<h1 id="isPasted">Introduction</h1><p>In less than three years since the arrival of ChatGPT, there has been a surge of interest in Large Language Models (LLMs) and Generative AI (GenAI). Hundreds of AI applications are rapidly transforming various areas of the economy, including finance, marketing, manufacturing, and other industrial sectors. The advent and widespread adoption of Generative AI is giving rise to a new computing paradigm, namely AI-based “intent computing”. In this paradigm, AI is transforming how multimedia content and software are written, but also how applications and systems are conceived, designed, and maintained.&nbsp;</p><p>Engineers can now articulate their system development goals in natural language, letting AI translate these intentions into actionable code, design blueprints, maintenance plans or system development strategies. This shift is already democratizing advanced engineering capabilities, as it makes them available to a broader range of professionals and organizations. At the same time, it is increasing the productivity of industrial organizations, which can now do more in less time and with improved quality. To better understand this productivity revolution, it's best to look at some of the most prominent engineering capabilities that are disrupted by AI technologies.</p><h1>Writing Original Software Algorithms with AI</h1><p>One of the areas that has been disrupted by GenAI is software development. Specifically, modern Generative AI tools enable a new approach to coding, where a considerable amount of code is produced by AI tools. AI-generated code can be later integrated into human-generated code to enable the development of larger scale software systems. In this way, software developers increase their productivity and become capable of implementing systems of increased complexity in less time.&nbsp;</p><p>During the last year, AI tools have also enabled new forms of software development, where business users develop simple applications and Minimum Viable Products (MVPs) without any programming knowledge. This paradigm is commonly known as “vibe coding” and is currently applicable to small scale applications without complex non-functional requirements (e.g., sophisticated security requirements). Nevertheless, the possibility of vibe coding more complex applications in the future cannot be ruled out.&nbsp;</p><p><strong>The Rise of AI Coding Assistants</strong></p><p>The above-listed capabilities are enabled by a wide range of AI-powered coding tools such as <a href="https://github.com/features/copilot">GitHub Copilot</a>, <a href="https://openai.com/codex/">OpenAI Codex</a>, and <a href="https://www.cursor.com/">Cursor AI</a>, which are rapidly gaining traction. These assistants leverage large language models trained on vast repositories of open-source code, which enable them to suggest code snippets, complete functions, and even write entire algorithms based on developer prompts. The result is a significant reduction in manual coding effort and a boost in productivity.</p><p><strong>Enhancing Developer Productivity</strong></p><p>Modern AI coding assistants go beyond simple conventional coding completion functions. They offer features like intelligent refactoring, automated code documentation, and test generation. These capabilities accelerate development cycles, while at the same time improving code quality and maintainability. With AI-assisted coding, engineers benefit from faster iteration, debugging help, and the ability to focus on higher-level design challenges rather than repetitive coding tasks.</p><p><strong>Lowering the Entry Barrie</strong>r</p><p>AI-assisted coding is the natural evolution of no-code and low-code platforms. Nowadays, these platforms are often powered by AI towards lowering the barrier to entry for algorithm development. Engineers without deep programming expertise can now create functional algorithms using intuitive interfaces and guided workflows. This accelerates rapid prototyping and innovation cycles, while at the same time enabling teams to test ideas and iterate on designs with unprecedented speed.&nbsp;</p><p>During the last couple of months, there are also business users that test alternative software development scenarios using “vibe coding” i.e., without writing any code themselves, but rather delegating code development to a Generative AI system like <a href="https://lovable.dev/">lovable.dev</a>. This has significantly lowered the time and cost needed for developing an MVPa. While video coding is still not adequate when it comes to writing enterprise software at scale, this could change in the future as AI research evolves towards AGI (Artificial General Intelligence).</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 813px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1753131060629-mouser+1+j.png" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner" spellcheck="false"><em id="isPasted">Generative systems are reducing the barrier to coding new products. Credit: Lovable</em></span></span></span></p><h1 id="isPasted">AI-Assisted Product and Component Selection</h1><p>Selecting the right components from millions of available electronic parts has always been a daunting challenge for engineers. This is mainly due to the sheer volume and complexity of choices considering the variety of components and their unique specifications, tolerance and intended applications. In this context, engineers must balance performance with cost based on the resolution of trade-offs between optimal technical specifications and project budgets. At the same time, it is important to consider availability, reliability, resilience and supply chain issues.