The adoption of artificial intelligence (AI) is growing worldwide, according to a new study released by the International Data Corporation which reveals the key factors driving the AI market.
The need to boost customer experience, employee efficiency and to accelerate innovation are the three main factors driving an increase in AI adoption.
More than 50% of surveyed 2,056 IT and line of business (LoB) decision-makers and influencers say that customer experience is their leading driver for AI adoption.
More than half of the survey participants also indicated that there is a direct correlation between AI adoption and superior business outcomes.
Over a quarter of all AI initiatives are already in production and more than one third are in advanced development stages. Organizations are reporting an increase in their AI spending this year, according to the study.
Although companies agree on the benefits of AI, there are huge variations in terms of how they deploy the solutions. The top use cases include IT automation, intelligent task/process automation, automated threat analysis and investigation, supply and logistics, automated customer service agents, and automated human resources.
Automated customer services agents and automated human resources are prioritised larger companies (5000+ employees), whilst small and medium firms (-1000 employees) prioritise IT automation.
AI continues to present challenges, particularly with regard to data and the challenges associated with data include the lack of adequate volumes and quality of training data.
Data security, governance, performance, and latency are also challenges whilst solution prices, performance and scale are the top data management issues.
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 the necessary expertise, lack of production-ready data, and lack of integrated development environment are reported as primary reasons for failure.
Ritu Jyoti, vice president of AI strategies at IDC, said: “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 rollout of AI solutions. Organizations worldwide are adopting AI in their business transformation journey, not just because they can but because they must be agile, resilient, innovative, and able to scale.
“An AI-ready data architecture, MLOps, and trustworthy AI are critical for realizing AI and Machine Learning at scale.”
Read more in the IDC report, AI StrategiesView 2020: Executive Summary