Building your AI Career Strategy:
Three Tips for Getting Started
Upskilling for the AI Era
The data is clear: AI is here to stay. Technical skills related to AI are in high demand, and soft skills will only grow in value to employers. Even technical skills for AI, like machine learning, are in high demand.
Upskilling doubtlessly required for all tech professionals who want to meet this demand. AI leverages the same foundational technologies and programming languages many tech professionals already know, which gives them an advantage in upskilling.
As AI handles low-level tasks, tech professionals can focus on mastering higher-level disciplines and competencies. Now is the perfect time to ensure your skill set encompasses language theory and programming architecture, skills that you may have overlooked or haven't had time to delve into yet. It is also a great time to improve your soft skills, so that you can be less dependent on technology trends and lean on your value to current and future employers as a creative, communicative and strategic asset that AI could never replace. Let's dig in.
"AI is a set of skills. There are a lot of ways that you can learn them. It's not such that you have to go to school or back to university to learn “AI.” There are a lot of individual courses that are associated with them. And, if I was a tech professional trying to get ahead in my career, I'd be starting to take those courses. Any certifications you get are all additive to your credibility and your ability to generate interest in yourself."
Art Zeile, Dice CEO
1. Identify Your Familiarity with the Core AI Competencies
No one is going to be able to acquire every AI related skill, but most teams are made up of tech professionals who can collectively do several core things. For more specific list of in-demand skills for the top five AI roles, check it out here.
Know Your Frameworks
Imagine you're tasked with building a robust software application. While you could meticulously code every single component from scratch, there's a more efficient approach. Frameworks act as pre-assembled toolkits, offering the essential building blocks to streamline your AI development process. Think of them like high-quality, pre-fabricated components for your software architecture.
Here we identify the types of frameworks used by each kind of AI tech professional. As you can see, there is a huge variety. Remember, there are many transferrable skills between these frameworks. As with most technologies, a foothold in the basics is all you need to expand in any direction you want to go.
Skills assessment tests can help you understand where your existing knowledge in these disciplines is. While there is no universal competency for many of the disciplines and tools noted above, competency tests can help you better gauge your existing knowledge in these disciplines. Platforms you want to implement may have skills tests you can take, but tools like HackerRank are a great place to start for general skills testing. If your employer doesn't have any competency testing for hiring available, platforms like Leetcode will still give you a good idea of your current skill level and the areas where you need to focus to upskill yourself for an AI future.
Given the trends in leveraging LLM in the corporate world, we’ve seen a shift in people who are primarily focused on development and engineering for AI and ML to candidates who can use LLM technologies to address certain use cases for the business, as well as resolve complex technical challenges. Five years ago, the focus was on people who could build, now there is a shift to people who can use what already exists.
Paul Wallenberg, Senior Director of Technology Recruiting Services at LaSalle Network
2. Improve Your Soft Skills
Technical skills and a deep understanding of AI are important, but soft skills like how you communicate results are critical. In fact, upskilling your soft skills is quietly one of the more important aspects of taking the next step in your journey with AI.
Not all businesses have the bandwidth or finances to hire a totally brand-new AI team, nor would that make sense if they did. This exciting energy behind AI means that demand for tech professionals will go up, while an already limited talent pool gets quickly snatched up. In this environment, tech professionals aware of the shifts in the market have a huge advantage.
This is a great time to improve on valuable soft skills. You're the expert, and it's more critical than ever that leadership and others at your company have the full view of what AI is doing and how you're helping drive results. Moreover, people at your company may not fully understand AI, or why it's being utilized, which is where soft skills come in handy.
Innovation and creativity
AI still struggles with tasks requiring originality, conceptual leaps, and "outside the box" thinking. Tech professionals who can leverage AI's capabilities while also bringing their creative problem-solving skills to the table will be well-positioned to develop groundbreaking solutions.
Communication
You will need to effectively collaborate with colleagues from various departments, including marketing, sales and product development. This will require clear, concise communication to ensure everyone is on the same page and working towards a common goal.
AI can analyze massive amounts of data and make complex decisions, but it often won't be readily apparent why it made a particular choice. Tech professionals will need to bridge the gap by translating AI's actions into clear, understandable terms for stakeholders who may not have a technical background. This could involve business leaders, clients or even the general public.
Improve your business sense
You should also keep an eye on how AI affects the bottom line. This is something leaders will want to know more about, moving forward. The efficiencies AI can help drive are important for your daily workflow, but those in leadership will also want to know how those efficiencies are helping drive revenue and decrease overall spending. Combine your understanding of business goals and technology to bring insight to the company that professionals outside the tech space don't have inherent access to.
3. Continue Your AI Education with Courses and Certifications
Remember that AI is evolving quickly. Upskilling and assessing your existing skill set are critical; continuing your education is vital. Today's AI landscape will quickly be a thing of the past as new language models, tools and platforms spring.
When you're ready to take your AI journey to the next level, it's likely time to take an online or in-person course dedicated to foundational AI disciplines. Some courses, like MIT's "Designing and Building AI Products and Services," are full-stack AI courses, and great options for those who are starting from scratch on AI. These types of courses are a bit more high-level than technical, so consider a course like this for when you're managing AI holistically.
Should you need to upskill yourself in a specific discipline, courses from platforms like Udemy can be beneficial. When choosing a course, be sure to find instructors with great reviews and high ratings. Whenever possible, try to find courses that offer a final test, which is a good measure of your understanding and expertise in what you've been learning. Courses that provide certifications of completion are beneficial as well. These allow you to demonstrate to your employer and others in your industry that you've taken the time to upskill yourself. These certifications are particularly impressive when they're from accredited universities — and those courses are typically available to anyone, not just alumni.