Applying AI to SMBs

Jeff Kimmel
Nov 5, 2020 · 4 min read

How does machine learning apply to your business?

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We live in a world where data is being generated all around us and large organizations are pushing through a digital transformation to extract as much value as possible from it. So with large enterprises driving towards the implementation of ML and AI, how can it be that only 51% of SMBs consider it to be important to their business?

I think the answer to the question lies in another question. How many understand how AI and ML could impact their business?

Incorporating machine learning and artificial intelligence into your organization can be time consuming and expensive. Time and money are typically not things that SMBs have to waste, and thus they are not targeted by the large technology providers. Because they are not the target of marketing and advertising, it is presented in a way that insinuates AI is not for them.

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Humans are often visual learners. They associate concepts with the imagery that is presented to them. If you do Google image search on “What is AI?”, the image above is one of the first to come up. Artificial Intelligence has been presented in popular culture not in business terms but in terms synonymous with robots and self-driving cars. It is not a surprise that SMBs don’t view AI as important to them if they are constantly told only a piece of what AI represents.

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In reality, AI encompasses a lot of different disciplines and technologies. It is like asking the question of what is a business without understanding that a business is the sum of its parts (operations, accounting, technology, etc). Understanding the sub-disciplines of AI will help SMBs to truly understand whether AI is important to their business. The answer may still be no, but we are holding SMBs back by not painting the full picture.

What is Machine Learning?

Machine learning (ML) is the study of computer algorithms that improve automatically through experience.[1] It is seen as a subset of artificial intelligence. (wikipedia)

What is Predictive Analytics?

One of the primary drivers for this is that people don’t view what they do as being predictive. We are taught to think of predictions in terms of weather, elections, etc. In fact, people make predictions in their business every day. We just refer to them as decisions. If you decompose your business into a series of decisions, predictive analytics can effectively analyze data to automate the repetitive decisions that you make every day. To take that further, AI has the ability to go beyond human capabilities in finding patterns in large data sets to uncover insights that may be critical to detecting faults or potential growth opportunities for your business. Examples of predictive analytics include:

Predicting an event: monitoring for potential failures of machines, monitoring the health of your crop, analyze whether payments will be received on time for cash flow forecasting

Predict a Value: optimize your product pricing, forecast the demand for products

Discover Outliers: uncover potential fraud, looks for potential defects, discover errors on invoices or accounting records

Group like items: segment customers for targeted marketing


At elipsa, we are building plug and play predictive tools through Approachable AI. We seek to simplify predictive analytics to allow SMBs to easily apply machine learning to their business data. Through an intuitive and easy to follow interface, users can build predictive models in order to truly see for themselves whether AI is applicable to the future of their business.


Approachable AI for Business Users

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