New research from the Infosys Knowledge Institute, suggests companies need to think differently about data and artificial intelligence (AI). They have invested heavily in AI systems in the past few years.
Global spending on AI-centric systems will approach $118 billion in 2022 and grow to more than $300 billion in 2026. All that work is not paying dividends. Companies have achieved basic AI capabilities. This is not what they want. The research suggests that three out of four companies in the survey want to operate AI at an enterprise scale.
Three areas for improvement
Infosys research identifies why AI fails to deliver on heightened expectations and recommends three areas for improvement.
- Develop data practices that encourage sharing,
- Bind explanations into advanced AI,
- Focus AI teams on business operations.
If companies improve on these fronts, the research indicates they can add up to 467 billion in profit growth collectively. Furthermore, increase internal satisfaction with data in AI.
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. Infosys Data+AI Radar: Making AI Real found that although three out 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 also found that 63% of AI models function only at basic capability, are driven by humans. They 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.
High-performing companies think differently
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%) 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 into 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.”
Enterprise Times: What this means for business.
This is report is pretty interesting. It makes the point that companies can no longer think of data as oil. I.e. extracted with great effort and valuable only when its refined. Data today is more like currency. It gains value when it circulates. This data-sharing economy is already up and running. The Infosys Knowledge Institute notes that companies that share data throughout the organisation, are more likely to have higher revenues. In addition to the better use of AI within the organisation. Refreshing data closer to real-time also correlates to increased profits and revenues. This makes sense. Managers and business owners who have access to more accurate, comprehensive AI-enabled data in real-time, will make more effective decisions. This will be reflected in better revenue streams and profits.
The Infosys Knowledge Institute suggests three ways data and AI can work better together. Enterprises need to get their data right and share it within the organisation. Build trust in advanced AI, by strengthening corporate ethics policy, getting management buy-in and deep learning AI and Cloud technology training across the organisation. The AI teams needs a balance with business leaders, as well as data scientists. Infosys says by combining these activities will enable enterprises to scale AI and unlock value within the organisation. Data,
