It is expected that 31% of organisations will be using Artificial Intelligence within the next twelve months, and 82% of those using machine learning have been able to better harness their data. These technologies are here to stay, and as more products emerge into the market, the ‘take over’ abilities of AI and machine learning are being called into question.

AI and machine learning can be applied to a variety of use cases. Start-ups in particular can benefit from using these tools as the research and data generated can help design more market relevant products and services, and take away some of the more administrative tasks, allowing smaller teams to focus their efforts where they matter most.

Established businesses can also benefit from the decision making and automated processing capabilities AI and machine learning are renowned for. Repetitive jobs such as data processing, can be enhanced beyond human capabilities and allow organisations to react more dynamically to the market. There’s a constant drive for companies to be competitive, and being able to change the user experience or offered service quick is vital to remaining at the forefront of customer needs.

Using AI and machine learning to extract data can help organisations better understand their business. This doesn’t mean that there needs to be a data overload. In fact, having a more selective approach to data can allow AI and machine learning systems to train themselves more effectively, and enable them to become more accurate and relevant, and ultimately make better recommendations and decisions. In order to train these technologies in this way however, it’s vital that teams first understand the data they really need, what they don’t and the business goals behind the data collection.

Humans apply subtle sense checks to all of the decisions they make, and while AI and machine learning can help businesses to positively innovate, the potential for the misuse of AI is a big topic of discussion.  Without understanding the reasons behind the technology and the ways in which it could, and should be applied, there is a risk that the AI ‘takeover’ may prove more detrimental than dynamic. With this in mind, it’s important to have the right team behind the tools.

Want to know more about how our team approach AI and where they think it can best make an impact? Take a listen to our podcast: https://soundcloud.com/softwire_techtalks/ai-machine-learning