AI Upskilling: Visibility and Community-First Learning

With AI a prominent topic in many sectors, workers globally ask: “How can I upskill for AI?”

McKinsey predicts that generative AI (artificial intelligence) could add as much as $4.4 trillion USD annually to industry profits, indicating a correlative need for AI upskilling. This impressive figure assumes that enterprise clients have the internal human resources to leverage AI tools and drive revenue. Only 47% of workers feel confident about using AI in their roles, and only 16% of CHROs deem their workforce AI-ready. 

Before achieving the dramatic returns AI promises, businesses must tackle their workforce's need for AI-related skills. From gaps in data science to a complete absence of AI experts, companies are increasingly turning inwards toward AI upskilling to solve the skills gap.

The AI Skills Landscape

Organizations that want to incorporate AI in the workplace must first scale their data processing, handling, and storage. Data is at the core of effective AI use, providing the foundational information for enterprise use-case-specific model training. 

Having skilled employees who can collect, clean, analyze, visualize, and feed data into statistical modeling simulations is the foundational skill set enterprises need for effective AI use. With a constant flow of sound, accurate, and well-structured data, businesses can train models and rely on third-party available AI software. 

Yet, beyond effective data management, artificial intelligence also requires AI specialists who have experience working with machine learning and creating large language models. While ML/LLM programmers overlap skills with data scientists, they often have more specialized skill sets.

As AI adoption in the workplace increases, businesses seek to hire more skilled personnel to fill this growing skill gap. The World Economic Forum forecasts that the demand for AI and ML specialists will increase by 40% by 2027. Yet, other AI-related roles, like big data engineers and data scientists, will also increase by around 25%. 

This global drive to develop and deploy in-house AI systems creates a hyper-competitive hiring environment. With present forecasts, there simply aren’t enough skilled individuals, further fueling the AI-related hiring frenzy. AI upskilling arises as a potential solution.

Can We Cross-Train Data Scientists and ML/LLM Programmers?

One strategy organizations have considered is to take their available workforce of data scientists and provide them with AI upskilling opportunities. After all, there are around 190,000 data scientists in the USA, while there are only around 30,000 AI specialists. Businesses can access skilled data scientists at a lower cost without competing with other organizations.

Skills like data analysis, problem-solving, and understanding machine learning are all straightforward. However, understanding the fundamental skills that an AI specialist has isn’t enough to transform a data scientist into a professional in this field. 

Commenting on this, one AI Sessions Atendee stated, ”One of the biggest challenges I’ve found is that there are different sub-specialties in the AI field. Our team is roughly broken into data scientists, machine learning engineers, and backend infrastructure engineers. The skills between them aren’t super fungible. It takes some time to specialize in any of those given group of skills.”

Organizations that rely on cross-training face several core problems:

• Depth Vs. Breadth: Achieving a high level of proficiency in both AI specializations and data science can be challenging. 

• Loss of Expertise: When going through a training program for AI upskilling, data scientists may lose proficiency in other areas. Often, employees must compromise in one area to achieve in another.

• Career Aspirations: Considering that nearly 80% of the US workforce has no trust or low trust in AI, many data scientists simply do not want to retrain a technology they don’t believe in. 

While cross-training data scientists is possible, there’s another effective method enterprises can use:  providing AI upskilling opportunities to existing employees from other fields. Let’s explore this further.

AI Upskilling for Existing Employees: A Sustainable Solution

Upskilling has been a central trend in the corporate workspace for the past decade. According to the World Economic Forum, six out of ten workers will need to acquire new skills to do their jobs by 2027. With limited resources, enterprises can leverage their existing talent pool and training programs to access the skills their business needs to thrive.

Thebenefits of AI upskilling are even more dramatic. Creating an available pool of talented individuals who possess the necessary skill set to interact with and deploy AI systems can achieve numerous benefits:

• Increased Productivity and Innovation: Investing in upskilling can improve employee performance by over 20%. 

