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How to respond to the challenge of AI
GIC’s annual thought leadership event, GIC Insights, was held here on 15 September 2017. It saw 110 prominent global business leaders deliberate long-term issues relevant to the international business and investment community. The theme was Asia’s Evolving Role in an Uncertain World, and topics included Asia’s Challenges and Prospects over the Next Decade, Artificial Intelligence for Traditional Industries and A Long-Term Future in an Uncertain World.
Below is the first of two excerpts from GIC on the topic AI for traditional industries:
How is AI changing the landscape?
- By mimicking elements of human intelligence, AI can deliver insights beyond human experts and transform organisations by optimising execution and learning in real-time.
- Small, AI-focused companies can solve problems at a lower cost and outperform traditional competitors. New entrants are beating the initial wave of disruptors, eg. Google and Tesla.
- Companies need to realise that understanding and adopting AI requires a fundamental rethink of their mind-set and approach.
- AI requires a new approach, mindset and capability
- Best to start small and scale from there
- AI needs continuous improvement and iteration
- Needs a whole system to create value
- A clear strategy is more important than ‘easy’
- Early adopters of an AI first approach are outperforming their peers.
How is AI changing traditional companies?
- How does AI work?
- Existing systems are programmed by humans, working on a minimal subset of rules that can be applied and scaled for profit. But as tasks get more complex, more rules are required, driving costs higher.
- AI apes human reasoning, and can beat any set of human beings by copying their moves to optimise its end-state. As the last barriers that prevent AI from understanding the world we live in (vision, speech, natural language processing, etc) disappear, AI gains the capabilities once limited to humans, and can address increasingly complex and previously unsolvable problems.
- There are many compelling examples of these capabilities in the small, AI-focused companies operating today. However, it is not just traditional companies that are facing disruption by AI. Older companies who had once been viewed as disruptors in their own fields are now being outdone by younger competitors.
- Zymergen, a 5-year old company, produced a fully functional replacement for Deet, a cancer- causing mosquito repellent within a span of 6 months using an automated lab, surpassing the efforts and investment by major pharmaceutical companies;
- Embark, a company started by college dropouts, has developed a system of autonomous trucks at less than US$10 million, ahead of Google and Tesla.
How should companies respond?
- AI provides a suite of capabilities that can be used to mitigate or anticipate upcoming disruptions. This helps companies manage risks, optimise functions and stay ahead of the game.
- Primer Intelligence uses AI to distill information from petabytes of documents, replacing the role of analysts and consultants
- Jupiter Intelligence uses AI to predict climate risks (sudden freezes, hurricanes, etc.) from 6 months to 50 years in advance, providing organisations more accurate risk assessments thantraditional insurance companies.
- Large organisations can leverage their existing position and assets to their advantage, and use AI to reinvent their business models. However, they need an enabler (eg. Element AI).
- Element AI, helps companies transit to AI by providing access to smart technology and datasets that they cannot build themselves. They have pioneered transfer learning and representation learning and via their AI as a service platform, they can offer customized AI models, delivered through easy to integrate APIs interacting with real time, mission-critical systems. Their platform improves continuously as it learns from each project.
How to capitalise on AI’s full potential
- Companies need to transit from a command-and-control role to teaching AI systems, improving and iterating on the algorithms to ensure the whole system creates value. This will involve generating a hypothesis, looking for the right insights, and testing them out with simulations of their organisation and the world around them. AI is a formidable capability but it cannot learn on its own; it requires human guidance and constant upkeep. Only by doing so can AI help generate novel solutions to the business problems these companies face.
- AI cannot be “wrapped” around existing systems and data. This is because the current data most organisations possess is based on a record of past operations and biases, and existing systems are built upon assumptions that are no longer accurate. Companies need to build new datasets to replace older ones as most existing data sets are simply not good enough for AI.
- They cannot use AI as a bolt-on “feature” to get more information, but need to incorporate it into their strategic planning and transform into digital businesses.
- Companies will need to look beyond their past corporate partners, and access founders and talents that may not move in the same circles. Many of these talents will be in Silicon Valley, but organisations should also look to hubs that are home to innovative companies linked up with universities and labs, such as Montreal, Toronto, London, Beijing, Tokyo and Singapore.