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As Spain celebrated the successful end of their campaign, Kambi was reflecting on a significant achievement of its own. For the sports betting technology provider, the tournament represented a major technological milestone: its first World Cup traded entirely by AI.

Across 104 matches and more than 100 million bets, AI handled every stage of the pricing lifecycle – from ingesting data and creating the offering to managing liabilities and settling bets. The platform recorded zero downtime, delivered an operator trading margin of 18% and, during the final alone, processed more than one million unique bet combinations.

Kambi had set itself the ambitious target of delivering the entire tournament through AI. By the time Spain lifted the trophy, it had provided compelling evidence that automated trading could withstand the scale and intensity of sport’s biggest betting event while improving the experience offered to operators and their customers.

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In the old model, Grenstad says, the product owner decided what to build, the developer decided how to build it, QA tested it and operations eventually handled the release.

As discussed last month, QA has become part of the process rather than a final stage. Product engineers consider from the planning stage what can be automated and what needs manual testing, while QA specialists now strengthen automation and identify gaps in coverage.

The responsibility for a piece of work increasingly extends from identifying the problem to building, releasing and measuring the result.

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The report singled out several marketing tactics which had been flagged as particuarly concerning.

The first was direct marketing (emails, texts, push notifications). Citing randomised studies, the committee noted that limiting direct marketing resulted in reduced betting and fewer short-term harms. 

Referencing an Australian study on direct marketing and its link to gambling harms, Dr Philip Newall, senior lecturer at the University of Bristol noted that “causality was established by getting a random subset of participants to opt-out of receiving direct marketing offers”.