&nbsp;</p><p>Nowadays, AI-driven platforms like <a href="https://www.celus.io/">CELUS</a> are used to automate the transformation of technical requirements into schematic prototypes. Similar AI-based selection functionalities are also offered by tools like the <a href="https://www.altium.com/altium-designer">Altium Designer PCB</a> design software and <a href="https://eda.sw.siemens.com/en-US/pcb/xpedition/enterprise/">Siemens Xpedition</a> (Mentor Graphics). &nbsp;These platforms can generate ECAD schematics and bills of materials with minimal manual intervention. Moreover, they provide real-time component recommendations and integrate with suppliers, which streamlines the design process and reduces time-to-market.</p><p>The benefits of AI-assisted selection of products and components extend to both engineers and suppliers. Engineers can focus on core design challenges, while suppliers gain insights into demand trends. Also, engineers can use these tools to collaborate more efficiently with design teams. The result is a more agile, responsive, and innovative engineering ecosystem.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 813px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1753131084267-mouser+j+2.png" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner" spellcheck="false"><em id="isPasted">AI-powered selection tools promise to speed up product design and offer novel methods for engineers to engage with components.&nbsp;</em></span></span></span></p><p id="isPasted"><strong>Generative AI and Design Optimization</strong></p><p>One of the most prominent industrial use cases of Generative AI is the rapid generation of alternative design options for products and components. In the scope of such use cases, GenAI uses machine learning to autonomously create and optimize design alternatives based on specified constraints and goals. This is very important in industries such as automotive, civil, and mechanical engineering, where Gen AI is enabling the creation of novel, efficient, and sustainable designs. Most importantly, the creation of these designs takes place faster than ever before. For example, AI-driven simulation tools allow engineers to rapidly validate designs, which reduces the need for costly physical prototyping. Furthermore, such simulation tools can provide real-time feedback on the performance and manufacturability of the prototypes.</p><p>Case studies in the above-listed sectors demonstrate that generative AI can lead to breakthrough innovations, such as lightweight automotive components, optimized building structures, and energy-efficient mechanical systems. Overall, GenAI endows modern engineers with the superpower of exploring a vast design space very quickly and efficiently. This is set to transform traditional engineering workflows.</p><h1>AI for Predictive Maintenance and Operations</h1><p>AI has been also revolutionizing maintenance strategies through predictive analytics. Specifically, AI algorithms have enabled a shift from conventional maintenance paradigms that lead to sub-optimal utilisation of critical assets (e.g., preventive maintenance) to CBM that aims to maximise the useful time of the asset in ways that lead to the best possible overall equipment efficiency (OEE). To this end, condition-based monitoring leverages sensors and other information in order to track and assess the actual condition of the asset prior to taking any service, maintenance and repair decisions. One of the most effective strategies in this direction is predictive maintenance, which leverages sensor data and machine learning to predict equipment failures before they occur. This enables proactive CBM in ways that maximize OEE and minimize unplanned downtime. Relevant AI-based implementation workflows typically involve data collection from IoT sensors, feature extraction, model training, and real-time monitoring.&nbsp;</p><p>In recent years, GenAI has boosted the development of predictive maintenance strategies through facilitating data analysis and the production of algorithms that estimate remaining useful life (RUL) and schedule maintenance activities. Most importantly, GenAI models can help with the generation of synthetic data whenever there is a lack of adequate volumes of historic data for predictive maintenance. Furthermore, the use of GenAI models (such as LLMs) can reinforce the contextual relevance of the maintenance strategies and predictions, leveraging on the vast training datasets and knowledge of the LLMs. Also, the use of state-of-the-art GenAI models can boost the development of multimodal capabilities that combine different types of data in order to produce more accurate and credible RUL predictions.</p><p>The benefits of AI-driven predictive maintenance are already substantial. They include reduced maintenance costs, minimized safety incidents, and improved operational efficiency. Based on AI and GenAI, industrial organizations can optimize resource allocation, extend equipment lifespans, and ensure higher levels of safety and reliability.</p><h1>Future Outlook: Accessible AI Tools for Every Engineer</h1><p>Overall, the democratization of advanced AI tools is empowering engineers across disciplines to leverage cutting-edge capabilities without requiring deep expertise in machine learning or data science. In the AI era collaboration and knowledge sharing are becoming much easier than in the past, as AI-driven platforms facilitate seamless communication and integration across teams and organizations.</p><p>Nevertheless, the AI landscape is still not free of challenges. For instance, AI adopters must deal with data quality issues, as well as with human worker’s limited trust in AI recommendations. To alleviate these issues there is a need for human oversight in AI processes. Moreover, regulatory compliance and ethical concerns must be addressed as AI becomes more deeply embedded in engineering workflows. Despite these challenges, the future of AI-based engineering design is bright. In the near future a richer set of accessible, intelligent and powerful tools will enable innovation at lower costs and shorter timescales.&nbsp;</p>

