Barrier to AI in the Enterprise: Access to High Quality Data

Ashesh Shah
December 8, 2017
673 Views

According to a recent Teradata study, 80% of IT and business decision-makers have already implemented some form of artificial intelligence (AI) in their business.
The study also found that companies have a desire to increase AI spending. Forty-two percent of respondents to the Teradata study said they thought there was more room for AI implementation across the business, and 30% said their organizations weren’t investing enough in AI.
Forrester recently released their 2018 Predictions and also found that firms have an interest investing in AI. Fifty-one percent of their 2017 respondents said their firms were investing in AI, up from 40% in 2016, and 70% of respondents said their firms will have implemented AI within the next 12 months.
While the interest to invest in and grow AI implementation is there, 91% of respondents to the Teradata survey said they expect to see barriers get in the way of investing in and implementing AI.
Forty percent of respondents to the Teradata study said a lack of IT infrastructure was preventing AI implementation, making it their number one barrier to AI. The second most cited challenge, noted by 30% of Teradata respondents, was lack of access to talent and understanding.
“A lot of the survey results were 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 an emerging space,” says Atif Kureishy, Global Vice President of Emerging Practices at Think Big Analytics, a Teradata company.
When it comes to barriers to AI, Kureishy thinks that the greatest obstacles to AI are actually found much farther down the list noted by respondents.
“The biggest challenge [organizations] need to overcome is getting access to data. It’s the seventh barrier [on the list], but it’s the one they need to overcome the most,” says Kureishy.
Kureishy believes that because AI has the eye of the C-suite, organizations are going to find the money and infrastructure and talent. “But you need access to high-quality data, that drives training of these [AI] models,” he says.
Michele Goetz, principal analyst at Forrester and co-author of the Forrester report, “Predictions 2018: The Honeymoon For AI Is Over,” also says that data could be the greatest barrier to AI adoption.
“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,” she says.
Read the source article at InformationWeek.com.
 
Source: AI Trends

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