AI to practice as you play

I’m a die-hard sports fan, as a player and watcher, so I find it interesting how parallels are drawn between work and sport – in particular, that “you practice as you play.” Alternatively, it’s implied that if you practice sloppy, you play sloppy. But if we look at a professional athlete that dedicates 40 hours per week to their craft, perhaps only an hour of that week is actually IN the event, leaving 39 hours to fine-tune FOR the event. In work, we don’t really get that luxury. If we work 40 hours, we rarely get the chance to practice for an outcome. We’re generally using most of the 40 hours to deliver outcomes IN the event. So at face value, I find this common analogy a strange one.

There are some areas of relevance for OSS though:

  • Crisis management – taking the time to prepare the team for crises to ensure mitigation of the negative contingencies that could effect business operation. We want to practice these events regularly like an athlete to ensure that we recover as quickly as possible from adverse conditions. Set the chaos monkeys loose!
  • In-flight training – Whereas an athlete can train certain practice drills to enhance performance before their event and monitor indicators to use as feedback for fine-tuning, OSS exponents don’t get that chance. However, we do have the ability to collect data, use it as feedback to improve our in-flight performance via decision support and trial different techniques to compare outputs, just like a coach refining an athlete’s drills
  • Match simulations / projections – An athlete will use feedback from training and events to identify strengths / weaknesses and simulate scenarios in their events. In OSS we are caught up in the event but still have to project forward. We have capacity planning tools and lots of historical data to prepare sophisticated predictive simulations (and optimisations) from

As you can see from the three points above, they’re all about utilising feedback whilst in the event because we don’t get much downtime before the event. Machine learning and artificial intelligence represent an exciting opportunity for OSS because they give us an opportunity to train and refine our OSS whilst we’re in the event, allowing us to indeed practice as we play.

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