• Nicky

Becoming AI Native: 7 Lessons from the Trenches

This article is originally posted on Gigster.com

A discussion on the road ahead for Artificial Intelligence on the development of new conventions and best practices with other major technology shifts.

Photo by Michael Dziedzic on Unsplash

“AI is the answer to so many real world challenges – moving cars, curing patients, delivering things on time, predicting agricultural yields. The number of applications is crazy.” – Hussein Mehanna, Former Director of AI Product & Engineering at Google

Previous technology shifts have largely been about improving business workflows – important, but somewhat abstract. AI, in contrast, applies directly to objects and decisions in the real world. The key insight is that unlike the PC, web and mobile revolutions that came before – AI has the ability to move and impact tangible things like cars, patients and crops. It’s a genuine big deal.

While there is no shortage of claims about the potential for AI in the enterprise, concrete examples of AI gains, as well as how to achieve them through repeatable systems, are harder to find. Gigster has delivered over 1,500 digital transformation projects, with over half of our projects now involving AI algorithms. This is one of two blog posts inspired by roundtable conversations Gigster had with partners and customers at the Google Next conference in San Francisco.

1. Point Projects at High Value Opportunities

“Everything comes down from the basic mission we set for ourselves.” – Victor Martin, VP of AI and Geosciences at Total

As the latest shiny object, AI is in danger of being over-applied as a pixie dust to every project. Many projects can benefit from AI, so the challenge is to select projects that will benefit the most.

At Total, this analysis starts by identifying the high level objectives for each project, such as reducing costs or increasing safety and security. These objectives are then mapped to holistic approaches that deliver the highest value and only incorporate AI as a means to an end. It’s critical to understand that while the effective use of AI does drive defensibility, it should be a component of a broader product strategy that’s laser focused on solving a real problem. Martin Casado of a16z wrote a good cautionary tale on assuming that just accumulating more and more data and leveraging AI will magically deliver more value to a business.

2. Knowing why is often more important than knowing what

“Explainability is the top problem forward thinking AI organizations have.” – Hussein Mehanna, Former Director of AI Product & Engineering at Google

There are crucial legal and ethical reasons to care about making models that can explain how they make their predictions so that we can understand the underlying “why.” In healthcare and finance, for example, the decisions being made by AI are in a heavily regulated context and without more insight into how these decisions were made, adoption will continue to be limited.

As AI methods like deep learning become increasingly sophisticated, figuring out what is going on inside the “black box” is critical. For example, Microsoft recently released InterpretML – a toolkit for building explainable systems giving them a leg up in the AI arms race.

3. Don’t be Sears

“You can’t fake AI commitment any more than you could fake e-commerce commitment. Either you were a company that went web native – like Amazon – or you were a company that put a website up and declared victory – like Sears.”Frank Chen

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