Turing CMO: Meet Phil
For Employers

Q&A Interview with Turing CMO Phil Walsh

Turing recently hired its first CMO and he shared with us what he’s working on, what he’s excited about, and where you might find him when he’s not working.

In today’s tech world, the role of chief marketing officer covers many areas and is constantly evolving in terms of what the CMO can–and should–be accountable for. A modern CMO’s responsibilities include brand, experience, and growth, and the role is wider and more accountable than ever before. 

Turing recently hired its first CMO, Phil Walsh, and he shared with us some insights on what he’s working on, what he’s excited about, and where you might find him when he’s not working.  

Turing: Phil, please introduce yourself and what you’re responsible for at Turing. 

Phil Walsh: Sure. I’m the Chief Marketing Officer here, and I joined in May 2023. I’m based in Denver, Colorado, in the U.S. and I’m super excited to be here.  A fun fact about me is that when I’m not working, I like to play a lot of golf in the summer and do some snow skiing in the winter. 

The CMO owns all things related to marketing. My team takes care of our brand, content, and marketing technology. We’re the ones who are building the website, driving demand gen, and creating leads for our sales teams. We’re also out there doing events—whether it be building an agenda, driving attendance, or participating in an industry conference, we’re there getting the Turing name out there. 

I also lead a team of people who work on what’s called marketing technology. That’s all of the tech infrastructure that helps us track the buyer’s journey with us both digitally and in person. 

Turing: You mentioned the buyer’s journey. How much of that lies within marketing?  

Phil: It’s my belief as a marketing leader that about 60–70% of a person’s buying journey is before they talk to a sales rep. That includes reading digital papers, sharing on social media, Google searching for topics, exploring people’s websites, watching videos, and other things like that. But people still want to buy from people they know and trust. So there’s an aspect of getting face-to-face with prospective buyers. As Jonathan [Siddarth, Turing’s CEO] says, “we need boots on the ground.” So we participate in large industry conferences and small audience events, like a dinner, to properly share and pitch our offering. 

Turing: That’s great. Who would you say your team works most closely with at Turing?  

Phil: The natural link is between sales and marketing, right? So a lot of our work as a marketing organization is to feed the sales team. We also work quite a bit with our product team to make sure that the customer experience that clients and prospects have—whether it’s emails they get or what they see on our website—is tightly aligned with some of the products that we built and with our back-end data and tracking. 

We’ve also been working closely with our fulfillment team to make sure that our leads actually turn into matches. My team is also responsible for helping to drive supply, or more partner developers, into our network, which ties directly to advertising and being able to attract the right type of talent for our platform. 

Turing: What are you most excited about since you started here? 

Phil: I’m most excited about the way our message is being received in the market. We have a product offering in a trillion-dollar tech services market that is truly differentiated. We have the ability to disrupt an industry that’s been pretty stale and doing the same thing for 20-25 years. So I’m really excited about bringing AI and our vetting and matching platform to the tech services world. For sure. 

Turing: Given that Turing is a data-driven organization, how much of your marketing work would you say is dependent on data, compared to the qualitative element that some might typically think about in marketing? 

Phil: When I started my career, you used to have to put a campaign out, hope things would work, and maybe get some feedback a few months later. In today’s digital world, we literally know within seconds who opened our emails, who clicked on our ads, who’s been on our website—that’s really rewarding information. However, if you don’t do something with that data, then it’s all for nothing. 

Marketing is still somewhat of an experimental practice. Nothing is 100% sure. A lot of what we do is hypothesize. But we can quickly measure the impact of that work and decide if we want to continue to invest in it again in the future. 

One hundred percent of marketing is data-driven. You have to be able to track and measure what you’re doing. But there’s also a very creative aspect to marketing. There always will be. I want people who are creative thinkers. I want people who are bringing new ideas to the forefront. 

Turing: Last question for you, Phil.  What’s some advice you would give a new hire as they start their onboarding journey at Turing? 

Phil: I think the best piece of advice I can give is network. In the past, you may have been able to walk to the water cooler and have a conversation with somebody. You can’t do that in a virtual world. So you have to create that for yourself.  

Maybe push out of your comfort zone a little bit. It could still be digitally. 

Participate. Have your camera on. Be engaged. Don’t just be the person who’s a blank screen with your name on it and never speaks, only listens. I mean, it is important to listen and absorb, but make sure that you add value and participate in the conversation. Do that because that’s how people will get to know you. That’s how people understand your point of view.  

Turing: That’s great advice, Phil. And thank you very much. We look forward to seeing great work from you and the marketing team. 

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By September 25, 2023
AI Code Review: Improving Software Quality and Efficiency
For Employers

AI-Enhanced Code Reviews: Improving Software Quality and Efficiency

AI code reviewers blend the power of code analysis, NLP, and continuous learning to provide comprehensive, context-aware assessments of code quality.

Code reviews have long been a critical practice in software development. They serve as a quality control mechanism, ensuring that code is not only functional but also maintainable, secure, and efficient. However, traditional manual code reviews come with their own set of challenges, such as time consumption and the potential for human error. This is where AI code reviews come in. 

In recent years, the rise of artificial intelligence (AI) has ushered in a new era of code reviews, with AI-driven tools and processes offering the promise of improved software quality and efficiency. In this blog, we’ll explore the significance of code reviews, the emergence of AI in this domain, and how AI-enhanced code reviews can revolutionize software development.

Table of contents:

  1. Why are code reviews important?
  2. This is why, AI code reviews
  3. How AI Code Reviewers Work
  4. Benefits of AI code reviews
  5. What are the challenges with AI code reviews?
  6. Real-life use cases of AI code reviews
  7. Conclusion
  8. FAQs

Why are code reviews important?

Code reviews are really necessary for keeping software quality. They involve developers examining code to identify and rectify issues before they can impact the final product. Here’s why they matter:

  1. Quality Assurance: Code reviews act as a software quality assurance checkpoint, catching bugs and defects before they reach production. This ensures a more reliable and stable software application.
  2. Knowledge Sharing: They promote knowledge sharing among team members, allowing developers to learn from each other’s coding styles and best practices.
  3. Maintainability: Code that passes through thorough reviews tends to be more maintainable, reducing technical debt and making future updates and enhancements easier.
  4. Security: Code reviews help in identifying security vulnerabilities, a critical concern in today’s interconnected world.
  5. Consistency: They enforce coding standards and maintain code consistency across a project, enhancing readability and collaboration.

Traditional code reviews, while effective, can be time-consuming and resource-intensive.

This is why, AI code reviews

AI is playing an increasingly prominent role in code reviews, delivering several critical advantages:

  1. Speed and Scalability: AI-powered code review tools possess the remarkable ability to analyze code at an unprecedented pace. This rapid processing significantly shortens review times, facilitating quicker software development cycles and expediting time-to-market. In a landscape where software delivery speed is paramount, AI’s speed and scalability offer a distinct competitive edge.
  2. Consistency: Unlike human reviewers who can experience fatigue and distractions, AI remains tirelessly consistent. It maintains unwavering attention to detail, regardless of the code’s complexity or duration of review. This unwavering consistency is especially beneficial for globally distributed development teams, ensuring continuous, high-quality reviews around the clock.
  3. Pattern Recognition: AI excels in recognizing intricate patterns and uncovering anomalies within code that human reviewers might overlook. This pattern recognition capability is invaluable for detecting subtle issues and identifying potential vulnerabilities. With each review, AI refines its pattern recognition skills, further enhancing the accuracy and depth of code analysis.
  4. Data-Driven Insights: AI-equipped code review tools provide data-driven insights into code quality. They monitor code metrics, such as complexity and adherence to coding standards, across the entire codebase. These insights empower teams to identify trends, prioritize areas for improvement, and make informed decisions. Additionally, AI offers actionable recommendations based on historical data and best practices, guiding developers to write high-quality code from the outset.
  5. Reduced Bias: AI code reviewers operate without human biases, ensuring a more objective assessment of code quality. This impartiality can lead to fairer evaluations and less friction among development teams.
  6. Language Agnostic: AI can analyze code written in various programming languages, making it a versatile solution suitable for diverse development environments.

