Back to Blog

Rethinking Your Relationship with Machine Learning

Jack Riewe

As we enter the last month of 2019, Obviously AI is looking towards next year’s goals. We discuss the necessities that need to happen: continue to help product managers and analysts utilize Natural Language Powered Data Science, grow as a business, always improve our platform, etc., but naturally, we start getting into philosophical discussions about the functions of machine learning in our user’s lives. One of the biggest challenges of next year is to help rethink the relationship you have with machine learning

Our Relationship With AI is Evolving.

In the 1990s, IBM’s Deep Blue was winning AI chess matches, but machine learning wasn’t available to the general public. In the age of modern machine learning, humans consume some sort of AI every day, but how machine learning works is still vaguely familiar. Businesses use to treat AI/ML as a mythical technology they had to hire very specialized statisticians to execute. Now, AI/ML is democratized and much more accessible.

For example, in 2006 Netflix offered 1 million dollars to anyone who could beat its consumer film rating algorithm. The BellKor team of AT&T scientists took the prize 3 years later.

In this video, you can see the complicated process it took to build and test an algorithm.


And just 3 years ago, Natural Language Processing entered the realm of personal shopping when IBM Watson and North Face teamed up to help shoppers find products through conversation. Now machine learning is solving bigger problems in online trolling, facial recognition, etc., but still, businesses are having trouble deciding what they want from AI.

How Businesses Want to Use AI/ML.

While researching this topic, we found businesses are interested in analytics and machine learning.


61% of companies in this survey by the Wall Street Journal are planning to use AI within 5 years for business analytics, 45% for Machine Learning and 21% for self-learning robots.


In the present day, no-code technology like Webflow, WordPress, Shopify, Weebly, AirTable, or Tiled allows product managers to build products without technical expertise. As the power is slowly slipping away from a monopoly of machine learning engineers building algorithms, the ML process is becoming more collaborative and available to those without a technical background. This shift allows a more seamless transition for those looking to add AI/ML into their workflow than ever before. 

While we’re constantly saying AI and ML are easy to use in the present day, that attitude won’t be adopted overnight. That’s why we want to introduce a new way of thinking about your relationship with machine learning to simplify explaining how an ML tool like Obviously AI can be used for business analytics. 

Think of Your Machine Learning Process as Google for Enterprise Data.

After talking to customers, one of the biggest challenges they face is the abilities that are available with machine learning. 

Going forward, we want our users to treat any of their data as queryable, so look at machine learning like this: Google for Enterprise Data.

It seems like we’ve been talking about Natural Language Predictions and Analytics a bunch in recent blog posts. We even created a whole guide on how to talk to your data using Natural Language, but to drive the point home, talking to your data in your Natural Language totally changes the machine learning process forever. If you want to learn more about the rise of no-code tools, read this uber-popular and important article.

Visit our Product Page to make your data queryable.

Share on social media 

More from the Blog

Give Up the SQL Query and Gain Data Independence

Being data-independent promotes collaboration, speed, and creativity with analyzing and making predictions.

Read Story

The Most Underrated Skill in Predictive Analytics

There is a skill that overshadows technical machine learning skills in solving problems, integrating data into your company’s workflow, and using data to its fullest potential.

Read Story

Your Team's Success Depends on Your Data Literacy

So you’ve added ML into your workflow, now what? How do you analyze and get value out of the data you collected? You have to be data literate.

Read Story

Never miss a minute.

We will never share your email address with third parties.