As companies around the world pour resources into their AI efforts, many struggle to translate their technology investments into measurable business outcomes.

This is the challenge that Data Bot is looking to address this issue with a series of new product updates announced today. DataRobot is not new in the AI ​​space, the company has actually been in business for 12 years, well before the current generative AI boom. One of the main objectives of the company since its creation has been to enable predictive analytics to help improve business results. Like many others in recent years, DataRobot has turned to Generation AI support.

With the new Enterprise AI Suite, announced today, DataRobot seeks to go further and differentiate itself in an increasingly crowded market. The new integrated platform promises to allow companies to start solving their business problems with AI out of the box, rather than having to bundle multiple services. The platform is designed to work in multiple cloud environments as well as on-premises, providing customers with more flexibility. The Enterprise AI Suite is a comprehensive platform that helps businesses build, deploy and manage predictive and generative AI applications while ensuring appropriate governance and security controls. DataRobot’s goal is to create tangible business value from AI, rather than simply providing the technology.

“How to take AI to the next level in terms of value creation? I tell people that customers don’t eat dummies for breakfast,” Debanjan Saha, CEO of DataRobot, told VentureBeat. “You have to create applications and agents, and not only that, you have to integrate them into their business fabric in order to create value. This is the goal of this version.

Meeting the challenges of implementing AI in business

According to a recent DataRobot study, 90% of AI projects fail to move from prototype to production.

“Training models don’t create any value for the business,” Saha said.

The new DataRobot Enterprise AI Suite introduces application templates that provide out-of-the-box functionality while maintaining customization flexibility. This approach bridges a common gap in the market between standard, rigid AI applications and resource-intensive custom development.

Saha explained that the models are designed to be horizontal, meaning they can be applied to different sectors, rather than being vertically specific. Although the templates are a starting point, businesses have the flexibility to customize them to suit their specific needs. This includes: modifying data sources, adjusting model parameters, modifying the user interface, and integrating applications with other systems in a technology stack.

Unifying predictive and generative AI

One of the key differentiators of the DataRobot platform is its unified approach to traditional predictive AI and generative AI capabilities.

The platform allows organizations to extend core models with enterprise data while implementing necessary security controls. DataRobot’s Enterprise AI suite supports a comprehensive Retrieval Augmented Generation (RAG) pipeline to help extend basic models such as Llama 3 and Gemini with enterprise data.

One of the new models combines both technologies to improve business results. As a potential use case, Saha said for example, a business could use the predictive model to predict which customer will unsubscribe, when they will unsubscribe and why they will unsubscribe. The data from this predictive model can then be used with a gen AI model to create a hyper-personalized next best deal email campaign.

The DataRobot platform includes built-in protections for predictive and generative models.

“These models have all kinds of problems in terms of accuracy, leakage of private data or secure data,” Saha noted. “So there’s a whole bunch of guard models that you want to surround yourself with.”

Advanced agentic AI brings new reasoning to enterprise use cases

Another notable feature of the new DataRobot platform is the integration of AI agent capabilities.

The agentic AI approach is designed to help organizations manage complex queries and workflows. The system employs specialized agents who work together to solve multi-faceted business problems. This approach is particularly useful for organizations dealing with complex data environments and multiple business systems.

“You ask your agent workflow a question, it breaks the questions down into a more specific set of questions, and then it routes them to agents who specialize in different areas,” Saha explained.

For example, a business analyst’s question about revenue might be routed to multiple specialist agents – one handling SQL queries, the other using Python – before combining the results into a comprehensive answer.

Observability and governance are keys to enterprise AI success

As part of the DataRobot updates, the company is also rolling out a new observability stack. New observability capabilities provide detailed insights into AI system performance, particularly for RAG implementations.

For example, Saha explained that an organization may have a corpus of enterprise data. The organization uses a sort of segmentation and integration model, mapping it to a vector database and then placing an LLM in front of it. What happens if the answers do not match those expected by the organization? This is where observability comes in. The platform offers advanced visualization and analysis tools to diagnose such issues.

“We have put in place a lot of instruments that allow people to understand visually, for example, if you have a lot of groupings of data in the vector database, you can get a spurious answer,” Saha said. “You’ll be able to see this if you find that your questions land in areas where you don’t have enough information.”

This observability extends to the platform’s governance capabilities, with real-time monitoring and intervention capabilities. The system can automatically detect and manage sensitive information, with customizable rules for different scenarios.

“We’re really excited about what we call AI that makes business sense,” Saha said. “DataRobot has always been focused on creating business value from AI – it’s not technology for technology’s sake. »