AI Across Industries – Where We Are, Where We’re Going

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HAL 9000 might be closer, more useful and friendlier than we think. Artificial Intelligence (AI) might not be here in a fully realized form yet, but it’s already disrupting a lot of industries that we work in.

As our customers discuss, question and evaluate the ways they can leverage AI to help streamline their workflows, I thought that I’d share some of the ways industry leaders leverage AI across media & broadcast, oil & gas and life sciences.

 

Media and Broadcast

Feeding Personalization

As viewers come to want and expect more personalized experiences when consuming content, media & broadcast companies have turned to AI to help deliver unique viewing experiences, tailored for each viewer.

One real-world example of how AI is personalizing the viewer experience is the customized recommendations across media platforms such as YouTube and Netflix. These companies rely on AI-based algorithms to create all of the personalized homepages and recommendation pages across their sites.

Keeping the Log

AI also works behind the scenes to streamline workflows and maximize ROI for broadcast companies, and one example of AI in production is logging.

Currently, staff manually view footage and log clips based on the content itself. For a hockey game, clips would be logged as “goal,” “penalty,” and so one, making it easier to search and access for highlights. AI is beginning to perform this task, removing the manual requirements. It's not quite here today, as AI still needs to be “taught” what to look for. But the more its used, the better it gets.

 

Oil and Gas

Finding Oil with AI

As resources become more scarce and remaining deposits become harder to extract from, oil & gas companies have to continually innovate and develop new ways to extract oil. AI has begun to help solve these problems by delivering new extraction methods, and one way is through intelligent robots.

New intelligent robots can identify natural “seepage” between rocks on the ocean floor, and since they can be controlled remotely, it removes the need to risk the safety of scientists and geologists physically exploring the ocean floor. “Our goal is to have these submersibles embody the reasoning of the scientists that program them. You want the explorer to do the science without the scientist there” - Professor Brian Williams, MIT.

 

Life Sciences

Better Engagement

We’ve seen “chatbots” answer customer questions, and now this type of AI is entering life science and pharma. When deploying machine learning across commercial departments, AI and machine learning can engage with customers on a more customized and personal level. As said Matthew Van Wingerden, head of machine learning services at Aktana stated, "Machine learning optimizes field execution by predicting the best message, channel and timing for each customer.”

Get Specific - Segmentation

A very manual process remaining in life sciences is segmentation - ordering and segregating patients by their needs, ailments etc. Being a manual process, segmentation is limited to human capabilities, so it remains basic and open to human error.

By using AI, segmentation can gain much deeper levels of complexity, allowing for limitless possibilities. With AI adding deeper granularity, segmentation can further personal engagement since they can be segmented on their specific needs, no matter how unique or marginal.

 

With Innovation, Comes Data

Across all of the industries above, and many more, the emergence of AI will bring something with it: even more data. Be it multiple versions of video content in broadcast, the data generated by robots exploring the ocean, or richer patient profiles, AI will generate even more data, meaning more data to analyze, to collaborate with and to share and potentially to move. Furthermore, since efficiency is a mantra of AI, it will generate this data much faster.

It's still a new world we’re entering, but we look forward to hearing more from our customers on the innovative ways they leverage AI, and how we can help them move all of the new data that is generated through these innovations.