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AI Success and the Early Wins

October 13, 2019
I help early-stage, and high growth companies build new products. My tool of choice is data science and AI. For many of these companies, this is their first adventure in this area. The executive team is talking about it, the investors are asking for it, but how to move forward is unclear. We are in the middle of defining this new field, and what works is a work in progress.

In a recent survey of C-level executives (mostly chief data, analytics, or information officers), we can see how the industry is rapidly changing. Of the respondents, 92% are increasing investment in data science and AI, while 62% have already seen measurable results in these areas.

In a less favorable light,

54% of executives reported that an inability to be nimble and compete on data presented the most significant competitive threat that they faced
79% of executives feared disruption from data-driven competitors.

Andrew Ng speaks of AI as the new electricity. Andrew talks about his early success in adapting AI at Google almost a decade ago, and he credits much of the success of starting small.

In the last few years, we have helped dozens of companies switch to AI and data-driven direction. In my experience, starting small with pilot projects does significantly increase the rate of long term success. We typically look for a plan with a high probability of success that will take 2 to 6 months, depending on the company's size and goals.

Data science and AI is a new language. All levels of a company need to learn to dialog in this language. Starting with pilot projects allows everyone to learn the process and see the potential. An early win provides buy-in for the more significant steps and makes that transition into a data-driven company.

Written by

Angela Wilkins

I like science, machine learning, start-ups, venture capital, and technology.
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