Sisense targets embedding AI with latest new features
Tools such as an MCP server and a natural language assistant demonstrate the vendor's evolution toward artificial intelligence as the interface for analytics.
Sisense on Tuesday launched new features, including an AI-powered assistant and Model Context Protocol server, aimed at enabling customers to model and explore data more efficiently.
In addition, the vendor unveiled Sisense Managed LLM, a managed service in private preview that provides access to analysis fueled by large language models (LLMs).
Sisense is not the first analytics vendor to provide an MCP server and AI-powered capabilities. For example, ThoughtSpot supports MCP and is fully automating its platform. However, given that Sisense is enabling customers to develop an intelligence layer they can embed in applications, the new features are significant additions, according to Michael Ni, an analyst at Constellation Research.
"Sisense isn't trying to win the BI platform war," he said. "MCP and a managed LLM extend Sisense's embedded analytics into supporting an intelligence supply chain to enable vendors to deliver their own insight-driven agentic experiences inside their SaaS products."
Regarding the uniqueness of the features, Ni acknowledged that other vendors provide assistants and MCP support, but Sisense is going a step further, Ni continued.
"Sisense is going beyond embedded analytics and chatbots and doing something more strategic," he said. "It's building the intelligence plumbing that lets other companies embed AI-driven decisions into their products and workflows without becoming AI platform companies themselves."
Based in New York City, Sisense is a longtime analytics vendor providing a platform that enables customers to develop and embed data products.
AI-powered analysis
With the emergence of agentic AI over the past two years and generative AI (GenAI) before that, AI has become an interface for data analysis, along with traditional business intelligence reports and dashboards.
Sisense isn't trying to win the BI platform war. MCP and a managed LLM extend Sisense's embedded analytics into supporting an intelligence supply chain to enable vendors to deliver their own insight-driven agentic experiences inside their SaaS products.
Michael NiAnalyst, Constellation Research
Agents and GenAI chatbots simplify modeling, exploring and analyzing data, often enabling users to carry out such tasks using natural language rather than code. As a result, some vendors have recreated their platforms with AI assisting users as they model data, develop data products and generate business insights.
In addition to ThoughtSpot, Tableau launched Tableau Next, a version of its platform featuring an AI-powered semantic layer and agents that help users prepare and analyze data, in April 2025. Now, Sisense is adding new AI capabilities that simplify using its platform with a mix of customer feedback and market trends providing the impetus, according to Ariel Katz, CEO of Sisense.
"Customer feedback played an important role, but the timing reflects a broader architectural shift in analytics," he said. "The real bottleneck in GenAI analytics is no longer access to LLMs -- it's making GenAI reliable when it's embedded into real products and real enterprise application workflows."
With an AI-first approach to analytics as a guide, Sisense's MCP server enables developers to connect agents and other AI applications to external data sources, including LLMs such as OpenAI's GPT models and Claude from Anthropic.
Created by Anthropic, MCP is a set of open source code that simplifies the complex process of connecting agents with the data that gives agents the context to accurately respond to queries and autonomously take on certain tasks. Because it reduces the burdens on developers, MCP has become so widely adopted that any vendor not providing an MCP server risks losing customers to competitors.
In addition to simplifying connections between AI applications and their source data, Sisense's MCP server ensures that query responses come from governed semantic models and maintains an enterprise's governance standards including access controls.
Sisense's AI-powered assistant is built on the foundation provided by the MCP server, enabling users to quickly and easily build data models, develop dashboards and other data products, and explore and analyze data using natural language prompts.
Finally, Managed LLM, which works in conjunction with Sisense's bring-your-own LLM offering, is a fully managed service that removes the burden of overseeing a complex AI infrastructure. By lowering operational barriers, the service is designed to make it faster and easier for end users to generate AI-informed business insights.
Sisense's AI-powered assistant is perhaps the most significant of the new features given that it simplifies accessing and analyzing data, according to David Menninger, an analyst at ISG Software Research. Meanwhile, like Ni, he noted that the three new capabilities collectively are important additions for Sisense users.
"These new features are significant from the perspective that they continue to incorporate and expand AI capabilities within the Sisense platform," Menninger said.
From a competitive standpoint, the new features keep Sisense in step with peers as well as demonstrate the vendor's focus on developers, he continued, pointing out that Sisense is focusing on a niche rather than battling hyperscale cloud vendors such as Microsoft and Salesforce -- which owns Tableau -- for market share.
"The analytics market is an extremely competitive and mature market dominated by some of the largest software providers in the world," he said. "Sisense has chosen to focus on the developer market as a way to avoid fighting a multi-front war. Our Buyers Guides for Analytics … shows Sisense clinging to their rating as an innovative provider in that segment, but their positioning has slipped a bit from last year."
Ni similarly noted that while Sisense is not releasing new features with same frequency as some other providers, particularly data management vendors such as Databricks and Snowflake that now offer AI development capabilities, the vendor's expertise in embedded analysis is a differentiator.
"Sisense didn't so much lose momentum as narrow its focus and step away from broad BI and chatbot visibility to concentrate on embedded analytics -- and now, embedded intelligence -- where execution matters more than noise," he said.
Looking ahead
As Sisense plots its product development roadmap for the first half of 2026, the vendor's focus is on adding more agents and assistants that enable users to explore and analyze data, according to Katz. In addition, initiatives include strengthening the semantic layer that helps developers discover relevant data and improving foundational features that allow customers to embed and govern agents, he continued.
"Together, these investments are designed to move AI in analytics from promising demos to trusted, production-grade capabilities that customers can deploy with confidence," Katz said.
Focusing on foundational capabilities that enable customers to embed agents and other AI tools in user workflows is wise, according to Ni.
"If Sisense wants to attract the next wave of customers, it should lean into being the intelligence layer for … domain intelligence," he said. "As SaaS competition tightens and AI leverage expands, vendors will differentiate less on features and more on the intelligence embedded in their products, and Sisense can position itself to be a key platform that enables that shift."
Menninger, meanwhile, suggested that Sisense add support for open source standards such as Agent2Agent Protocol that build on MCP to coordinate how agents interact with one another.
"If Sisense wants to remain competitive in the developer market, it should consider expanding beyond MCP to support inter-agent coordination," he said. "MCP provides context but does not facilitate communication directly between different agents. Custom AI-based applications will need to coordinate the actions of multiple agents."
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.
