Cloudera has published the findings from its survey, The State of Enterprise AI and Modern Data Architecture. The report – which is based on a survey of 600 IT leaders located in the U.S., EMEA, and APAC regions – explored the challenges and barriers that exist for enterprise AI adoption across global enterprises and current applications. It also explored plans for AI adoption, the state of data infrastructure, and the benefits of hybrid data management in relation to enterprise AI adoption. The survey revealed that although a high majority of enterprises are adopting AI in some capacity (88%), many are still lacking the necessary data infrastructure and employee skills to truly benefit from it.
In recent years, AI has become a global phenomenon, namely for its ability to supercharge business operations, enable informed decision making, accelerate innovation, and enhance experiences for both employees and customers. However, not every organization has been able to reap the benefits. The survey found that the top barriers to adopting AI were worries about the security and compliance risks that AI presents (74%), not having the proper training or talent to manage AI tools (38%), and AI tools being too expensive (26%). These findings signal that despite rapid AI adoption, many pillars of a resilient AI strategy are being neglected or forgotten.
A key finding of the survey is that all AI efforts are ultimately tied back to trustworthy data. While 94% of respondents said that they trust their data, 55% also said they would rather get a root canal than try to access all of their company’s data. This frustration is driven by challenges including contradictory datasets (49%), an inability to govern data across platforms (36%), and too much data (35%). These areas of frustration signal that many enterprises might be missing a modern data architecture that empowers organizational-wide access to data – wherever it may live – in a secure, accessible and trustworthy manner.
From automating and streamlining IT processes, to building chatbots capable of supporting front-line customer needs quickly and effectively, to leveraging analytics to foster better decision-making, the survey revealed the top use cases for AI included improving customer experiences (60%), increasing operational efficiency (57%), and expediting analytics (51%).
Improving customer experience: Companies are applying AI technology to enhance security and fraud detection (59%), automate aspects of customer support (58%), leverage predictive customer service (57%), and power chatbots (55%), all with a goal of giving customers a safer, simpler, and more intuitive experience.
Increasing operational efficiency: AI is being integrated into nearly every facet of business. The survey found that IT departments are not the only ones using AI, 52% of respondents reported using it for customer service like better informed chatbots, and 45% indicated it’s used for marketing, such as analyzing call center data to offer more targeted incentives to customers.
Expediting analytics: Faster, easier, and more dependable access to analytics means more informed decision making, giving the companies leveraging AI a distinct competitive advantage. Nearly 80% of respondents said it is either “completely” or “very” true that their company is using all of the data at its disposal to make smarter business decisions. This data provides mission critical information, so access to all of an organization's data is critical.
“For the majority of companies, the quality of their data is not great, it’s distributed across various infrastructures and not documented in an efficient manner, and we’re seeing the fallout from that presented in the challenges identified by the survey,” said Cloudera Chief Strategy Officer, Abhas Ricky. “Managing data where it resides is the most important thing when it comes to adopting AI - being able to run models in a cost efficient manner where that data already lives. Instead of bringing the data to the models, enterprises are starting to realize the advantages of bringing AI models to their data.”