AI is transforming engineering by enabling professionals to express system development goals in natural language, which AI can then convert into code, designs, or strategies. This shift is democratizing access to advanced engineering tools and boosting productivity.

The Future of AI-based Engineering Design: From Code Generation to Predictive Maintenance

Article #4 of Confronting AI Series: AI's rapid rise brings transformative potential and serious risks; without transparency, regulation, education, and justice, unchecked deployment may entrench bias, erode trust, and harm society more than it helps.

Revisiting AI's Ethical Dilemma: Balancing Promise and Peril

<h2 dir="ltr" id="isPasted" data-element-id="headingsMap-4-0">Introduction</h2><p dir="ltr">The modern workplace is rapidly changing and AI is emerging as a transformative force. With the growing use of AI-powered tools, companies are rethinking how to approach hiring, employee onboarding, and enhancing the efficiency, development, and well-being of employees.</p><p dir="ltr">In this article, we delve into the role of AI in the workplace, highlighting its impact on hiring processes, improving employee retention, and workplace conditions. We present case studies of companies who have found success with AI tools. We also explore future trends and ethical considerations, providing an overview of how AI is positioned to help companies hire more effectively and create better places to work.</p><h2 dir="ltr" data-element-id="headingsMap-5-0">AI in the Hiring Process</h2><p dir="ltr">Hiring is time-consuming and costly for companies, and often frustratingly involves processes that overlook the most qualified candidates. Some problems with candidate screening, matching, and interviewing are:</p><ul><li dir="ltr"><p dir="ltr">Over-reliance on keywords and algorithms, which may screen out very qualified candidates whose resumes do not precisely match job descriptions.</p></li><li dir="ltr"><p dir="ltr">Bias in recruitment, which can hinder diversity and inclusion efforts and lead to a homogenized workforce.</p></li><li dir="ltr"><p dir="ltr">High-volume applications and initial screenings, a task that relies heavily on the judgment of recruiters and is subject to many human factors.<sup>[1]</sup></p></li></ul><p dir="ltr">Companies can address these challenges through the implementation of AI technologies in the recruitment process. The following are a few examples of the potential for AI to help companies make hiring more efficient and inclusive.</p><h3 dir="ltr" data-element-id="headingsMap-6-0">Streamlining candidate screening</h3><p dir="ltr">AI-powered applicant tracking systems can streamline sourcing and screening automating resume analysis and identifying qualified candidates efficiently and accurately. These are some case studies where AI is already improving efficiency in candidate screening.</p><ul><li dir="ltr"><p dir="ltr">IBM Watson recruitment at Hilton&nbsp;– By integrating IBM Watson for candidate screening and matching, the Hilton hospitality chain saw a 50% reduction in time-to-fill, improved candidate quality, and enhanced hiring manager satisfaction.</p></li><li dir="ltr"><p dir="ltr">Pymetrics gamified assessments at Deloitte&nbsp;– Deloitte, the US auditing and tax services company incorporated Pymetrics’ AI platform for gamified assessments into their candidate screening process. As a result, the company saw a 60% reduction in time spent on initial screenings, a 30% increase in diverse hires, and enhanced candidate experience.</p></li><li dir="ltr"><p dir="ltr">HireVue video interviews at McDonald’s&nbsp;– The fast food chain McDonald’s implemented HireVue’s AI-enhanced video screening to streamline their high-volume hiring process. Use of the platform resulted in a 40% reduction in time-time hire, improved candidate engagement, and enhanced scalability.