A recent International Data Corporation (IDC) survey of more than 2,000 IT and line of business (LoB) decision makers confirms that adoption of artificial intelligence (AI) is growing worldwide. Over a quarter of all AI initiatives are already in production and more than one third are in advanced development stages. And organizations are reporting an increase in their AI spending this year.

Delivering a better customer experience was identified as the leading driver for AI adoption by more than half the large companies surveyed. At the same time, a similar number of respondents indicated that AI’s greatest impact is in helping employees to get better at their jobs. Whether it is an improved customer experience or better employee experience, there is a direct correlation between AI adoption and superior business outcomes.

“Early adopters report an improvement of almost 25 percent in customer experience, accelerated rates of innovation, higher competitiveness, higher margins, and better employee experience with the roll out of AI solutions. 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,” said Ritu Jyoti, program vice president, Artificial Intelligence Strategies.

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While there is considerable agreement on the benefits of AI, there is some divergence in how companies deploy AI solutions. IT automation, intelligent task/process automation, automated threat analysis and investigation, supply and logistics, automated customer service agents, and automated human resources are the top use cases where AI is being currently employed. While automated customer services agents and automated human resources are a priority for larger companies (5000+ employees), IT automation is the priority for smaller and medium sized companies (<1000 employees).

Despite the benefits, deploying AI continues to present challenges, particularly with regard to data. Lack of adequate volumes and quality of training data remains a significant development challenge. Data security, governance, performance, and latency (transfer rate) are the top data integration challenges. Solution price, performance and scale are the top data management issues. And enterprises report cost of the solution to be the number 1 challenge for implementing AI. As enterprises scale up their efforts, fragmented pricing across different services and pay-as-you-go pricing may present barriers to AI adoption.

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Other key findings from the survey include:

• Enterprises report spending around one third of their AI lifecycle time on data integration and data preparation vs. actual data science efforts, which is a big inhibitor to scaling AI adoption.
• Large enterprises still struggle to apply deep learning and other machine learning technologies successfully. Businesses will need to embrace Machine Learning Operations (MLOps) – the compound of machine learning, development, and operations – to realize AI/ML at scale.
• 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 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,” added Jyoti.

The IDC report, AI StrategiesView 2020: Executive Summary (Doc #US46261720), summarizes the results of a global survey of 2,056 IT and line of business decision makers and influencers about their current and future current and future AI investments, business drivers, and use cases and applications. The survey was designed to provide a better understanding of what drives customer buying behavior for AI on a worldwide basis across different IT and LOB personas, from CXOs to middle management to practitioners (including data scientists, data architects, data engineers, and MLOps engineers).

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