The buy side generally has a poor track record of adopting new technologies and artificial intelligence looks to be the latest entry on a long list.

In a recent survey of 60 buy-side head traders conducted by the Tabb Group, only 7% are using cognitive technology in their trade execution workflow.

Of the rest of the respondents 20% want to implement the technology but feel they do not have the technical resources to do so, and another 20% said they were investigating the technology, according to Dayle Scher, a senior analyst at Tabb Group and who conducted the survey.

“However, 56% of the head trades have no plans at all to implement any kind of cognitive technologies across their trade execution workflows,” she said as she moderated a panel during her firm’s FinTech Festival conference.

The results are in line with what panelist Gaurav Chakravorty, co-founder and head of portfolio management at investment firm Qplum, has seen across the buy side.

“If you think of the $164 trillion landscape of asset management,  the use of quant trading is around $300 to $320 billion,” he said. “Out of that, AI trading probably accounts for not more than $4 to $5 billion in assets under management.”

Given quants’ expertise, it is not surprising that quant houses lead the way in AI adoption, noted fellow panelist Lucien Foster, head of fintech strategy & partnerships at BNY Mellon.

“In other industries, you’ll see first adopters having the knowledge that will trickle down to more traditional asset management base,” he added. “We are getting a lot of inbound interest in collaborating to co-develop or work around certain issues that clients would like to see improved, which is s a long process.”

Although many industry discussions focus on AIs outthinking and outperforming traders, the panel’s consensus was that firms should look at the use of AI beyond the front office.

For BNY Mellon, the custodian bank is taking a somewhat back-to-front approach when rolling out AI-based implementations. “Some of this is tactical and operational in nature,” said Foster. “If you can save money, that’s great, and if you can execute trades, that is great too.”

The bank is using AI to improve intelligent routing of email, credit and debit matching, as well as forecasting trade and settlement breaks.

“All of those processes work to make the bank more efficient,” said Foster. “It obviously saves money in a lot of different areas and serves the client quite profoundly in the near-term.”

BNY Mellon also plans to implement AI-based predictive analytics in the future.

Cost savings is an attractive driver for AI adoption, agreed Chakravorty. “Costs are why people feel that 100-year old indexing strategy is better than anything new,” he said. “Because anything new is priced at a point where it does not make sense.”