Finding AI talent is difficult. Here’s how tech companies like Traveloka are coping

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This is a Discuss post, where we feature short but insightful opinions from the Asian tech community on startup, entrepreneurship, and tech topics. Got a topic or question to suggest? Drop us an email or leave a comment.

The applications of AI are incessantly growing, from medicine, finance, and urban planning to winning debates, monitoring wildlife, and even recreating the periodic table of elements!

But apparently, a lot of companies, especially startups, can’t find enough AI talent. What’s making the hunt difficult? How can things become easier for startups?

Representatives from different tech companies share their thoughts.

Editor’s note: Answers have been edited for clarity.

Daryl Manning, vice president of data at Traveloka

AI is a vast, multifaceted field, which makes finding suitable AI talent challenging.

From our experience, one key challenge/factor that stands out the most is understanding what kind of AI talent is required for a specific role. For instance, should the candidate be knowledgeable in credit risk? Should he/she be an NLP (natural language processing) expert? Further, depending on the team structure and charter, some roles may require greater focus on large-scale production systems vs in-depth algorithmic knowledge for more research-focused groups. The key takeaway here is to understand and identify your team’s goals and needs in the AI space at a granular level.

The second challenge is finding AI talent that possess strong business acumen and technical knowledge. As a result of the democratization of AI (think Coursera, Udemy, Medium, etc.), the market is flooded with candidates who look good on paper but disappoint under scrutiny. The table below provides an illustration of this conundrum across various seniority levels: