Generative AI is quickly changing the landscape of the business world, with rapid adoption rates across nearly every industry. Businesses are turning to gen AI to streamline business processes, develop proprietary AI technology, and reduce manual efforts in order to free up employees to take on more intensive tasks. A recent survey of senior IT professionals from Foundry found that 57% of IT organizations have identified several areas for gen AI use cases, 25% have started pilot programs, and 41% are engaged in training and upskilling employees on gen AI.\n\nIn the next six to 12 months, some of the most popular anticipated uses for gen AI include content creation (42%), data analytics (53%), software development (41%), business insight (51%), internal customer support (45%), product development (40%), security (42%), and process automation (51%). Organizations are also optimistic that gen AI will boost productivity and improve business outcomes, with 58% saying that they believe gen AI will make employees more productive, 55% saying that gen AI\u2013infused products lead to better business outcomes, and 55% saying that gen AI enables employees to focus more on value-adding tasks.\n\nAs this technology becomes more popular, it\u2019s increased the demand for relevant roles to help design, develop, implement, and maintain gen AI technology in the enterprise. Foundry\u2019s AI survey also identified several roles that companies are looking to hire to help with the integration of gen AI in the workplace. Here are the top 11 roles companies are currently hiring for, or have plans to hire for, to directly address their emerging gen AI strategies.\n\n1. Data scientist\n\nAs companies embrace gen AI, they need data scientists to help drive better insights from customer and business data using analytics and AI. For most companies, AI systems rely on large datasets, which require the expertise of data scientists to navigate. Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software. It\u2019s a role that requires experience with natural language processing, coding languages, statistical models, and large language and generative AI models. According to the survey, 28% of respondents said they have hired data scientists to support generative AI, while 30% said they have plans to hire candidates.\n\n2. Machine learning engineer\n\nMachine learning engineers are tasked with transforming business needs into clearly scoped machine learning projects, along with guiding the design and implementation of machine learning solutions. This role is responsible for training, developing, deploying, scheduling, monitoring, and improving scalable machine learning solutions in the enterprise. It\u2019s a role that requires a wide range of skills including model architecture, data and ML pipeline creation, software development skills, experience with popular MLOps tools, and experience with tools such as BERT, GPT, and RoBERTa, among others. The goal of a machine learning engineer is to ultimately make machine learning more accessible across the organization so that everyone can benefit from the technology. According to the survey, 22% of respondents say they have already hired machine learning engineers to support generative AI, while 28% say they have plans to hire for the role.\n\n3. AI researcher\n\nAI is new territory for businesses, and there\u2019s still a lot to discover about the technology, which is why they\u2019re looking to hire AI researchers to help identify the best possible applications of AI within the business. AI researchers help develop new models and algorithms that will improve the efficiency of generative AI tools and systems, improve current AI tools, and identify opportunities for how AI can be used to improve processes or achieve business needs. AI researchers need to understand data and automation infrastructure, machine learning models, AI tools and algorithms, data science, programming, and how to build AI models from scratch. According to the survey, 31% of respondents say they have already hired AI researchers to support generative AI, while 19% say they have plans to hire for the role.\n\n4. Algorithm engineer\n\nAlgorithm engineers, sometimes referred to as algorithm developers, are tasked with building, creating, and implementing algorithms for software and computer systems to achieve specific tasks and business needs. The role of algorithm engineer requires knowledge of programming languages, testing and debugging, documentation, and of course algorithm design. These engineers are responsible for solving complex computational problems in the organization, often working with large data sets to design intricate algorithms that address and solve business needs. Businesses rely on algorithm engineers to help navigate gen AI technology, relying on these experts to scale and deploy gen AI solutions, consider all the ethical and bias implications, and ensure they\u2019re aligned with all compliance and regulatory requirements. According to the survey, 16% of respondents say they have already hired algorithm engineers to support generative AI, while 31% say they have plans to hire for the role.\n\n5. Deep learning engineer\n\nDeep learning engineers are responsible for heading up the research, development, and maintenance of the algorithms that inform AI and machine learning systems, tools, and applications. Deep learning is a subset of AI, and vital to the development of gen AI tools and resources in the enterprise. This role is responsible for building and maintaining powerful AI algorithms, identifying data requirements, and finding better ways to automate processes in the business to improve performance. Technologies such as chatbots, virtual assistants, facial recognition, medical devices, and automated cars rely on deep learning to create effective products. As companies continue to embrace gen AI, deep learning engineers are critical for businesses that want to capitalize on AI and integrate it into business processes, services, and products. According to the survey, 16% of respondents say they have already hired deep learning engineers to support generative AI, while 28% say they have plans to hire for the role.