88% of companies plan to boost AI hiring through 2025 with employee productivity top priority

Artificial intelligence has moved from Silicon Valley buzzword to boardroom imperative, fundamentally reshaping how organizations approach everything from customer service to strategic planning. This technological shift is creating an unprecedented demand for AI talent across industries, as companies scramble to build internal capabilities that can harness these powerful tools effectively.

Recent research from Foundry, a leading technology research firm, reveals the scope of this hiring surge. Their survey of senior IT professionals shows that 88% of organizations have already invested or plan to invest in AI capability-building tools, with 61% planning to increase their AI spending through 2025. Only 1% of companies plan to reduce their AI investments, signaling sustained confidence in the technology’s business value.

The driving forces behind this hiring boom are clear. Employee productivity tops the list of priorities, with 68% of organizations citing it as their primary motivation for AI adoption. Close behind are improving customer service (55%), enabling innovation (54%), and expanding revenue opportunities (47%). These aren’t abstract goals—they represent concrete business outcomes that require skilled professionals to achieve.

As organizations rush to integrate AI into their operations, they’re discovering that successful implementation requires more than just purchasing software licenses. It demands specialized expertise across multiple disciplines, from technical development to ethical oversight. Here are the 11 most in-demand AI roles that companies are actively hiring for, ranked by current and planned hiring activity.

1. Machine learning engineer

Machine learning engineers serve as the bridge between business requirements and technical implementation, transforming abstract needs into functioning AI systems. These professionals design, build, and maintain the infrastructure that powers AI applications, handling everything from initial model training to ongoing performance monitoring.

The role demands a unique combination of software engineering skills and statistical knowledge. Machine learning engineers must understand model architecture, create data pipelines, and work with MLOps tools—specialized software that helps manage machine learning workflows at scale. They also need familiarity with advanced language models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), which power many modern AI applications.

Consider a retail company implementing personalized product recommendations. A machine learning engineer would analyze customer behavior data, select appropriate algorithms, train models to predict preferences, and build systems that can process millions of customer interactions in real-time while continuously improving accuracy.

Currently, 20% of surveyed organizations have already hired machine learning engineers, while 55% plan to add these roles—making it the most sought-after position in AI hiring.

2. Data scientist

Data scientists function as organizational detectives, uncovering patterns and insights that drive AI-powered decision making. While their role predates the current AI boom, the explosion of generative AI tools has dramatically expanded their responsibilities and importance to business operations.

These professionals combine statistical analysis with programming skills to extract meaningful insights from vast datasets. They build predictive models that can forecast customer behavior, identify market trends, or optimize business processes. In the AI era, data scientists also help organizations transition from traditional analytics to AI-enhanced systems that can process unstructured data like text, images, and voice.

A healthcare organization might employ data scientists to analyze patient records, medical imaging, and treatment outcomes to develop AI models that can assist doctors in diagnosis or predict patient risks. This requires not only technical skills but also domain expertise to ensure the models produce clinically meaningful results.

The survey found that 26% of organizations have hired data scientists for AI initiatives, with 53% planning future hires.

3. AI chatbot developer

AI chatbot developers create conversational interfaces that handle customer inquiries, provide support, and automate routine interactions. These professionals combine programming expertise with understanding of natural language processing—the AI technology that enables computers to understand and respond to human language.

Modern chatbots go far beyond simple question-and-answer scripts. They can understand context, maintain conversations across multiple topics, and even exhibit personality traits that align with brand values. Chatbot developers must understand both the technical aspects of language models and the practical considerations of customer service workflows.

A financial services company might deploy chatbots to handle account inquiries, loan applications, and investment advice. The chatbot developer would need to ensure the system can understand financial terminology, comply with regulatory requirements, and escalate complex issues to human agents when appropriate.

Currently, 21% of organizations have hired AI chatbot developers, with 44% planning to add these roles.

4. AI researcher

AI researchers serve as an organization’s scouts in the rapidly evolving landscape of artificial intelligence, identifying emerging technologies and their potential business applications. These professionals stay current with academic research, experiment with new AI models, and develop custom solutions for specific business challenges.

The role requires deep technical knowledge combined with strategic thinking. AI researchers must understand the theoretical foundations of machine learning, stay current with the latest research publications, and translate complex academic concepts into practical business solutions. They often work on cutting-edge problems that don’t have established solutions.

A manufacturing company might employ AI researchers to explore how computer vision could improve quality control, or how predictive maintenance algorithms could reduce equipment downtime. This involves not just implementing existing technologies, but potentially developing new approaches tailored to specific industrial processes.

Among surveyed organizations, 18% have hired AI researchers, while 52% plan to add these positions.

5. Algorithm engineer

Algorithm engineers design and implement the mathematical procedures that power AI systems. These professionals tackle complex computational problems, creating efficient solutions that can process large amounts of data while maintaining accuracy and speed.

The role combines mathematical expertise with practical engineering skills. Algorithm engineers must understand data structures, optimization techniques, and computational complexity while also considering real-world constraints like processing time, memory usage, and scalability. They often work on problems that require custom solutions rather than off-the-shelf software.

A logistics company might employ algorithm engineers to optimize delivery routes, taking into account factors like traffic patterns, vehicle capacity, fuel costs, and delivery time windows. This requires developing algorithms that can process thousands of variables in real-time to find optimal solutions.

Currently, 17% of organizations have hired algorithm engineers, with 51% planning to add these roles.

6. Natural language processing engineer

Natural language processing (NLP) engineers specialize in helping computers understand and generate human language. These professionals work on systems that can analyze text, translate between languages, summarize documents, and engage in conversations with users.