[2]</p></li></ul><h2 dir="ltr" data-element-id="headingsMap-7-0">Enhancing candidate matching</h2><p dir="ltr">AI algorithms can be used to match job skills and requirements without the over-reliance on specific job description keywords that is often the case with traditional algorithms. AI algorithms can use natural language processing to analyze candidate data and match actual qualifications with job requirements, improving the accuracy of candidate matching.<sup>[3]</sup></p><p dir="ltr">Additionally, by focusing on skills, qualifications, and experience rather than personal characteristics, AI can help reduce unconscious bias in recruitment, leading to a more diverse and inclusive workforce.<sup>[4]</sup></p><h3 dir="ltr" data-element-id="headingsMap-8-0">Interview and assessment automation</h3><p>Companies can harness the power of AI using deep learning and natural language processing to improve the interview process for both candidates and recruiters. Image credit: mikemacmarketing | Source: wikimedia.org</p><p>AI-driven recruitment strategies can help make the interview process a much better experience for candidates and recruiters alike. AI tools can automate interview scheduling, conduct initial screenings, ask follow-up questions, and use natural language processing to analyze responses, making the interview process more effective and efficient.[5] In the case studies discussed above, the introduction of AI tools improved the recruitment experience for hiring managers and candidates, and improved candidate engagement.</p><p>By incorporating AI tools into hiring, organizations can streamline candidate screening, improve matching accuracy, reduce bias, enhance efficiency, and ultimately create a more inclusive and effective hiring environment.</p><h2 dir="ltr" data-element-id="headingsMap-9-0">AI's Role in Employee Onboarding and Retention</h2><p dir="ltr">While it is important to find and hire the best candidates for the job, it is equally important to make sure that new employees are quickly brought up to speed and productive. Perhaps even more critical is to keep turnover rates low and retain employees by making sure that they are satisfied with the job. Companies can use AI-powered tools to build a more effective and engaged workforce.</p><h2 dir="ltr" data-element-id="headingsMap-10-0">Automated onboarding processes</h2><p dir="ltr">AI-powered recruitment software can analyze the experience, knowledge, and learning style of a new hire and generate tailored onboarding programs. This type of onboarding takes the employee’s personality and background into account, to help a new employee quickly become engaged and part of company culture.</p><p>This type of streamlined onboarding means that a new employee becomes productive faster and may be more satisfied with the job overall. Research shows that a successful onboarding experience leads to greater employee retention — employees are twice as likely to be satisfied at work and contribute to a positive company culture.[6]</p><h2 dir="ltr" data-element-id="headingsMap-11-0">Predictive analytics for employee turnover</h2><p dir="ltr">AI is a powerful tool because it can collect and process large volumes of data. This ability to sift through data and identify patterns and correlations can be leveraged to pinpoint the things that contribute to employee turnover. Companies can use historical turnover data to develop models that predict potential turnover risks.</p><p>With the power of AI, companies can better understand the specific reasons for turnover, make improvements, and test the outcome. For example, if historical turnover data points to dissatisfaction with limited opportunities for growth, the company can offer career development programs, use AI to monitor the result, and make improvements to the programs.<sup>[7]</sup></p><p>AI tools can have a significant impact on employee onboarding and retention, leading to more engaged, productive, and loyal employees.</p><h2 dir="ltr" data-element-id="headingsMap-12-0">Improving Workplace Conditions with AI</h2><p dir="ltr">Working conditions are another factor in employee satisfaction and retention. Companies can leverage AI-powered tools to optimize the workspace, support career development, and maintain employee health.