David manages the software research and advisory for IT and leads the expertise in AI at ISG Software Research. He oversees the team and expertise areas for software used by IT and for what is used by business. David leads the software research efforts in AI for IT and AI-infused software building on over three decades of experience in data and analytics. For decades, he has brought to market leading-edge analytics and data products in marketing and product leadership positions at Pivotal (a division of EMC), Vertica Systems, Oracle, Applix, InforSense, and IRI Software. David earned his MS in Business from Bentley University and a BS in Economics from the University of Pennsylvania.
narration area
Executive Summary
AI Analytics
For decades, organizations have refined and expanded the use of analytics software, sometimes referred to by its historical name of business intelligence (BI), to improve operations. Providers have made dramatic improvements to BI products, with highly interactive visualizations and the ability to process and display very large volumes of data quickly. However, enterprises' quest for more continues, searching for ways to make analytics accessible to more of the workforce. Generative AI (GenAI) is being applied to all aspects of data analytics software to make the products easier to use. Machine learning (ML) models are being used to deliver more advanced analytics and automated insights. Agentic AI is being used to make many aspects of software products easier to use. It should come as no surprise that ISG Market Lens research shows that 87% of participants are now AI-enabling analytics and BI applications.
ISG Research defines AI analytics as the use of GenAI, agentic AI and other AI/ML techniques to enhance analytics processes. It includes providing conversational interfaces, recommending data preparation steps, suggesting visualizations of data and documenting analytics processes. It includes using AI/ML to provide automated insights and natural language generation. AI analytics includes predictive, descriptive and prescriptive analytics. It also incorporates agentic AI to help implement decisions made using analytics software.
Adopting AI/ML has proven more complicated than many had expected. Ideally, BI software products could simply be extended to include a complete set of AI/ML capabilities, but that has not yet fully happened. AI/ML requires skills that are beyond the reach of many analysts, and organizations have had difficulty finding skilled resources. As a result, we expect that through 2027, more than one-half of enterprises will realize their AI competencies and skills are insufficient and will require new investments to avoid being at a competitive disadvantage.
Faced with this gap, BI software providers have invested in ways to make AI/ML more accessible by augmenting system capabilities. With the advent of GenAI, elements of AI/ML can be more easily incorporated into analytics experiences. For example, AI/ML can drive automated insights to identify and explain relationships in the data as well as recommend which actions to take.
AI/ML can augment analytics in various ways. One of the most common and beneficial uses of GenAI is natural language processing (NLP) to support conversational analytics with natural language queries and narrative responses. Automated machine learning (AutoML) automates the process of creating ML models, making more sophisticated analytics, such as customer segmentation using clustering techniques, accessible to more individuals. GenAI can be applied to many tasks in analytics and data processes to make those actions easier to design and perform.
In addition to conversational analytics, one of the biggest opportunities for GenAI is to assist with data preparation. Data preparation continues to be the area where organizations spend the most time in the analytics processes. GenAI can be used to suggest which tables of data to combine and how to merge those tables. It can automatically construct a logical data model from a physical data model. AI/ML can augment data quality processes, identifying outliers and anomalies in the data and even recommending potential corrections for those data points.
While efforts to apply AI/ML have been underway for some time, the sudden explosion of GenAI capabilities has fueled more interest in how to augment BI, such as co-pilots and assistants. GenAI is also used to generate SQL to access data sources and, in some cases, GenAI produces documentation of data pipelines for analytics processes, enhancing the understanding and lineage of the data. In some ways, it is the wild, wild west, with providers racing to outdo each other to apply GenAI. The technology holds much promise, and we expect it will have a significant impact on the analytics market, but it is still early days.
Generative AI is making conversational analytics more common and more capable than it is today.
AI analytics will continue to evolve. Many features are still under development or in initial release. Generative AI is making conversational analytics more common and more capable than it is today. It will enable better support for multilingual capabilities, which have been lacking in many analytics products. And it will likely lead to increased automation in data preparation processes and in creating initial analyses, making analysts much more productive.
