Advances in generative AI have revolutionized work as we know it. From copilots (like MS Copilot), integrated application AI assistants (like Salesforce’s Agentforce), AI agents, and intelligent process automation, AI-powered enterprise systems are transforming how we operate and what we expect from our workforce.
Yet, while AI transformation is a strategic priority for most organizations and dominates the conversation for many C-suite leaders and executives, many are falling short of realizing ROI. From research, writing, and coding, GenAI is already handling knowledge-based tasks that previously were manual, time-consuming processes. These knowledge workers make up 40% of the US workforce and require contextual, role-tailored upskilling to adapt to new AI-led tasks quickly.
One key culprit is a widening AI skills gap and the need to support employees when asked to learn an entirely new way of working. Employees across departments lack the foundational knowledge, contextual understanding, and confidence to use AI effectively in their day-to-day workflows. This impact is vast and difficult to accept fully. LinkedIn’s Future of Jobs reports that 40% of core skills will change due to GenAI, and the World Economic Forum estimated that AI and automation will displace 85M jobs by the end of the year.
Transformation won’t happen without user adoption, and adoption won’t happen without upskilling. BCG reports that organizations are currently allotting 1.5% of their total budget to AI upskilling, which is a start, but clearly requires more attention and resources to support such a massive shift in how employees achieve their goals and effectively manage their responsibilities.
In this article, we’ll explore how organizations can close the AI skills gap with a modern, embedded approach to employee training—one that aligns with real work, accelerates time-to-productivity, and unlocks AI-powered business outcomes.
What Is the AI Skills Gap?
According to the latest Thomson Reuters Future of Professionals Report, 77% of professionals believe AI will greatly impact their careers. While that impact is mostly seen as positive, a critical gap exists between AI’s growing presence and employee readiness.
This AI skills gap encompasses:
- A lack of foundational knowledge in AI and data literacy.
- Inadequate familiarity with AI-powered tools and their use cases.
- A shortage of hands-on experience using AI in real-world scenarios.
- Persistent discomfort or resistance toward AI’s role in the workplace.
Institutions like Reuters estimate that 70% of workers will require reskilling to meet this challenge. IBM places that number at 40%. The gender disparity is also alarming—264 million fewer women are connected online than men, per the Digital Cooperation Organization.
The pressure is mounting for enterprise leaders to close this divide and build an AI-ready workforce.
In response, leaders recognize a serious need to train their employees in foundational AI literacy and related conversations around AI ethics. Companies like IBM are deploying initiatives like SkillsBuild in support, offering free AI courses and other resources for employees and organizational leaders.
Why Closing the AI Skills Gap Is a Business Priority
- Failure to achieve ROI from AI investments: Organizations are investing heavily in AI, but without employee adoption, those tools collect dust and become failed AI investments. Upskilling ensures AI tools are used to their full potential, safeguarding ROI.
- Sluggish innovation: AI-powered decision-making, productivity, and automation unlock new levels of efficiency. But without reskilled teams, progress slows, and opportunities are lost.
- Widening workforce inequality: Neglecting AI upskilling will deepen digital divides and disadvantage underrepresented groups. Closing the gap fosters inclusion and reduces bias by ensuring AI tools reflect diverse perspectives.
- Low adoption, low impact: Employees won’t adopt what they don’t understand. Investing in personalized, role-specific training increases usage, boosts satisfaction, and amplifies the business value of AI.
Key AI Skills Employees Need Today
Enterprise teams don’t need to become data scientists. But they do need a core set of competencies to work with AI confidently and responsibly:
- Role-specific AI use case: Employees must understand how AI can enhance their unique workflows. For example, marketers using AI for personalization, HR teams using it for intelligent candidate screening, etc.
- AI fluency and data literacy: A working understanding of how AI operates, including data hygiene, output interpretation, and critical evaluation of bias.
- Prompt engineering: The ability to structure effective prompts to guide AI tools toward desired results.
- Machine learning basics: Familiarity with how models learn and evolve to inform responsible usage.
- Responsible AI awareness: Grounding in AI ethics, including fairness, privacy, and accountability.
How to Train Employees to Use AI
The extreme shortage of AI skills can be overcome through focused learning and development initiatives. Here are some effective steps to take to eliminate the AI skills gap within your organization:
Assess current skills and AI maturity
Determine your team’s existing AI skills through surveys and formal assessments, if possible. From there, you can map out your team’s current AI-powered tech stack to determine the degree of your internal AI skill gap. This information will serve as the foundation for your AI development plan.
Define AI roles and learning pathways
Next, outline the different roles on your team or organization and determine whether they fall into the categories of AI user, AI collaborator, or AI Builder.
- AI Users use AI as a tool to complete specific aspects of their work.
- AI Collaborators work in partnership with AI to fulfill responsibilities. Often working back and forth with AI agents to engage in complex tasks.
- AI Builders have a direct role in calibrating AI tools and developing AI procedures and protocols for use across an organization or team.
Use these role paths with your AI readiness assessment to map out each role’s AI skill gaps to develop custom learning paths.
