In 2016 and 2017 I spoke with dozens of venture capitalists, many of whom have a specific and overt focus on artificial intelligence technologies. I wanted to know what made an AI company worth investing in, and what business models were generally the most appealing for investment.
Episode Summary: At Emerj, we like to look around the corner at where AI is impacting industries and how people can make better business decisions based on that information. AI and data-driven software for enterprise is an emerging topic of interest, and in this episode we get a venture capitalist's perspective on where AI will play a vital and necessary role with real results in software and industry.
[This story has been revised and updated.]
Big data has turned out to be a key ingredient in turning machine learning from an abstract technology into a potentially invaluable tool of insight and foresight for businesses across industries. The burgeoning cognitive technologies of predictive analytics and data visualization are opening new windows of opportunity to companies trying to solve complex problems with multiple moving parts. From finding ways to retain new customers to more efficiently monitoring multiple performance metrics and easing performance volatility, more companies are gravitating towards machine learning-based data analysis tools in an effort to optimize operations and find innovative solutions and opportunities that were once too obscure for only the human eye.
This article is the third in a series part in a series about AI product development.
In the first installment in this series, we covered how to develop AI product ideas with both near-term adopt-ability and long-term potential.
Whether you're a startup or an enterprise, developing AI products is challenging.
Not only do you have to wrestle with the challenges of finding a use-case that where AI can actually deliver value into an enterprise workflow, but you also have UI concerns, and - often - much higher demands to monitor algorithmic drift and other technical issues.
Many books could be written on the subject of AI strategy, and we've seen that "strategy" means something different from one enterprise to the next. This article is a brief overview of the common steps in creating an AI strategy - in roughly the order that the steps are usually executed.
In July 2017, The State Council of China released the “New Generation Artificial Intelligence Development Plan," outlining China's strategy to build a US$150 billion Chinese AI industry in a few short years, and to become the leading nation in AI by the year 2030. Other nations followed suit quickly with national AI strategies of their own - with the US trailing behind by nearly two years before developing a semblance of an AI initiative. The proposed 2021 budget for the national security budget in the US is $740 billion - with a billions of dollars being earmarked for AI specifically (learn more: US Public Sector AI Opportunity Report).
Many business leaders make the mistake of believing that AI and machine learning are like regular IT, but this could not be further from the truth. In large part, this is because, unlike simple software solutions for discreet business problems, it can be very difficult to measure the ROI of machine learning.
Companies looking to apply AI are looking for a competitive advantage in their industry, something that will give them an edge in the market and help them grow. However, not every AI application can give a company a competitive advantage. Many AI applications are simply going to become the new normal.