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 769px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTQ5ODYxMjUyLTFOQUdCUVpiVVVZelNIVl9ja3cwWl9zRzJoZTZkQ284LmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Employers can leverage AI to optimize the working environment while improving employee well-being and retention. Image credit: Alice Mai Ah | Source: wikimedia.org</span></span></span></p><h2 dir="ltr" id="isPasted" data-element-id="headingsMap-13-0">AI for workspace optimization</h2><p dir="ltr">Using AI, companies can create smart work environments that adapt to employee patterns, preferences, and usage. Automatic environmental adjustments like lighting, temperature, and desk configurations can lead to increased comfort, productivity, and energy cost savings for the company.</p><p dir="ltr">AI systems can also be used to optimize resources like meeting rooms, workstations, and common areas, making sure facilities are used effectively and that employee needs are supported.</p><h2 dir="ltr" data-element-id="headingsMap-14-0">AI-enhanced training and development</h2><p dir="ltr">As discussed above, AI can be used during onboarding to get new employees working quickly. In addition to new hire training, AI tools can be used for ongoing growth, tailoring development programs to each employee’s learning styles, career goals, and performance. Using AI analytics, organizations can track the effectiveness of development programs and continuously improve them, leading to better career growth opportunities and satisfied employees.</p><h2 dir="ltr" data-element-id="headingsMap-15-0">Ensuring health and well-being</h2><p dir="ltr">Predictive AI analytics can play a crucial role in improving employee mental health by analyzing patterns in employee data and feedback to catch signs of stress or burnout. Companies can use this data to take proactive measures that support the well-being of employees.</p><p dir="ltr">AI-powered ergonomic assessments and activity reminders can also help improve the workplace. Wearable devices and AI applications can provide data on workplace ergonomics and suggest improvements. Wearables can also remind employees to take breaks and stretch to maintain both mental and physical health.</p><h2 dir="ltr" data-element-id="headingsMap-16-0">Case Studies: AI-Driven Success in Hiring and Workplace Enhancement</h2><p dir="ltr">Companies today are already leveraging powerful AI tools to enhance hiring and the workplace. These are just a few case studies that show how companies are seeing success with these tools.</p><ul><li dir="ltr"><p dir="ltr">ClearCompany&nbsp;– ClearCompany, a recruiting software platform, has automated employment verification, document submission, and analysis of learning pase using AI-driven onboarding software. This has resulted in a more efficient onboarding process and increased focus on strategic work that drives company growth.<sup>[6]</sup></p></li><li dir="ltr"><p dir="ltr">Genpact&nbsp;– This company has used AI to automate their entire onboarding experience, providing personalized experiences for new hires, which has helped speed up the onboarding process and improve retention.<sup>[8]</sup></p></li><li dir="ltr"><p dir="ltr">Tier 4 Group&nbsp;– This talent acquisition firm worked with TechScreen to streamline their tech hiring processes, resulting in a more effective, ethical, and considerate candidate screening process.<sup>[9]</sup></p></li><li dir="ltr"><p dir="ltr">Iceland Foods – During the pandemic, Iceland Foods partnered with Sapia.ai to adapt their recruitment processes to a changing world. AI automation helped the company find the right candidates in the midst of global upheaval, demonstrating the resilience of AI-driven recruitment.<sup>[10]</sup></p></li></ul><p dir="ltr">These examples illustrate the diverse applications of AI in hiring and workplace enhancement, from improving candidate experience and streamlining the recruitment process, to fostering internal development and supporting ethical hiring practices.