The key to operational AI: Modern data architecture

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data—have begun exploring AI. However, there’s a significant difference between those experimenting with AI and those fully integrating it into their operations. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.

AI enhances organizational efficiency by automating repetitive tasks, allowing employees to focus on more strategic and creative responsibilities. Today, enterprises are leveraging various types of AI to achieve their goals. Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models. To fully benefit from AI, organizations must take bold steps to accelerate the time to value for these applications. This is where Operational AI comes into play.

Operational AI involves applying AI in real-world business operations, enabling end-to-end execution of AI use cases. It integrates AI into business processes, processes real-time data, and provides actionable insights to automate tasks, improve efficiency, and make data-driven decisions. Ultimately, it simplifies the creation of AI models, empowers more employees outside the IT department to use AI, and scales AI projects effectively.

Adopting Operational AI

Organizations looking to adopt Operational AI must consider three core implementation pillars: people, process, and technology.

• People: To implement a successful Operational AI strategy, an organization needs a dedicated ML platform team to manage the tools and processes required to operationalize AI models. This team serves as the primary point of contact when issues arise with models—the go-to experts when something isn’t working. The team should be structured similarly to traditional IT or data engineering teams. Just as DevOps has become an effective model for organizing application teams, a similar approach can be applied here through machine learning operations, or “MLOps,” which automates machine learning workflows and deployments.
• Process: To build confidence in the reliability of an organization’s AI implementation, it’s essential to standardize the processes and best practices for deploying models into production. For example, there should be a clear, consistent procedure for monitoring and retraining models once they are running (this connects with the People element mentioned above). As organizations integrate more AI into their operations and expand their use cases, standardizing these practices helps maintain a high level of confidence in both the methods and the models.
• Technology: The workloads a system supports when training models differ from those in the implementation phase. While in the experimentation phase, speed is a priority, the implementation phase requires more attention to resiliency, availability, and compatibility with other tools. For this reason, organizations looking to leverage Operational AI need an Operational AI platform that specifically supports the requirements for operationalizing, managing and monitoring models in production. 

Operational AI offers organizations significant benefits, including time and cost savings, and critical competitive advantages in today’s business landscape. Key benefits of Operational AI include:

• Increased efficiency through task automation
• Improved service delivery
• Reduced time to market for new AI models
• Lower operational costs
• Enhanced decision-making capabilities

Additionally, Operational AI provides greater oversight of AI models, which is crucial for regulated industries that must diligently manage risk.

However, the biggest challenge for most organizations in adopting Operational AI is outdated or inadequate data infrastructure. To succeed, Operational AI requires a modern data architecture. These advanced architectures offer the flexibility and visibility needed to simplify data access across the organization, break down silos, and make data more understandable and actionable. They support the integration of diverse data sources and formats, creating a cohesive and efficient framework for data operations. Ensuring effective and secure AI implementations demands continuous adaptation and investment in robust, scalable data infrastructures.

Bringing Operational AI to Enterprises

In an effort to address the biggest hurdles in AI deployments by enabling organizations to effectively build, operationalize, monitor, secure, and scale models across the enterprise, Cloudera acquired Verta’s Operational AI Platform and team, deepening its intellectual property and adding even more talent to better serve its customers with unmatched expertise and innovative solutions.

In leveraging Verta’s platform, Cloudera is now equipped to simplify the process of bolstering customers’ private datasets to build custom retrieval-augmented generation (RAG) and fine-tuning ​applications​. As a result, ​developers — regardless of their expertise in machine learning — will be able to develop and optimize business-ready large language models (LLMs). The Verta Operational AI platform supports production AI-ML workloads in the most complex IT environments. The Verta Model Catalog, Model Operations, and GenAI Workbench have helped customers ranging from AI startups to Fortune 100 enterprises seamlessly manage, run, and govern AI-ML models on-prem and in the cloud. 

Adopting an Operational AI mindset helps organizations fully leverage AI benefits across their companies. It’s the difference between a handful of AI success stories and reaching the point where the whole enterprise is running on intelligence.

Learn more about how Cloudera can support your enterprise AI journey here.