The problem with AI: it's not you, it's the data

The problem with AI: it’s not you, it’s the data

Hand writing AI on transparent circuit board


Companies are leaving billions on the table because they can’t bring their data together. If they want to be successful in driving value through data-driven initiatives like artificial intelligence, they need to better align and support the backend data that powers those systems.

That’s the gist of the latest study, based on a survey of 2,500 executives and published by Infosys Knowledge Institute, which estimates that companies could collectively generate more than $460 billion in additional profits if only people could manage a little better their data resources.

This involves improving data practices, placing more trust in advanced AI, and integrating AI more closely into business operations. Commercial value is still elusive.

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The survey identified three barriers to effective AI implementation: lack of a cohesive and centralized data strategy, weak data verification, and lack of appropriate infrastructure. Most companies don’t have a cohesive data management strategy.

Respondents want to manage data centrally, but most currently do not. Analysis of survey results “shows that centralized data management is linked to better profit and revenue growth. 26% of respondents currently have a centralized approach; 49% would like to have adopted this approach by ‘next year.

“Data is not the new oil,” said study authors Chad Watt and Jeff Kavanaugh, both of the Infosys Institute. “Companies can no longer afford to view their data as oil, extracted with great effort and valuable only when refined.”

Data today is more like currency: “It gains value as it flows. Companies that import data and share their own data more intensively achieve better financial results and show greater progress towards the idea of ​​enterprise-wide AI – a critical goal for three out of four companies in the survey,” say Watt and Kavanaugh.

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The success of currency depends on trust, and this also applies to data. “Advanced AI requires trust,” the authors state. “Trust your own data management and that of others, and trust AI models. Pristine data and perfectly programmed AI models mean nothing if humans don’t trust and use what data and AI produce.”

Companies that have shared data, inside and outside their organization, are more likely to have higher revenues and better use of AI, the survey found. “Data refresh closer to real time is also correlated with increased profits and revenue.”

Another anti-oil analogy made by the study authors is that the data looks more like nuclear energy than fossil fuels. “Data is rich in potential, requires special handling and is dangerous if you lose control. Data in the 21st century has a long half-life. When to use it, where to use it and how to control it are as critical as where the put.”

Most companies are new to AI, according to the survey. More than 8 in 10 companies, 81%, only deployed their first true AI system in the last four years, and 50% in the last two. Additionally, 63% of AI models only operate at their base capabilities and are piloted by humans. They often lack data verification, data practices, and data strategies. Only 26% of practitioners are very satisfied with their data and AI tools. “Despite the AI ​​siren song, something is clearly missing,” the survey authors say.

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The survey authors identified high-performing companies, which tend to focus heavily on three areas:

  • They transform data management into data sharing. “Companies that embrace the data sharing economy generate greater value from their data,” say Watt and Kavanaugh. “Data increases in value when it is treated as currency and disseminated through star data management models. Companies that refresh data with low latency generate more profit, revenue, and subjective measures of value.”
  • They have moved from data compliance to data trust. “Companies who are very satisfied with their AI (currently only 21%) have consistently trusted, ethical and responsible data practices. These prerequisites address the challenges of data verification and bias, build trust, and enable practitioners to use deep learning and other advanced algorithms. »
  • They engage everyone in the AI ​​process. “Expanding the AI ​​team beyond data scientists. Companies that apply data science to practical requirements create value. Business leaders matter as much as data scientists. AI typically involve multiple disciplines.” Data verification is the biggest challenge moving forward, along with AI infrastructure and compute resources.

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