Recent releases, including the Agentic Analytics Platform and Agentic Semantic Layer, demonstrate that the vendor continues to be creative amid the changing analytics industry.
ThoughtSpot, which was regarded as one of the more innovative analytics vendors before the burgeoning era of AI-driven business intelligence, has evolved in step with ongoing trends and continues to provide customers with capabilities that industry insiders assert set it apart from competitors to some extent.
With agentic AI -- applications capable of reasoning and context awareness that can act autonomously to surface insights and execute certain tasks -- now the vanguard, ThoughtSpot in April launched its Agentic Analytics Platform. To underpin its agentic AI capabilities, the vendor on June 2 made its new Agentic Semantic Layer generally available.
In addition, with data management vendors Snowflake and Databricks each holding their annual user conferences over the past two weeks, ThoughtSpot unveiled SnowSpot and DataSpot, which are versions of its platform that natively integrate with Snowflake and Databricks.
"ThoughtSpot remains a leader, specifically in AI-driven analytics," said Donald Farmer, founder and principal of TreeHive Strategy. "[ThoughtSpot is] differentiated from traditional BI platforms such as Qlik, Tableau and Microsoft Power BI by an agentic AI architecture and superior natural language search."
[ThoughtSpot is] differentiated from traditional BI platforms such as Qlik, Tableau and Microsoft Power BI by an agentic AI architecture and superior natural language search.
Donald FarmerFounder and principal, TreeHive Strategy
Mike Leone, an analyst at Enterprise Strategy Group, now part of Omdia, likewise noted that ThoughtSpot's current capabilities, which build on the vendor's historic roots in AI-powered analytics, are advanced among analytics vendors.
Based in Mountain View, Calif., ThoughtSpot made AI part of its platform from its start in 2012, providing a natural language interface that enables users to analyze data without writing code.
However, before improvements in generative AI (GenAI) technology that began with OpenAI's November 2022 launch of ChatGPT, users needed data literacy skills and some degree of technical expertise to use ThoughtSpot, given the previous limitations of natural language processing.
"Despite the progress and press some of the biggest players in the market receive, ThoughtSpot continues to be a contender and over the last years has leaned in to their AI-native strengths," Leone said.
Recent developments
With GenAI's potential to make workers better informed and more efficient, many enterprises increased their investments in AI development after ChatGPT's launch. Data management and analytics vendors responded by building tools that easily connect GenAI models with the proprietary data needed to train GenAI systems to understand an individual enterprise's unique operations.
Throughout 2023 and into 2024, GenAI development largely consisted of building chatbots that enable end users to engage with data using natural language. As 2024 progressed, agentic AI emerged and is now the dominant trend.
Agents, unlike chatbots that act only when prompted, are semiautonomous, responding to user prompts as well as acting on their own.
ThoughtSpot launched Spotter, an AI-powered agent that enables analytics using natural language, in November 2024. Five months later, with the release of the Agentic Analytics Platform, ThoughtSpot made Spotter one component of a larger suite that also includes Analyst Studio for users to prepare data for AI and analytics-based analysis.
In addition, the Agentic Analytics Platform enabled users to embed agents in any application so that end users could benefit from agentic AI without leaving their normal workflows. Now, ThoughtSpot is adding versions of its Agentic Analytics Platform so that customers of Snowflake and Databricks can natively integrate ThoughtSpot's agentic analytics capabilities with the AI development and data management capabilities of both vendors.
"There's a lot to like in the Agentic Analytics Platform, but I am particularly keen on Analyst Studio," Farmer said. "This is unique in how it carefully enables the specific workflows of data analysts. ThoughtSpot has really thought through what it is like to be a data analyst on a daily basis, and it shows."
Leone likewise praised ThoughtSpot's approach to agentic AI, which considers how users interact with data and rearchitects ThoughtSpot's platform rather than merely adding AI on top of existing capabilities.
"Their pure-play focus on truly conversational, agent-driven analytics puts them at the bleeding edge amongst the leaders," Leone said.
While ThoughtSpot's Agentic Analytics Platform lets customers essentially provide employees with their own analyst, the vendor's Agentic Semantic Layer aims to improve the accuracy of agentic outputs. AI's accuracy has been a significant concern for many enterprises ever since ChatGPT's launch.
Now, accuracy is perhaps of even greater importance as agentic AI becomes more ubiquitous and the tools developed are capable of taking action on their own. AI developers are continually working to improve the accuracy of their models. To further improve the accuracy of agents, vendors such as ThoughtSpot and Tableau -- which, like ThoughtSpot, has made agentic AI the foundation of its platform -- are adding semantic layers tailored for agentic AI.
Semantic layers are tools with which organizations can define key metrics and standardize terms that describe data. The intent is to make data consistent and discoverable, aid data quality, and avoid potential data duplication.
ThoughtSpot has long had a semantic layer to fuel dashboards. Now, as BI is increasingly consumed using AI, it has tailored one for agentic development.
Given that the Agentic Semantic Layer builds on ThoughtSpot's preexisting semantic layer by automatically applying key contextual information to agents in a uniform way across an organization's data, it's a significant addition, according to Leone.
"It's a natural evolution of their original vision," he said.
Farmer similarly called its addition valuable. However, he noted that ThoughtSpot's claim that its Agentic Semantic Layer sets a new standard for data foundations is somewhat dubious.
"The ability to apply automated context ... is very useful," he said. "It's good, but I do think they oversell it."
Ketan Karkhanis, who took over as ThoughtSpot's CEO in September 2024, called the Agentic Analytics Platform a continuation of the vendor's journey, representing its evolution from visualization-based analytics to agentic analytics, with fully autonomous analytics as the next step. Similarly, the Agentic Semantic Layer represents the evolution of longstanding ThoughtSpot capabilities, he added.
"Our biggest differentiation in the world of AI is the BI we built five years ago," Karkhanis said. "It's the semantic model we built, the ThoughtSpot modeling language we built, the search on structured data that we built."
Looking ahead
As ThoughtSpot plots its roadmap, connected insights, agentic AI and smart applications are focal points, according to Karkhanis.
The vendor's push related to connected insights is to enable customers to embed ThoughtSpot, including a network of interoperable agents, in work applications such as Salesforce and ServiceNow. The next step with agents is to make them fully autonomous, including greater interoperability with frameworks such as the Model Context Protocol -- which ThoughtSpot now supports -- and Agent2Agent protocol. ThoughtSpot's push related to smart applications is to enable users to embed AI in any homegrown application.
"Every enterprise needs these three," Karkhanis said.
Farmer, meanwhile, suggested that ThoughtSpot do more to provide potential new customers with affordable pricing options. The vendor provides consumption-based pricing and offers three tiers, starting with an Essentials version for $1,250 per month for 20 users and 25 million rows of data.
In addition, ThoughtSpot could expand its offering by adding industry-specific tools and new partnerships, according to Farmer.
"Verticalize [by providing] prebuilt agents for industries such as retail demand forecasting and healthcare analytics," he said. "[And] expand GenAI partnerships ... to enhance Spotter's reasoning."
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.
