AI

Innovation, employee efficiency, improved CX drive AI-cloud adoption

The latest survey by the International Data Corp. (IDC) identified that one of the drivers for AI (artificial intelligence) is improved customer experience (CX). This is the response from more than 2,000 IT and line of business (LoB) decision-makers.

The survey said more than half of the companies survey believes that AI allows them to give a better customer experience. According to IDC, organizations are reporting an increase in their AI spending this year and that over a quarter of all AI initiatives are already in production and more than one third are in advanced development stages.

“Early adopters report an improvement of almost 25% in customer experience, accelerated rates of innovation, higher competitiveness, higher margins, and better employee experience with the rollout of AI solutions,” said Ritu Jyoti, program vice president, Artificial Intelligence Strategies. “Organizations worldwide are adopting AI in their business transformation journey, not just because they can but because they must, to be agile, resilient, innovative, and able to scale.”


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IDC said companies have recognized the advantages of using AI in business operations. However, “there is some divergence in how companies deploy AI solutions.” Currently, AI is being utilized in IT automation, intelligent task or process automation, automated threat analysis and investigation, supply and logistics, automated customer service agents, and automated human resources.

Automation

The survey also found that larger companies (5000+ employees) use AI for automated customer agents and automated human resources while smaller and medium-sized companies saw the use of AI for IT automation.

Because of all these benefits, spending on AI increased, as found in the survey. Organizations said they spend around one-third of their AI lifecycle time on data integration and data preparation against actual data science effort, which, according to IDC, is a big inhibitor to scaling AI adoption.

It is interesting to note that based on IDC’s survey, “large enterprises still struggle to apply deep learning and other machine learning technologies successfully.”

But adopting AI is not the end of it. To fully maximize its use, companies have to learn and “embrace” Machine Learning Operations (MLOps), the compound of machine learning, development, and operations.

The research firm also said trustworthy AI is fast becoming a business imperative. Fairness, explainability, robustness, data lineage, and transparency, including disclosures, are critical requirements that need to be addressed now. Around 28% of the AI/ML initiatives have failed. Lack of staff with the necessary expertise, lack of production-ready data, and lack of integrated development environment are reported as primary reasons for failure.

“An AI-ready data architecture, MLOps, and trustworthy AI are critical for realizing AI and Machine Learning at scale,” said Jyoti.

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