectively with others and can contribute to the success of the team and the organization. Teamwork ability is a key skill for achieving common goals, fostering creativity, and promoting a positive work environment.

5. Self Leadership

Employers value individuals with strong self-leadership skills as it involves setting goals, making plans and taking action to achieve those goals, and being self-motivated, self-disciplined, and accountable for one’s own actions.

# Learning AI-based Tools to stay competitive

According to the Global AI Survey, three in four businesses (75%) are either exploring or implementing AI and are increasingly recognizing AI’s potential to transform their operations and create new business opportunities. The survey also revealed that the adoption of AI is still in its early stages, with many businesses facing challenges such as a lack of skilled talent to use these tools, difficulty integrating AI with existing systems, and concerns about data privacy and security.

If there’s one thing that can give designers a competitive edge, it is the use of AI generative tools. AI generative tools are designed to assist designers in creating new designs, patterns, and layouts using machine learning algorithms. These tools can generate a wide range of options based on a set of input parameters, allowing designers to quickly explore different possibilities and find new inspiration.

1. Natural Language Generation Tools (NLG)

NLG tools use algorithms to generate text based on predefined rules or templates. These tools are commonly used for report generation, news article writing, and chatbot interactions. For example GPT-3, Wordsmith, Quill, Articoolo, Textio, etc.

2. Content Ideation Tools

These tools use AI to generate ideas for content topics based on keyword analysis, social media trends, and other data sources. They can help the sales and marketing team identify new topics and angles to explore. For example BuzzSumo, SEMrush, ContentIdeator, Clearscope, etc.

3. Video and Image Generation Tools

There are various AI tools available for image and video generation, which use deep learning algorithms and computer vision techniques to create realistic and engaging visual content—for example Midjourney, DALL-E, Adobe Sensei, Lumen5, etc.

4. Music and Sound Generation Tools

AI can analyze existing music to create new compositions or generate realistic sound effects for video and gaming applications. For example Amper Music, AIVA, Jukedeck, etc.

The Road Ahead:

It’s important to note that while these tools can save time and resources, they also have limitations. They may lack the creativity and nuance that comes with human-generated content, and there is a risk of producing low-quality or biased content if the data inputs or algorithms used are flawed. So a designer who is familiar with these tools will have a better understanding of how to work with them and may be more in demand by companies and clients looking to integrate AI into their design processes. Embrace these opportunities and be open to new ways of thinking.

Disclaimer: I am not an economist. Everything mentioned in this article is supported by extensive research and is not my personal view. I hope this article gives you some clarity and confidence heading into these uncertain economic times. 

About the Author: Unnathi is a UI/UX designer, currently working at Mantra Labs. She is passionate about research and has expertise in building digital systems that provide engaging experiences.

Want to know more about designing?

Read our blog: Personalization in Mobile UX

Cancel

Knowledge thats worth delivered in your inbox

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.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot