5 Reasons Enterprises are Finding it Hard to Adopt AI

5 Reasons Enterprises are Finding it Hard to Adopt AI

Last year may have been AI’s biggest year. Yet its application in real-world use cases are still very far. Why are companies and enterprises struggling to adopt AI? We asked leading AI expert Yves Bergquist to explain the AI – enterprise bottleneck.

Erik: How do you summarize what happened with AI in 2018?

Yves: The past year was a pivotal one in AI. With buzz way down and real-life applications way up, practitioners, policymakers and community leaders started having serious discussions about it. There was intense and necessary talk about accountability, fairness, and transparency. In the lab, scientists started broadening the application of Deep Learning with great results, and even started tinkering with hybrid neural net-probabilistic graph models that many, including myself, consider key to more general AI. Challenges still abound in the private sector, but they’re getting clearer, and the goal of seeing real AI products and services is nearer than ever.

How you feel about what happened in Artificial Intelligence in 2018 depends on where you look from. If you’re a researcher, 2018 was truly a pivotal year. You got to play with a gaggle of exciting new toys, witness deliciously pugnacious debate between two giants of the field, and you got to tinker with emerging and thrilling methods like probabilistic graph models and deep learning-enabled natural language processing.

If you’re an executive in a forward-thinking organization full of machine intelligence ambitions, you’re part of the lucky few who probably got to apply the gaggle of new machine learning frameworks to real world business problems such as logistics, drug discovery, autonomous driving, chatbots, or image/video/audio analysis. Congrats, you have the best job in the world.

If you’re an AI enthusiast in one of the remaining organizations, which is the majority of the world, you’re probably still fighting 2017’s dragons: myopic leadership, fragmented data, lack of funding, business unit politics, and a general misunderstanding of what AI really is.

Erik: What makes AI adoption so hard?

Yves:  Even today, applied AI is still very hard. Make no mistake: the tech IS there. What’s happening is that 19th century organizational models are trying to solve 21st century problems with 22nd century technology. It’s largely a problem of mindsets, investments and organizations.

To apply AI in enterprise, you need 5 things most companies don’t have:

  • Educated leaders.
  • Lots of properly curated data.
  • Lots of money to experiment.
  • A large and diverse team of absurdly expensive quants.
  • Most importantly – and rare- you need the freedom to apply it all to pursue uncertain goals through unproven methods … and fail miserably.

Despite its progress, AI is still very experimental. And it takes a very special kind of organization to put “give me $10 million and maybe you’ll get something” into action. The lab is where all of these conditions are met, not the boardroom. And once again it’s in the lab that 2018 was the most generous.

Erik: What’s going to help dislodge the AI – enterprise bottleneck?

Yves: The tech of AI is, for the most part, reliable enough to be carefully implemented. For the first time organizations have the ability to own the data infrastructure to support AI. You can buy petabytes of object storage at pennies per gigabyte to support massive implementations while taking advantage of fast data processing with NVMe™ flash memory and endless new processing modules for compute.

But mindsets, organizations, and business models, are not there yet.

This is much more than a challenge of skill, data infrastructure or even education. Sure, the gaping deficit of AI talent is a big factor holding back the application of AI in enterprise. And there’s even going to be a dedicated “AI college.” Having more executives specifically trained in artificial intelligence will definitely create a much stronger pull towards applied AI. But it’s unlikely that this will in and of itself dislodge the enterprise bottleneck.

What is needed is a true cultural revolution: organizations large and small will have to transform how they think about human vs machine knowledge, and how to augment the former with the latter. They will have rethink how they approach organizational agency and the power of the human mind to control and reduce risk. To deploy AI at scale, they will have to gradually shift some of that power and responsibility over to machines which they don’t yet fully trust or fully understand.

If you’re attending the Hollywood Professional Association (HPA) Tech Retreat, please join me! I’ll be talking about next generation workloads for the media industry in several roundtables and speaking sessions. Yves will also present a new paper on AI in Media & Entertainment that we helped sponsor.

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Yves Bergquist is an AI researcher and the Director of the “AI & Neuroscience in Media” Project at the Entertainment Technology Center (ETC), where his team is developing next-generation applications drawn from AI and neuroscience for the media and entertainment industry. At ETC, Yves and his team manage a half dozen research and development projects applying advanced AI and neuroscience methods to the challenges facing the entertainment industry, including AI-augmented content production, postproduction, and marketing.

Yves is also the founder and CEO of AI startup Corto, which is building a comprehensive knowledge engine to help media and entertainment companies develop deep, “genomics”-type insights into how their content resonates with audiences.

Erik Weaver is Global Director, Media & Entertainment Market Development at Western Digital, focused on the intersection of cloud and the media and entertainment industry. Addressing the ever-changing and evolving media landscape, he brings deep insights for every stage of the workflow – from object storage and all-flash arrays to hybrid cloud storage solutions and archive.

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