How to build AI skills across your workforce

What are AI skills?

It's common to equate AI skills with technical expertise, but that definition is too narrow. Most of the workforce doesn't need to build or fine-tune AI models. Rather, most employees just need to be able to use AI tools effectively, safely and reliably.

The need for general AI literacy is broad, while advanced specialist expertise remains concentrated in a much smaller share of roles. The following taxonomy describes the AI skills needed across the enterprise:

4 pillars for building AI skills

To effectively build AI capabilities and capture value, enterprises need to focus on the following four pillars:

1. AI literacy for leaders

Executive and IT leadership set the terms for how their organizations use AI. This includes which use cases receive funding, which risks are acceptable and which controls the business requires. Leaders don't need to understand the minutiae of how a model works. But they must understand two things: where AI creates measurable business value and how to govern AI responsibly. Leaders overseeing agentic deployments must also define which decisions agents can make autonomously, which require human approval and who's accountable when an agent makes a mistake.

In this area, AI governance is the key responsibility of leadership. McKinsey's 2025 "The state of AI: How organizations are rewiring to capture value," survey of 1,491 respondents across industries found that executive-level oversight of AI governance correlates with higher bottom-line impact. Governance is also a compliance matter. The EU AI Act creates an explicit AI literacy obligation for organizations that deploy or operate AI systems. NIST's AI Risk Management Framework establishes AI risk management as an organizational capability.

2. Role- and model-specific proficiencies

Generic AI training is one of the most common reasons enterprise upskilling programs lose momentum. Teaching every employee the same content wastes time by giving them unnecessary information. AI proficiency should be role-specific and related to the AI systems in use.

Consider the various roles throughout an organization. Each of these roles will interact with AI differently and be responsible for different facets of the technology. Executives require proficiency in prioritizing use cases, governing risk, defining accountability and setting agent authority boundaries and approvals. Engineers and data teams would benefit from understanding how to use AI coding assistants, review AI-generated code for security issues, integrate and orchestrate models with agents, monitor deployed systems and secure multistep workloads.

Skill requirements also vary by the type of AI technology. Consider the following:

• Generative AI. Users should understand prompt engineering, output verification, citation discipline, and IP- and privacy-safe use.
• Predictive ML. Users should understand data quality, model monitoring, fairness evaluation and documentation.
• Enterprise automation. Users should understand process mapping, exception handling and audit trail maintenance.
• Agentic AI systems. Users should understand task decomposition, tool orchestration, HITL supervision, permission scoping, multistep evaluation, failure recovery and audit logging.

It's essential to develop these model-specific proficiencies because each model presents unique threats. Generative AI can hallucinate and be susceptible to data leakage, while predictive ML models can experience degradation and agentic systems can perform unsafe actions autonomously. Different models require different strategies and skillsets.

3. Continuous learning

AI tools, risks and best practices can change faster than annual training cycles. However, employees will resist training that's long, abstract or disconnected from their work. Programs that embed learning into the workflow are more effective; these include short task-based modules, peer review of AI-generated outputs, internal case reviews when something goes wrong and retraining when tools or policies change.

A practical approach is a mix of external platforms for foundational content and internal development of workflow- and application-specific training. Use the following platforms to improve employees' AI skills:

• AWS Training and Certification
• Coursera
• DataCamp for Business
• Google Cloud Skills Boost
• IAPP
• LinkedIn Learning
• Microsoft Learn
• Pluralsight
• Udemy for Business

4. Organizational change management

AI rollouts create anxiety among employees. Effective change management is key to countering these issues. It has three components. First, address workforce anxiety directly. Employees will adapt faster if they understand which tasks AI will automate -- and not replace -- and what the new workflow will look like. Second, redesign manager routines before scaling end-user training, because managers determine whether employees use AI outputs in production. Third, for agentic deployments, redesign the work itself: define which tasks are for agents, which are for humans, and how exceptions and escalations move between them.

AI training implementation strategies

The most common failure is deploying tools and assigning courses while leaving work routines, incentive structures and workflow ownership unchanged. Before choosing a training platform or vendor, leaders should be able to answer the following:

• Where is AI already in use, and is it sanctioned or shadow AI?
• Which workflows have the highest potential value and the most risk?
• What's the organization's current governance posture?
• What would measurable success look like at six months?

Without clear answers, training programs can target the wrong roles, deploy in the wrong sequence or cover the wrong content.

Establish a baseline that includes a skills inventory of roles and workflows; a governance maturity check, such as approved tools, data policies, audit trails and agent permission scope; and a workflow map to identify AI-addressable tasks.

For most enterprises, a hybrid model works well. Businesses can procure foundational AI literacy training externally at scale and build any specific organizational resources they need internally. This includes workflow-specific training, policy compliance, governance routines, agent supervision procedures, and tool-specific instruction that require approval flows and permission boundaries. HR, IT and business unit leadership must share ownership of these efforts. Partner with outside experts as needed.

Design and deliver the programs aligned to the enterprise AI roadmap outlined below:

• Pilot phase. Pilots focus on high-value use cases and workflows, approved tool access and end-user readiness training and last several weeks. Successful pilots achieve measurable cycle-time or throughput improvements and zero policy incidents.
• Scale phase. When scaling, it's important to identify in-depth, role-based training opportunities, establish governance consistency and repeatable use cases. The scaling phase begins three to six months into deployment. Measure scaling success by depth of enterprise adoption, stability of controls, reproducible and measurable outcomes.
• Institutionalization phase. To further embed AI programs, organizations continuously evaluate and improve oversight and audit capabilities, agentic workflow controls and continuous learning infrastructure. Success indicators in this phase include AI policy adherence, quantified business value, risk reduction and agent audit readiness. This continuous phase begins after six months.

5 challenges of AI skilling programs

Organizations building an AI skills program at scale will encounter a common set of constraints, including the following:

1. Lack of internal expertise. This creates dependency on external providers. However, external partnerships can reduce time to competency, particularly for organizations early in their AI journey. Key partner selection criteria include role-based learning paths, verified competency assessments, content updated for tools and coverage of AI governance requirements.
2. Workforce anxiety. Clear, specific communication can reduce workforce anxiety. Employees who understand the scope of AI projects, particularly which tasks AI will augment and not replace, adapt faster and are more motivated to engage in upskilling.
3. Budget and resource pressure. When businesses fail to measure ROI effectively, they can undercut the budget and resources required for effective AI deployments. Consider that an AI-proficient workforce commands a 56% wage premium, according to PwC analysis. This makes hiring AI talent an expensive option; therefore, adequate budget for AI upskilling is a priority.
4. Security, compliance and agent-specific risks. Each of these requires dedicated training attention. Prompt injection, insecure output handling and data leakage are common risks in generative AI deployments. Agentic systems introduce additional risks, such as goal hijacking, tool manipulation, privilege escalation and unsafe autonomous execution. Training must address these threats in policy and practice.
5. Keeping training current and effective. This is an ongoing effort. Business and training leaders should use modular content so they can update components independently. Also, tie refresh triggers to tool changes. Measure AI effectiveness through outcomes, not completion rates.

Kashyap Kompella, founder of RPA2AI Research, is an AI industry analyst and advisor to leading companies across the U.S., Europe and the Asia-Pacific region. Kashyap is the co-author of three books, Practical Artificial Intelligence, Artificial Intelligence for Lawyers and AI Governance and Regulation.