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Monday, July 13, 2020

Artificial Intelligence Drives Superior Business Outcomes

Across the globe, more CEOs and their leadership team now say that business process automation has become a key driver of their organization's digital growth. Moreover, a survey of more than 2,000 Information Technology (IT) and Line of Business (LoB) decision-makers confirms that the adoption of artificial intelligence (AI) is growing rapidly.

Over a quarter of all AI initiatives are already in production and more than one third are in advanced development stages. Besides, organizations are reporting an increase in their AI technology investment this year, according to the latest worldwide market study by International Data Corporation (IDC).

Artificial Intelligence Market Development

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 survey respondents indicated that the greatest impact of AI is helping employees to improve productivity.

The study findings are conclusive: 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 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," said Ritu Jyoti, vice president at IDC.

While there's agreement on the benefits of AI, there is some divergence in how companies deploy AI solutions. IT automation, intelligent task or 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 it's true that automated customer service agents and automated human resources are a priority for larger companies (5000+ employees), in contrast, IT automation is the priority for smaller and medium-sized companies.

Despite the benefits, deploying AI continues to present challenges -- particularly with regard to data preparation. Lack of adequate volumes and quality of 'training data' remains a significant development challenge for software developers and their LoB leaders.

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 the cost of a solution to be the number one challenge for implementing AI.

According to the IDC assessment, as enterprises scale up their efforts, fragmented pricing across different services and pay-as-you-go pricing models may present barriers to further AI adoption.

Key findings from the IDC survey:
  • 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 combination of machine learning systems, software development, and IT 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 percent 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," added Jyoti. We'll continue to monitor the evolution of machine learning operations best practices, and report on other insightful survey and market study findings.