Look to the future

With the increase in AI within businesses, and the increase in spending on AI within businesses that already have AI projects, comes a larger number of mistakes.

The IDC predicts that global spending on AI will reach nearly $120 bn in 2022 and $300 bn by 2026. This means that even if your company has been putting off moving into the Artificial Intelligence space, it would be unwise to delay further, especially in coming times of economic instability when automation, faster processing and mining value from data could be the deciding factors of your future success.

We have found the biggest three mistakes you could make, so that you can avoid these pitfalls and hit the ground running.

1. Mixing up the terms

If you are to be involved with your organisation’s AI project, you will need to understand that there is no such thing as an interchangeable term when it comes to this space. There are a few abbreviations to learn as well.

AI = Artificial intelligence – tech that can understand things like a human can

DL = Deep Learning – algorithms that learn to understand patterns over an extended period of time

ML = Machine Learning – machines running tests in order to gain an understanding

DS = Data Science – using techniques and tools on large amounts of data to uncover patterns and find useful information

DL and ML are a subset of AI. DS uses DL and ML to build predictive models. DL and ML run tests and learn information then feed that information into an AI system so that it can do its job of sensing, navigating and executing actions within its environment.

Knowing how these systems and concepts interact helps businesses build great teams

2. Thinking You Don’t Need AI At All

It is a myth that AI is a business luxury. Of course, some companies do not need it. To write it off as a trend that will fall out of fashion is a mistake you cannot afford to make as it is highly likely that, once up and running, it will start returning on the investment within a relatively short period by automating, analysing and generating insight. Once you have made a serious case for the adoption of AI within your business, it is time to build a team.

3. Misunderstanding How AI Works

To sum it up as simply as we can, AI uses algorithms to study data and use insights to improve itself. Once it is knowledgeable enough it can be left to decide on issues, each time becoming more accurate. Some businesses have tech that does the first part but does not do the second part, which drains resources without giving them maximum returns. Having the best team means you have people who have experience in what to do and what not to do in order to make the teaching of the AI not so labour intensive before it becomes self-sufficient. Quality, pre-sifted data is needed in this task so it is important that you understand that data experts will be required too. Think of it as a human reading one badly-researched book. They will take the wrong lessons from that book and apply that information to make decisions, which will be wrong. The more you read, the more you learn and the more you learn how to sort good info from bad. AI is prone to bias, as humans are, so it is important to ensure diversity of input, which means both the datasets and the people working on the team.

If you are recruiting for AI, Data, ML, DL or Cloud, it really helps to have a dedicated, specialist tech team building recruitment partner to give advice and find the best hidden candidates.

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.