Key Highlights
- A recent study said that Global spending on artificial intelligence (AI) centric systems would reach $118 billion in 2022 and increase to more than $300 billion by 2026.
- But all this spending needs to pay more dividends.
- Infosys Data+AI Radar found that companies could generate more than $460 billion in incremental profits if AI implementation were improved.
The three things companies require to generate profits are improved data practices, trust in advanced AI, and AI integration with business operations. Despite high expectations for data and AI, many companies need to act in these areas to convert data science to business value.
According to the Infosys Data+AI Radar ‘Making AI Real’ report, though three of four companies want to operate AI across their firms, many companies are new to AI and face daunting challenges to scale. At least 81 percent of respondents deployed their first actual AI systems in only the past four years and 50 percent in the past two.
Report Findings
The report also found that 63 percent of AI models functioned only at essential capability, were driven by humans, and frequently fell short on data verification, practices, and strategies. Only 26 percent of practitioners were satisfied with their data and AI implementation tools.
Satish HC, executive vice-president and co-head for Delivery at Infosys, said companies that build foundations to trust and share their data are more agile and scale their AI implementation. Data management and trust in AI form dual solutions to grow business capability and financial rewards.
The survey covered 2,500 AI practitioners and found that 81 percent deployed their first AI systems in the past four years. However, most companies (85 percent) have yet to achieve advanced capabilities, and humans still drive most AI models (63 percent). Outcomes were mediocre at best: Users were delighted with their data and AI implementation results only about a quarter of the time.
The reports said the aging process, i.e., extracting, transforming, and load data into a private warehouse, faced limits. Followers of that process could only apply AI to the data contained in the four walls of their warehouse. Data management strategies that aided data sharing, both importing in and sharing out, extended the universe of available data.
Three Focus Areas
Infosys Knowledge Institute identified that high-performing companies think differently about AI and data, and these leaders concentrate on three areas:
- Transform data management into data sharing
- Move from data compliance to data trust
- Extend the AI team beyond data scientists
These areas increase AI usage and unlock its potential value, transforming AI dreams into insights, operational effectiveness, and improving the human experience. Infosys research found the financial services industry recorded the most substantial satisfaction with its data and AI uses, followed by retail and hospitality, healthcare, and high tech.
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