MicroStrategy recently introduced a new feature designed to enhance business analytics within organizations. On Tuesday, they unveiled MicroStrategy Auto, an AI-powered bot aimed at making business intelligence more accessible and efficient.
MicroStrategy Auto is built on the company’s AI framework, which was first launched in October 2023. This bot offers customization options, allowing users to adjust its appearance, language, and detail level to fit their specific needs.
Auto can function both as an independent application and be integrated into third-party platforms. It leverages GPT-4 technology to understand and respond to user inquiries using natural language.
Saurabh Abhyankar, MicroStrategy’s Executive Vice President and Chief Product Officer, noted that while GPT-4 handles the natural language processing, MicroStrategy’s platform adds a layer of analytic capability. This integration enables the bot to execute specific queries, such as inventory checks, by accessing and processing the relevant data while adhering to security protocols and rules.
Abhyankar highlighted the difference between MicroStrategy Auto and general-purpose language models like ChatGPT. He emphasized that Auto combines the conversational abilities of LLMs with a structured approach to enterprise analytics, providing the necessary context, business knowledge, and governance required for accurate and secure responses.
Unlocking User Value
MicroStrategy’s new AI feature, Auto, aims to streamline decision-making by making business analytics more accessible. The company claims that Auto allows users to obtain insights without navigating complex dashboards by simply using natural language to ask questions.
Nena Pidskalny, Director of Supply Chain Strategy and Planning at Federated Co-operatives Limited, noted that MicroStrategy AI could offer substantial value by simplifying access to insights that previously required more effort to obtain. She described it as beneficial for self-service analytics.
Mark N. Vena, President and Principal Analyst at SmartTech Research, suggested that broader access to business intelligence could enhance decision-making and agility across departments. However, he also warned that increased accessibility might lead to risks such as data breaches and misuse if not properly managed.
Rob Enderle, President and Principal Analyst at the Enderle Group, highlighted that customized AI bots like MicroStrategy Auto might outperform general-purpose models like ChatGPT in specific tasks due to their focused design. He also pointed out that these specialized bots could offer enhanced security because they are tailored to avoid undesired behaviors and use smaller data libraries.
Addressing AI Concerns
Custom AI bots can mitigate concerns about data security that come with using large, general-purpose chatbots. Will Duffield, Policy Analyst at the Cato Institute, explained that businesses may worry about sharing proprietary information with tools that could use or misrepresent it. In contrast, business-oriented tools often have clear contractual terms governing data use.
Saurabh Abhyankar, Executive Vice President and Chief Product Officer at MicroStrategy, explained that their system stores data within the customer’s environment, only sending metadata to the AI for processing. This approach minimizes the risk of data leakage and avoids training the AI with sensitive data. By handling calculations internally, MicroStrategy ensures that the AI’s responses remain accurate and contextually appropriate.
Enhancing Productivity
Making business intelligence more accessible could boost productivity. Rob Enderle noted that easier access to data can lead to more informed and timely decisions, contributing to operational success.
Saurabh Abhyankar added that MicroStrategy Auto’s self-service capabilities can enhance the productivity of data analysts by reducing the volume of routine queries they handle. This allows analysts to focus on more complex tasks.
Sharad Varshney, CEO of OvalEdge, emphasized that generative AI technologies are transforming data analytics by simplifying data discovery for non-technical users. However, he stressed that effective data governance is crucial. Data must be accurately governed and validated to ensure quality and compliance before it is used for analysis. Tools that automate these governance tasks can help maintain data integrity and usability.