AI’s integration into code reviews represents a fundamental transformation in how software development teams operate. It not only automates and expedites the review process but also brings a level of consistency, pattern recognition, and data-driven decision-making that significantly enhances code quality and development efficiency. 

How do AI code reviewers work?

How do AI code reviewers work?

How do AI code reviewers work?

Understanding the inner workings of AI code reviewers reveals the power and precision of these tools. They employ a combination of advanced techniques, primarily centered around machine learning and natural language processing (NLP):

  1. Code Analysis

    AI code reviewers begin by scanning the source code thoroughly. This process involves identifying and flagging various aspects, including:
    • Syntax Errors: AI checks for violations of the programming language’s syntax rules, ensuring that the code is structurally sound.
    • Code Style Violations: They analyze the code against coding standards and guidelines, highlighting deviations in coding style.
    • Potential Bugs: AI utilizes its knowledge of common coding errors and bug patterns to identify potential issues. This proactive approach helps catch bugs early in the development process.
  2. Natural Language Understanding (NLP)

    In addition to code analysis, AI code reviewers incorporate NLP techniques to comprehend the context and intent behind code changes:
    • Comments and Documentation: NLP enables AI to understand comments, documentation, and commit messages associated with code changes. This contextual awareness helps AI reviewers make more informed assessments of code quality.
    • Semantic Analysis: NLP can perform semantic analysis of code comments, extracting meaningful information and identifying connections between code and comments. This aids in identifying discrepancies or misalignments.
  3. Learning from Data

    AI code reviewers continuously learn and evolve from historical code reviews and codebases. This learning process is fundamental to their ability to identify issues and provide recommendations:
    • Historical Data: AI draws insights from past code reviews, code repositories, and the collective knowledge of developers. This historical context helps AI reviewers become more effective over time.
    • Adaptive Recommendations: AI adapts its recommendations based on historical data. If certain types of issues have been addressed in specific ways in the past, AI can provide tailored guidance to developers.
  4. Auto-Correction (Advanced Feature)

    Some advanced AI code reviewers have the capability to go beyond flagging issues; they can suggest or automatically implement code fixes. This feature streamlines the development process, as developers can choose to accept or modify AI-generated fixes, significantly reducing manual intervention.
  5. Language Agnosticism

    AI code reviewers are designed to work across multiple programming languages, making them versatile and adaptable to diverse development environments.

AI code reviewers blend the power of code analysis, natural language understanding, and continuous learning to provide comprehensive, context-aware assessments of code quality. Their ability to identify errors, enforce coding standards, and even suggest fixes contributes to improved software quality and development efficiency. 

Benefits of AI code reviews

Benefits of AI code review

Benefits of AI code reviews

AI code reviews come with a plethora of benefits that have a profound impact on software development:

  1. Enhanced Error Detection: AI is highly proficient at identifying common coding errors, such as null pointer exceptions, memory leaks, and boundary condition problems. Its ability to catch these errors early reduces the likelihood of defects reaching production, resulting in more reliable software.
  2. Comprehensive Security: AI code reviewers excel at detecting security vulnerabilities, including potential entry points for cyberattacks. They can recommend patches and coding practices that bolster the software’s resilience against security threats, helping protect sensitive data and user privacy.
  3. Efficient Resource Utilization: By automating routine code reviews, AI frees up valuable developer time. Developers can redirect their efforts toward more complex, creative, and strategic tasks, such as architectural design and innovation.
  4. Scalability Without Resource Expansion: AI-powered code reviews are highly scalable. As project sizes grow, teams can handle the increased workload without the need for proportional expansions of human resources. This scalability is particularly advantageous for organizations with fluctuating development demands.
  5. Consistent Code Quality: AI maintains a consistent standard of code quality throughout a project. It enforces coding standards, best practices, and company-specific guidelines consistently, promoting uniformity in coding style and practices across the entire development team.
  6. Reduction in False Positives: AI code reviewers, when properly tuned, can significantly reduce the occurrence of false positives compared to manual reviews. This means developers spend less time investigating and addressing issues that aren’t actual problems, boosting productivity.
  7. Increased Code Review Coverage: AI can efficiently analyze and review a higher percentage of the codebase, including areas that might be overlooked in manual reviews. This extended coverage reduces the risk of undiscovered issues surfacing later in development.
  8. Faster Time-to-Market: The speed at which AI conducts code reviews accelerates software development cycles, leading to quicker time-to-market for software products. This agility is a competitive advantage in rapidly evolving industries.
  9. Enhanced Collaboration: AI code reviewers provide objective assessments, reducing subjective biases that can sometimes emerge in human code reviews. This fosters a more collaborative and constructive atmosphere within development teams.
  10. Continuous Improvement: AI-driven code review tools learn and adapt from each review. They gain insights from historical data and developers’ actions, improving their ability to identify issues and provide recommendations over time. This continuous learning benefits code quality and development efficiency.

AI code reviews offer a multitude of benefits that span error reduction, security enhancement, resource efficiency, scalability, consistency, and more. These advantages collectively contribute to the improved quality of code, shorter development cycles, and ultimately, a more competitive and agile software development process.

What are the challenges with AI code reviews?

What are the challenges with AI code reviews?

What are the challenges with AI code reviews?

While AI brings significant advantages to code reviews, it is not without its challenges and considerations:

  1. False Positives and Negatives: AI code reviewers may occasionally generate false positives by flagging issues that are not actual problems, or false negatives by missing real issues. This necessitates human intervention to validate and fine-tune AI recommendations. Striking the right balance between minimizing false alerts and capturing genuine issues is an ongoing challenge.
  2. Learning Curve: Implementing AI code reviews introduces a learning curve for development teams. They must adapt to new tools, processes, and workflows. This transition can require time and effort, potentially affecting productivity in the short term. Providing comprehensive training and support can help mitigate this challenge.
  3. Human Expertise: While AI is a valuable tool for automating code reviews, human expertise remains essential for making nuanced decisions. Developers bring domain-specific knowledge and contextual understanding that AI may lack. Project-specific requirements and business logic often demand human judgment for optimal decision-making.
  4. Over-Reliance on AI: Over-reliance on AI can be a concern. Teams may become complacent in performing manual code reviews, assuming that AI will catch all issues. This can lead to the neglect of critical aspects, particularly subtle or context-specific problems that require human judgment. Striking a balance between automated and manual reviews is crucial to maintain code quality.
  5. Privacy and Data Security: AI code reviewers analyze code, which may contain sensitive information or intellectual property. Ensuring the privacy and security of code repositories and review data is paramount. Implementing robust data protection measures and compliance with data regulations are essential considerations.
  6. Customization and Tuning: AI code reviewers often require customization and tuning to align with specific project requirements and coding standards. Teams must invest time in configuring AI tools to deliver optimal results. Regular adjustments may be necessary to adapt to evolving coding practices.
  7. Maintenance and Updates: AI models and tools require ongoing maintenance and updates to remain effective. Staying current with the latest AI advancements and ensuring that AI code reviewers evolve alongside changing coding practices is crucial.
  8. Ethical Considerations: AI code reviewers should be designed and used ethically. Developers and organizations must consider biases in training data and ensure that AI reviews adhere to ethical coding standards.

While AI significantly enhances code reviews, addressing challenges such as false alerts, learning curves, and over-reliance is crucial for its effective implementation. Organizations should approach the adoption of AI in code reviews thoughtfully, considering the specific needs and dynamics of their development teams and projects. Striking a balance between AI automation and human expertise is key to optimizing code quality and development efficiency.

