Why You Should Think Of AI As A Team Sport

  • “Domain expertise is key; machines don’t have the depth of context that people have, and people need to know the business and data well enough to understand which actions to take based on any insights or recommendations that are surfaced.
  • The 2021 Gartner® The State of Data Science and Machine Learning (DSML) report states that “client demand is shifting, with less-technical audiences wanting to apply DSML more easily, experts needing to improve productivity and enterprises requiring shorter time to value for their investments
  • With a more iterative, revise-and-redeploy model building processes, people with business context can get value from AI faster—even deploying new models to thousands of users within days to weeks, instead of weeks to months. This is especially powerful for those teams whose unique challenges may not be a high priority for data science teams, but can benefit from the speed and thoroughness of AI analysis.
  • McKinsey’s latest global survey on AI revealed that within 34% of high-performing organizations “a dedicated training center develops nontechnical personnel’s AI skills through hand-on learning,” compared to only 14% of all others surveyed. Additionally, in 39% of high-performing organizations “there are designated channels of communications and touchpoints between AI users and the organization’s data science team,” compared to only 20% of others.”


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