Infosys: AI and data must join forces to deliver value

Despite high expectations for data and artificial intelligence (AI), most companies fail to act on these areas to convert data science to business value

Companies could be generating more than US$460bn in incremental profit if they improve data practices, trust in advanced AI, and integrate AI with business operations, according to new research from the Infosys Knowledge Institute

The research, by the thought leadership and research arm of Infosys, Data+AI Radar: Making AI Real, found that although three of four companies want to operate AI across their firms, most businesses are new to AI and face daunting challenges to scale. 81% of respondents deployed their first true AI system in only the past four years, and 50%, in the last two.

The report, which surveyed 2,500 senior technology leaders and executives across 13 industries across the US, UK, France, Germany, Australia, and New Zealand, also found that 63% of AI models function only at basic capability, are driven by humans, and often fall short on data verification, data practices, and data strategies. Only 26% of practitioners are highly satisfied with their data and AI tools. Despite the siren song of AI, something is clearly missing.

Thinking differently about AI and data

Infosys Knowledge Institute found that high-performing companies think differently about AI and data. It also found leaders focus in three areas:

  • Transform data management to data sharing. Companies that embrace the data-sharing economy generate greater value from their data. Data increases in value when treated like currency and circulated through hub-and-spoke data management models (US$105bn incremental value). Companies that refresh data with low latency generate more profit, revenue, and subjective measures of value.
  • Move from data compliance to data trust. Companies highly satisfied with their AI (currently only 21%) have consistently trustworthy, ethical, and responsible data practices. These prerequisites tackle challenges of data verification and bias, build trust, and enable practitioners to use deep learning and other advanced algorithms.
  • Extend the AI team beyond data scientists. Businesses that apply data science to practical requirements create value. The report found that business—data scientist integration accelerates efficiencies and value extraction (additional $45 billion profit growth). For intelligent data, business and IT are much better together.

Infosys says that, combined, these areas not only scale AI usage but unlock its potential value – transforming AI dreams to insights and operational effectiveness and improving the human experience. Infosys research found the financial services industry recorded the strongest satisfaction with its data and AI uses, followed by retail and hospitality, healthcare, and high tech.

Satish H.C., EVP and Co-Head Delivery, Infosys, said, “Companies that build foundations to trust and share their data are more agile and scale their AI. Companies that don’t trust their data risk a vicious cycle of “pilot purgatory” and only use data and AI to solve small problems. Data management combined with trust in AI are the dual solutions to increase business capability and financial rewards.”

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