Real-life use cases of AI code reviews

AI-driven code reviews have gained prominence in various industries and are being utilized by leading tech companies to enhance code quality and development efficiency:

  1. GitHub’s CodeQL

    GitHub, one of the world’s largest code hosting platforms, leverages CodeQL, an AI-powered static analysis tool. CodeQL’s sophisticated AI algorithms automatically identify security vulnerabilities in code. It doesn’t stop at detection; it also suggests fixes and patches. This AI-driven approach helps protect millions of open-source projects hosted on GitHub by proactively addressing security concerns. By finding and fixing vulnerabilities early in the development process, CodeQL contributes significantly to the overall security of the software ecosystem.
  2. Facebook Infer

    Facebook employs Infer, an AI-based code analysis tool, to enhance software reliability and prevent issues from reaching the production codebase. Infer uses static analysis to identify a wide range of programming errors and potential crashes, even in complex and large-scale codebases. By catching bugs and issues before they propagate, Infer helps Facebook maintain the high quality and stability of its applications while reducing costly post-release bug fixes.
  3. Google’s DeepCode

    DeepCode, developed by Google, is an AI-driven code review tool that goes beyond error detection. It provides intelligent suggestions for code improvements, offering specific recommendations to developers. By analyzing code patterns, coding styles, and best practices, DeepCode assists developers in writing cleaner, more efficient code. This not only reduces the likelihood of errors but also accelerates development by automating code enhancements. DeepCode is particularly valuable for optimizing development workflows and reducing coding errors, ultimately saving time and resources.
  4. Uber’s Aibolit

    Uber has developed its AI-based code analysis tool called Aibolit. Aibolit is designed to identify code smells, which are indications of potential issues in code quality. It helps Uber’s developers maintain codebases that are clean and efficient. Aibolit assists in ensuring code adherence to the company’s coding standards and best practices, ultimately contributing to a smoother development process and improved code maintainability.
  5. Microsoft’s IntelliCode

    IntelliCode, developed by Microsoft, enhances the code review process by providing AI-generated code completion suggestions and recommendations. By analyzing coding patterns and contextual information, IntelliCode assists developers in writing code faster and with fewer errors. This AI-powered tool integrates seamlessly with popular development environments, such as Visual Studio, improving productivity and reducing coding inconsistencies.


In the world of software development, where code quality directly impacts the success of a project, AI code reviews offer a powerful solution. They combine speed, consistency, and error detection capabilities that surpass human capabilities. While challenges remain, the benefits of integrating AI into your development workflow are undeniable. Embracing AI code reviews can significantly improve software quality and efficiency, ensuring that your projects meet the highest standards.


  1. What is the AI code reviewer in GitHub?

    GitHub’s AI code reviewer, known as CodeQL, is a powerful tool that automatically scans code for security vulnerabilities. It not only identifies issues but also suggests fixes, helping developers enhance the security of open-source projects hosted on GitHub.
  2. Can AI be used for code review?

    Yes, AI can be employed for code reviews. AI-driven code review tools analyze code for errors, style violations, and security vulnerabilities, significantly improving code quality and development efficiency.
  3. Will AI code review process replace developers?

    No, AI code review processes will not replace developers. While AI enhances code reviews and automates certain tasks, human expertise, creativity, and decision-making remain essential in software development. AI is a valuable tool that complements developer skills but does not replace them.
  4. What is AI code reviewer?

    An AI code reviewer is a software tool that uses artificial intelligence and machine learning techniques to analyze and review source code. It scans for errors, style violations, security vulnerabilities, and more, providing recommendations to improve code quality and efficiency. AI code reviewers are used to enhance the code review process in software development.
  5. Can we use AI for code?

    Yes, AI is widely used in coding. It aids developers by generating code snippets, providing real-time code completion suggestions, and analyzing code for errors and vulnerabilities. AI-driven testing tools automate test case generation and execution, enhancing code quality and efficiency. While AI supports coding tasks, it complements, rather than replaces, human developers who bring creativity and problem-solving skills to software development.
  6. Is code review automation possible?

    Yes, code review automation is possible and increasingly common. AI-powered tools can automate the code review process by analyzing code for errors, style violations, and security vulnerabilities. They provide detailed feedback and recommendations, significantly speeding up the review process and improving code quality. However, human oversight and expertise remain valuable for addressing nuanced issues and making context-specific decisions.

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By September 25, 2023
Open Source vs Commercial Software License
For Employers

Open Source vs. Commercial Software License: What Do You Need?

Open source vs commercial software: Open source software is a licensed software whose source code is freely & publicly available whereas commercial software…

Open source vs commercial software license: Which one do you need? Choosing a suitable software license is crucial when starting a new software project. The software license you opt for protects your software and controls the rules for collaboration on the software. Software license also ensures you adhere to restrictions of third-party components in your software.

You might wonder what license would be best for your project. While there’s no right or wrong answer to this question, your choice should address your project and business needs. Hence, in this blog post, we will learn the common types of licenses – open source and commercial – understand their differences, obligations, and restrictions, and help you make an informed decision. Without further ado, let’s dive right in.

Table of contents:

  1. Understanding the available software license types
  2. What is open source software?
    1. Top 5 examples of open source software
    2. Types of open source software
    3. Does open source mean free of cost?
    4. Advantages of choosing an open source license
    5. Disadvantages of choosing an open source license
  3. What is commercial software?
    1. Examples of commercial software
    2. Types of commercial software licenses
    3. Advantages of choosing a commercial license
    4. Disadvantages of choosing a commercial license
  4. Open source and proprietary software similarities
  5. Open source vs. commercial software: What are their differences?
  6. Factors to consider when choosing a license

Understanding the available software license types

What is a software license? Software license is a legal contract between individual developers or software companies and a software user. It governs how an end-user can legally use or distribute copies of your software.

Software licenses can either be open source or commercial.

What is open source software?

What is open source software

What is open source software?

Open source software is a licensed software whose source code is publicly available for free. Open source software license allows anyone to learn, share, modify, and distribute your source code for free.

Top 5 examples of open source software

There are several open source software solutions, each serving a purpose. Listed below are some examples of open source software

  • VLC media player: VLC media player is one of the most popular media players we have today. It supports most video and audio files, including MOV and MP3. It also supports streaming. 
  • Mozilla Firefox: Firefox is one example of an internet browser that offers a free and safe browsing experience. Firefox has similar features to Opera Mini and Chrome browser and has features that protect you while browsing. It’s customizable and supports browser extensions.
  • WordPress: WordPress is a content management system (CMS) and website builder that allows you to create and store website content. It has very basic features, which you can extend by installing plugins and themes. You can download and install a standalone version on your server, or you can buy a managed hosting with WordPress already installed from WordPress.
  • React: Reactjs, a front-end JavaScript library allows you to build single-page applications (SPA). React allows you to break your application’s user interface (UI) into reusable server or client components.
  • TestDisk: TestDisk is a file recovery software that can be used to recover lost partitions on your PC. TestDisk can recover files from different file systems including NTFS, exFAT, and FATX. TestDisks also works on other storage devices including USB drives and memory chips.

Types of open source software 

Depending on the restrictions and rules for collaboration on software, an open source license can be considered as either permissive or copyleft.

Permissive license: Permissive licenses are the less restrictive type of open source software license. Permissive licenses allow anyone to freely modify and share your software, use your source codes as part of their software, and distribute it in proprietary works. Often, they only require you to provide attribution to the original developers when distributing the software.

Examples of permissive licenses include the Massachusetts Institute of Technology (MIT) license, the Berkeley Source Distribution (BSD) license, and the Apache License.

Copyleft license: Unlike permissive licenses, copyleft licenses are very restrictive. Copyleft licenses require anyone distributing software that contains source codes protected under a copyleft license to do so under the copyleft terms.

Copyleft licenses intend to make the source codes of modified versions of software available to the public to prevent being used in proprietary works without proper attribution.

Examples of copyleft licenses include General Public License(GPL), Affero General Public License(AGPL), and Mozilla Public License(MPL).

Does open source mean free of cost?

