The recently released 2024 Annual Work Trend Index from Microsoft and LinkedIn surveyed 31,000 people in 31 countries and revealed that AI is already changing the world of work. Fully 75% of knowledge workers now use AI at work, citing benefits like saving time, becoming more creative, and enjoying their work more. The report also found that while 79% of leaders agree AI adoption is critical to remain competitive, 60% of leaders worry their organisation lacks a plan and vision to implement AI.

"The urgency every company is feeling to build AI skills gives talent development pros a new seat at the table," says Aneesh Raman, workforce expert at LinkedIn. "The biggest thing that will set any organisation up for success is building a culture of continuous learning."

This moment requires both thinking differently and a new plan for upskilling your employees. Talent development teams must help their organisations take a strategic, personalised approach to learning AI skills across levels.

LinkedIn has developed a framework to upskill employees across a wide range of roles and levels of proficiency, from project managers, marketers, and administrative assistants requiring introductory AI knowledge to engineers who require highly technical skills to build and deploy company-specific AI systems. LinkedIn has also curated learning paths featuring more than 50 LinkedIn Learning courses that can help lead organisations’ AI upskilling efforts.

The framework for AI upskilling in this new era of work focuses on AI skills, which will be critical for nearly every role, but the level of AI readiness will vary: An entry-level sales representative, seasoned marketing professional, data analyst, and engineer will all need different skills to incorporate AI into their day-to-day work.

It can be complex to navigate these different use cases; that's why the LinkedIn Learning team crafted a five-level framework with a structured learning model to upskill organizations on AI. The framework was built utilizing insights from LinkedIn's one billion members across 200 regions and countries; consulted LinkedIn Learning instructors who are some of the top AI experts in their field; and validated the model with LinkedIn's top AI engineering experts across R&D and IT.

The framework is organised into five levels of AI expertise. The first two contain foundational AI knowledge that all employees will need, while the top three levels require deep technical skills and specialised expertise: level 3 is designed for business power users, developers, and data engineers; level 4 for machine learning engineers; and level 5 for cloud specialists, cybersecurity professionals, data scientists, researchers, and those preparing for tech certifications.

Some of the levels of upskilling should happen concurrently. For example, you may introduce general AI learning to your whole organisation while you launch hands-on skill building to certain functional roles and deep specialisation to highly technical engineers in parallel. While rolling out AI upskilling can feel overwhelming, your IT and tech teams, internal AI experts, and LinkedIn's courses and insights will all help organisations thrive in this new era of work.

Let's take a deeper dive into what upskilling looks like at each level of employee AI expertise.

Level 1 / Understanding - Building an AI foundation for all employees

To begin an organisation's AI journey, every employee - from business leaders to creatives to tech professionals - will need to understand AI basics.

Employees need to understand what AI can help with (first drafts of emails, finding patterns in data) and what it can't help with (human-to-human connection, relationship building). From there, it's important to share a consistent message around how AI should be used based on the organisation's own goals and guardrails, including guidelines for responsible AI use.

"Over the next few years, we'll see people who have the same job title, but who are doing entirely different work," says Doug Rose, AI strategist, trainer, organizational change consultant, and LinkedIn Learning instructor, whose course Introduction to Artificial Intelligence is featured in this level. "It won't just be about learning how to interact with AI systems, but also about getting better at tasks in which humans still have a distinct advantage."

If an organisation is just getting started with AI, it’s in good company.

"We're still figuring a lot out in the AI space, like I'm sure many organisations are," says Terence Morley, VP of global talent development at NBCUniversal. "We're excited about AI's potential to fast-track learning, help leaders in the moment, and improve performance."

Expert example - When Kraft Heinz was planning its 2023 Ownerversity Day, an annual 24-hour global learning event to accommodate employees across time zones, they decided on the theme "Revolutionising Creativity and Collaboration Through the Power of AI."

