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Avinash Tripathi is a VP Analytics at University of Phoenix, thought leader and keynote speaker with over 20 yrs of experience in the field.
The realm of AI is expanding from $86.9 billion in 2022 to $407 billion by 2027. The World Economic Forum suggests that AI-led automation will lead to a significant shift in the division of labor by 2025, with 85 million jobs displaced and 97 million new roles created.
As this transformation unfolds, a key question arises: How can professionals stay relevant and thrive in this changing environment? The key lies in enhancing our practical hands-on skills in AI, which is essential for advancing our careers and staying competitive in an AI-driven future.
While the growing number of AI certifications may seem like a logical step to stand out in the job market, they often fall short of providing the practical experience that employers increasingly demand. According to a 2024 report by the Credentialing Industry Research Institute, there has been a notable increase in AI-related programs and certifications.
However, these credentials alone may not suffice to meet the demands of an industry where real-world problem-solving and hands-on project experience are becoming more valuable than theoretical knowledge.
AI Skills Gap: Why Employers Struggle To Keep Up
Despite the talk about enhancing skills, LinkedIn Learning highlights several reasons why employers are struggling to advance in upskilling and closing the skills gap. One main issue is the pace of advancements, which makes it tough for companies to stay abreast of the latest skills required in the workforce. Moreover, there is often a mismatch between the skills that employers want and the available training programs.
The emergence of the “Great Talent Stagnation” presents another pressing issue as businesses struggle to find skilled workers while employees feel undervalued. To address this, companies should adopt a data-driven approach to identify internal talent and invest in reskilling and upskilling initiatives. The 2024 University of Phoenix Career Optimism Index study highlights this issue, revealing that businesses are overlooking their existing workforce’s potential while employees feel unappreciated.
Crafting Your AI Career Path: Hands-On Projects As Your Competitive Edge
As an analytics practitioner and hiring manager, I am frequently approached by recent graduates and early mid-career professionals eager to learn AI and transition into AI-powered analytics or data roles. The vast array of AI certifications and certificates available can be overwhelming.
While numerous AI certifications and certificates exist, their actual value in attracting employers for these specific fields is debatable. A key finding of the credentialing report is a substantial discrepancy between the educational content learners desire and what issuers are currently providing. One main reason hiring managers find it challenging to fill AI-related roles, despite candidates having AI credentials, is a lack of practical, hands-on experience.
When considering AI education for these career paths, I recommend focusing on the following:
• Hands-On Experience: Practical skills and projects often hold more value than theoretical knowledge. Especially for recent college graduates, I tend to prefer candidates who have actively contributed to open-source projects or gained hands-on experience through internships. For instance, one could explore the potential of AI in open-source endeavors by utilizing existing APIs to develop a prototype solution for real-world business challenges. An illustration of this concept is conducting sentiment analysis on social media data, which entails utilizing an NLP library integrating with social media APIs and crafting a tool for sentiment analysis. Other project suggestions include delving into predictive or prescriptive analytics for forecasting revenue growth using machine learning tools and data visualization libraries, as well as embarking on Chatbot development through the use of NLP techniques and language model APIs. These experiences showcase an applicant’s ability to apply AI concepts in practical settings, which is what employers truly seek.
• Specialization: In the evolving realm of AI, there is a rising need for professionals versed in the ethical dimensions and impacts of AI advancements. A focus on aspects like AI ethics and data privacy signifies a dedication to AI practices, which can greatly boost your attractiveness to employers who value development in AI.
• Continuous Learning: The landscape of data and AI is constantly changing. By learning and keeping up with the developments and best practices, you can ensure that your skills stay relevant and that you can contribute effectively to AI projects. I personally prefer programs that delve into domains like machine learning or deep learning using Python or design principles and the various stages of the design process instead of general introductory courses. This targeted learning approach ensures you develop expertise in high-demand areas, keeping you competitive in the job market.
The AI Education Dilemma: Certifications, Degrees Or Both?
Although certifications have always been valued, there is a growing belief that this may change over time. One of the reasons for this change is the pace at which technology is advancing, meaning that some certifications become outdated more quickly than in the past. While there are parallels with the buzz around “big data,” AI’s impact is broader and more transformative, indicating that the current emphasis on AI careers and education isn’t a temporary trend but a crucial evolution in the tech landscape.
A McKinsey report found that 35% of businesses mentioned that they had hired data engineers and 39% software engineers to support their AI initiatives in 2022. With the increasing integration of AI in businesses, the need for AI-supporting roles is on the rise.
Pursuing a four-year AI degree can be beneficial since there is a demand for professionals in this field. Degrees like computer science, data science and computer engineering, all with an AI emphasis, offer comprehensive knowledge and skills. For those seeking specialization or stand out in the field, pursuing a two-year master’s degree in AI can be a valuable alternative or addition, especially if they already have a related undergraduate degree.
Ultimately, the key is to align one’s learning journey with career objectives, all while ensuring that it is complemented by hands-on experience working on AI-related projects.
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