Open source software is usually distributed for free. However, additional features and services may come at a cost. Commercial Open Source Software companies have developed business models that help them commercialize free software. These models often revolve around selling support or hosting or selling add-on features to complement the free software. Software run under any one of these business models is referred to as Commercial Open Source Software (COSS).

Commercial Open Source Software examples include WordPress, Unreal Engine, and MongoDB.

Advantages and disadvantages of choosing Open Source License (1)

Advantages and disadvantages of choosing open source licensing model

Advantages of choosing an open source license

Choosing an open source license has several advantages. Some of these are:

  • Community collaboration: Choosing an open source license invites a global community of developers, designers, and users to collaborate on your project. You get to improve your software and fix bugs for free. 
  • Rapid iteration: With a larger pool of contributors, development cycles can become faster. Bugs are identified and fixed quickly, new features are proposed and implemented, and your project can evolve more rapidly than you could have imagined.
  • Quality improvement: Changes to open source software are often peer-reviewed. The scrutiny of the open source community can lead to higher code quality. Contributors review the code base thoroughly to ensure best practices, identify vulnerabilities, and enhance overall reliability. 
  • Innovation: Open source fuels many modern-day inventions. Many technologies we rely on today are open source. Such an example is the internet.  Choosing an open source license allows anyone to take your original idea and make something new from it.
  • Mass adoption: According to GitHub’s octoverse 2022 report, 90% of companies rely on open source software. Making your software open source means you are tapping into the population of businesses already using open source software.

Disadvantages of choosing an open source license.

Let’s look at some disadvantages of choosing an open source license for your project.

  • Limited support for users: Open source software often lacks dedicated support teams to help users resolve issues with the software. Contributors are usually more interested in building and shipping new features to users than in supporting users to resolve issues they encounter while using the software. Often open source software users would need to rely on discussion forums like stackoverflow to resolve an issue.
  • Bad documentation: Open source software documentation often receives less attention. The software documentation is usually written by the community of developers working on the projects. Sometimes, the software documentation is adapted for users with technical knowledge and may be difficult to understand as a normal user with less technical knowledge.
  • Security issues: Attackers can learn and find vulnerabilities in open software much easier compared to closed source software. Sometimes, the vulnerability might come from your software dependencies, which are exposed to attackers. In other cases, some developers might contribute bugs to your software to make it vulnerable and easy to exploit.
  • Limited funds: Oftentimes, free open source projects not backed by big companies rely on crowdfunding or donations. With limited funds, it can be hard to invest in further development of your software.
  • Project abandonment: Open source software contributors are more likely to abandon your software for other open source software, and it can become challenging to find new contributors for software whose core developers have stopped working on it.

What is commercial software?

What is commercial software

What is commercial software?

Commercial software refers to software distributed to make profits. Commercial software is usually proprietary. Commercial software is licensed to users at a fee under an agreement that aims to protect the business and preserve the rights of the developer.

Examples of commercial software

Commercial software examples include:

  • Adobe Photoshop: Photoshop is a photo-editing software that allows you to edit and save your photos and graphics. It offers similar features to other photo-editing software like Figma and Gimp and supports feature extensions through plug-ins.
  • DigitalOcean: Digital Ocean is an example of a commercial open source cloud service provider. Digital Ocean offers cloud computing and lets you host your website and applications back-end on a cloud infrastructure.
  • Wondershare Filmora: Filmora is a video editing software that allows you to edit videos and audio. Filmora offers similar tools to other video editing software like Adobe After Effects. Filmora also supports video export in different file formats including MOV, 3GP, and MP4.
  • Bigcommerce: Bigcommerce is an example of an open source Software as a Service(SaaS) ecommerce provider. Bigcommerce provides retailers tools to set up an online store without much hassle.
  • Zoom: Zoom is a virtual meeting software that offers video, audio, and messaging tools to communicate effectively with others over the internet. Zoom also offers other features like meeting transcription and virtual whiteboard as part of their software. 

Types of commercial software licenses

Commercial ( or proprietary) software licenses come in various types, each with its terms and conditions set by the software vendor or developer. Here are some common types of proprietary software licenses:

  • Single-user license: As the name implies, single-user licenses allow a single person to use one installed copy. This means other users of the software need separate copies of the software license for themselves.
  • Volume license: Volume licensing is suitable for organizations that need multiple copies of the software. These licenses allow you to share copies of software in the organization using only one license.
  • Perpetual license: A perpetual license grants the right to use the software indefinitely, usually with the option to purchase maintenance and support separately.
  • Subscription or annual license: With these types of software, you purchase a license that grants you access to a copy of the software for a particular period (often a year), after which you need to renew your software license if you wish to continue using the software.
  • Floating or concurrent license: Floating licenses allow a specified number of users to access the software on a network. These types of licenses are managed by a license server. The license server tracks and maintains the specified number of users using the software simultaneously.
Advantages and disadvantages of choosing commercial licensing model

Advantages and disadvantages of choosing commercial licensing model

Advantages of choosing a commercial licensing model

Several benefits come with commercial license models. Commercial licenses give you flexibility and control over your software. Let’s discuss some reasons why you should consider commercial license models 

  • Protects interest: Choosing a commercial license for your proprietary works protects your interest in the software.  As mentioned earlier, commercial software licenses protect your business and preserve your rights. These licenses may include clauses to restrict certain activities, such as reverse engineering your software and redistributing copies of your proprietary works.
  • Maintains ownership: Commercial license models often do not license ownership or the rights to modify and distribute a software copy to the end user. Commercial software licenses usually restrict others from using your source code.
  • Maintains competitiveness: Distributing your software under commercial licenses gives you a competitive advantage over open source software. Some users prefer licensed software over open source software for several reasons, including security and support.
  • Maintains control: Licensing your software to users gives you control over your software. Simply put, you control who gets access to your source code and who can work on your software. To an extent, you can also control how the end user uses your software.
  • Develops funds: Licensing your software to end users generates revenue used to fund further research and developments. Funding gives you the advantage of employing people to help develop features and improve the software.

Disadvantages of choosing a commercial licensing model.

While licensing your software to end-users might be lucrative, it poses certain disadvantages. Let’s discuss some disadvantages of choosing commercial licensing models

  • Impending liability: Commercial software owners or companies are often liable for any damage caused by defects in their software. They’re responsible for the data protection and privacy of their users and may be subjected to litigation if any issues arise.
  • Software piracy: Commercial software is often pirated by users who do not want to pay for a license. Such activities affect your ability to raise funds from your software. Piracy could also harm your brand’s identity in the long run.
  • Manufacturer dependence: Commercial software usually offers little customization options for users. As a result, users tend to depend on the software manufacturer to fix bugs in the released software and add features they need in further updates.
  • High costs: Commercial software can be expensive to build, maintain, and scale. Commercial software owners would usually need some funds upfront to build fully functional software for their target users.  
  • Slower development cycle: Commercial software projects are often developed by a small number of developers. With fewer people working on the software your development cycle would be much slower compared to a similar but open source software.

Open source and proprietary software similarities

Similarities between open source and proprietary software include:

  • Product documentation: Both open source and commercial software are distributed with documentation to help end users complete tasks using the software.
  • Skilled developers: Open source and commercial software are developed and maintained by a community of skilled developers.
  • Customer support: Both open source and commercial software may have technical support teams to help users troubleshoot and resolve issues relating to the software.
  • Security concerns: Open source and proprietary software are both vulnerable to hacking. As a result, individual developers or companies invest time and effort to fix vulnerabilities in their software.
  • Compiled versions: Open source and proprietary software are typically distributed in compiled form. However, contrary to proprietary software, open source software source codes are made publicly available.
  • Copyrighted: Both open source and proprietary licenses are subject to copyright laws.

Open source vs. commercial software: What are their differences?

Here are the major differences between open source and commercial software:

Open Source Software

Commercial Software

Open source software is released under licenses that grant users freedom to access, modify, and distribute the source code.