More products will offer AutoML capabilities. Among the providers we evaluated, AutoML is most often used to generate forecasts and perform customer segmentation analyses. Over time, AutoML capabilities will expand to support more types of analyses and will produce models with improved accuracy. The exact intersection between AutoML in GenAI analytics products and the models produced from more sophisticated AI/ML products remains to be seen. Today, some AI analytics products can work with these models, but it is still a loosely coupled process.
Organizations should be aware of changes going on in the market and understand what capabilities providers currently offer. Enterprises should also be comparing offerings from current providers with what other providers have to offer. In evaluating AI analytics, one must consider the underlying analytics capabilities. GenAI can only do so much if the foundation of the underlying analytics is weak. Consequently, this Buyers Guide combines an assessment of AI analytics capabilities with core analytics capabilities to determine the provider’s overall rankings. Organizations can then use this report not only to help guide purchasing decisions but to guide conversations with providers about the roadmap for AI analytics. The market is still evolving rapidly, but organizations can realize value today that will improve the analytics processes.
The 2025 ISG Buyers Guide™ for AI Analytics evaluates software providers and products in three key areas of data, analytics and communications. It also includes capability requirements used in our overall Analytics Buyers Guide, spanning analytics-specific areas: discover, integrate and predict analytics; act, collaborate, inform and manage analytics; access; and models of data.
This research assessed the following providers: Alibaba Cloud, AWS, Cloud Software Group, Databricks, Domo, Epicor, Google, IBM, Infor, insightsoftware, Microsoft, OpenText, Oracle, Qlik, Salesforce, SAP, SAS, Sisense, Strategy, ThoughtSpot and Zoho.
Buyers Guide Overview
ISG Research has conducted market research for over two decades across vertical industries, business applications, AI and IT. We have designed the ISG Buyers Guide™ to provide a balanced perspective of software providers and products that is rooted in an understanding of business and IT requirements. Utilization of our research methodology and decades of experience enables our Buyers Guide to be an effective method to assess and select software providers and products. The findings of this research provide a comprehensive approach to rating software providers and rank their ability to meet specific product and customer experience requirements.
ISG Research has designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of business and IT requirements.
The 2025 ISG Buyers Guide™ for AI Analytics is the distillation of continuous market and product research. It is an assessment of how well software providers’ offerings address enterprises’ requirements for AI analytics software. The Value Index methodology is structured to support a request for information (RFI) for a request for proposal (RFP) process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with software providers. The ISG Buyers Guide evaluates customer experience and the product experience in its capability and platform.
The structure of the research reflects our understanding that the effective evaluation of software providers and products involves far more than just examining product features, potential revenue or customers generated from a provider’s marketing and sales efforts. It can ensure the best long-term relationship and value achieved from a resource and financial investment We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of AI analytics software can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its potential. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on opinions or historical biases.
ISG Research believes that an objective review of existing and potential new software providers and products is a critical strategy for the adoption and implementation of AI analytics software. An enterprise’s review should include an analysis of both what is possible and what is relevant. We urge enterprises to do a thorough job of evaluating AI analytics software and offer this Buyers Guide as both the results of our in-depth analysis of these providers and as an evaluation methodology.
Key Takeaways
AI analytics has evolved from traditional BI into a discipline that layers conversational interfaces, automated insights and assisted data preparation onto core analytic workflows. Enterprises now look to these platforms to simplify the use of AI/ML, reduce reliance on scarce specialist skills and accelerate interpretation of increasingly complex data. The category reflects rapid innovation driven by GenAI and agentic assistance, even as underlying analytics capabilities remain essential for value realization. These shifts highlight the need for solutions that balance advanced automation with robustness, scalability and operational clarity.
