What is the AI skills gap?

The AI skills gap is the mismatch between the AI capabilities organizations need and the skills employees currently have. It is growing as generative AI, AI agents and automation change how work gets done.

The skills gap includes technical expertise, such as machine learning (ML), software development, data science, natural language processing (NLP), prompt engineering and model governance. It also includes broader workplace capabilities, such as AI literacy, change management, strategic leadership and the ability to work effectively with generative AI tools and AI agents.

AI is moving from specialized technical teams into everyday business workflows. Generative AI, large language models (LLMs) and popular AI assistants (e.g., ChatGPT) have made artificial intelligence more visible to general users. At the same time, enterprise AI systems are becoming more capable; AI agents can complete multistep tasks, connect to business applications and support decision-making across departments.

This need for AI across the enterprise means that the AI skills gap shows up in everyday moments. For example, an employee is asked to use a generative AI assistant but is unsure what company data can be entered. A manager knows that AI can improve productivity but does not understand which workflows are appropriate for automation. A software developer starts by using an AI coding tool but still needs to review generated code for security, accuracy and maintainability. A business leader approves an AI initiative but lacks a reliable way to measure business value.

The result is a broader definition of workforce readiness. Organizations still require data scientists, machine learning engineers and software developers who can build and deploy AI systems. Organizations also need employees across functions who can use AI tools responsibly, evaluate outputs, understand risks and adapt as work changes.

According to IBM Institute for Business Value research, only 25% of workers regularly use AI as part of their jobs, even though 86% of CEOs believe that their employees have the skills to collaborate with AI. The same research found that 83% of CEOs believe AI success depends more on people’s adoption than on the technology itself.

There is an AI skills gap because AI capabilities, business expectations and job requirements are changing at the same time.

Many organizations are still determining how artificial intelligence should be used in day-to-day work. Some teams use generative AI for drafting, summarization, customer support or software development. Some teams are experimenting with AI agents that can perform tasks across systems. Other teams are building machine learning models, using predictive analytics or automating workflows.

These changes create several overlapping skills gaps:

• The technical gap: Organizations need people who understand data engineering, algorithms, machine learning, model evaluation, natural language processing, cybersecurity and software development. They also need specialists who can build, tune, deploy, monitor and govern AI systems.
• The practical gap: Employees who are not technical specialists still need to understand how AI applies to their roles. They need to know how to use tools safely, how to evaluate AI outputs and when to escalate a task to a human expert.
• The managerial gap: AI adoption often changes workflows, roles, performance expectations and decision rights. That makes change management and leadership important parts of AI readiness.

The World Economic Forum’s Future of Jobs Report 2025 found that employers expect 39% of workers’ core skills to change by 2030. The report also found that 50% of the workforce completed training as part of long-term learning strategies, up from 41% in 2023.

IBM’s 2026 CEO study also points to the scale of workforce change. Surveyed CEOs said that they expect 53% of employees to need upskilling between 2026 and 2028 to perform their current roles more effectively, while 29% will need to reskill for different roles.

The impact of AI on workforce skills falls into several areas.

Many employees now encounter AI inside tools they already use, such as productivity software, customer service platforms, analytics tools, HR systems and software development environments. Workers might need to know how to give more precise prompts, provide context to a model or decide whether an AI-generated answer is reliable. These tasks require AI literacy and critical evaluation, not necessarily advanced coding skills.

Technical teams are also seeing changes in how work gets done. Software development teams can use generative AI coding assistants (such as IBM Bob) to generate code, document functions, explain legacy code and create unit tests.

Software development expertise remains essential, especially for helping broader teams review, test, secure and maintain generated code.

AI systems can analyze large amounts of data, identify patterns and recommend actions. In some cases, AI can support decisions that used to be too time-consuming to make manually. But it can create new risks if the recommendation is not explainable or appropriate.

This increases the importance of governance, ethics and accountability. Employees need to understand when AI can inform a decision, when it should not be used and when human review is required.

AI adoption is no longer only the responsibility of technical teams. Leaders in HR, finance, operations, marketing, legal, risk and customer experience increasingly need enough AI literacy to understand how the technology affects their domain.

“The CEOs delivering real results from AI transformation aren’t just deploying AI faster, they’re redesigning their organizations to bring together the best people with the best technology,” says Mohamad Ali, Senior Vice President of IBM Consulting.

The skills needed for AI vary by role, industry and level of responsibility. In general, they fall into several categories.

AI literacy is the ability to understand, use and evaluate artificial intelligence systems. It includes knowing what AI can do, what its limitations are and how to use it responsibly.

Prompt engineering is the practice of giving AI systems clear, useful instructions. For most employees, this can mean learning how to frame a task, provide context, specify the desired format and ask follow-up questions. For more advanced users, prompt engineering can involve designing reusable prompts, building prompt libraries and combining prompts with approved data sources.

Critical evaluation is the ability to assess whether an AI output is accurate, relevant, complete and appropriate. Workers need to know how to check sources, identify bias and decide whether a human expert should review the result.