Commercial software is often distributed under licenses that restrict access to the source code and require users to buy a license.

Users of open source software have access to the source code, allowing them to view, modify, and redistribute it.

Commercial software users do not have access to the source code, which means they cannot modify the software. 

Open source software is accessible to users without upfront costs. 

Commercial software usually involves upfront licensing costs, subscription fees, or one-time payments. 

Open source projects are usually community-driven, and ownership is distributed among contributors. Users and developers have collective control over the project’s direction.

Commercial software users have limited influence over the software’s development roadmap.

Open source software users can customize the software to suit their use case

Customization options for commercial software may be limited due to restrictions imposed in a license

Open source software usually doesn’t have dedicated support teams set up to help users resolve issues.

Commercial software often has dedicated teams whose task is to help users troubleshoot and resolve common errors.

Open source software documentation is often written by the same community of developers and may be difficult to comprehend as a normal user.

Documentations are usually written by a team of technical writers. Commercial software documentation is adapted for different users and is easier to understand.

Open source software is harder to set up, especially for users with very little technical knowledge

Commercial software is relatively easy to set up and use.

Factors to consider when choosing a license

Although choosing the right license is subjective. Here are five factors to consider when choosing a license:

License compatibility: If you used other developers’ source code in your software, you might want to consider choosing a license compatible with theirs to avoid lawsuits. If the software license used isn’t clear to you or there’s no license, you can ask the original developer to permit you to use their source code.

Project goals: It’s necessary to consider your project goals before choosing your license. It’s easier to grow and market your software if your license aligns with your goals.

Target audience: It’s often necessary to keep your target audience in mind when choosing a license if you’re not the only person going to use the software. It’s better to choose a licensing model that they’re used to.

Market trend: Looking at the market gives you insights into what your customers are already using or prefer. It helps you know whether free software will help you get to your goals faster, or if selling licenses to users will be a better option.

Operational cost: Another factor to consider when choosing a licensing model is your operational cost. Developing and maintaining software is often costly, so the license you choose should reflect on the costs to give you a good return on investment. 


As you’ve read in this blog post, each license has its terms and conditions making it a better choice for a particular use case. Knowledge of their differences and restrictions should help you navigate license options and help you choose a license suitable for your needs.

As mentioned above, open-source software is typically free, open, and collaborative. Commercial software is closed, expensive, and tightly controlled. 

Open source software is an excellent option when flexibility, cost-effectiveness, and community-driven support are paramount. It provides access to the source code, allowing customization and adaptation to unique requirements. Additionally, open source solutions often benefit from a collaborative community, leading to rapid development and robust troubleshooting. 

On the other hand, commercial software can be the preferred choice when comprehensive support, specialized features, and a clear warranty are critical. It comes with professional customer service, dedicated maintenance, and regular updates, ensuring a higher level of reliability and security. Organizations might opt for commercial solutions when compliance, scalability, and seamless integration with existing systems are non-negotiable. 

To sum up, striking the right balance between open source and commercial software depends on a thorough evaluation of specific project requirements, budget constraints, and long-term strategic objectives.


  1. Is open-source software always free?
    Open-source software is usually free, but here, “free” means freedom instead of free of cost. Some open-source software do have associated costs for support, customization, or specialized versions.
  2. Is open-source software always of lower quality than commercial software?
    No, open-source software can be of good quality. In fact, open-source software is used in important applications across industries. What’s more, several popular open-source projects have proactive contributors who ensure the software’s quality and reliability.
  3. Can I modify open-source software for my own use?
    Yes, you can. One of the most important principles of open-source software is the freedom to modify the source code for personal or organizational use. This way, you can customize the software to meet specific needs.
  4. Can I sell open-source software?
    Yes, you can sell open-source software. But you must follow the terms of the software’s license. Some open-source licenses, like the GPL, require that any derived work must also be distributed under the same open-source license.
  5. Is commercial software more secure than open-source software?
    Security cannot be determined solely by whether a software is open-source or commercial. Both software can be secure if they’re well-maintained. Since open-source software has several eyes on the code, identifying and fixing security threats can be quicker.
  6. Can I use both open-source and commercial software in my organization?
    Yes, you can. In fact, several organizations use a combination of open-source and commercial software, known as a mixed-source environment. This environment enables them to leverage the strengths of each type of software to best meet their needs and budget.

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By September 21, 2023
Kafka vs JMS
For Developers

JMS vs Kafka: Which Message Broker Should You Use?

JMS vs Kafka: JMS is used to communicate between Java-based applications and other software components. Apache Kafka is an open-source distributed event..

JMS vs Kafka: which message broker is the best? JMS and Kafka are widely used message brokers to transmit data between different applications. JMS or Java Message Services is used to communicate between Java-based applications and other software components. Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications. 

This blog post will take you through the similarities and differences between JMS and Apache Kakfa to help you pick the best one. Keep reading.

Table of Contents:

  1. What are message brokers?
  2. What is Apache Kafka?
  3. What is JMS?
  4. Similarities between Kafka and JMS
  5. JMS vs Kafka: Differences
  6. JMS vs Kafka: What should you choose?
  7. Conclusion
  8. FAQs

What are message brokers?

Message brokers are software systems or components that facilitate the exchange of messages between different applications or components within a distributed system. They serve as intermediaries, ensuring that messages are delivered from senders to receivers efficiently and reliably. Message brokers play a crucial role in enabling asynchronous communication, decoupling sender and receiver systems, and ensuring that messages are processed in a scalable and fault-tolerant manner.

What is Apache Kafka?

What is Apache Kafka?

What is Apache Kafka?

Apache Kafka is a distributed streaming system that enables you to pass messages from one point to another. Kafka maintains a stream of records within a cluster of servers, offering a robust logging mechanism for distributed systems. Kafka helps users publish and subscribe to streams of records, process records in real time, and store streams of records. With Apache Kafka, developers can create streaming data applications and pipelines.

What is JMS?

What is JMS?

What is JMS?

Java Message Service or JMS is an API that facilitates communication between Java-based applications and other software components. JMS comes with pre-defined messaging protocols that support the Java programming language. The messaging standard allows users to create, send, receive, and read messages between computers in a network. With JMS, developers can make software applications written in different programming languages talk to each other.

Similarities between Kafka and JMS

While the architecture and design of these popular message brokers are different, there are a few similarities between the two. Let’s take a look:

  1. Messaging Middleware: Both Kafka and JMS are messaging middleware solutions used to facilitate communication between different components or systems in a distributed architecture. They provide a way to send, receive, and process messages asynchronously.
  2. Message Brokers: Kafka and JMS both involve the use of message brokers. In the case of Kafka, it’s Apache Kafka, and for JMS, it can be various JMS providers like Apache ActiveMQ, RabbitMQ, or IBM MQ. These brokers are responsible for managing the routing and delivery of messages.
  3. Messaging Patterns: Both Kafka and JMS support common messaging patterns, such as publish-subscribe and point-to-point messaging. While Kafka primarily focuses on publish-subscribe, JMS provides support for both patterns, making it versatile for various use cases.
  4. Message Durability: Both Kafka and JMS can be configured to provide message durability. Kafka stores messages for a configurable retention period, ensuring data availability even after consumption. JMS offers message persistence options to prevent data loss.
  5. Integration: Kafka and JMS can be integrated with various programming languages and frameworks, making them suitable for a wide range of applications. Kafka clients are available in multiple languages, and JMS provides a standardized API for Java applications.
  6. Scaling: Both Kafka and JMS can be scaled to handle increased message volumes. Kafka achieves scalability through horizontal scaling by adding more broker nodes, while JMS implementations may offer scalability options depending on the provider.
  7. Acknowledgment: Both Kafka and JMS allow for acknowledgment mechanisms. Producers can receive acknowledgments when messages are successfully delivered and processed, ensuring reliable communication.
  8. Message Transformation: Both Kafka and JMS offer ways to transform message formats. Kafka supports various serialization formats, and JMS may provide message conversion capabilities.

