Enterprise IT leaders across industries are tasked with preparing their organizations for the technologies of the future – which is no simple task. With the use of AI exploding, Cloudera, in partnership with Researchscape, surveyed 600 IT leaders who work at companies with over 1,000 employees in the U.S., EMEA and APAC regions. The survey, ‘The State of Enterprise AI and Modern Data Architecture’ uncovered the challenges and barriers that exist with AI adoption, current enterprise AI deployment plans, and the state of data infrastructures and data management.
The State of Enterprise AI
It will likely come as little surprise that businesses across the world are swiftly incorporating AI into their operations, with 88% of surveyed companies already utilizing this transformative technology. AI is starting to revolutionize industries by changing how a business operates and the teams within. The departments leading this adoption are IT (92%), Customer Service (52%), and Marketing (45%). Across these business areas, AI is enhancing efficiency in IT processes, improving customer support with chatbots, and leveraging analytics for better decision-making.
Among various AI implementations, Generative AI (GenAI) stands out as the most popular, with 67% of respondents utilizing generative models in some capacity. Companies are deploying GenAI using several architectures: exposing data to open-source models without training on it (60%), training open-source models on their data (57%), using open-source models trained on-premises or in private clouds (50%), and developing proprietary Large Language Models (LLMs) or Small Language Models (26%).
In addition to GenAI, respondents noted they are deploying predictive (50%), deep learning (45%), classification (36%) and supervised learning (35%) applications.
Challenges in Implementing AI
Implementing AI does not come without challenges for many organizations, primarily due to outdated or inadequate data infrastructures. While every business has adopted some form of data architecture, the types they use vary widely. The majority of organizations store their data in private clouds (81%), but other architectures are also prevalent, including public clouds (58%), on-premises mainframes (42%), on-premises distributed systems (31%), other physical environments (29%), and data lakehouses (19%).
Navigating the complexity of modern data landscapes brings its own set of challenges. Key issues include data security and reliability (66%), escalating data management costs (48%), compliance and governance challenges (38%), overly complex processes (37%), siloed and difficult-to-access data (36%), mistrust in connecting private data and inaccuracies in AI models (32%), and the need for standardized data formats (29%).
Adding to these complexities is the rapidly evolving nature of data technologies and the growing volume of data businesses must manage. Ensuring that AI implementations are effective and secure requires continuous adaptation and investment in robust, scalable data infrastructures. This is essential for businesses aiming to leverage AI for competitive advantage and operational efficiency.
Leveraging Modern Data Architectures
In today’s landscape, the only way to ensure data reliability is through the adoption of modern data architectures. These advanced architectures provide critical flexibility and visibility, acting as a blueprint for accelerating the extraction of insights and value from data. They simplify data access across organizations, breaking down silos and making data easier to understand and act upon.
When asked about the most valuable advantages of hybrid data architectures, respondents highlighted data security (71%) as the primary benefit. Other significant advantages include improved data analytics (59%), enhanced data management (58%), scalability (53%), cost efficiency (52%), flexibility (51%), and compliance (37%).
Modern data architectures support the integration of diverse data sources and formats, providing a cohesive and efficient framework for data operations. This integration is essential for businesses aiming to leverage data-driven strategies, ensuring that their data infrastructure can meet the demands of evolving technologies and increasing data volumes. By adopting these architectures, organizations can position themselves to unlock new opportunities and drive innovation through reliable and accessible data.
The enhanced security, transparency, accessibility, and insights provided by modern data architectures directly contribute to a business’s agility, adaptability, and informed decision-making. These factors are crucial for future-proofing data infrastructure, ensuring it remains robust over time, and achieving tangible ROI from AI implementations.
To gain more insights from Cloudera’s latest survey report, click here.