Editorial
Our homes and cities have become smart, AI has become a part of every workplace and even kindergarteners are learning to code. Notably, the rise of AI — especially user-friendly, no-code models, like ChatGPT and Gemini — has rewritten the rules of being an effective employee today. Within months of ChatGPT launching, skills related to the generative AI model, such as prompt engineering, rose 15x from January to May last year. Some of the hottest jobs in the coming year are predicted to be AI-related, including a host of AI leadership opportunities across the federal government. Clearly, there are some great career moves to be made if you have the right skills.
Monumental Changes Ahead
Generative AI is still relatively narrow in the activities that it can complete. Imagine what will happen when AI can match humans in most tasks. But human-level AI is expected to be in our workplaces within the next century, causing monumental changes workforces are unprepared for. If the current knowledge-based learning trend continues, employees will remain woefully unprepared.
Everyone Should Have Tech Skills
The future job market will fuse human and machine collaboration, where AI augments human capabilities to an increasing extent as the technology becomes more advanced. It is predicted to impact two-thirds of jobs in Europe and the U.S. The good news is that humanity is well-versed in re-invention. What is new today is the scale of impacted jobs and the diversity of skills needed to future-proof your career. Due to AI, everyone will, to some degree, have technology skills.
Identifying the Right Skills
Cultivating skills that enable workers to collaborate with AI is vital. These skills include adaptability, creativity, emotional intelligence, teamwork (including human-machine teams), resilience and critical thinking. Digital and data literacy also matter for those building and working directly with AI models and in technology roles, decision makers assessing investments in AI, leaders responsible for AI and data governance and anyone acting on AI’s outputs. President Joe Biden’s recent executive order requires federal agencies to appoint a chief AI officer before the end of the year. This underpins the importance of strong AI ethics and governance skills and may even start a bidding war for those who have such skills. If the highest office in the U.S. wants a senior leader to be accountable for AI, why wouldn’t every other organization do that?
Emotional Intelligence, Teamwork and Critical Thinking
Although AI will impact jobs, only a handful will be eliminated entirely. Even ChatGPT admits that roles heavily emphasizing human judgment, decision making, complex communication, physical dexterity and social and emotional intelligence cannot be easily automated. Hybrid human-AI teams will become commonplace with AI taking on repeatable, easily computerized tasks and humans doing more nuanced work with oversight. A doctor may use AI to analyze medical images, but consultations, discussions about health care options and communication with loved ones will sit squarely with human professionals. In the learning industry, AI can serve as a copilot, providing instant feedback but not guidance.
Power Skills: Adaptability, Resilience and Creativity
It’s impossible to fully predict what tasks and roles will be impacted, and in what way, by AI. This calls for a degree of flexibility as we continuously re-invent ourselves in the workplace. Skills that help workers navigate ever-shifting sands will help when a new ChatGPT hits the market or general AI is finally achieved. It also helps to be creative and open to new opportunities and career paths if automation and AI eliminate your role. One of the greatest uses of generative AI is its ability to give you a first draft of virtually anything, in any tone, format or structure. This is particularly useful for content creators, cutting significant time and cost from the content development process. It has reduced the skill set required for an author or developer to get started. More people can now build better content in less time.
The Need for Robust Cybersecurity
Cybersecurity skills deserve their own call-out, because as AI and other emerging technologies, like smart cities and the internet of things (IoT), proliferate, the potential and complexity of cyberattacks rise exponentially. An attack on an IoT device can be a gateway to a larger attack on a system or to access sensitive data. Attacks are also becoming more advanced thanks to AI, helping cybercriminals by automating attacks, generating personalized phishing content, scanning attack surfaces and more.
However, cybersecurity teams can also use AI to find network vulnerabilities, set up alerts for specific keywords or data and identify unusual patterns or behaviors. To achieve this, cybersecurity professionals must constantly refresh their knowledge and skills to stay up to date on new AI solutions that can help them address new threats and employ the latest tactics.
On an Upskilling Deadline
Employees will have to develop many skills quickly to thrive in the AI-augmented workplace. That cannot happen through knowledge-based learning alone.
Consider your earliest experiences of learning. As babies and toddlers, we learn by doing. You don’t expect a toddler to read a book about how to navigate obstacles. You encourage them to crawl and then walk. In school, we learn to write by putting pen to paper and reading books and develop hand-eye coordination through sports. But all that hands-on learning grinds to a halt in corporate learning and development (L&D).
The norm in L&D is to impact theoretical knowledge, because it’s simply to share content with the masses. The high-demand skills of the future, like teamwork and critical thinking, cannot be easily learned via books, blogs and podcasts, at least not to a job-ready level.
It's Time to Get Practical
Looking to build AI skills in your workforce? Explore practical ways to reinforce and deepen the skills. As AI evolves, using content or consumption as proof of knowledge as a reliable form of skills assessment is becoming less desirable and less accurate. There are many examples in recent months of AI demonstrating that by having access to knowledge, it can accomplish feats, such as passing law and business school exams.
In many areas of business, it’s crucial to know — not just infer — that someone has the skill. If you care about skills validation and you want to collect verifiable evidence that a person possesses a skill, asking them to perform tasks that generate evidence becomes exponentially more important.
Pilots have simulations and surgeons have shadowing. What’s the equivalent in your workplace?
Providing hands-on learning opportunities shows someone how a skill will apply to their role (or future job) in the real world. Simulations and simulated tests can help people confidently say that they have a skill ready to be applied on the job. This particularly applies to business-critical skills, like cybersecurity, where a simulated phishing attack or preventing a DDOS attack can test someone in the heat of the moment.
Broaden Your Talent Pool
Given the rise of new roles like chief AI officer, AI ethicist and even robot teaming coordinator, using performance-based learning to validate that someone has a specific skill becomes more important. Candidates can’t rely on decades of experience to prove their capabilities in this area, so employers will have to open their minds (and hiring criteria) to find people who have built AI skills in other roles or through learning.
Offering practical training can make learning more accessible to those who don’t learn effectively through knowledge-based methods. Since only one-half of workers feel they have adequate training opportunities (a disparity that’ll only widen with automation and AI), leveling the field in this way can create a more equal future for everyone and widen technology talent pools.
The Future is Built on Continuous Skill Development
We are entering a new era where humans and machines work (hopefully) harmoniously together. But to realize that, we need to build the right skills to critically assess our AI investments, understand and act on AI’s recommendations, and create effective teams. Combining traditional L&D with practical, hands-on learning is the only way to upskill in time and to the depth needed before AI augmentation becomes commonplace.
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