Now that we know what’s similar, let’s head to the differences between JMS vs Kafka and see which one’s better for your needs.

JMS vs Kafka: Differences

Major Differences between JMS vs Kafka

Major Differences between JMS vs Kafka

Let’s see the major difference between JMS and Kafka and get to know which of the two message brokers will be a better choice for your business requirements.

  1. Apache Kafka vs JMS: Style of programming

    JMS adheres to an imperative programming style. Developers write specific code to solve particular problems, executing a series of instructions sequentially. JMS operations often occur synchronously, with the sender waiting for acknowledgment of message receipt and processing. This style is well-suited for applications where precise control over the order of operations is critical.

    Kafka follows a reactive programming style, which revolves around asynchronous data streams and event-driven processing. Developers work with data as it flows through the system, and events trigger actions across the application. Kafka leverages reactive programming libraries and frameworks to handle events efficiently. This style suits real-time data processing and event-driven architectures.
  2. JMS vs Kafka: Segregating the Content

    JMS segregates content using queues and topics. Queues are typically used for point-to-point messaging, ensuring that messages are delivered to one consumer. Topics are used for publish-subscribe messaging, allowing multiple subscribers to receive the same message.

    Kafka segregates content using topics. Topics allow for the categorization of messages into different streams, enabling efficient routing and processing of related data. Producers and consumers subscribe to specific topics of interest, facilitating a publish-subscribe model.
  3. Type of Message Programming

    JMS traditionally deals with messages in text or binary format. While custom serialization is possible, it may require additional configuration and implementation effort compared to Kafka.

    Kafka supports messages in various formats, such as Avro, JSON, or custom serialization and deserialization. This flexibility enables developers to work with data in the format that best suits their needs, making it versatile for different use cases.
  4. JMS vs Kafka: Filter Method

    JMS provides message selectors for filtering messages. However, the effectiveness of filtering may vary depending on the JMS provider. JMS selectors are more suitable for simple filtering criteria.

    Kafka offers robust filtering capabilities through Kafka Streams or consumer group subscriptions. Kafka Streams provides a powerful stream processing API for data transformation and filtering. Consumer groups allow multiple consumers to subscribe to topics, each receiving a copy of the data, enabling parallel processing and filtering.
  5. Routing System

    JMS offers both point-to-point and publish-subscribe routing mechanisms. Queues are used for point-to-point communication, ensuring that a message is delivered to only one consumer. Topics are used for publish-subscribe communication, where multiple subscribers can receive the same message.

    Kafka employs a publish-subscribe model with topic-based routing. Producers publish messages to topics, and consumers subscribe to specific topics of interest. This approach simplifies message distribution in a distributed system.
  6. JMS vs Kafka: Storage

    JMS typically does not retain messages beyond delivery. Message persistence depends on the specific JMS broker configuration. In some cases, additional setup may be required to achieve message durability.

    Kafka provides durable message storage with configurable retention periods. Messages are stored for a specified duration, allowing consumers to replay historical data. This feature is valuable for applications that require data auditing, analytics, or replayability.
  7. Apache Kafka vs JMS: Queuing

    JMS excels in queuing scenarios. It offers point-to-point messaging with guaranteed message delivery. Queues ensure that each message is consumed by only one recipient, making JMS suitable for scenarios where strict message ordering and processing are essential.

    While Kafka can mimic queuing behavior using single-consumer consumer groups, it is primarily designed for publish-subscribe patterns. Queuing behavior can be achieved with a single consumer per partition.
  8. Partitioning of Topics

    JMS topics do not natively support partitioning. Scalability in JMS is typically achieved by deploying multiple instances of a topic, and each instance is responsible for handling a subset of messages.

    Kafka allows topics to be partitioned, enabling parallelism and scalability in message processing. Each partition can be handled by a separate consumer, ensuring efficient distribution of work.
  9. Message Logs

    Message durability in JMS depends on the broker’s configuration. While JMS brokers offer message persistence, the level of persistence may vary between different JMS providers.

    Kafka acts as a distributed commit log, making all messages persistent by default. It provides strong durability guarantees, ensuring that messages are not lost even in the case of broker failures.
  10. Apache Kafka vs JMS: Scalability and Availability

    The scalability and availability of JMS implementations can vary. Achieving high availability often requires configuring redundancy and failover mechanisms. Scalability may also vary depending on the specific JMS provider and deployment architecture.

    Kafka is designed for horizontal scalability, allowing you to add more brokers to handle increased load. This design makes Kafka highly scalable and available. Kafka’s distributed architecture ensures fault tolerance and high availability.

JMS vs Kafka: What should you choose?

The choice between JMS (Java Message Service) and Kafka (Apache Kafka) depends on various factors, including the specific requirements and use cases of the company. Both JMS and Kafka have their strengths and weaknesses, so the decision should be made based on the following considerations:

  1. Messaging Style and Use Case

    • Choose JMS if: Your company primarily deals with traditional enterprise messaging scenarios, requires strict point-to-point messaging, or needs a standardized API for Java-based applications. JMS is well-suited for scenarios where precise control over message ordering and processing is critical.
    • Choose Kafka if: Your company focuses on real-time data streaming, event-driven architectures, log aggregation, or needs to handle large volumes of data efficiently. Kafka excels in scenarios where you want to process data as it flows through the system and requires horizontal scalability.
  2. Scalability and Volume

    • Choose JMS if: Your company’s messaging volume is moderate, and you don’t require extensive scalability. JMS can be scaled, but it may require more effort and custom configurations to achieve high scalability.
    • Choose Kafka if: Your company deals with high message volumes, requires horizontal scalability, and needs a system capable of handling massive data streams efficiently. Kafka’s architecture is designed for scalability and high throughput.
  3. Message Persistence and Retention

    • Choose JMS if: Message durability and persistence are a primary concern, and your company relies on the JMS provider’s features for message storage. JMS brokers often provide configurable message persistence options.
    • Choose Kafka if: You need both durability and the ability to retain messages for extended periods. Kafka retains messages for a configurable retention period, making it suitable for use cases that require data auditing, analytics, or data replay.
  4. Programming Paradigm

    • Choose JMS if: Your development team is more familiar with an imperative programming style and you are comfortable with writing sequential code for messaging operations.
    • Choose Kafka if: Your development team is comfortable with a reactive programming style and is looking to leverage event-driven processing, asynchronous data streams, and reactive libraries.
  5. Ecosystem and Integration

    • Choose JMS if: You require a messaging solution that integrates seamlessly with Java-based technologies and frameworks. JMS has a long history of integration with the Java ecosystem.
    • Choose Kafka if: You need a broader ecosystem with connectors, stream processing tools like Kafka Streams, and extensive monitoring solutions. Kafka offers a rich set of tools and libraries for various integration scenarios.
  6. Vendor Neutrality

    • Choose Kafka if: Your company prefers an open-source, vendor-neutral solution that is not tied to a specific vendor.
    • Choose JMS if: You are comfortable with vendor-specific JMS implementations and don’t require vendor neutrality.

JMS vs Kafka: Conclusion

In the end, the decision between JMS and Kafka boils down to your specific needs and goals. If you’re looking for a messaging system that’s like following a well-structured recipe, ensuring messages are delivered with precision and control, then JMS is your choice. It’s like cooking with a detailed cookbook, step by step, ensuring everything happens in a particular order.

On the other hand, if your applications thrive on real-time data streams, massive data volumes, and a dynamic, event-driven environment, then Kafka steps in. Think of Kafka as the express highway for data, where information flows rapidly and asynchronously, connecting everything seamlessly. Moreover, it’s open-source and integrates effortlessly with various technologies, making it incredibly versatile.

So, whether you opt for the meticulous orchestration of JMS or the high-speed, data-centric nature of Kafka, both serve as reliable messengers, facilitating seamless communication between your applications. Your choice ultimately depends on the conversation you want your applications to have – structured and precise or dynamic and data-intensive.