Software Provider Summary
The ISG Buyers Guide™ for AI Analytics evaluates 21 software providers offering products supporting AI-enhanced analytics across data, analytics and communication capabilities. The research ranked the top three overall leaders as Oracle, Databricks and Domo. Providers were classified using weighted performance in Product Experience and Customer Experience for ISG quadrant placement. AWS, Databricks, Domo, IBM, Microsoft, Oracle, Qlik, Salesforce, SAP and Zoho were rated as Exemplary, with Strategy rated as Innovative. Google, Infor, OpenText, SAS and ThoughtSpot were rated as Assurance, and Alibaba Cloud, Cloud Software Group, Epicor, insightsoftware and Sisense were rated as Merit.
Product Experience Insights
Product Experience, representing 80% of the evaluation, focuses on Capability (50%) and Platform (30%), which includes adaptability, manageability, reliability and usability. Oracle, Domo and IBM achieved the highest performance as Leaders in this category, supported by the breadth and depth across AI analytics capabilities and strong underlying platform foundations enabling secure operations, scalable performance and intuitive user experiences. Leaders demonstrated enterprise-grade platform capabilities across varied roles and contexts.
Customer Experience Value
Customer Experience, representing 20% of the evaluation, focuses on validation and TCO/ROI. Oracle, Domo, Salesforce and ThoughtSpot were the Leaders in this category, showing strong customer advocacy and clear investment in success outcomes. Providers with lower performance often lacked publicly available customer validation or failed to demonstrate structured ROI measurement and proactive lifecycle engagement.
Strategic Recommendations
Enterprises should treat AI analytics platforms as strategic investments that unify core analytics functionality with expanding AI-driven automation and assistance. Buyers should prioritize providers that combine strong foundational analytics, clear AI roadmaps and demonstrable customer value. Solutions that streamline data preparation, enhance interpretability and support guided analytics experiences will enable more consistent adoption across business roles. Using these criteria, organizations can align provider capabilities with long-term needs for scalability, operational clarity and AI readiness.
How To Use This Buyers Guide
Evaluating Software Providers: The Process
We recommend using the Buyers Guide to assess and evaluate new or existing software providers for your enterprise. The market research can be used as an evaluation framework to assess existing approaches and software providers or establish a formal request for information from providers on products and customer experience and will shorten the cycle time when creating an RFI. The steps listed below provide a process that can facilitate best possible outcomes in the most efficient manner.
- Define the business case and goals.
Define the mission and business case for investment and the expected outcomes from your organizational and technological efforts.
- Specify the business and IT needs.
Defining the business and IT requirements helps identify what specific capabilities are required with respect to people, processes, information and technology.
- Assess the required roles and responsibilities.
Identify the individuals required for success at every level of the enterprise from executives to frontline workers and determine the needs of each.
- Outline the project’s critical path.
What needs to be done, in what order and who will do it? This outline should make clear the prior dependencies at each step of the project plan.
- Ascertain the technology approach.
Determine the business and technology approach that most closely aligns to your enterprise’s requirements.
- Establish software provider evaluation criteria.
Utilize the product experience: capability and platform with support for adaptability, manageability, reliability and usability, and the customer experience in TCO/ROI and Validation.
- Evaluate and select the software provider and products properly.
Apply a weighting the evaluation categories in the evaluation criteria to reflect your enterprise’s priorities to determine the short list of software providers and products.
- Establish the business initiative team to start the project.
Identify who will lead the project and the members of the team needed to plan and execute it with timelines, priorities and resources.
Using the ISG Buyers Guide and process provides enterprises a clear, structured approach to making smarter software and business investment decisions. It ensures alignment between strategy, people, processes and technology while reducing risk, saving time and improving outcomes. The ISG approach promotes data-driven decision-making and collaboration, helping choose the right software providers for maximum value and return on investment.
The Findings
The software providers and products evaluated in the research provide product and customer experiences, but not everything offered is equally valuable to every enterprise or is needed to operate in business processes and use cases. Moreover, the existence of too many capabilities in products may be a negative factor for an enterprise if it introduces unnecessary complexity. Nonetheless, you may decide that a more comprehensive set of capabilities in the product is important, and where they match your enterprise’s requirements.
