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When Bill Gates was asked what he’d do if he had to drop out from Harvard and start a new company today, he replied, “I would start an AI firm whose motive is to teach computers how to read so that they can have all the knowledge of the world.”

Since then, several AI startups have been launched in healthcare, education, finance, etc.  There are two types of companies in the current AI market: startups and giant corporations.

However, the reality is that the current economic environment lets tech giants like Google and Baidu succeed, while SMBs companies are struggling to survive in this data-driven world.

This is because large corporations have access to more data, excellent talents, and resources, huge investments. What about the companies with annual revenue of €50 million and €1 billion? How are you supposed to compete with these giant companies?

Fortunately, mid-scale companies have enough revenue that they can scale their business and implement AI strategies. However, they often lack in finding talented resources to implement ideas.

Gladly, midsize companies can collaborate with AI vendors to fetch data and talent without recruiting teams or going bankrupt.

To survive in this AI-driven world, SMBs need to look at alternative methods they never thought of before. Polling data across companies can be a great option to help them thrive in the current market.

Let’s look at a few other perks which joint AI ventures can provide to mid-scale companies:

1) Joint AI ventures get access to enormous data; they can deploy via machine learning for revenue generation business operations. The vertical data pooling approach integrates all data into one chain, and machine learning algorithms leverage rich data chains for smooth operations.

Meanwhile, horizontal data pooling can be performed between companies that are not in competition with each other. It is executed to enhance the accuracy of ML systems and the value of AI-augmented providing.

2) Secondly, developing a robust AI-driven solution needs a team of expert data scientists, ML engineers. However, we know attracting such talented people for mid-scale companies is difficult. By sharing financial resources within AI ventures, these firms can hire in-house AI teams to leverage the vast data files. 

This integrated approach needs a paradigm shift from short-term benefits to network-centered perks. It is more helpful for SMBs to collaborate with AI companies to stay ahead of the competition. Though many AI companies have already implemented this, it’s still not too late for companies to take this step.