“Artificial intelligence” (AI). No, we’re not talking about the post-apocalyptic, superiority complex-inflicted, misanthropic machines you’ve seen in the Terminator and Matrix movies (not yet, at least).
AI is the latest favorite buzzword in Southeast Asia’s startup ecosystem. PR folks use it as a catch-all term for technologies as wide-ranging as machine learning, natural language processing, and image recognition, among others.
Nevertheless, these are at the center of a revolution taking place in the region and the wider world, and they are helping businesses to be more efficient, generate revenue from new sources, and improve access to things like transportation, healthcare, and educational resources. And it just so happens that Indonesia has all the right ingredients to be a perfect testing ground for startups looking to break into this revolutionary field.
Data first
The fundamental element to this whole class of technologies is data. Without the collection of masses of data that can be analyzed and understood, the “learning” part of machine learning can’t happen.
In the opinion of Sachin Chitturu, digital core leader for Southeast Asia at McKinsey & Company, this is where Southeast Asian startups face their biggest challenge.
“Data is a big gap for startups,” said Chitturu, speaking on stage at Tech in Asia Jakarta 2017. “They either need to collaborate with big companies, or generate data themselves, which takes time.”
Value increases as scale does.
Only a relatively small handful of Southeast Asian corporates possess the large customer bases that are preferable to test AI algorithms on. According to Chitturu, these typically fall into two groups: telcos, and fast-moving consumer goods (FMCG) businesses.
Partnering with these types of companies is therefore a great starting point for AI startups, he added.
It’s a virtuous circle. If startups have technology that can make the corporates’ lives easier by boosting efficiency or creating new revenue streams, then they become an attractive partner. As a partner, they can get access to the corporate’s large customer base and the masses of precious data it generates. In turn, this allows the startup to hone and improve its AI, making it an even more attractive partner for customer-facing corporates.
Virtuous circle
One such collaboration is that between Indonesian chatbot developer Kata.ai and Telkomsel, the mobile network subsidiary of Telkom, the country’s biggest telco.
“We can see amount of data they’re collecting… and their business is growing in double digits each year,” said Kata.ai co-founder and CEO Irzan Raditya.
With this huge number of customers comes a huge amount of interaction with them, a whole load of admin work – and, inevitably, more than a few complaints. Kata.ai’s AI-driven chatbot can handle much of this work for Telkomsel, allowing the network operator’s employees to focus on those issues that require a human touch.
“We saw customer service agents mostly doing mundane things, repetitive tasks,” said Raditya. “We see AI helping them to be more productive, to do things more efficiently. And it can pass all those things that require ‘high-touch’ handling – like complaints – to humans.”
Meanwhile, the smart chatbot can answer customer inquiries like “Where’s the store nearest to me?” and “What pricing plans do you offer?”
Kata.ai co-founder and CEO Irzan Raditya speaking onstage at Tech in Asia Jakarta 2017. Photo credit: Tech in Asia.
Beyond organizational efficiency and improved customer engagement, Kata.ai’s chatbot also has the potential to open new revenue streams for Telkomsel. “They could never monetize their channels on Instagram, Facebook, and so on,” Raditya explained. The chatbot makes it more feasible to sell packages through social media and website interactions.
Indonesia rocks
Another way of leveraging a larger dataset is to roll out your AI tech in big markets. And Indonesia is as big as it gets in Southeast Asia, accounting for about half the region’s total population and with a widely variable demographic make-up.
“Value increases as scale does,” said Chitturu. “So playing in Singapore, with five million people, as opposed to Indonesia, with 260 million – the economies of scale, the cost improvement are so much better.”
Nevertheless, there are a few significant obstacles to overcome before Indonesia can fully begin to unlock this potential.
One key challenge is access to talent, Chitturu said, though he added that this seems to be a global problem. “Even companies in the US are struggling,” he said. “Because there is a very limited number of data scientists, and they’re very expensive.
Sansan – a Japanese startup that uses AI to digitize business cards and create a social network from the data – employs 10 to 15 data scientists depending on how you define the term, said COO Rio “PopEye” Inaba. He suggested that startups might have more success in hooking AI experts if they do more to understand what these specialists are looking for in a job.
Relationship advice
For Sansan, it’s more about thinking of how to “have a relationship” with candidates, rather than simply looking at what they can do in their day-to-day work. Offering an opportunity to work on something they consider to be new and revolutionary may be even more important to them than the paycheck they can take home.
It is one thing to capture data, but another to make it available to everyone.
“Some talented engineers are not looking for money – of course, money’s important – but they’re looking more for an experience,” he said, citing Sansan’s own set-up as an example. “Since we have all this business card information, they may want to join us to experience how they can work with that kind of clustering coefficient, that particular kind of network.”
Broader infrastructure and support for startups would also be welcome, said Chitturu. “It is one thing to capture data, but another to make it available to everyone. Singapore has a huge government initiative to capture data and open it up. In Indonesia, that is not there – it would give startups a huge amount of data to train their algorithms. Every use case – transport, education, healthcare – can be improved with AI.”
Despite highlighting the public sector’s role in nurturing an ecosystem for AI startups, Chitturu believes the impetus for adoption will come from business. “That’s why I think the private sector will drive AI, not government,” he said. “If they find they can make additional revenue or get cost efficiencies, private companies will push it, and startups that can collaborate with them to achieve that will go there.”
Original article here by Tech in Asia.