Adoption of artificial intelligence (AI) is growing worldwide, according to an IDC survey of more than 2,000 IT and line of business (LoB) decision makers.
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.
Better customer experience is a major driver for AI adoption
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.
Despite the benefits, challenges remain
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 (less than 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 one 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.
Other key findings
- 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.