</p><h2 dir="ltr" data-element-id="headingsMap-17-0">Looking ahead: the next generation of AI in the workplace</h2><p dir="ltr">The integration of AI into the workplace is poised to have a huge impact on how we work. Here are some ways that we predict AI will transform the workplace in specific industries.</p><ul><li dir="ltr"><p dir="ltr">Healthcare&nbsp;– By integrating data like medical imaging, lab results, and checkups, AI advancements will likely lead to more efficient healthcare systems, as well as more effective care, with better outcomes for patients.</p></li><li dir="ltr"><p dir="ltr">Finance&nbsp;– The role of AI in fraud detection, personalized financial advising, and record keeping is likely to grow, but a strong emphasis on human oversight and ethical decision-making will remain a key part of the finance industry.</p></li><li dir="ltr"><p dir="ltr">Education&nbsp;– In education, AI will be able to analyze and provide insights into student learning, and will help institutions create more engaging and personalized experiences for both students and teachers.</p></li><li dir="ltr"><p dir="ltr">Retail&nbsp;– Predictive analytics will enable retailers to understand customer behavior, manage inventory, and improve customer service. AI has the potential to boost customer satisfaction and operational efficiency.</p></li><li dir="ltr"><p dir="ltr">Manufacturing&nbsp;– AI will significantly impact how work is done in the manufacturing industry as well. Collaborative robots (cobots), predictive maintenance, and smart factories are AI-powered tools that will likely improve the safety and efficiency of manufacturing.</p></li></ul><h2 dir="ltr" data-element-id="headingsMap-18-0">Ethical considerations: balancing technology and humanity</h2><p dir="ltr">AI is a promising tool for improving workplaces and the efficiency, health, and well-being of workers. And because AI is for the benefit of humanity, it is important to examine ethical considerations and maintain a balance between technology and human values.</p><p dir="ltr">When using AI tools in hiring and HR functions, It is critical for companies to remain vigilant against bias and discrimination. Laws, standards, and guidelines that require AI tools to be audited for bias are already in place. It is also important for companies to monitor AI tools for bias during recruiting, hiring, and promotion.<sup>[11]</sup></p><p dir="ltr">In an increasingly automated workplace, it is also essential for companies to balance AI and human interaction. While AI magnifies our human capabilities and streamlines the work that we do, it is important for companies to foster human relationships and empathy, and to take an ethical stance to put humans first when it comes to AI strategies.</p><h2 dir="ltr" data-element-id="headingsMap-19-0">Conclusion</h2><p dir="ltr">The integration of AI into the hiring process, employee onboarding, and workplace improvement is a significant shift towards a more data-driven, efficient, and employee-centric approach to company management. As underscored by the case studies above, there are already tangible benefits to AI-driven strategies, from streamlined hiring processes to personalized onboarding experiences, to optimized workplace conditions that prioritize employee health and well-being.</p><p dir="ltr">As AI continues to evolve and is increasingly integrated into our workplaces, it is important to address ethical considerations and maintain a balance between technological advancements and human values. The future of AI in the workplace holds great promise for improving how we hire and work, and has the power to transform industries, improve the lives of workers, and shape how we think about work.</p><h2 data-element-id="headingsMap-20-0">References</h2><p>[1] “Navigating the Maze: The Fracture’s in Today’s IT Recruiting Process &amp; the AI Solution”. TechScreen. November 27, 2023. Accessed from <a href="https://techscreen.com/navigating-the-maze-the-fractures-in-todays-it-recruiting-process-the-ai-solution/" target="_blank">https://techscreen.com/navigating-the-maze-the-fractures-in-todays-it-recruiting-process-the-ai-solution/</a>&nbsp;</p><p>[2] Jessica Dennis. “AI Recruiting: Uses, Advantages, &amp; Disadvantages”. TechnologyAdvice. 