AI governance and ethics skills help organizations use AI responsibly. These skills include understanding data privacy, bias, fairness, transparency, explainability, auditability, regulatory requirements and human oversight. Toolkits such as IBM watsonx.governance might help organizations establish responsible AI use.

Some roles require deeper technical expertise. These skills are especially important for data scientists, AI and ML engineers, software developers, MLOps specialists and cybersecurity professionals.

High-demand AI skills include:

• Data science and statistical analysis
• Machine learning and deep learning
• Natural language processing systems
• Model development, testing and evaluation
• Data engineering and data quality management
• Software development and API integration
• Retrieval-augmented generation
• Model monitoring and MLOps
• Cybersecurity and privacy engineering
• Algorithm and model behavior analysis

Strategic leadership is the ability to connect AI initiatives to business goals. This includes deciding which tasks should be automated, which should be augmented and which should remain human directed. It also includes funding the right upskilling program, supporting career development and building a future-ready workforce.

AI agents are artificial intelligence systems that can perform tasks with a degree of autonomy. Unlike basic chatbots, AI agents can plan steps, use tools, interact with software systems and more. This affects the AI skills gap because employees might no longer only ask AI systems for information. They may also supervise AI systems that initiate actions.

In practice, this creates new skill requirements. Employees may need to understand how to:

• Define agent goals
• Set boundaries,
• Review actions
• Monitor performance
• Decide when human approval is required

For example:

• Human resources: An AI agent might answer employee questions, retrieve policy information, schedule interviews or start onboarding tasks. HR teams need to know when the agent should escalate sensitive issues.
• Finance: An AI agent might gather enterprise data, summarize budget variances or draft forecast explanations. Finance teams still need to validate the data, assumptions and conclusions.
• Customer service: An AI agent might classify requests, retrieve account details, recommend resolutions or initiate refunds and service tickets. Employees need to know which actions can be automated and which require approval.
• Supply chain operations: An AI agent might monitor inventory, identify delays or recommend alternate suppliers and shipping routes. Operations teams need to understand the data behind the recommendation before acting.

These examples show why agentic AI expands the skills conversation. The issue is not only whether employees can use AI. It is whether they can work within systems where humans and AI agents share responsibility for parts of a workflow.

Bridging the AI skills gap typically involves several key steps:

AI readiness is the degree to which an organization has the people, processes, data, governance and technology needed to use artificial intelligence effectively.

An AI readiness assessment can examine:

• How AI is currently being used across teams
• How AI performance and risk are measured
• Which roles are most exposed to AI-driven change
• Which workflows are candidates for automation or augmentation
• What tools employees already use
• Whether employees understand data privacy and security
• Where technical expertise is concentrated
• Whether managers can support AI-enabled change
• Which workers need upskilling or reskilling

This helps organizations avoid generic training that does not match their actual needs.

To build AI literacy, organizations can start with broad training on AI basics, generative AI, LLMs, automation and AI agents. These programs create a shared foundation for using AI tools safely and effectively.

Different roles need different levels of skills development. A customer service team might need training on AI-assisted support workflows. A software development team might need training on generative AI coding tools, secure code review and test automation. Role-specific learning paths can make upskilling more practical and relevant.

Employees are more likely to build lasting AI skills when training is connected to their actual work. This can include sandbox environments, guided exercises, peer workshops, office hours, internal communities of practice and hands-on projects.

Some employees will need reskilling, not just upskilling. Reskilling prepares workers for different roles as tasks are automated, reorganized or augmented by AI. This training can help employees move into new roles.

Managers and functional leaders help employees understand expectations for AI use. They can help identify useful workflows, make time for learning and evaluate how AI-supported work should be measured. They also need enough AI literacy to make decisions about strategy, risk, staffing and investment.

Organizations should match AI skills development to their goals. Broad AI literacy programs can include internal academies, certifications and guided learning paths. Technical teams may need platforms for model development, deployment, monitoring and governance. Teams adopting AI agents may need orchestration tools to manage workflows, approvals and performance. Software teams may benefit from AI coding assistants. The most useful tools align with clear use cases, workforce needs and responsible AI practices.

Organizations might also consider low-code and no-code tools where appropriate. Low-code and no-code platforms can make AI tools more accessible to employees without extensive programming experience. These platforms allow business users to build applications, automate workflows or configure AI agents through visual interfaces and prebuilt components. They do not eliminate the need for technical expertise, governance or oversight, but they can help more employees participate in AI adoption while technical teams focus on complex development and integration work.

Organizations can measure AI skills development through both learning metrics and business metrics. Learning metrics might include course completion or certifications. Business metrics might include productivity, quality, customer satisfaction or error reduction.

The AI skills gap will continue to evolve as AI systems become more capable and more embedded in work. Skills that are valuable today may need to be updated as tools, regulations, workflows and business expectations change.

That makes continuous learning programs an important part of workforce readiness. Organizations may need to regularly update training materials, review governance policies, reassess skills needs and revise career development paths. Employees need ongoing opportunities to practice with new tools, ask questions and develop confidence over time.

Strategies for closing the AI skills gap require more than hiring technical talent. Organizations need AI literacy, role-based training, responsible AI practices and continuous learning across the workforce. And they need to connect those skills effectively to overall business goals.