They focused learning on three topics to craft a foundational knowledge of AI:

• The first was ‘What is generative AI?’, says Pamay Bassey, Kraft Heinz's chief learning and diversity officer. "To kick off the conversation, we first explored what AI is, how it's intended to be used, its power, and the challenges that are faced when considering and leveraging AI. We explored traditional and generative AI so people had a strong foundation."
• The second topic was ‘How is AI used at Kraft Heinz?’, which was an exploration of the AI projects and pilots that were already underway in the company.
• The third was ‘What does AI mean for each of us?’. "As we considered this question," Pamay says, "we discussed the ways that AI can be leveraged both in our personal and professional lives."

When hosting an ‘AI Learning Day’, companies need to identify which teams (for example, compliance, IT, and legal) are charged with establishing policies and guidelines around the use of AI. The teams need to be aligned early and often, as they will be the go-to partners throughout the journey on everything from privacy and security considerations to details like what data can and cannot be inputted into AI.

The next step on the learning path for employees is building AI Literacy.
 

Level 2 / Applying - Helping all employees apply AI to everyday work
The second level is for employees who have gained basic AI fluency and are ready to get hands-on with AI. Core skills include chatbot prompting and collaborating with AI assistants, as well as how to think critically when using AI. Level 2 moves beyond education (how and why AI can help with parts of the job) to practice, so people can learn firsthand how to harness AI for specific tasks and roles.

This level is appropriate for all professionals, including leaders and managers. Every employee can benefit from topics such as building a generative AI strategy or using AI to foster a collaborative team culture.

Expert example - PwC, which was recently honoured by LinkedIn as a 2024 Top Company for career growth in the U.S., pledged to invest $1 billion over the next three years toward expanding and scaling its AI offerings across every level of the organisation. To do so, they had to lay the groundwork for their 75,000 employees in the United States and Mexico.

"When it comes to learning and development at PwC, we know that a one-size-fits-all approach just doesn't cut it," says Leah Houde, chief learning officer at PwC. They first rolled out mandatory eLearning modules about how to use AI at PwC to all their employees, but they didn't stop there. PwC paired those modules with in-person seminars, gamification, hands-on workshops, bitesize video content, and access to generative AI tools to offer learning adaptable to different departments' needs.

"We want our employees to feel empowered and engaged throughout their learning journey," Leah says, "not intimidated or overwhelmed with emerging topics like Gen AI."

The first step here is to reach out to team leaders (sales, marketing, partners across HR) to assess how employees are using AI today so examples can be shared company-wide and inspire people to get started. While talking to those leaders, it is crucial to know what their business goals are in order to speak to how AI aligns with them. Whether time is allotted at an all-hands, a lunch-and-learn is scheduled, or a dedicated AI Learning Day is created, it's important to build a culture of curiosity around AI and get people comfortable with digging in and learning.

Level 3 / Building - Creating with AI for business power users and developers

While the first two levels focus on using AI, the third is centred on creating with it. For many L&D leaders, this is where collaborating with technical leaders on AI upskilling can drive the greatest business impact.

Level 3 includes business power users working with no- and low-code tools, as well as seasoned developers. It covers skills like working with APIs (application programming interfaces) and getting hands-on practice with AI tools like Hugging Face and Semantic Kernel to do things like build custom GPTs for the organisation. Employees at this level will benefit from hands-on practice, whether it's building an AI application or using low-code methodologies to leverage large language models.

Expert example - "We strongly believe that all of our people need a fundamental understanding of AI technology to advise clients through this transformation and to leverage it in their own work," says Leah Houde. "We also know that strategy needs to continue to evolve for all of our people - and for those in deeply technical roles." Leah also explains that, for technical employees already fluent in AI basics, "intermediate training will help accelerate engineers' skills development, making this corps of engineers some of the top talent in the field."

After PwC rolled out beginner-level courses to all their employees as highlighted in level 2, they took concrete steps to link technical AI upskilling and talent-development strategies:

1. releasing intermediate courses to their 75,000 employees in the U.S. and Mexico
2. starting to offer advanced courses and activities for their professional technologists and "gen-AI super users" to further equip them with the knowledge and skills to drive innovation
3. actively recruiting talent from across business lines into an internal team of AI technologists

Here the first step, as the 2024 Annual Work Trend Index from Microsoft and LinkedIn advises, is that L&D pros prioritise rolling out AI to those functions that will see the most ROI first. Keep track of wins and learnings, and share learnings widely as the effort is scaled.