22 May 2023. Accessed from <a href="https://technologyadvice.com/blog/human-resources/ai-recruiting/" target="_blank">https://technologyadvice.com/blog/human-resources/ai-recruiting/</a>&nbsp;</p><p>[3] Ranganath Ponnaboyina, et al. “Smart Recruitment System Using Deep Learning with Natural Language Processing”. Intelligent Systems and Sustainable Computing, Vol. 289. 29 May 2022. Accessed from <a href="https://link.springer.com/chapter/10.1007/978-981-19-0011-2_58" target="_blank">https://link.springer.com/chapter/10.1007/978-981-19-0011-2_58</a>&nbsp;</p><p>[4] “Pros and Cons of Using AI in Recruiting”. Recruiter.com. Accessed from <a href="https://www.recruiter.com/recruiting/pros-and-cons-of-using-ai-in-recruiting/" target="_blank">https://www.recruiter.com/recruiting/pros-and-cons-of-using-ai-in-recruiting/</a>&nbsp;</p><p>[5] Paul Arolye. “Navigating Recruitment Challenges: 5 Ways AI Can Revolutionize HR”. eLearning Industry. 27 January 2024. Accessed from <a href="https://elearningindustry.com/navigating-recruitment-challenges-ways-ai-can-revolutionize-hr" target="_blank">https://elearningindustry.com/navigating-recruitment-challenges-ways-ai-can-revolutionize-hr</a>&nbsp;</p><p>[6] ClearCompany. “3 Benefits of Automating Employee Onboarding with AI-Driven Software”. 5 September 2023. Accessed from <a href="https://blog.clearcompany.com/benefits-of-automating-employee-onboarding-with-ai" target="_blank">https://blog.clearcompany.com/benefits-of-automating-employee-onboarding-with-ai</a>&nbsp;</p><p>[7] Cabot Jaffee. “Leveraging AI to predict and reduce employee turnover.” Talent Management. 14 August 2023. Accessed from <a href="https://www.talentmgt.com/articles/2023/08/14/leveraging-ai-to-predict-and-reduce-employee-turnover/" target="_blank">https://www.talentmgt.com/articles/2023/08/14/leveraging-ai-to-predict-and-reduce-employee-turnover/</a>&nbsp;</p><p>[8] Maria Korolov. “AI takes aim at employee turnover”. CIO. 7 April 2022. Accessed from <a href="https://www.cio.com/article/307980/ai-takes-aim-at-employee-turnover.html" target="_blank">https://www.cio.com/article/307980/ai-takes-aim-at-employee-turnover.html</a>&nbsp;</p><p>[9] TechScreen. “Case Studies in Tech Hiring: AI Success Stories and Lessons Learned from Top Companies”. 28 December, 2023. Accessed from <a href="https://techscreen.com/case-studies-in-tech-hiring-ai-success-stories-and-lessons-learned-from-top-companies/" target="_blank">https://techscreen.com/case-studies-in-tech-hiring-ai-success-stories-and-lessons-learned-from-top-companies/</a>&nbsp;</p><p>[10] Sapia.ai. “How Iceland Foods smashed its volume hiring goals”. Accessed from <a href="https://sapia.ai/resources/case-study/iceland/" target="_blank">https://sapia.ai/resources/case-study/iceland/</a>&nbsp;</p><p>[11] Roy Mauer. “HR Must Be Vigilant About the Ethical Use of AI Technology”. SHRM. 26 September 2022. Accessed from <a href="https://www.shrm.org/topics-tools/news/technology/hr-must-vigilant-ethical-use-ai-technology" target="_blank">https://www.shrm.org/topics-tools/news/technology/hr-must-vigilant-ethical-use-ai-technology</a>&nbsp;</p>

 AI is having an impact on hiring processes, employee retention, and workplace conditions. 

How AI will help companies hire more people and make the workplace better

In this episode, we discuss how the popularity of generative AI has led to the emergence of prompt engineering which begs the question: what is prompt engineering?

Prompt Engineering

Latest Posts

The Future of AI-based Engineering Design: From Code Generation to Predictive Maintenance

Revisiting AI's Ethical Dilemma: Balancing Promise and Peril

How AI will help companies hire more people and make the workplace better

Podcast: Prompt Engineering: Latest Meme or The Next Big Thing?