Embracing an AI-Enabled Future
Where people can’t interact face to face, artificial intelligence steps into the breach. An IDC survey released in June 2020 of more than 2,000 IT and line of business (LoB) decision-makers confirms that the adoption of artificial intelligence (AI) is growing worldwide. As of last year, 25% of all AI initiatives were already in production. More than one-third were already in the advanced development stages. Organizations had begun reporting an increase in their AI spending.
Delivering a better customer experience was identified as the leading driver for AI adoption by more than half of 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% increase 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,” according to IDC Artificial Intelligence Strategies program vice president Ritu Jyoti.
Challenges in AI deployment
Based on the data IDC gathered in its survey, 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 its benefits, deploying AI continues to present challenges, particularly with regard to data. Inadequate volumes and quality of training data remain a significant development challenge, IDC researchers found. Data security, governance, performance, and latency (transfer rate) are among the top data integration challenges. Solution prices, performance, and scale are the top data management issues. Enterprises have reported the cost of their solutions to be the top challenge for implementing AI.
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.
“An AI-ready data architecture, MLOps, and trustworthy AI are critical for realizing AI and Machine Learning at scale,” Jyoti added.
Leveraging AI to Adapt
According to IDC’s “FutureScape: Worldwide Future of Intelligence 2021 Predictions – APeJ Implications” released in 2020, the COVID-19 pandemic has highlighted organizations’ need to stay resilient, and many firms are leveraging the benefits of becoming an Intelligent Enterprise to future-proof themselves against further disruptions.
“IDC’s Future of Intelligence FutureScape highlights the fact that the organizations who will succeed in the next normal will be those that can learn fast from their changing environments and scale that learning into action,” according to Dr. Chris Marshall, associate vice president for AI, Big Data, and Analytics research at IDC Asia/Pacific.
In July last year, IDC reported that “over half of Australian and New Zealand enterprises implementing AI solutions say the deployments are enabling changes to their business models. Conversational AI is transforming the customer care model and predictive analytics is shifting traditional maintenance business models.”
IDC’s report “AI in Australia and NZ: Viable Use Cases Driving Adoption” found that the main drivers behind investment are automation for productivity, customer satisfaction, business agility, and accuracy. “Organizations are shifting workloads to AI solutions where the system can make decisions and act faster than employees can,” according to IDC associate market analyst Liam Landon. “AI is augmenting the employee workforce, driving changes to business models to capture real returns on improved productivity, satisfaction, agility and accuracy”.
In Australia and New Zealand, organizations deploy conversational AI to provide consistent and accurate responses to straightforward customer queries and free up their human frontline staff so they can spend more time helping customers with complicated queries. More complex uses of AI that leverage large data sources include assisted diagnoses—especially within healthcare—a need that grows as the technology matures, and the pandemic has yet to end.
Over 65% of the organizations in India have leveraged automation for improved productivity and faster time to market with new products and services—and this is one of the top drivers for using AI there.
Organizations surveyed by IDC across multiple verticals have been leveraging AI, driven by their own needs. All of them are considering intelligent systems to either reduce the effort for mundane tasks or establish specialized real-time insights for faster decision-making. This adoption has been varied across industries, but it has remained steady.
Health care institutions and hospitals are deploying AI for clinical imaging and point-of-care applications. The business and financial services institutions (BFSI) vertical is exploring multiple use cases, varying from chatbots and conversational AI platforms to fraud detection in claims and insurance. The public sector is also implementing a similar arc of adoption.
What used to be on a wish list for business and government organizations is now a potent tool for survival and recovery.