Artificial intelligence is becoming a part of everyday corporate workflows, but many companies struggle to scale their AI projects and achieve tangible results. A key challenge is the inability to upskill teams with AI knowledge and capabilities.
In its 2024 North American IT Skills Survey of 1,015 IT leaders, IDC predicted that 90% of organizations around the world will suffer from an IT skills shortage by 2026, with AI skills being the most in-demand skills. This can cause organizations to experience project delays, quality issues, and lost revenue.
McKinsey & Company’s report, Superagency in the Workplace: Empowering People to Realize the Full Potential of AI, surveyed 3,613 employees and 238 executives across a variety of roles. According to the report, only 1% of companies say they have reached AI maturity. In 2026, the AI skills crisis will become even more urgent as companies move from chatbots and co-pilots to agent-based AI applications.
Business owners and IT leaders need a practical framework to bridge the AI skills gap in their enterprises. Learn what’s important about AI skills, how to build them, and how to evaluate whether your training investments are working.
What are AI skills?
It is common to equate AI skills with technical expertise, but that definition is too narrow. Most employees don’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.
While general AI literacy needs are widespread, advanced expertise remains concentrated in far fewer roles. The following classification describes the AI skills needed across the enterprise.
Four pillars for building AI skills
To effectively build AI capabilities and capture value, companies must focus on four pillars:
1. AI literacy for leaders
Executives and IT leaders set the terms for how AI is used in their organizations. This includes which use cases receive funding, what risks are acceptable, and the controls your business requires. Leaders do not need to understand how the model works in every detail. However, you need to understand two things: where AI creates measurable business value and how to manage it responsibly. Leaders overseeing agent deployment must also define which decisions the agent can make autonomously, which decisions require human approval, and who will be held accountable if the agent makes a mistake.
In this space, AI governance is a key responsibility of leaders. McKinsey’s 2025 State of AI: How organizations are rewiring to capture value surveyed 1,491 respondents across industries and found that executive-level oversight of AI governance is correlated with improved revenue. Governance is also a compliance issue. EU AI law establishes explicit AI literacy obligations for organizations implementing or operating AI systems. NIST’s AI Risk Management Framework establishes AI risk management as an organizational capability.
2. Role and model specific proficiency
General AI training is one of the most common reasons why corporate upskilling programs lose momentum. Teaching all employees the same content wastes time by giving them unnecessary information. AI proficiency should be role-specific and relevant to the AI system being used.
Consider different roles throughout the organization. Each of these roles interacts with AI differently and is responsible for different aspects of the technology. Executives must be adept at prioritizing use cases, managing risk, defining accountability, and setting authority boundaries and authorization for agents. Engineers and data teams will benefit from understanding how to use AI coding assistants, review AI-generated code for security issues, integrate and tune models with agents, monitor deployed systems, and secure multi-step workloads.
Skill requirements also vary depending on the type of AI technology. Consider the following:
- Generative AI. Users need to understand rapid engineering, output validation, citation discipline, and IP- and privacy-secure use.
- Predictive ML. Users need to understand data quality, model monitoring, fairness assessment, and documentation.
- enterprise automation. Users need to understand process mapping, exception handling, and audit trail maintenance.
- Agent AI system. Users need to understand task decomposition, tool orchestration, HITL monitoring, privilege scoping, multi-step evaluation, disaster recovery, and audit logging.
Each model has unique threats, so it is essential to develop these model-specific capabilities. Generative AI can hallucinate and be subject to data breaches, while predictive ML models can suffer from degradation and agent systems can autonomously perform dangerous actions. Different models require different strategies and skill sets.
3. Continuous learning
AI tools, risks, and best practices can change faster than your annual training cycle. However, employees resist training that is lengthy, abstract, or disconnected from work. Programs that incorporate learning into their workflow are more effective. These include short task-based modules, peer review of AI-generated output, internal case reviews if something goes wrong, and retraining if tools or policies change.