• Improved Employee Engagement and Retention: Employee learning and development opportunities can reduce turnover by up to 28%.

• More Efficient Allocation of Resources: Instead of spending HR budgets to enter into the hyper-competitive AI expert hiring market, businesses can pour money back into their own top talent to enhance available skills. Around 75% of employees are excited about learning new skills in their workplace.

AI upskilling springboards off the previous success of learning and development programs and synergizes with the impressive returns of AI-first organizations. However, businesses must achieve clear visibility over their workforce to ensure successful implementation. 

How to Support AI Upskilling for Employees

One of the most significant barriers to artificial intelligence's entry into enterprise environments is a lack of visibility over employee skills. Even after determining that AI upskilling is the most precise and profitable course of action, businesses can still not motivate their employees to pursue these skills.

Research by Gallup suggests that leaders rarely know whether or not employees are using artificial intelligence in their workflows. Even within organizations that push for AI adoption, figures remain staggeringly low. Despite what sensationalist news headlines and LinkedIn reporters may suggest, 70% of Americans never use AI in the workplace; only one out of every 10 uses AI at least once a week. 

With 75% of Americans now perceiving that AI will reduce the overall number of jobs over the next decade, many businesses are unable to motivate their workforce to interact with AI tools.

When leaders don’t have visibility into their workforce, they cannot begin to turn the tides in favor of AI adoption. To enable AI upskilling, businesses must first gain complete visibility over their organization and then prioritize these in-demand skills for all. 

One organization, Walmart, achieves this nationally by providing a sign-up educational program. It directly contacts employees it considers to have the potential to digitally upskill, hoping to enlist them in a monitored program. Known as Correlation One, Walmart’s cohort-based system provides visibility into employee activity and allows it to train numerous employees carefully in data-related skills.

While Walmart has achieved digital upskilling nationally, PwC gained complete visibility over its workforce and launched a successful AI-first upskilling program globally.

PwC: A Global Upskilling Effort 

Back in 2017, PwC identified that the rapid development of technology would lead to fundamental shifts in the modern workplace. To adapt to this changing world, they focused on helping their employees learn new skills that would allow them to thrive using these new digital technologies.

PwC created and launched a $3 billion USD global upskilling program (including AI upskilling), offering immersive data analytics and applied data skills modules. Over three years, PwC took 37,000 employees through these data-first courses, significantly increasing their organization's baseline knowledge of technology and preparing its workforce to interact with digital technologies.

Instead of hiring AI experts, PwC can use its highly skilled workforce to fill the AI skills gap without hiring. This approach reduces their reliance on external talent hiring and forges loyalty in employees as they, too, benefit from the skills that PwC offers them. PwC ensured that its learning modules directly catered to the areas where employees could gain the most value from upskilling.

The key to their success was complete visibility over the workforce. They created an enterprise-wide learning culture, actively discussing learning opportunities with employees and highlighting the learning achievements of those who would engage with their programs. By monitoring what modules different employees had taken, they drove up interaction with these learning modules.

Leading by example, PwC has inspired its partners worldwide to take a similar course of action with AI upskilling. Their program structure is used by partners worldwide, and it has now reached over 8.3 million total employee students.

Deploying Upskilling in an Enterprise Setting

Artificial intelligence is the future of the workplace. Yet, to achieve the efficiency and productivity gains that AI promises, businesses must first address the skills gap in their organization and create learning and development schemes to upskill their workforce.
As companies like Walmart and PwC have demonstrated, gaining visibility over the workforce and providing engaging and accessible upskilling will allow employees to find ways to enhance their skills in a controlled environment. These learning opportunities will create the data engineers, AI experts, and technicians that enterprises need to leverage AI in the coming digital era. 

For more insight into how AI impacts the workplace, be sure to sign up for the Virtasant AI Sessions.

‍Note: This was one of the key topics at our monthly AI Sessions event, an exclusive and free 5-person event designed specifically for senior AI professionals. Apply to attend the next event; space is limited.