An effective customer relationship with a software provider is vital to the success of any investment. The overall customer experience and the full lifecycle of engagement play a key role in ensuring satisfaction and long-term success. Providers with dedicated customer leadership, such as chief customer officers, tend to invest more deeply in these relationships and prioritize customer outcomes to TCO and ROI expectations. It is equally important that this commitment to customer success is clearly demonstrated throughout the provider’s website, buying process and customer journey.
Overall Scoring of Software Providers Across Categories
The research finds Oracle atop the list, followed by Databricks and Domo. Providers that place in the top three of a category earn the designation of Leader. Domo and Oracle have done so in five categories, Databricks and IBM in two and Salesforce and ThoughtSpot in one category.
The overall representation of the research below places the rating of the Product Experience and Customer Experience on the x and y axes, respectively, to provide a visual representation and classification of the software providers. Those providers whose Product Experience have above median weighted performance to the axis in aggregate of the two product categories place farther to the right, while the performance and weighting for the Customer Experience category determines placement on the vertical axis. In short, software providers that place closer to the upper-right on this chart performed better than those closer to the lower-left.
The research categorizes and rates software providers into one of four categories: Assurance, Exemplary, Merit or Innovative. This representation of software providers’ weighted performance in meeting the requirements in product and customer experience.
Exemplary: This rating (upper right) represents those that performed above median in Product and Customer Experience requirements. The providers rated Exemplary are: AWS, Databricks, Domo, IBM, Microsoft, Oracle, Qlik, Salesforce, SAP and Zoho.
Innovative: This rating (lower right) represents those that performed above median in Product Experience but not in Customer Experience. The provider rated Innovative is: Strategy.
Assurance: This rating (upper left) represents those that performed above median in Customer Experience but not in Product Experience. The providers rated Assurance are: Google, Infor, OpenText, SAS and ThoughtSpot.
Merit: This rating (lower left) represents those that did not surpass the median in Customer or Product Experience. The providers rated Merit are: Alibaba Cloud, Cloud Software Group, Epicor, insightsoftware and Sisense.
We advise enterprises to use this research as a supplement to their own evaluations, recognizing that ratings or rankings do not solely represent the value of a provider nor indicate universal suitability of a set of products.
Product Experience
The process of researching products to address an enterprise’s needs should be comprehensive and evaluate specific capabilities and the underlying platform to the product experience. Our evaluation of the Product Experience examines the lifecycle of onboarding, configuration, operations, usage and maintenance. Too often, software providers are not evaluated for the entirety of the product; instead, they are evaluated on market execution and vision of the future.
The research results in Product Experience are ranked at 80%, or four-fifths, using the underlying weighted performance. Importance was placed on the categories as follows: Capability (50%) and Platform (30%). Oracle, Domo and IBM were designated Product Experience Leaders.
Customer Experience
The importance of a customer relationship with a software provider is essential to the actual success of the products and technology. The evaluation of the Customer Experience and the entire lifecycle an enterprise has with its software provider is critical for ensuring satisfaction in working with that provider. The ISG Buyers Guide examines a software provider’s customer commitment, viability, customer success, sales and onboarding, product roadmap and services with partners and support. The customer experience category also investigates the TCO/ROI and how well a software provider demonstrates the product’s overall value, cost and benefits, including the tools and resources to evaluate these factors.
The research results in Customer Experience are ranked at 20%, or one-fifth of the 100% index, and represent the underlying provider validation and TCO/ROI requirements as they relate to the framework of commitment and value to the software provider-customer relationship.
The software providers that evaluated the highest in the Customer Experience category are Oracle, Domo, Salesforce and ThoughtSpot. These category leaders best communicate commitment and dedication to customer needs. While not a Leader, SAP was also found to meet a broad range of enterprise customer experience requirements.