According to a McKinsey analysis of 16 business functions, "just four - customer operations, marketing and sales, software engineering, and research and development - accounted for approximately 75% of the total annual value from generative AI use cases." This indicates that starting a pilot program on AI upskilling with developers will enable an organisation to reap the benefits.
 

Level 4 / Training and Maintaining Models - Levelling up skills for engineers who are training and maintaining AI models

While machine learning engineers have long used AI to build software and develop and train AI models, the momentum and speed of change to AI-driven software requires frequent upskilling and reskilling to help them stay on track. This pace of change only exacerbates an existing issue: Talent with deep technical expertise is both harder to find and more expensive to hire. IDC predicts a global shortfall of four million developers by 2025. This looming talent gap makes upskilling technical talent mission critical.

Level 4 of the framework focuses on the skills needed by employees in engineering and coding-heavy roles who are building AI systems and products. Topics include deep learning and neural networks, as well as training, maintaining, and fine-tuning AI models.

L&D is well positioned to help these engineering leaders upskill their teams - both current employees and those new to the company - so they are better equipped to quickly and effectively deliver the AI-powered applications that the business needs.

The first step here is meeting with senior engineering leaders and discussing two key questions: ‘What AI skills do their teams currently have?’, and ‘What AI skills are they lacking?’. A monthly time should be set up to sync so that reskilling can happen on an ongoing basis for the organisation's engineers.

Level 5 / Deeply Specialising - Empowering technical and R&D specialists with AI tools

Level 5 of the framework focuses on helping specialists’ roles. Here the focus is on DevOps, data scientists, and R&D teams with the goal of sharpening their skills in the cybersecurity applications of AI, as well as AI cloud solutions like AWS Cloud, Azure, and Google Cloud Platform (GCP).

This group of technical specialists has a particular challenge: The AI skills that they need change on a weekly basis, rather than monthly. This rapid pace calls for continuous, in-depth skill building to help specialists be more agile and productive.

L&D pros can help this group by offering expert-taught courses that involve deep skill building, hands-on practice, and certification prep, all of which are important to advancing both their individual careers and the business.

The critical first step for this super specialised population that needs frequent upskilling is to open and maintain regular, open lines of communication with the engineering and IT teams. One challenge technical talent often faces is making time for learning, so L&D working alongside department leads to help technically skilled talent make time for regular upskilling is critical - from working with managers to tie learning to career growth to rewarding top learners.

Final thoughts - This is L&D's moment

The Microsoft and LinkedIn report on the state of AI at work revealed that nearly half (45%) of U.S. executives are not currently investing in AI tools for employees, and only a quarter (25%) of L&D teams globally plan to offer any generative-AI training this year.

As a result, employees are taking things into their own hands: The report showed that 78% of AI users are bringing their own tools to work. However, without guidance or clearance from the top, organisations are missing out on the benefits that come from the strategic use of AI at scale.

And if the C-Suite needs to be convinced to invest in AI learning, a recent study from IDC "The Business Opportunity of AI" showed that for every $1 a company invests in AI, it is realising an average return of $3.50.

Companies that channel employee AI experimentation into an intentional strategy that drives bottom-line impact will pull ahead. Those that stand still will fall behind, and L&D pros are in the driver's seat to lead their organisations to seize this opportunity.

This framework is intended to be a starting point to upskill every employee based on their specific level of AI readiness and technical proficiency. As companies dig in and start to undertake this activity based on their own needs, they have to stay on their toes: The AI landscape will continue to evolve and they'll need to continue to be proactive, to adapt, and to srefine their upskilling strategy.

LinkedIn will be doing the same, continuing to offer the latest expert insights and resources to help organisations on the journey to AI literacy, one skill at a time.