A practical approach is to combine external platforms for foundational content with internal development of workflows and application-specific training. Use the following platforms to improve your workforce’s AI skills:
- AWS training and certification
- Coursera
- Data camp for business
- Google Cloud Skill Boost
- IAPP
- LinkedIn Learning
- Microsoft Learn
- multiple vision
- Udemy for business
4. Organizational change management
The introduction of AI creates anxiety for employees. Effective change management is key to addressing these issues. It has three components. First, directly address employee concerns. Employees will be able to adapt faster if they understand what tasks and new workflows will be automated rather than replaced by AI. Second, managers decide whether employees will use AI output in production, so redesign their routines before scaling up end-user training. Third, for agent deployment, redesign the work itself. Define which tasks are assigned to agents and which tasks are assigned to humans, and how exceptions and escalations move between agents.
AI training implementation strategy
The most common mistake is introducing tools and assigning courses without changing work routines, incentive structures, or workflow ownership. Before choosing a training platform or vendor, leaders need to be able to answer the following questions:
- Where is AI already in use? Is it licensed AI or shadow AI?
- Which workflows have the most potential value and the highest risk?
- What is your organization’s current governance structure?
- What does visible success look like after six months?
Without clear answers, training programs may target the wrong roles, unfold in the wrong order, or cover the wrong content.
Establish a baseline that includes a skill inventory for roles and workflows. Governance maturity checks such as approved tools, data policies, audit trails, and scope of agent privileges. A workflow map to identify tasks that can be addressed with AI.
For most companies, a hybrid model works well. Companies can source basic AI literacy training externally at scale and build the specific organizational resources they need internally. This includes tool-specific instructions that require workflow-specific training, policy compliance, governance routines, agent supervision procedures, and approval flows and permission boundaries. Human resources, IT, and business leaders must share responsibility for these efforts. Collaborate with outside experts as necessary.
We design and deliver programs that align with the enterprise AI roadmap outlined below.
- Pilot stage. The pilot will focus on high-value use cases and workflows, access to approved tools, and end-user readiness training and will last several weeks. A successful pilot results in measurable improvements in cycle time or throughput and zero policy incidents.
- scale stage. As you scale, it’s important to identify in-depth role-based training opportunities and establish governance consistency and repeatable use cases. The scaling phase begins 3 to 6 months after deployment. Measure scaling success by a company’s depth of implementation, stability of controls, and repeatable and measurable results.
- Institutionalization stage. To further incorporate AI programs, organizations continually evaluate and improve their monitoring and auditing capabilities, agent-based workflow controls, and continuous learning infrastructure. Success metrics for this phase include AI policy compliance, quantified business value, risk mitigation, and agent audit readiness. This ongoing phase begins after 6 months.
Five challenges for AI skills programs
Organizations building AI skills programs at scale will face common constraints, including:
- Lack of in-house expertise. this A dependency on an external provider is created. However, external partnerships can speed up the time to competency, especially for organizations in the early stages of their AI journey. Key partner selection criteria include role-based learning paths, validated competency assessments, updated content for tools, and coverage of AI governance requirements.
- worker anxiety. Clear and specific communication reduces employee anxiety. Employees who understand the scope of an AI project, especially the tasks that AI will augment rather than replace, will adapt faster and be more motivated to upskill.
- Budget and resource pressures. when Companies may not be able to effectively measure ROI, reducing the budget and resources needed for effective AI implementation. Consider that the wage premium for an AI-skilled workforce is 56%, according to PwC analysis. This makes hiring AI talent an expensive option. Adequate budgets for AI upskilling should therefore be a priority.
- Security, compliance, and agent-specific risks. Each of these requires dedicated training. Instant insertion, insecure output processing, and data leakage are common risks in generative AI deployments. Agent systems introduce additional risks such as goal hijacking, tool manipulation, privilege escalation, and insecure autonomous execution. Training must address these threats in policy and practice.
- Keep your training current and effective. this This is an ongoing effort. Business and training leaders should use modular content so that components can be updated independently. Also, tie update 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 in the US, Europe, and Asia Pacific. Kashyap is the co-author of three books: “Practical Artificial Intelligence,” “Artificial Intelligence for Lawyers,” and “AI Governance and Regulation.”