\n\n6. NLP engineer\n\nNatural language processing (NLP) engineer is a vital role for embracing gen AI in any organization. Gen AI relies heavily on NLP to improve communication and to create chatbots and other AI services that need to communicate effectively with users, no matter the query. This role is responsible for training NLP systems, developing models, running experiments, identifying proper tools and algorithms, and performing regular maintenance and analysis of the models. Candidates typically have experience in big data, coding, model selection and customization, language modeling, language translation, and text summarization using NLP tools. NLP plays a big role in technologies such as text-to-speech (TTS) and speech-to-text (STT), chatbots and virtual assistants, and other gen AI tools that are designed to interact in real-time with users. According to the survey, 15% of respondents say they have already hired NLP engineers to support generative AI, while 27% say they have plans to hire for the role.\n\n7. AI chatbot developer\n\nChatbots are one of the earliest and most common uses of gen AI in a business setting \u2014 it\u2019s very likely that you have interacted with an AI chatbot at some point in the past several years. They help direct customers to the right associates, connect users with important documentation, and can alleviate some of the load on customer service representatives. With gen AI, chatbots are becoming even more sophisticated, with the rise of services such as ChatGPT, Bard, Replika, Cleverbot, and others, which have shown to be powerful tools that are useful to businesses. Chatbot technology is in demand across every industry, and businesses are eager to develop their own chatbot tools to help streamline customer service, appointment scheduling, social media engagement, user support, and even marketing and promotions. According to the survey, 15% of respondents say they have already hired AI chatbot developers to support generative AI, while 27% say they have plans to hire for the role.\n\n8. Prompt engineers\n\nPrompt engineers are responsible for ensuring that tools using gen AI, especially text-to-text and text-to-image AI models, can accurately assess user prompts and deliver the correct information. It\u2019s a role that requires extensive knowledge of natural language processing, coding, natural language queries, and artificial neural networks. Examples of prompt engineering can be seen in tools such as ChatGPT, which takes user queries and generates a unique response, and AI image tools such as Midjourney, which produces unique art and imagery based on user requests. For businesses interested in leveraging AI, especially with chatbots, automated assistants, and image generators, prompt engineering is a vital role to ensure those tools are effective and useful. According to the survey, 11% of respondents say they have already hired prompt engineers to support generative AI, while 26% say they have plans to hire for the role.\n\n9. Chief AI officer\n\nChief AI officer is a relatively new senior executive position that helps organizations tackle the rapid progress of and demand for AI in the workplace. There are so many considerations when integrating AI into the workplace, especially around security, bias, compliance, and privacy. A chief AI officer is responsible for overseeing AI strategy development by navigating and overseeing the development and implementation of AI in the business. Other responsibilities include overseeing data management and governance, business unit collaboration, ethics and compliance, risk management, talent acquisition and team building for AI, and monitoring overall performance and analytics reporting on AI tools. According to the survey, 11% of respondents say they have already hired a chief AI officer to support generative AI, while 21% say they have plans to hire for the role.\n\n10. AI writer\n\nMore companies are turning to AI for content creation, including writing blog posts, product descriptions, and more. But the results aren\u2019t always perfect \u2014 and often need a human eye to edit and rework gen AI results into something that sounds more human and relatable to readers. Companies are looking for experienced writers and editors who can help turn around content quickly, using generative AI, while ensuring that the content is well-written and easy to understand by the intended audience. According to the survey, 10% of respondents say they have already hired AI writers to support generative AI, while 21% say they have plans to hire for the role.\n\n11. AI artist\n\nAI art is one of the newer applications of gen AI, with tools such as Midjourney and Stable Diffusion taking off in the past year. These tools can take a prompt, or an image, and either create entirely unique content, or make specific edits to already-existing imagery. There is a lot of potential for organizations looking to create marketing materials, product images, stock images, and other art-related content. Organizations are seeking experienced artists and graphic designers who can take that expertise and knowledge to get the most out of image generation tools. Artists have the right knowledge and expertise to create prompts that will garner better results from generative AI. They know the lingo, terminology, and nuances of various artistic areas \u2014 be it film, artwork, or visual graphics \u2014 which can help ensure that businesses get the results they want from these services. According to the survey, 7% of respondents say they have already hired AI artists to support generative AI, while 15% say they have plans to hire for the role.