Its 2018 and if you want to get in on this right?!
Your first step is to study AI on a basic level to bring up some of your own nasty AI solutions.

1. Free online videos

Some excellent playlists to watch (ordered for better understanding):

Learn Python for Data Science

Intro to Tensorflow

Intro to Deep Learning (Udacity Nanodegree)

The Math of Intelligence

2. Online courses

Second you have to suck in knowledge from online courses, some excellent resources are:

Andrew Ng - Deep Learning Specialization

https://www.coursera.org/specializations/deep-learning
About Andrew Ng: Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain and really an "AI rockstar" in the AI community

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Jeremy Howard - fast.ai

http://course.fast.ai/

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3. Books

Third, read some books! One great choice would be this one:

Ian Goodfellow and Yoshua Bengio and Aaron Courville - Deep Learning

It helps you to with the terms in math of deep learning.
Available for free on http://www.deeplearningbook.org/

4. Collect your Data

Ever heard about "garbage in, garbage out"?
The quality of your data is the most important part of the machine learning pipeline even more so than the architecture of your AI model.

Github

The easy way to get data is to search for public datasets. You found awesome datasets on Github.
https://github.com/awesomedata/awesome-public-datasets

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UC Irvine Machine Learning Repository

https://archive.ics.uci.edu/ml/index.php

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Kaggle Datasets

https://www.kaggle.com/datasets

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Create/buy your own datasets

DataCircle - where you can buy or directly exchange datasets with other people
https://datacircle.io/

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Scrape data yourself using python with a library like Scrapy, Selenium, Beautiful Soup 4 etc.
Example: Retrieve Images from Wikipedia - https://gist.github.com/iwek/3100809
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Diggernaut - turn website content into datasets. No programming skills required
https://www.diggernaut.com/
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Last words

Keep in mind, big companies like Google have a huge advantage when it comes to building horizontal products that can apply to many industries like image recognition, language translation or infrastructure. But the advantage that smaller players have, like you, is that you can move fast on a single problem vertically. The big ones dont have to time to tackle every single niche problem. But you do! You can focus on the enterprise and build some niche solution that would help companies (and sell it to them).

One way to raise awareness of your product is to raising your own personal profile, establish yourself as an AI thought leader and publish blog posts on https://medium.com . Create blog content that answers fundamental questions about AI and build an audience. Then share it on Social Media or on Hacker News (https://news.ycombinator.com).
Example https://medium.com/@dtfoster

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Let me end this with an inspirational meme :

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The finest resources for Machine Learning & Deep Learning (coursera, fast.ai, kaggle and many more )