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How AI Is Transforming the World of Work

Scaling AI Lynn Heidmann

When the role of data scientist first started taking shape in the early 2000s, the general attitude toward working with data (and, later, AI) was that it should be handled by experts, potentially sitting in a center of excellence or a data science team somewhere. Today, it’s all about Everyday AI. But what does that mean, and how is it transforming the way we work?

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“To me, Everyday AI just means making [working with data] a very ordinary, pedestrian, routine part of the business. Which doesn’t mean it’s not special and interesting work, it just means we’re starting from things that really matter to the business.” 

 — Shaun McGirr, EMEA RVP of AI Strategy

Watch Shaun McGirr, EMEA RVP of AI Strategy, as he shares one of his favorite real-life Everyday AI stories to illustrate how this technology is becoming ingrained in the day-to-day of work, even for roles that don’t have “data” or “analyst” in their title.

 

How Dataiku Is Transforming the World of Work

Dataiku is transforming the landscape by helping people on the business side move from observers in the AI revolution to AI creators. By bringing an easy-to-use, unified, collaborative AI platform — complete with AutoML and a visual, code-free interface — Dataiku empowers everyone to get insights from data.

Practically, this might look like:

  • A supply chain manager or merchandiser empowered to leverage massive amounts of data to extract key business patterns from sales and optimize product assortment.
  • Factory operators armed with AI-powered dashboards that help monitor production quality in real time and react to alerts.
  • Marketing teams incorporating machine learning to better understand the customer mix and deliver as well as respond to insights

In this case, as Shaun describes in the video, a product manager’s work was transformed by Dataiku. This person had no data science background and, in fact, doesn’t want to be a data scientist. What they’re responsible for for their company is the quality of service for the incoming email requests that they get. Before Dataiku, they used a black-box solution to route those incoming emails to the right people to respond. 

However, they hadn’t been able to understand from that black-box provider what “good” looks like when it comes to service quality, what the benchmarks are, and how they were improving over time. So this product manager took a bunch of data from their emails, put it in Dataiku, and in one afternoon — with no prior data science or machine learning experience — was able to automatically generate a bunch of models, and that model won’t be the one that goes into production, but it gave them something that they needed, which was a baseline. Something to measure against.

The Bottom Line: One Use Case at a Time

The world of work won’t be upended overnight by AI — augmenting human intelligence with machine intelligence to help people work smarter (not harder), such as in the examples above, happens one use case at a time.

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