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Implementing AI In Business

Tips On Introducing Artificial Intelligence In Your Business

As continues to grow and permeate seemingly every aspect of business, it’s important to cut through the noise and focus on where fits in your organization and how to best implement it.

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There are myriad articles on and its application in business. As continues to grow and permeate seemingly every aspect of business, it’s important to cut through the noise and focus on where fits in your organization and how to best implement it.

I’m the founder and CEO of an -based customer relationship management platform. Through this experience, I’ve learned a few ways leaders can determine their own approach to .

A Brief Overview Of Application In B2B 

Broadly speaking, is a branch of computer science concerned with replicating human intelligence in machines. Depending on whether you run a business-to-consumer or business-to-business company, you might find some types of more relevant to your business than others.

In B2B, is all about data and analysis to make better-informed decisions. For example, if you have enough sales and customer data, you can use to figure out your ideal customer profile and/or potential customer base and adjust your marketing strategy and campaigns accordingly.

In more technical terms, applications in B2B can be broken into three types of : supervised, unsupervised and reinforcement.

In the case of supervised learning, you or someone with business intelligence skills feeds the data to the learning algorithm (a statistics algorithm) and sets a goal (what you want to get to or what you’re looking for). The machine then tries to match that goal.

In , the algorithm looks at the data and searches for patterns. As the name suggests, there are no instructions given prior to the analysis. For example, it can look at your customer data and decide that you have a cluster of customers in the manufacturing industry that looks really promising.

Finally, in reinforcement learning, which is more advanced, the algorithm looks at the data and comes up with a set of conclusions. You don’t provide a predefined dataset or any guidance; it’s more of a trial-and-error method. You look at the results and tell it whether the conclusions are correct, and it continues to reinforce the right steps to get to an endpoint.

How can benefit your business?

For businesses that collect a lot of customer data at every point, being able to use to derive meaning from that data can help get ahead of the competition. You can spot trends early and identify areas where you’re losing revenue or where you could potentially gain revenue. You can then make data-driven decisions and quickly adapt to changes.

can also impact your CRM system and team productivity by helping identify leads, building effective nurture campaigns or personalizing the customer experience. (A number of companies, my own included, offer CRM and marketing solutions.) […]

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