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Training future AI talent means understanding Gen Z
Artificial intelligence and machine learning are critical to the future of organizations. Along the AI/ML journey, there will be disruption before there is innovation and transformation. As Gen Z slowly takes over as the largest percentage of the workforce, IT professionals need to anticipate the skills needed to flourish in the emerging world of artificial intelligence. This blog explores how to prepare the next generation of AI practitioners to surf the wave of incoming disruption.
If you had the opportunity to go back in time and become an expert in a technical field before it hits its peak, would you?
Believe it or not, you and your organization are in a similar scenario now. Artificial intelligence and machine learning are not only becoming ingrained in our tech, but are defining, impacting, and transforming industries of all kinds. From healthcare to the public sector to retail, developing AI skills now and continually sharpening them will keep organizations advancing down the road while super-charging careers.
Being able to identify and stay on top of disruptive technology trends before they go mainstream is a key differentiator for successful organizations and leaders. And the next big disruption is already in motionโAI adoption across various industries will contribute $15.7 trillion to the global economy by 2030. This means incredible potential for new and exciting roles to open up.
AI gets thrown around a lot as technology that shouldnโt be ignored, and while thatโs certainly true, it would be a more productive exercise to focus on how you can actually execute AI methodologies in ways that benefit your organization and its business outcomes. Perhaps the most crucial part of your organizationโs AI strategy is to build the next generation of tech talent to pull it offโparticularly, Gen Z workers looking to break intoโor move up withinโthe industry.
Gen Z is more than just a blanket term used to describe post-millennial digital natives. Theyโre now an integral part of the workforce, and theyโve got enormous influence in the digital realm. In fact, Gen Z will make up 30% of the workforce by 2030. So how can organizations bridge the gap between AI skills needed and the younger personnel who will take on these challenges? Think clear, business-driven outcomes to AI methodologies, combined with working and learning styles suited for unique generational preferences.
Building the next generation of AI talent
Having the right talent to pull off the strategy makes all the difference. And with seasoned workers leaving and younger personnel entering the workforce, building the next generation of AI talent should be at the top of your priority list. In particular, a focus on getting Gen Z workers ready to solve AI challenges in todayโs increasingly AI-centric world.
Gen Z has its own preferred style for learning new skills, such as:
- Digital learning formats, particularly videos. 56% of Gen Z-ers said training would be more engaging if there were more videos in the learning material.
- Mobile-optimized learning to gain new skills โon the goโ. 62% of Gen Zs want to access and complete training from their smartphones.
- Hands-on, collaborative learning. Gen Z learners want to feel like the learning theyโre doing is connecting them with others, and therefore has meaning.
These are all important factors to consider when it comes to training the next generation of talent to flourish in an AI-centric worldโthe learning methods will make a difference in the quality of knowledge transfer that occurs.
Which AI skills to focus on?
AI is a broad industry with different areas of focus, so where should prospective AI practitioners start?
Elliott Lynch, a data scientist specializing in the cloud, shares what he thinks workers should focus on when it comes to being an integral part of the AI disruption:
โIn order to succeed, an AI practitioner will require a continuous hunger to learn and expand their skills as well as donning their โDevOpsโ thinking cap in order to thrive in an increasingly Containerized MLops-centric world. While there is certainly plenty of overlap and ambiguity between the roles of Data Science, ML engineer and Data Engineer, itโs evident that the industry at large is moving to a place where those who can embrace and work within cross-disciplinary areas stand to thrive and solidify their value to organizations.โ
When it comes to gaining proficiency in AI/ML, here are some areas of focus:
- All things data: data is at the heart of AI
- Natural language processing (NLP)
- Image classification, object detection
- MLops
- Infrastructure
Lynch also recommends focusing on deploying models in production. โThe more knowledge and expertise you build in this area, the more your core ML knowledge will be able to shine through and provide its utmost value to your organization.โ
Getting the most out of emerging technologies requires learning much more than just the technology itself. Prepping Gen Z to become AI practitioners means prepping your organization for a future-proofed and durable path ahead, so strike while the iron is hot.
HPE is a leader in creating the artificial intelligence of tomorrow, making us uniquely positioned to help you grow AI skills and knowledge. HPE Education Services offers courses in artificial intelligence, machine learning, and deep learning, to get you skilled in todayโs fastest-growing technical fields. Explore our course offerings and start your learning journey today!
Kristina Lechuga
Hewlett Packard Enterprise
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KristinaLechuga
I am an HPE Education Services Marketing and Social Media Specialist with a love for all things creative. I love marketing especially because I get to combine storytelling with an analytical approach to create exciting content. I'm an avid reader, painter, and music lover.
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