NLP engineers must understand both the technical aspects of language processing and the nuances of human communication. They work with technologies like text-to-speech (TTS) and speech-to-text (STT) systems, develop chatbots and virtual assistants, and create tools that can analyze sentiment, extract key information, and generate human-like responses.

A legal firm might employ NLP engineers to develop systems that can review contracts, identify potential risks, and summarize key terms. This requires understanding both legal terminology and the subtle ways that language can convey different meanings in legal contexts.

The survey shows 17% of organizations have hired NLP engineers, with 18% planning future hires.

7. Deep learning engineer

Deep learning engineers focus on the most advanced forms of AI, working with neural networks that can recognize patterns in complex data like images, audio, and text. Deep learning is the technology behind many breakthrough AI applications, from image recognition to language translation.

These professionals design and train sophisticated neural networks that can learn from vast amounts of data. They must understand advanced mathematical concepts, work with specialized hardware like graphics processing units (GPUs), and optimize models for both accuracy and efficiency. The work often involves experimental approaches to solve problems that traditional programming cannot address.

An automotive company developing autonomous vehicles would employ deep learning engineers to create systems that can recognize objects, predict pedestrian behavior, and navigate complex traffic scenarios. This requires models that can process visual information in real-time while making split-second decisions.

Currently, 15% of organizations have hired deep learning engineers, with 54% planning to add these positions.

8. Prompt engineer

Prompt engineers represent one of the newest specializations in AI, focusing on crafting effective instructions for generative AI systems. These professionals understand how to communicate with AI models to produce desired outputs, whether that’s generating text, creating images, or writing code.

The role requires both technical understanding and creative problem-solving skills. Prompt engineers must understand how different AI models process instructions, experiment with various approaches to achieve specific outcomes, and develop systematic methods for improving AI outputs. They often work closely with other teams to integrate AI capabilities into existing workflows.

A marketing agency might employ prompt engineers to develop templates and processes that enable content creators to generate consistent, on-brand materials using AI tools. This involves understanding both the technical capabilities of AI systems and the practical needs of marketing workflows.

Among surveyed organizations, 15% have hired prompt engineers, with 54% planning future hires.

9. Chief AI officer

Chief AI officers (CAIOs) provide executive-level leadership for organizational AI initiatives, combining technical expertise with strategic vision and leadership skills. These senior executives guide companies through the complex process of integrating AI into business operations while managing risks and ensuring ethical implementation.

The role requires understanding both the technical possibilities of AI and the practical challenges of organizational change. CAIOs must navigate questions of data privacy, algorithmic bias, regulatory compliance, and workforce impact while building teams and establishing governance frameworks for AI development and deployment.

A healthcare system might appoint a CAIO to oversee the integration of AI across multiple departments, ensuring that new technologies improve patient care while maintaining privacy standards and regulatory compliance. This involves coordinating between technical teams, clinical staff, and executive leadership.

Currently, 16% of organizations have hired CAIOs, with 44% planning to add these positions.

10. AI writer

AI writers combine traditional writing skills with expertise in generative AI tools, creating content more efficiently while maintaining quality and brand consistency. These professionals understand how to use AI to generate initial drafts, conduct research, and optimize content for different audiences and platforms.

The role requires strong writing abilities, understanding of SEO principles, and knowledge of AI capabilities and limitations. AI writers must also navigate evolving questions about copyright, attribution, and the ethical use of AI-generated content. They often work closely with marketing teams, content strategists, and legal departments.

A technology company might employ AI writers to produce blog posts, product documentation, and marketing materials. This involves using AI tools to generate initial content, then editing and refining the output to ensure accuracy, brand alignment, and engagement with target audiences.

Among surveyed organizations, 14% have hired AI writers, with 44% planning future hires.

11. AI artist

AI artists use generative AI tools to create visual content, combining artistic vision with technical expertise to produce images, graphics, and other visual materials for marketing, branding, and product development. These professionals understand both creative principles and the technical capabilities of AI image generation systems.

The role requires artistic skills, understanding of design principles, and knowledge of prompt engineering techniques for visual AI systems. AI artists must also navigate questions about copyright, originality, and the ethical implications of AI-generated artwork while working within brand guidelines and project requirements.

A consumer goods company might employ AI artists to rapidly prototype packaging designs, create marketing visuals, and develop product concepts. This involves using AI tools to generate initial designs, then refining and customizing the output to meet specific brand and functional requirements.

Currently, 10% of organizations have hired AI artists, with 41% planning to add these roles.

Strategic considerations for hiring

The rapid evolution of AI technology means that hiring strategies must balance immediate needs with long-term adaptability. Organizations should consider several factors when building AI teams:

Skills over titles: Many AI roles overlap significantly, and the most valuable candidates often possess interdisciplinary skills that span multiple specializations. Focus on core competencies rather than rigid job descriptions.

Internal development: Given the competitive market for AI talent, investing in training existing employees can be more effective than competing for external hires. Many traditional software engineers, data analysts, and domain experts can transition into AI roles with appropriate training.

Ethical considerations: As AI systems become more powerful and pervasive, organizations need professionals who understand not just technical implementation but also ethical implications, bias mitigation, and regulatory compliance.

Closing thoughts

The AI hiring surge reflects a fundamental shift in how organizations approach technology and business operations. Success in this new landscape requires more than just technical expertise—it demands professionals who can bridge the gap between AI capabilities and business needs while navigating the complex ethical and practical challenges of AI implementation.

Companies that invest thoughtfully in AI talent today will be better positioned to leverage these transformative technologies tomorrow. The key is building teams that combine technical excellence with strategic thinking, ethical awareness, and the adaptability to evolve alongside rapidly advancing AI capabilities.