While boards of directors demand the implementation of artificial intelligence, IT leaders, like chief information officers, know that there is more to the story than having a solid AI use case. .
The challenge preventing technology leaders from implementing AI isn’t actually generating a model and launching it, said Prukalpa Sankar, co-founder of data catalog and governance software Atlan. Instead, he said it is not possible to have ready data for AI. “Everything is ready for AI except your data,” Sankar said.
In a recent global study of more than 1,300 technology and data executives, only 18% of companies say they are fully ready for AI implementation, meaning that their data is fully accessible and unified (another 40% if consider mostly ready, but not enough here).
To get to that point of readiness, Sankar said companies have to overcome several hurdles. The first is to find and organize all your data, a job mainly for data engineers. “You’re trying to bring together data that was otherwise siloed across different business units to actually deploy it for a specific use case,” he said.
Companies also need to complete complex data labeling and classification, mainly to keep private datawith “Depending on who’s asking the question, I can change the data behind it,” Sankar said. For example, a human resources chatbot may be able to use payroll data while a general chatbot may not.
With AI, data governance is not so cut and dry
All of this falls under the umbrella of data governance, or how a business manages data assets through policies, processes and standards. Matt Carroll, CEO and co-founder of data security platform Immuta, said data governance isn’t new, but AI is changing how it’s done.
“When you think about traditional business intelligence, which we’ve been doing for 30 years, governance was still a structured, well-oiled machine,” Carroll said. “When you introduce AI, you can’t do it the same way.”
This is because companies need to constantly add new data to support AI models from internal and external sources.
Ultimately, Carroll said, AI readiness comes down to three things: “They need to be able to find the data, they need to be able to use it, and they need to be able to observe how it’s being used.”
Having a mature data governance pipeline is not common in the industry, or at least not yet. A 2024 AI readiness report from MIT found that data governance, trust and security are a greater focus in government and financial institutions versus other industries. Carroll said this practice should extend far beyond banks and government, as they are not the only industries that handle sensitive data. All companies pursuing generative solutions or other types of AI solutions have to perform a dance between IT, the leaders of the legal and wider organizations, as well as the departments into which it trickles down.
Additionally, Carroll wants to see more businesses implement ongoing data readiness even after implementing AI. One such way that companies can do this is through an AI hotline, which can be a full-on hotline in a large company, or a more accessible managed Slack channel in a smaller company. What is important is that domain experts have a direct line to the engineering team to report problems such as hallucinations or incorrect data tags.
“They need that feedback loop, so maybe a review model of the model can take or reevaluate, or potentially flag for retraining and revalidation,” said Carroll, “which, by the way, is not a negative thing. game”.
This is, of course, in addition to continuous tests on the models to look for anomalous behavior and make sure they meet the company’s quality standards.
Businesses are getting creative to prepare for AI
From the start of AI implementation journeys, Sankar said he sees companies creating AI readiness scores to help quantify the process of getting their data in order. The measurable score for AI readiness could rank a given date out of 5.0, for example, based on a number of factors. “Unless you measure, nothing moves,” he said.
Another trend experts are seeing is adding a secondary title of data steward to an employee’s primary role. “You’re in business, you happen to know the domain, but now, all of a sudden, you’re going to own this set of data that may or may not be used for AI,” Carroll said. In addition, he said, highly specialized data managers (who could have an official title of data governance managers or data management engineers, for example) are hard to find, but increasingly important and something we can see more of in the future
Sankar compares the data infrastructure ecosystem to a market. “On one side of the market you have business-ready AI use cases,” he said. “And on the other side is your complicated data infrastructure.”
For organizations pursuing AI, experts agree that data preparation must come first. But even the broad category of data readiness breaks down further. Before even tackling the first step, Carroll said, it’s worth asking what may be an unpopular question in the C-suite: “In data preparation, there’s also a question of, should you do it at all?” Therefore, Carroll means that there is an ethical decision that all companies must make regarding whether or not you should expose certain types of data in your systems. Only with this approach, companies can really pursue AI preparation.