Various organizations all around the globe are investing heavily in the likes of artificial intelligence (AI), and that’s a trend that’s likely to carry on for quite some time. A recent Teradata study revealed that around 80% of the IT managers and other important decision makers surveyed said they’d already implemented AI into their businesses in one way or another.
However there are still obstacles for many firms to overcome before AI can be fully integrated into their businesses, including a lack of infrastructure. “A lot of the survey results weer in alignment with what we’ve experienced with our customers and what we’re seeing across all industries — talent continues to be a challenge in emerging space,” said Atif Kureishy, Global Vice President of Emerging Practices at think Big Analytics.
According to Kureishy the hardest part of working with AI is gaining access to data. “It all comes down to how do you make sure you have the right data and you’ve prepared it for your AI algorithm to digest,” says principal analyst at Forrester, Michele Goetz. “AI is really recognized by companies as a way to create better relationships and better experiences with their customers.”
The integration and adoption of AI across the world will affect the way owners think about their own business models. “It’s very resource intensive to adopt [AI] without a clear understanding of what [it] is going to do,” says Goetz. “So, you’re seeing there’s more thought going into [the question of] how will this change my business process.”
However, Goetz is also keen to point out that AI’s not about replacing employees. Instead, it’s about get the most value out of them as possible. “Instead of focusing on drudge work or answering questions that a virtual agent to answer, you can allow those employees to be more creative and think more strategically in the way that they approach tasks,” she says.
To succeed in business today, firms need to grab a slice of that AI pie, and they in which they do that is through data engineering. Data engineers are responsible for “compiling and installing database systems, writing complex queries, scaling to multiple machines, and putting disaster recovery systems into place,” according to a recent Udacity blog post. So basically they do all the prep work ready for the data scientists and at the moment are in very high demand.