This Survey Says AI-Driven Companies Are Putting the AI Cart Before the Horse

Firms are bringing artificial intelligence into the house, but many don’t have the resources set up to optimize gains.

In the drive to get on board the AI train, a growing number of companies are making a big mistake in not having the technology infrastructure ready before installing new AI tools and systems.

That’s the outlook from a new UK-based study that shows 90% of leading financial organizations have rolled out new AI solutions since the start of 2023. Unfortunately, many of those businesses are doing so “without first optimizing their data architecture to support AI”, says global analytics giant EXL Services in a survey released this week.

The rollouts from 100 of the UK’s top financial institutions, including banks and insurers, are big in scope. About 90% of the companies surveyed are pouring at least 7.9 million pounds ($10 million in US dollars) into their AI investments, primarily in areas of high data quality needs like marketing, compliance, and claims processing.

Even so, 47% of company CFOs surveyed say they’re only gaining “minimal data-driven results” due to “underdeveloped” data operations that weren’t designed to support major AI initiatives.

Not Thinking Things Through

The evidence shows that many of the companies surveyed rushed to deploy AI without having a solid technology infrastructure in place that could maximize their investments.

“The risk is that mounting competitive pressure can lead to AI investment that isn’t properly thought through,” said Kshitij Jain, chief strategy officer for analytics at EXL. “Getting data architecture and governance right upfront greatly enhances AI outcomes, and neglecting it can waste budgets and elevate risks.”

That scenario could easily lead to downside results due to a lack of adequate safeguards, the study reported. 70% of financial executives say they have “deep concerns” over potential brand damage, runaway risk from autonomous systems, and data errors propagating via generative models.

Left unaddressed, the companies facing an infrastructure/AI problem may not see the results they expected from their artificial intelligence investments.

“Enterprise-wide AI success requires great strategy, governance, and employee training – not just flashy technology,” Jain noted. “Firms properly identifying priorities, planning steady integration on robust data foundations, and mitigating risks will pull ahead as the pack thins out.”

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