The Toronto Star | Ajay Agrawal, Joshua Gans, Avi Goldfarb | Nov 26, 2022

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Five years ago, artificial intelligence (AI) experts would have guessed that the first AI “unicorn” startup (a venture valued at more than $1 billion) in Canada would be from Toronto, Montreal, or Edmonton. But the experts were wrong. 

• We thought the first AI unicorn would be where the cutting-edge research took place. Better technology would be all it took.  Instead, Canada’s first unicorn AI startup hailed from St John’s, Newfoundland, far from where Canada’s research leaders were developing cutting-edge AI. Verafin gave financial institutions the tools to weed out money laundering and fraud. In 2020, NASDAQ bought it for $2.75 billion (U.S.).
  • What did we all get so wrong? We were focused on the location of AI expertise rather than where AI could be most easily implemented.
• AI prediction machine: Verafin was already in the prediction business and already parsed financial transactions to find fraud.  When AI’s latest wave of progress arose 10 years ago, Verafin took those inventions and improved its product with AI at the centre. Its banking customers wanted prediction; AI supplied it.

See:  Why Artificial Intelligence Will Solve All Our Money Problems

• Chastened by our own predictive failure, we started to look closely at where AI was really being used — not by the tech stars, but by businesses.  Only about 11 per cent of businesses use AI somewhere in their operations. But most large businesses had spent meaningful investment dollars attempting to use AI. Why were they coming up short?
  • Example: When Air Canada developed an AI system to forecast freight demand and dramatically reduce the likelihood of empty cargo holds, it found that to put it in place, it had to train workers to pack the planes differently. In other words, it couldn’t simply adopt the enhanced prediction capability; it had to adapt the system in which it was embedded.
• Verafin gave us a clue. For them, adopting AI was easy. They were supplying predictions to their customers, who knew what to do with them. But in many other businesses, AI could supply predictions — say, about demand or supply-chain issues — but many businesses were not equipped to use it.

This is par for the course for radical technologies. AI is coming. It will transform many industries. To take the current, modest uptake as a forecast that dramatic disruptive change isn’t coming would be a big mistake.

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