Since first launching MicroStrategy AI, the vendor has added features such as AI Explainability, which aims to engender trust in generative AI outputs by providing explanations showing how Auto arrived at its response, and capabilities that enable users to embed Auto within employee workflows.
Generative AI has been the dominant trend in data management and analytics in the two years since OpenAI's launch of ChatGPT brought significant improvement in large language model capabilities.
Two of the main potential benefits of generative AI are true natural language processing that lets workers of any skill level query and analyze data to inform decisions and process automation that enables data experts to be more efficient.
As a result, vendors such as MicroStrategy, Domo and Qlik -- among others -- have created suites that make it simple for customers to combine generative AI capabilities with their own proprietary data to develop tools that understand their business.
Accuracy, however, has been a problem for generative AI tools. Even when trained with quality data, generative AI models and applications sometimes deliver incorrect and even bizarre responses called AI hallucinations.
Data quality helps reduce the likelihood of hallucinations. So does data volume -- the more quality data a model or applications is trained on, the less likely it is to make up a response. But they aren't foolproof, which is one reason MicroStrategy provided explanations showing how Auto arrived at a response.
Now, by upgrading the large language model underpinning Auto AI to OpenAI's GPT-4o, MicroStrategy is hoping to further improve the accuracy of generative AI responses to make them more trustworthy. With GPT-4o working in concert with an enterprise's proprietary data, Auto is now better at understanding the context of a user's query, which results in a more relevant and accurate response.
Beyond attempting to improve the accuracy of Auto's responses, new MicroStrategy AI features include Auto for Teams, an integration that lets users query Auto directly in Microsoft Teams so they don't have to leave their collaboration environment to work with data.
The suite also includes Auto Narratives, a tool that provides automatically generated summaries of text and visualizations to provide users with context related to the data they're using, and a feature that enables developers to train bots using multiple datasets without requiring them to first consolidate their data into a single, unified set.
Each are significant and stand to benefit MicroStrategy customers, according to Leone.
"Auto for Teams is a feature that I think anyone with Teams would appreciate -- simply message an expert on a certain topic and get a relevant response," he said. "And ... Auto Narratives really feeds into the idea that explainability matters. Enabling more folks to understand data remains a critical area for businesses, and this serves as a reliable shortcut."
As MicroStrategy adds new features to MicroStrategy AI and tries to improve existing ones, customer feedback provides the primary motivation for deciding what to feature in a given platform update, according to PeggySue Werthessen, the vendor's vice president of go-to-market strategy.
"The impetus for these enhanced features of Auto and actionable analytics was working directly with our customers to understand how GenAI can deliver real business value," she said.
Complementing customer feedback is MicroStrategy's ongoing effort to ensure that its platform is "accessible, flexible and performs with reliability and scalability to support demanding use cases," Werthessen added.
Beyond the new MicroStrategy AI features, new MicroStrategy One capabilities include the following:
Of particular note are the Python Action Buttons, according to Werthessen.
"This means that a front-line knowledge worker can initiate workflows for individual items in a list with a click of a button," she said.