Thinking of embarking on a career in AI? Here are some tips from people who work with the tech on what you need to know before taking the plunge.
This is a very exciting time to be looking for a career in AI because it is arguably no longer just a subset of the technology sector.
AI is very much a part of our everyday lives. It powers everything from business automation processes to customer service chatbots to social media algorithms.
Yes, AI is a technology, but its applications are constantly broadening. Sectors such as health, agriculture, law, education and entertainment rely on AI tech to function – and this is only going to become even more pronounced.
If you want to work in AI there’s no better time to take the plunge and see the difference your skills can make. Not everyone can be disruptors on the level of OpenAI founder Sam Altman, but there is space out there for anyone with decent AI skills and a willingness to put them to work.
As part of our AI and Analytics week at SiliconRepublic.com, we asked some current AI pros for tips on working in the field.
Here are five pieces of advice they had for anyone who wants to work in AI.
Understand AI’s broader applications
As we touched on in the introduction, AI is being deployed by many sectors for all kinds of reasons. As an AI professional, you should be aware of its potential as well as its limits.
According to Owen Fenton, who is a manager with KPMG’s Belfast-based applied intelligence team, it is “becoming increasingly vital” for those who want to work in AI to “understand company operations, organisational standards and broader industry practices.”
And that’s on top of the tech skills you’ll be expected to have.
“By effectively blending this knowledge into business-domain acumen, individuals will be able to develop problem-solving skills and solutions that are tailored to the unique challenges and opportunities within specific industries, such as finance, transport, or retail,” says Fenton.
“Understand the business needs you’re trying to address. To successfully deliver value via AI or analytics, you need to know what’s important to the business and how your work will impact that,” say Andrew Keogh and Caoilte Guiry of Workhuman’s engineering team.
Ethics
“The ethical questions surrounding data, privacy, bias and governance are huge and only likely to become more central,” according to Keogh and Guiry.
“Consider the ethical ramifications of the work you are doing, and be prepared to address bias in the data and think about how to avoid it in your outputs,” they warn.
“As data becomes more and more integral to successful business operations, it is becoming imperative to be aware of the ethical implications of using it responsibly and to grasp the potential moral issues that may arise from misusing it,” says Fenton.
“To address this, understanding data governance, management, and authorisation practices is crucial.”
Learn by doing
BearingPoint’s Gary Mullane says that practical experience is “essential to building expertise” in AI and analytics.
Mullane is responsible for the growth of the consulting firm’s data analytics and artificial intelligence business. (You often see AI and analytics mentioned together because AI is used to extract meaning from data to aid businesses).
Mullane recommends that people who want to work in AI get hands-on and pick up some practical experience.
“Participate in online workshops or hackathons, work on personal projects, or contribute to open-source projects to get real-world experience and build a portfolio of your work.”
Soft skills as well as tech skills
As well as building up your portfolio and honing your tech skills, Mullane says soft skills need attention.
“Develop soft skills and collaborate as much as possible. Soft skills such as communication, problem-solving, and teamwork are critical to success in AI and analytics.
“Focus on building these skills to work effectively with others and communicate ideas, to become a well-rounded professional and stand out in the field,” he advises.
He says that those who think their soft skills need work can start improving them by joining AI and analytics communities, attending conferences and connecting with others in the field.
Deloitte’s Adam Grant also has a tip for improving soft skills: public speaking.
“I would recommend getting good at PowerPoint and public speaking, this will aid you in both the recruitment process and the ability to work on interesting projects once employed.”
Public speaking is not a part of communication that many people relish, but it is important in the context of explaining to people without AI and analytics knowledge how the tech can help them.
“Analytics is all about deriving meaning and context from the data, in order to do this, subject matter experts are critical to delivering a successful project. Get used to working in teams and incorporating feedback into your work. What makes sense to you may not make sense to the end users of the solution. Always remember – you are building for them, not yourself,” says Grant.
Quality not quantity
If you’re feeling a little overwhelmed about all the things you need to stay on top of to build a good career in the AI sector, don’t worry.
Grant advises that it’s all about quality rather than quantity when it comes to learning in AI and analytics.
“There is a lot to learn, and you can find yourself doing course after course. My main piece of advice here is to not move forward unless you build a portfolio project with a specific technology. The portfolio process is key to internalising the skills learned.
“When interviewing, I am far more impressed with an individual who has built and can talk about projects using a limited numbers of tools than someone who has completed dozens of courses.”
This reiterates the point above about soft skills like communication and, of course, adaptability.
“AI and analytics evolves fast, learning how to adapt fast is the most critical skill,” says Grant.
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