Upskilling Seen As Most Practical Way for Workers to Learn AI
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Upskilling Seen As Most Practical Way for Workers to Learn AI
Approaches vary, but all recognize the need to keep the workforce current on increased digitization and automation in industry; adaptive learning platforms are personalized, flexible
By John P. Desmond, Editor, AI in Business
The AI skills gap means the talent may not be available to execute individual projects, and new hires may be conversant in AI but not have the business knowledge to identify use cases. Given these obstacles, some businesses may decide to abandon the drive to recruit the top hire with the needed combination of AI and business skills in favor of upskilling the in-house business analysts in AI.
“Internal upskilling programs can be the answer to AI skills gaps,” stated Gregory Herbert, SVP and GM, Dataiku, an AI and ML company, in a recent account in GulfBusiness. “Modern AI platforms are starting to cater to professional-development paths for all levels of knowledge worker, from the intermediate spreadsheet user to the most knowledgeable data scientist,” he stated.
Approaches can vary. Some organizations may opt for a program where skills rather than department determine who trains with whom Excel power users from finance, for example, may bind themselves sitting in classrooms with Excel power users from warehousing in this training model. “This approach is useful to train large groups at a time and to let a little domain knowledge from each business unit seep into others,” Herbert stated.
Another approach is functional upskilling, where the use case is the focus and employees from the same department with similar business skills but differing tech skills–spreadsheet users and database administrators from finance, for example–learn together to address a specific problem or set of problems. “This approach is used where rapid adoption and time to value are the priorities,” Herbert stated.
AI Governance Frameworks a Fit for Upskilling Efforts
An AI governance framework with a common AI platform in place as the organization is learning AI skills can help monitor and measure the progress. “Many useful metrics emerge from the common-platform approach,” Herbert stated. Measurements can be made for ROI, tech budget percentage spent on AI and ML, AI assets produced per data worker per quarter and talent retention rates, for example. Milestones can be set for six, 12 and 24 months, to help measure progress. “Working models that add value to the business while empowering their creators and never straying far from core business objectives,” he stated.
AI governance readiness is a new level of maturity for AI in business. The AI Risk and Security (AIRS) organization, an informal group of practitioners, conducted a survey recently with the Wharton School of the University of Pennsylvania, on AI governance readiness. The results showed: 40 percent of companies responding had an agreed-upon definition of I/ML; 30 percent had an AI/ML center of excellence; 30 percent had defined roles and responsibilities for the jobs needed to implement AI/ML systems; and 20 percent had a centrally managed and budgeted department for AI governance.
Knowing the risk exposure of your AI systems is becoming de rigueur. For example, the AI Act recently approved by the European Union defines high-risk, limited-risk and minimal-risk AI systems. High-risk AI systems that would be prohibited in Europe include systems for mass surveillance, those that manipulate before to cause harm, and social scoring systems. For US companies that want to prepare for regulations to come, a good strategy would be to assess AI system risk in comparison to the model of the AI Act.
A practical guide to upskilling the workforce recently published by AIHR, the Academy to Innovate HR, suggested much of the solution can be handled internally with management moves.
Handling Upskilling Internally
Upskilling has grown more relevant for several reasons. “The most important one is the growing digital skills gap many companies are facing; the difference between what employers want or need their employees to be able to do and what those employees can actually do,” stated Neelie Verlinden, content creator with the Academy to Innovate HR (AIHR), based in Rotterdam, in an account posted recently on the AIHR blog.
The main causes of the skills gap are: an “aging workforce,” with more retirements of baby boomers creating open positions that are difficult to fill with equivalent skills and knowledge; and the move towards digitalization across all industries, where demand for knowledge of AI has become paramount.
Strategies for upskilling the workforce include:
Learning and development
Job rotation
Job enlargement
Job enrichment
Peer coaching
Peer mentoring
External experts
For learning and development, four phases are needed, Verlinden advised: a skills gap analysis to identify the training requirement; specification of learning objectives; design of training content and method; mentoring and evaluation.
Approaches will vary. “Some organizations will prefer online courses in combination with real-life lectures and seminars, while others will go for peer coaching and an ‘upskill track’ on their LMS (Learning Management System),” Verlinden stated.
Job rotation is the practice of moving employees between jobs in an organization, often laterally, as a way to transfer skills, knowledge and competency.
Job enlargement is the practice of adding duties to existing jobs; job enrichment is the practice of adding higher responsibility and dimensions to existing jobs.
Peer coaching is the practice of having two or more colleagues work together to expand, refine, build new skills and teach one another how to solve problems in the workplace.
“Peer coaching offers people the opportunity to build leadership skills such as active listening, effective feedback, timely communication and the ability to teach and mentor,” Verlinden stated. A recent report in the Netherlands found that 50 percent of the country’s workers need to be upskilled on digital skills, she reported.
Among the options for upskilling course content, she recommended: courses from her own company, AIHR, such as People Analytics, content she described as “world-class, online education programs available anywhere, anytime.”
She also recommended Udacity, a platform offering many upskilling opportunities including AI, data science and cloud computing, as well as concentrations for business leaders and within specific industries, such as AI for healthcare.
Also, Udacity has “partnered with leading technology companies to teach critical tech skills that organizations are looking for in their workforce.”
Another option is Kokoroe, a platform offering soft skills as well as professional and technological skills. Courses include: Inclusion: Why It Matters; Giving Good Feedback at Work and How to Deal with Conflict.
Arla Foods Upskilled Its Workforce of 3,500
Citing upskilling examples, Verlinden mentioned Arla Foods of Denmark, a farmer-owned dairy company that embarked on a project to upskill its entire workforce in order to remain competitive. Over 3,500 workers participated in taking courses, including math, to help deal with increased automation in the industry. At Arla, “A key success factor was the positive image of training among the Arla workforce,” Verlinden stated.
Adaptive Learning With AI Can Be Personalized for Each Student
Adaptive learning platforms are in a good position to provide the personalization and flexibility needed to upskill a workforce in AI. The platforms provide a customized learning experience catering to each student’s needs, and a way to measure progress. The platforms are able to adjust instruction content, pacing and delivery methods to optimize for each student.
Key features in an adaptive learning platform, according to a recent account from The Silicon Valley Innovation Center, include: customized content, aligned to each student’s current knowledge and skills; immediate feedback; individualized pacing and data-driven insights derived from comprehensive analytics.
Types of adaptive learning platforms include: content-based; assessment-driven; game-based; and comprehensive, combining elements of all.
An example in higher education cited by the Innovation Center as the McGraw-Hill ALEKS platform for college math courses. The offering uses AI to determine each student’s knowledge gaps, thus is able to provide targeted instruction, resulting in higher pass rates and improved retention.
Edtech startups mentioned in the account included: Knewton, Smart Sparrow and Cerego.
The further integration of AI into adaptive learning platforms will enhance personalized learning, and over more advanced real-time feedback and assessments.
Read the source articles and information in GulfBusiness, in a survey from the Wharton School, on the blog of the Academy to Innovate HR (AIHR) and from The Silicon Valley Innovation Center,
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