If you’re planning to recruit – or “buy” – talent from the open market to close the artificial intelligence (AI) skills gap in your organization, you may be in for a rude awakening. Tech professionals with AI skills or related skillsets are both costly and in-demand, making them difficult to source. In fact, in our analysis of tech job postings, conducted to create our AI hiring guide, we found that 14% of tech job postings in early 2024 sought candidates with skills in artificial intelligence or machine learning (ML).
This is a growing demand, and teams will need to look internally to find talent in order to keep up. Yet only 64 percent of tech professionals looking to get into the AI world think they’re receiving the training they need to upskill in to this area, and 57 percent found their training to be inadequate.
Training tech professionals with the skills your company will need may not only be easier than recruiting, but it can be way more cost effective. Having said that, upskilling or reskilling an existing tech workforce to succeed in the age of A.I. requires a new approach and mindset. Here are four ways to create an effective program.
Develop a Learning Culture
Since tech professionals skilled in AI and ML will need to learn technical and soft skills–like critical thinking and problem solving– from each other as well as structured training courses, a thriving learning culture and support from managers and executives are foundational must-haves for upskilling success.
“Your company’s culture affects how managers coach and connect employees, assign projects and even their willingness to transfer employees to other teams to achieve learning objectives,” noted Lynne Mayers, senior consultant with TalentNeuron, a provider of labor market intelligence.
The good news is that when you present a compelling business case for upskilling to your leadership team, and point out how it can support talent attraction and retention and the attainment of business objectives, you invite cooperation and a cultural shift toward continuous learning. Of course, it will take more effort to complete the journey, but it is possible to do both things in parallel.
Identify and Prioritize the Skills to Learn
The best way to prioritize your staff’s learning needs is to start by identifying the roles that will be fundamentally changed by AI along with the new and adjacent skills that will be needed today and in the near future to support the business strategy.
Given how quickly the skillsets are changing, human resources should consult external talent intelligence as well as the CIO and business leaders to create a skills inventory by position.
“Consulting both internal and external data sources can help you see around corners,” Mayers said.
For instance, external data sources support decision making by offering insights on labor market trends, projected supply and demand but also the areas where A.I is having the greatest and most immediate impact - such as supply chain, business process automation, robotic automation etc.
“Human resources needs to provide some guidance and direction on what’s a priority and what’s not to allocate resources effectively,” advised Hannah Yardley, Chief Human Resources Officer at Achievers.
For instance, Yardley is providing basic technical fluency training on AI, generative AI and machine learning to everyone in the organization, but she put the engineering and product teams as well as the company’s entire leadership team on the top of the training list.
You can't train everyone. At least, not all at once. You need to prioritize your upskilling efforts based on business goals and the logical order for teaching and applying the new skills. For certain leaders, gaining a fundamental understanding of A.I. and its core concepts is essential to leading the rest of the company and making informed decisions that affect customers.
Conduct a Skills Gap Analysis
Once you’ve identified the AI-related knowledge and skills that employees will need to meet specific job requirements, use a skills gap analysis to assess the difference (or gap) between your workforce's current capabilities and what is required to meet the current or near-term business strategy.
Then, develop strategies to close the gaps but with these provisos:
“First, focus on teaching adjacent or complementary skills initially. The main advantage of prioritizing adjacent skills is speed,” Mayers explained. You don’t have to start from scratch. Your staff can leverage their existing knowledge and skills to tackle new duties and responsibilities more quickly.
Next, be very agile in your approach to developing skills, especially soft skills which are more nuanced and subjective. Be open to using peer coaching, gamification, technical vendors, third-party training firms or even creating an internal talent marketplace to help tech employees develop and grow their careers.
Remember, tech workers need more than basic instruction or course work; they need to know how to apply what they’ve learned or relate it to everyday tasks and that requires hands-on training as well as job shadowing or job sharing.
“Move away from job titles,” Mayers said. Leverage skills that are integral to the business plan, wherever they sit in the organization. By adopting a growth mindset and breaking down internal silos, talent can flow to where it's most needed.
Measure the Results, Adjust and Improve
When executed effectively, an AI upskilling and reskilling program lets businesses move at the speed of need. But you won’t know whether your strategies and actions are hitting the mark, unless you measure the impact quickly, effortlessly and continuously.
For instance, Yardley is using pulse surveys to measure the results of her A.I. upskilling program in three key areas. Her program is similar to the Kirkpatrick Model, which assesses the effectiveness of training programs based on four criteria: reaction, learning, behavior and results.
What's going to happen at the end of the day depends on how organizations respond to the changes and the strategies they use to help their workforce learn new skills and remain relevant in the age of AI.
