How to Decide Which AI Solution Your Business Needs


In the past several years we have witnessed exponential growth in the quantity of digital data created, gathered, and analyzed by businesses, using artificial intelligence (AI). Using AI, businesses are able to optimize churn analysis, sales forecasting, product recommendations, and many more. A recent survey demonstrated that companies that utilize AI for predictive purposes, on average, outperform companies that do not. We commonly hear that these days, introducing AI into your business has become easier than ever before. 

AI: Why isn’t everybody doing it?

If introducing AI into your business is so easy and so effective, then we need to ask the obvious question:

Why doesn’t everybody do it?

We believe there are three main reasons for this: 

There is a shortage of data scientists

Data scientists are professionals whose expertise lay in manipulating and analyzing vast amounts of data using AI algorithms. This very specific expertise relies on many years of academic education in mathematics, statistics, and computer science. The extreme growth in demand for data scientists coupled with the long and rigorous process it takes to train one has made it very difficult and expensive for organizations to recruit them. 

AI projects are extremely expensive

A recent article by Forbes states that the cost of employing a data scientist in the United States might be as high as $300,000 per year. Of course, the shortage of data scientists means this price isn’t expected to drop anytime soon, making the cost of AI projects expensive. 

The performance of AI systems degrades over time

common misconception is that AI tools, like many other software- based tools, will maintain a stable performance over time. The reality is that an AI model represents the signals and trends in the data in a specific time frame. Once the reality of the business changes, the data and the performance of the AI model degrades since it only represents the signal in the original data. Sometimes, retraining an AI model to match the new data solves this problem, but often, we need to create a new model which means starting the R&D process from scratch (which, again, means spending more time and money).

AI: How To Make It Your Own 

In order to avoid these pitfalls, a business needs to clearly define its AI needs and choose the solution that best works for them.

There are a few ways to go about this: 

  • Outsource
  • Build an In-house Data Science Team 
  • Turn to Data Science as a Service 

Recruiting a data science team to address your AI needs is probably the best way to ensure the performance quality of your AI tools. However, a lot of businesses avoid this approach because it is extremely hard to recruit quality data scientists and because of the high costs that come with it. 

Outsourcing to an experienced data science provider brings you a mature AI solution in less time than it takes to build an in-house team. However, the cost of outsourcing AI projects is still very high, and this approach does not deal with the issue of performance degradation. When businesses first commit to outsourcing such projects, they do not take into account the cost of ongoing support and end up paying more than they had planned on maintenance. 

Since there is a growing demand for AI analysis, companies specializing in AI have recently started providing automated services that enable non-technological or semi-technological users to utilize these types of tools. These tools, if designed properly, will phrase analytical questions in business terms rather than in mathematical terms, and their results will be easily interpreted by a layman. Connecting to these tools should be easy via simple APIs or by using basic tools such as Microsoft Excel. 

Turn to Data Science as a Service 

The performance and accuracy of data science services might not be as high as solutions designed by experts to solve a specific problem. However, these tools have two main advantages:

(a) They are significantly cheaper than solutions that require an R&D process

(b) The degradation of performance and accuracy is no longer an issue. The service providers invest in the needed R&D to prevent the degradation and always keep up to date and integrate the most efficient and safe tools and apps.


There are a few questions you can ask yourself to uncover the solution is best for your business needs.

Click here to get the full  white paper with these questions and a guide to how to choose your solution.


Nili Goldberg

Author Nili Goldberg

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