Artificial Intelligence (AI) and Generative AI (GenAI) are transforming the way businesses operate. These technologies are driving innovation and improving efficiency across many industries. However, despite the vast potential of AI, many organizations face challenges in fully benefiting from these technologies. A recent Gartner survey revealed that a lack of skilled staff is one of the key barriers to successfully implementing AI. As AI adoption continues to grow, this skills gap is expected to widen.
This challenge will increase as AI and GenAI tools become more widely used. While AI creates new roles and changes existing ones, it also brings an urgent need for reskilling and upskilling employees. In this article, we will explore the impact of AI adoption, the new roles emerging to support AI, and how organizations can close the skills gap to remain competitive in an AI-driven world.
The Current State of AI Adoption
In 2024, AI adoption has grown rapidly. About 72% of organizations now use AI in at least one business function. AI is helping businesses drive innovation, improve efficiency, and create new opportunities. It is also playing a key role in automating tasks, analyzing large data sets, and providing valuable insights, which makes it crucial for staying competitive.
Despite this growth, many organizations are finding it hard to find the right talent to manage and improve their AI systems. The use of AI and GenAI requires specific skills that are in short supply. Investing in new talent has become a top priority. Unlike traditional roles, AI-related jobs need specialized skills that are not widely available, making the talent shortage even more critical.
The Skills Gap in AI and Its Impact on Organizations
The shortage of skilled AI professionals is one of the biggest challenges organizations face when trying to integrate AI into their operations. Many organizations struggle to hire and retain employees with the expertise needed to manage AI systems. This shortage can slow down AI implementation, limit innovation, and affect a company’s ability to compete in the marketplace.
There is no standard size for an AI team, as it depends on factors like the complexity of AI projects, the industry, and the company’s goals. However, it is clear that the need for AI talent is growing, and organizations that do not address this gap risk falling behind.
Emerging AI Roles Organizations Should Consider
While roles like data scientists, data engineers, and machine learning engineers are already crucial, the rapid development of AI is creating new and specialized roles. As AI adoption increases, organizations should consider adding the following positions, according to Gartner, to ensure they have the necessary expertise:
Model Manager
A model manager is responsible for the development, deployment, and maintenance of AI models. They ensure the models are properly trained, tested, and aligned with business goals.
Model Validator
Model validators test AI models to ensure accuracy, fairness, and compliance with regulations. They help reduce the risks of biased or incorrect AI results.
Knowledge Engineer
Knowledge engineers design and maintain the systems that AI models use to make decisions. They work with experts to encode knowledge that helps improve AI decision-making.
Analytics Engineer
Analytics engineers build the infrastructure needed to support AI-driven analytics. They ensure that data pipelines are robust and scalable for handling large volumes of data.
AI Architect
AI architects design the overall structure of AI systems. They work with IT teams and other stakeholders to create an AI strategy that supports the company’s long-term goals.
AI Risk and Governance Specialist
As AI becomes more common, AI risk and governance specialists create policies to ensure AI systems are ethical, transparent, and compliant with regulations.
AI Product Manager
AI product managers oversee the development of AI-powered products and services. They ensure that AI solutions deliver value to customers and meet market needs.
UX Designer for AI
UX designers for AI create user-friendly interfaces for AI systems. They make sure AI technology is easy to use and accessible to non-technical users.
Decision Engineer
Decision engineers design AI systems that help with decision-making. They work with business leaders to create systems that provide actionable insights.
AI Developer
AI developers write the code for AI models and applications. They work with data scientists to implement machine learning algorithms and integrate AI into existing systems.
Reskilling and Upskilling: Addressing the AI Talent Shortage
As AI creates new roles and reshapes current ones, reskilling and upskilling employees have become important strategies for addressing the skills gap. Instead of relying solely on external hires, many organizations are investing in training their existing workforce in AI-related skills such as data analysis, machine learning, and AI model management.
Upskilling involves providing advanced training to employees who already have some AI knowledge. This could include training in areas like AI ethics, governance, or advanced machine learning techniques. By continuously developing employee skills, organizations can stay competitive and have the talent needed to handle AI technologies.
Partnering with Educational Institutions and AI Training Programs
To address the AI skills gap, organizations can also partner with educational institutions and AI training programs. Many universities and online platforms now offer AI courses and certifications. These programs equip individuals with the skills needed for AI-related roles. By collaborating with these institutions, companies can gain access to a pipeline of skilled AI professionals.
Some organizations are also creating internal AI academies or training programs. These programs allow employees to gain hands-on experience with AI technologies, which they can then apply directly in their roles.
Conclusion
As AI adoption accelerates, the demand for skilled AI professionals will continue to grow. While the skills shortage is a major challenge, it is also an opportunity for businesses to invest in their workforce through reskilling and upskilling initiatives. By building a strong pipeline of AI talent and keeping up with AI trends, companies can set themselves up for success in an AI-driven future.
AI deployment is more than a technological change; it is a workforce transformation. To fully unlock the potential of AI, organizations must close the skills gap by investing in talent, building internal capabilities, and promoting continuous learning. The future of business is AI-driven, and those who adapt will thrive in this new era of innovation.