Develop or curate role-specific training programs
Now, it’s time to establish measurable goals and benchmarks for improvement. Develop AI learning plans to bridge the AI skills gaps for each role. Base these plans on learning needs, AI roles, and established goals. Review existing L&D programs to determine how AI upskilling can be incorporated. Take advantage of AI personalization features in eLearning platforms to automate learning path customization and facilitate continual optimization.
Leverage internal SMEs and external learning partners
The benefits of closing the AI skills gap are extensive, but many organizations, especially smaller ones, may not have many AI experts on staff. Tap into the knowledge of AI-savvy team members and, if possible, enlist external learning partners to help develop learning content and facilitate training.
Incorporate real-world, use-case-driven learning
Relevant, real-world learning experiences help learners retain more information and begin using what they’ve learned more quickly. Build real-world examples and immersive experiences into AI upskilling programs to optimize learning with valuable and relevant content.
Embed user support in the flow of work
Use in-app messaging software like a digital adoption platform (DAP) to provide relevant, timely guidance as employees acclimate to new AI tools. Tools like Whatfix DAP allow L&D teams to easily embed contextual guidance and step-by-step tutorials into employee tasks and daily workflows. This embedded, “learning in the flow of work” approach to training will help employees learn to use new AI tools according to their needs, improving the training experience, supporting your employees with contextual experiences that don’t impact productivity, and empowering your organization to achieve full ROI of AI technology investments.
With Whatfix, you can govern how employees use GenAI, guide users on how to best use AI tools, alert them to new use cases or process changes, and analyze how employees are engaging with them. You can learn more about how Whatfix is driving AI adoption here.
Track impact and iterate on learning programs
Throughout the development and implementation of your training program, refer back to established goals and benchmarks and use analytics software to assess the effectiveness of upskilling programming. Use these data to monitor progress and improve training experiences continually.
Common Barriers to AI Upskilling
Providing effective support as your teams work to eliminate AI skill gaps can be challenging. Here is a list of some of the most common barriers to AI upskilling and solutions for overcoming them:
| Barrier | Solution |
| Lack of executive buy-in | Present the program as a strategic enabler of AI ROI and competitive advantage. |
| Fear of AI replacing jobs | Reinforce AI as a tool for augmentation, not automation. Position AI adoption as career growth and skill development. |
| No structured learning path | Create phased, role-specific plans tied to measurable business outcomes. |
| One-size-fits-all training | Customize training by function and workflow to maximize relevance and retention. |
| Lack of hands-on experience | Provide scenario-based learning, application sandboxes for simulation training, and in-app guidance and embedded workflow support within enterprise AI applications. |
Real-World Examples of AI Upskilling at Scale
If you still need inspiration to invest in AI skills training for your organization, here are some real-life examples of AI upskilling success:
Airline industry
Airline companies use artificial intelligence to improve flight operations, ensure safety, and provide better customer service.
German airline Lufthansa worked with IBM to incorporate AI into customer service and other business operations. By helping employees hone AI and data analytics skills, Lufthansa was able to streamline workflows and empower its data scientists to develop innovative new AI tools for internal use.
Healthcare
Today’s healthcare industry looks very different from what it was even ten years ago. Artificial intelligence has been integrated into administrative tasks, diagnostic tools, and patient care.
Institutions like St. John’s Hospital are taking a proactive approach to healthcare AI by implementing robust AI upskilling programs, facilitating the integration of AI tools into patient care. As technology evolves and healthcare worker shortages continue, AI upskilling will be used to fill gaps, reduce the burden on workers, and keep patients safe and healthy.
Shipping Industry
Shipping companies often use AI to manage complex navigation plans, fuel consumption, and cargo optimization. CMA CGM, a French shipping company, supports employees with an AI skills accelerator program, training C-suite leaders first to enable top-down transmission of AI knowledge.
Human Resources
Many common human resources software, like applicant tracking systems, onboarding and training software, and performance management tools, include artificial intelligence features. Organizations can optimize AI use in this area by providing HR teams with thorough training not only on AI skills, but the ethical considerations around using AI in hiring practices and people management.
AI Upskilling Clicks Better With Whatfix
The digital revolution shows no signs of slowing, and business leaders must adapt. Prepare your teams for the AI-powered workplace with intuitive guidance and personalized support.
Whatfix DAP empowers employees to upskill in real-time by embedding intuitive guidance directly into the software they use every day. Through interactive walkthroughs, contextual tooltips, and on-demand self-help, Whatfix reduces learning friction and accelerates AI-driven tools and processes adoption.
Beyond training delivery, Whatfix Analytics offers no-code event tracking and AI-powered insights, enabling leaders to see how employees interact with software. These insights help optimize training programs, eliminate workflow bottlenecks, and drive measurable productivity improvements.
In a world where upskilling is critical to staying competitive, Whatfix gives organizations the edge.
Ready to learn more about Whatfix for AI adoption? Request a Whatfix expert-led demo today!
The post How to Close the AI Skills Gap With Upskilling appeared first on The Whatfix Blog | Drive Digital Adoption.