Global spending on AI-centric systems will approach $118 billion in 2022 and grow beyond $300 billion by 2026, said a recent study. But all this spending isn’t paying dividends. A study by Infosys Data+AI Radar found that companies can generate over $460 billion in incremental profit if AI implementation improves.
The three things that companies need to focus on to get these gains are to improve data practices, trust in advanced AI, and integrate AI with business operations. However, despite high expectations for data and artificial intelligence (AI), most companies fail to act on these areas to convert data science to business value.
The report, Infosys Data+AI Radar: ‘Making AI Real’, found that though three of four companies want to operate AI across their firms, most businesses are new to AI and face daunting challenges to scale. Eighty one per cent of the respondents in the study deployed their first true AI system in only the past four years, and 50 per cent, in the past two.
The report also found that 63 per cent 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 per cent of practitioners are highly satisfied with their data and AI tools. Despite the siren song of AI, something is clearly missing.
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.”
The survey, which covered 2,500 AI practitioners, found that 81 per cent deployed their first AI system in the past four years. However, most companies (85 per cent) have not achieved advanced capabilities, and most AI models (63 per cent) are still driven by humans. Compounding this, outcomes are middling at best: Users are highly satisfied with their data and AI results only about a quarter of the time.
Infosys Knowledge Institute found that high-performing companies think differently about AI and data, and these 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 ($105 billion 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 per cent) 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.
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.
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