To pivot or not to pivot to AI: that’s the question for startups

In 2016, Kamarul A Muhamed read a report that the drone industry would be worth US$128 billion by 2020.

Kamarul was ecstatic. He had founded Aerodyne, a Malaysian drone company based in Cyberjaya, just two years earlier.

Image credit: Timmy Loen

“So I was celebrating,” he told the crowd at the Tech in Asia Conference in Kuala Lumpur in July. He studied the paper, reading it back to back several times, and even flew to London to meet the writer.

But Kamarul soon learned that the report’s assertions were “far from the truth,” he tells Tech in Asia in a follow-up interview. He says that demand failed to materialize from the ecommerce sector and government regulations proved to be an obstacle.

“Fast forward to 2020, I don’t think it’s even 25% of the way [to US$128 billion],” the founder adds.

But in 2017, Kamarul noticed something else. Drones were not only capable of offering a service, such as security or delivery, but were also a prime source of data. That sent him working on a data solution that same year.

Seven years later, Kamarul says that Aerodyne has successfully pivoted, focusing on AI solutions based on the data that the drones provide. He describes the firm as “more of a data science company that uses data from drones to provide predictive analysis and enterprise resource planning solutions.”

Photo credit: Aerodyne

The company now has over 600 people with a team of “150 data scientists, AI engineers, and software developers” working on data.

Kamarul says the firm will now be focused on combining data from the drones with an enterprise-level large language model (LLM) that will integrate data with procurement and finance as well as automatically generate reports.

The first application will be with electrical transmission towers. The drones will monitor and photograph the towers, and the LLM will include industry-specific information relevant to maintenance and operations of the towers.

He adds that this AI-driven system would be able to forecast maintenance issues and allow the firms to schedule repairs and order parts in advance.

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