If you’re a skilled developer looking for remote  JMS jobs or Apache Kakfa jobs, try Turing today. Work with some of the best US companies, earn salaries higher than the market average, and enjoy a fantastic work-life balance.


  1. What is the difference between JMS and Kafka?

    JMS (Java Message Service) is an API for messaging in Java applications, while Kafka is an open-source distributed event streaming platform. JMS is suited for traditional messaging, while Kafka excels in real-time data streaming and handling large data volumes.
  2. What is the purpose of JMS?

    JMS facilitates communication between Java-based applications and other software components, providing standardized messaging protocols.
  3. What is the replacement of JMS?

    JMS doesn’t have a direct replacement, but Kafka and other messaging systems can offer alternative solutions based on specific use cases.
  4. What is Kafka used for?

    Kafka is used for building real-time data pipelines, streaming applications, and log aggregation. It enables publishing, subscribing, and processing data streams.
  5. What is JMS used for?

    JMS is used for point-to-point and publish-subscribe messaging between Java-based applications, ensuring reliable and ordered message delivery.
  6. Which is better JMS or Kafka?

    The choice between JMS and Kafka depends on specific use cases. JMS suits traditional messaging, while Kafka excels in real-time data streaming, making one better than the other based on project requirements.

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By September 20, 2023
AI for recruiting: Procure the Right People Trusting AI Vetting for Software Engineers
For Employers

Procure the Right People: Trusting AI Vetting for Software Engineers

Procure the right people with Turing’s AI-powered vetting process that uses a structured approach to source, vet, and manage world-class developers.

Great employees are the foundation of successful companies. But great talent takes time to come by. And this is where AI for recruiting comes in. McKinsey’s Steven Hankin coined the term “war for talent” in 1997, and it continues to be true today. The competition has only gotten fiercer over the years, and while the recessionary conditions over the last year may suggest a surplus of talent in the market, getting the right talent is still difficult. A survey by Manpower Group* reveals that in 2023 nearly 4 out of 5 employers face difficulty in finding the right talent. So how can AI for recruiting be beneficial? Let’s find out.

Table of Contents:

  1. Reasons why hiring the right talent is hard
  2. What is AI for recruiting?
  3. Benefits of AI for recruiting: How can AI improve the recruiting process?
  4. What are the challenges companies can face when adopting AI for recruiting?
  5. How AI will change the recruiter role?
  6. What are some myths about AI in recruiting?
  7. Is there a solution?
  8. Transforming Hiring with Turing’s Comprehensive AI Vetting Process
  9. FAQs

Reasons why hiring the right talent is hard

Today, hiring the right talent has become more difficult than ever. Here are a few reasons why: 

  1. Unhelpful resumes: Resumes often lack comprehensive or accurate information about a candidate’s technical and soft skills. Given these inconsistencies, hiring managers and talent specialists end up wasting precious hours going over hundreds (sometimes even thousands) of resumes. 
  2. Incorrect job descriptions: A job description is an important tool in finding and onboarding the right talent. Poorly translated job descriptions can create disconnects and inefficiencies in the hiring process, as the actual job requirements may differ from what hiring managers have in mind.
  3. Inefficient assessment process: Relying solely on unstructured interviews can result in random outcomes and wasted time for hiring managers.
  4. High competition: Intense competition for skilled professionals can make it challenging to attract and retain top talent.
  5. Cultural mismatch: Finding candidates who not only possess the required skills but also align with the company’s culture and values can be a significant challenge.
  6. Skill shortages: Shortages of qualified candidates in certain industries or roles further complicate the hiring process.
  7. Global sourcing challenges: For companies looking to hire talent from around the world, navigating different labor laws, immigration processes, and cultural nuances can add complexity to the hiring process.
  8. Inconsistent candidate experience: Negative experiences during the recruitment process can deter top talent from considering a company. Ensuring a positive candidate experience is essential to attracting and retaining the right candidates.
  9. High cost of hiring: The recruitment process can be expensive, from advertising job openings to conducting interviews and assessments. Managing these costs of hiring while finding the right talent is a delicate balance.
  10. Uncertain market conditions: Economic conditions, industry trends, and geopolitical factors can influence the availability of talent. Adapting to these external factors is an ongoing challenge for HR and talent acquisition teams.

What is AI for recruiting?

AI for recruiting refers to the use of artificial intelligence (AI) and machine learning (ML) technologies to enhance and streamline various aspects of the hiring and talent acquisition process. This includes automating tasks like resume screening, sourcing candidates, assessing qualifications, and even predicting candidate-job fit. AI in recruiting aims to make the process more efficient, data-driven, and fair by reducing human biases and optimizing candidate-job matches.

One of the primary benefits of AI in recruiting is its capacity to automate time-consuming administrative tasks. For instance, AI-powered systems can sift through vast numbers of resumes, screening candidates based on predefined criteria, and identifying the most promising individuals. This not only saves valuable time but also ensures a more objective assessment, reducing the risk of bias in the early stages of recruitment.

Additionally, AI can significantly improve the accuracy of candidate-job matching. By analyzing vast datasets, AI can identify patterns and correlations that may not be apparent to human recruiters. This results in a better fit between candidates and positions, ultimately leading to more successful hires and reduced turnover rates.

Let’s take a look at the major benefits of leveraging AI for recruiting. 

Benefits of AI for recruiting: How can AI improve the recruiting process?

AI for recruiting: What are the benefits of using AI for recruiting

What are the benefits of using AI for recruiting

Using AI for recruiting offers several benefits that can significantly improve the efficiency and effectiveness of the recruitment process. Here are some key advantages:

  1. Efficiency and time savings

    AI can automate various aspects of the hiring process, such as resume screening, candidate sourcing, and initial assessments. This reduces the time and effort spent by HR teams and hiring managers on administrative tasks, allowing them to focus on strategic aspects of recruitment.
  2. Improved candidate matching

    AI algorithms analyze a candidate’s qualifications, skills, and experience to match them with the most suitable job openings. This leads to more accurate candidate-job fit, reducing the likelihood of mismatches and improving overall hiring quality.
  3. Bias reduction

    AI can help minimize unconscious bias in the hiring process. By relying on objective data and criteria, AI-driven systems are less prone to biases related to gender, race, or other factors that can affect human decision-making.
  4. Cost reduction

    Automating parts of the hiring process can result in cost savings. Companies can reduce expenses associated with job postings, manual resume screening, and lengthy interview processes.
  5. Data-driven decision-making

    AI tools generate data and analytics that provide insights into the effectiveness of recruitment strategies. This data can help organizations make informed decisions about their hiring processes and optimize them over time.
  6. Enhanced candidate experience

    AI-driven chatbots and automated communication can provide candidates with timely updates, answer their questions, and offer a smoother application and interview experience, enhancing the overall candidate experience.
  7. Predictive analytics

    AI can predict a candidate’s likelihood of success in a role based on their skills, qualifications, and historical data. This helps organizations identify candidates who are more likely to perform well in the long term.
  8. Scalability

    AI can handle large volumes of candidate data and job openings simultaneously, making it easier for companies to scale their recruitment efforts when needed, such as during periods of rapid growth.
  9. Continuous learning

    AI systems can continuously learn and adapt to changing job requirements and candidate preferences. This adaptability ensures that the recruitment process remains up-to-date and effective.
  10. Global talent pool access

    AI-powered platforms can source candidates from a global talent pool, providing access to a diverse range of candidates with different skills and backgrounds.
  11. Reduction in human error

    Automation reduces the risk of human error in tasks like resume screening and data entry, leading to more accurate and consistent evaluations of candidates.
  12. Faster time-to-fill

    AI can identify qualified candidates more quickly, reducing the time it takes to fill job openings. This is particularly valuable for roles that require immediate staffing.

What are the challenges companies can face when adopting AI for recruiting?

AI for recruiting: What are the challenges companies can face when adopting AI for recruiting

What are the challenges companies can face when adopting AI for recruiting

While AI comes with a plethora of benefits if used for hiring, there are some challenges that companies might come across while using AI for recruiting. Here are the major challenges:

  1. Avoiding bias and being fair

    One big challenge is making sure that AI doesn’t make unfair decisions. Sometimes, AI can pick up biases from old data, which can lead to unfair hiring choices. It’s crucial to use AI in a way that’s fair to everyone.
  2. Getting good data

    AI needs good information to make smart decisions. Sometimes, companies struggle to find and use the right data. If the data is messy or not diverse enough, it can make AI hiring less effective.
  3. Being clear and honest

    AI can seem like a bit of a mystery. It’s not always easy to explain why AI picked one person over another for a job. Being clear and honest about how AI works is important to build trust.
  4. Following the rules

    Laws about hiring are pretty complicated. Companies need to make sure that AI hiring practices follow all the laws. These laws can be different depending on where you are, so it can be a challenge to keep up.
  5. Keeping candidates happy

    While AI can help speed up the hiring process, it shouldn’t make things worse for job applicants. Making sure that candidates have a good experience during the hiring process, even with AI involved, is important for hiring top talent.

How AI will change the recruiter role?

AI is poised to revolutionize the recruiter role in two key ways. Firstly, it will automate repetitive tasks like job description writing and interview scheduling, allowing recruiters to focus on relationship-building and candidate engagement. While AI can assist in outreach, recruiters’ human touch remains vital for building connections.

Secondly, AI will drive a shift towards skills-first hiring, reducing human bias. Recruiters will rely on AI to assess candidates based on skills and experience, fostering diversity and equity. As AI tools become integral, recruiters will need to emphasize soft skills like negotiation and storytelling. These skills will be essential in providing a personalized and efficient hiring process, ensuring candidates are a cultural fit and possess potential.

AI will transform recruiters into strategic partners who leverage automation for efficiency and equity while emphasizing essential human skills to engage candidates effectively, creating a more streamlined and inclusive recruitment process.

What are some myths about AI in recruiting?

AI for recruiting: What are some myths about AI in recruiting

What are some myths about AI in recruiting

There are several myths surrounding the use of AI in recruiting that can sometimes mislead businesses. Let’s debunk these misconceptions:

  1. AI replaces humans: Contrary to the belief that AI replaces human recruiters entirely, it primarily enhances efficiency in certain parts of the recruiting process, like resume screening and skill assessment, while human expertise remains invaluable in relationship-building and understanding nuanced candidate needs.
  2. AI simplifies candidate search: While AI provides valuable insights into the hiring process, it doesn’t necessarily make candidate sourcing easier. It can streamline certain aspects, but identifying the right talent still requires human judgment and context.
  3. AI eliminates jobs: AI tends to create more opportunities by generating high-level roles for those who can effectively utilize these tools. It complements human efforts rather than replacing them, leading to the emergence of new job categories.
  4. AI is only for large companies: AI is accessible to businesses of all sizes. It can benefit small and medium-sized enterprises (SMEs) just as effectively as larger corporations by improving recruitment processes and reducing operational risks.
  5. AI recruiting is expensive and complex: AI solutions vary in cost and complexity, with many offering free trials. These technologies can help analyze data and make faster, more informed decisions without requiring a substantial financial commitment.
  6. AI is only good for simple tasks: AI has evolved far beyond simple tasks and can excel in complex and specialized domains, from healthcare diagnostics to creative content generation.
  7. AI is just an algorithm: AI encompasses a broad spectrum of capabilities, and recruiting AI is a specialized application designed to automate and enhance recruitment processes through data analysis and predictive modeling.
  8. You can teach AI anything: Training AI requires time, effort, and continuous monitoring to ensure it understands and adapts to changing requirements.

Is there a solution?

Turing experts believe that having a system that collects proper representation and information on the following three fronts can help find the right candidate.

  1. Accurate qualifications and competencies of job seekers
  2. Actual job requirements aligned with reasonable expectations
  3. Valid and reliable assessment of the fit between the job and the candidate

A limited or regional talent pool can increase the difficulty of finding the right talent in a fast and cost-efficient manner. This is why Turing built an AI-powered platform that uses 20,000+ ML data signals to source, vet, and manage world-class developers. Following a structured approach, AI supports Turing’s vetting process and is Turing’s solution to the hiring conundrum.

Transforming Hiring with Turing’s Comprehensive AI Vetting Process

In the dynamic world of tech recruitment, Turing stands at the forefront with its innovative AI-based vetting system. With a global talent pool exceeding 2 million software professionals, Turing offers career growth opportunities for developers and the assurance of being vetted once for a lifetime of prospects.  In this video, Turing’s CEO Jonathan Siddharth explains how Turing uses AI to evaluate developers. 

Progressive Assessment Tailored to the Job

Turing’s AI vetting process revolves around two key vectors: role types and dominant tech stacks. This tailored approach ensures that candidates are assessed based on the specific skills required for their roles, whether they’re Front-end Engineers or Back-end Engineers, specializing in React, Node, Python, Java, and more.

Stage 1: Fundamental Knowledge Evaluation

The journey begins with a deep dive into candidates’ fundamental knowledge. Turing’s AI system conducts automated knowledge tests, examines code blocks, and tests language or technology concepts. Dynamic scoring based on question difficulty enhances candidate quality assessment. With over 150 automated tests catering to 10+ roles, foundational skills are thoroughly scrutinized.

Stage 2: Coding Proficiency Assessment

Successful candidates progress to Stage 2, where they face coding challenges aligned with their tech stack. These challenges assess algorithmic prowess, data structure understanding, and coding best practices, emphasizing efficiency and performance. Performance metrics, including testing, debugging, code deployment, and API interaction, ensure candidates possess practical skills.

Stage 3: Soft Skills Evaluation

Recognizing the importance of soft skills, Turing’s AI evaluates candidates’ core values, communication, collaboration, and remote work adaptability in Stage 3. This holistic approach ensures candidates meet the technical and interpersonal requirements for the job.

Stage 4: Perfect Match Assurance

In the final stage, Turing’s AI system meticulously matches job requirements with candidate skills, ensuring a harmonious fit between abilities and job demands.

Turing’s four-stage assessment process, encompassing technical proficiency, soft skills, role-specific knowledge, and job compatibility, revolutionizes hiring. With a commitment to excellence and a 97% retention rate, Turing has earned the trust of 900+ companies, including industry giants like Pepsi, Disney, and Dell. Transform your hiring experience with Turing’s AI-powered Talent Cloud and reap the benefits of precision and efficiency in talent acquisition.

FAQs related to AI for recruiting

  1. What is AI recruiting?
    AI recruiting involves using artificial intelligence to assist in various stages of the hiring process, from sourcing and screening candidates to assessing their qualifications and predicting job fit.

  2. What is the role of AI in hiring process?
    The role of AI in hiring includes automating repetitive tasks, enhancing candidate matching, reducing bias, providing data-driven insights, and improving the efficiency and effectiveness of recruitment.

  3. Can AI replace humans in recruiting process?
    While AI can automate many aspects of recruiting, it cannot fully replace humans. Human judgment, communication, and empathy remain essential in evaluating soft skills and cultural fit.

  4. How AI is changing the hiring process?
    AI is changing the hiring process by streamlining tasks, reducing bias, enabling data-driven decisions, and enhancing the candidate experience, ultimately making recruitment more efficient and fair.

  5. What are the challenges of using AI in hiring process?
    Challenges of using AI in hiring include potential bias in algorithms, data quality issues, transparency concerns, legal compliance, and ensuring a positive candidate experience.

  6. What are the benefits of AI powered recruitment?
    Benefits of AI-powered recruitment include efficiency gains, better candidate matching, reduced bias, cost savings, faster time-to-fill positions, predictive analytics, and improved overall hiring quality.

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By September 18, 2023
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