Software providers that did not perform well in this category were unable to provide or make sufficient information readily available to demonstrate success or articulate their commitment to customer experience. The use of a software provider requires continuous investment, so a holistic evaluation must include examination of how they support their customer experience.
Appendix: Software Provider Inclusion
For inclusion in the 2025 ISG Buyers Guide™ for AI Analytics, a software provider must be in good standing financially and ethically, have at least $150 million in annual or projected revenue verified using independent sources, sell products and provide support on at least two continents, and have at least 50 customers. The principal source of the relevant business unit’s revenue must be software-related and there must have been at least one major software release in the past 12 months.
The product must be actively marketed as an analytics product that includes generative AI, agentic AI or machine learning capabilities to support the analytics processes with an organization including assisting with data access and preparation, automated analyses and insights, and natural language query or chat interfaces.
The research is designed to be independent of the specifics of software provider packaging and pricing. To represent the real-world environment in which businesses operate, we include providers that offer suites or packages of products that may include relevant individual modules or applications. If a software provider is actively marketing, selling and developing a product for the general market and it is reflected on the provider’s website that the product is within the scope of the research, that provider is automatically evaluated for inclusion.
All software providers that offer relevant AI analytics products and meet the inclusion requirements were invited to participate in the evaluation process at no cost to them.
Software providers that meet our inclusion criteria but did not completely participate in our Buyers Guide were assessed solely on publicly available information. As this could have a significant impact on classification and ratings, we recommend additional scrutiny when evaluating those providers.
Products Evaluated
| Provider | Product Names | Version | Release Month/Year |
|---|---|---|---|
| Alibaba Cloud | Quick BI | 6.0 | October 2025 |
| AWS | Amazon Quick Suite | NA | November 2025 |
| Cloud Software Group | Spotfire | 14.6 LTS | October 2025 |
| Databricks | Databricks Platform | NA | November 2025 |
| Domo | Domo | NA | November 2025 |
| Epicor | Grow | 15.0.0 | October 2025 |
| Looker | 25.2.0 | November 2025 | |
| IBM | IBM Cognos Analytics | 12.1.1 | October 2025 |
| Infor | Infor Birst | NA | November 2025 |
| insightsoftware | Logi Symphony | NA | November 2025 |
| Microsoft | Power BI | 2.148.1477.0 | November 2025 |
| OpenText | Analytics Cloud | NA | November 2025 |
| Oracle | Oracle Analytics Cloud | NA | November 2025 |
| Qlik | Qlik Cloud Analytics | NA | November 2025 |
| Salesforce | Tableau | NA | November 2025 |
| SAP | SAP Business Data Cloud | NA | November 2025 |
| SAS | SAS Viya | 2025.11 | November 2025 |
| Sisense | Sisense Platform | NA | November 2025 |
| Strategy | Strategy One Platform | NA | November 2025 |
| ThoughtSpot | ThoughtSpot Cloud | 10.14.0.cl | November 2025 |
| Zoho | Zoho Analytics | NA | October 2025 |
Executive Summary
AI Analytics
For decades, organizations have refined and expanded the use of analytics software, sometimes referred to by its historical name of business intelligence (BI), to improve operations. Providers have made dramatic improvements to BI products, with highly interactive visualizations and the ability to process and display very large volumes of data quickly. However, enterprises' quest for more continues, searching for ways to make analytics accessible to more of the workforce. Generative AI (GenAI) is being applied to all aspects of data analytics software to make the products easier to use. Machine learning (ML) models are being used to deliver more advanced analytics and automated insights. Agentic AI is being used to make many aspects of software products easier to use. It should come as no surprise that ISG Market Lens research shows that 87% of participants are now AI-enabling analytics and BI applications.
ISG Research defines AI analytics as the use of GenAI, agentic AI and other AI/ML techniques to enhance analytics processes. It includes providing conversational interfaces, recommending data preparation steps, suggesting visualizations of data and documenting analytics processes. It includes using AI/ML to provide automated insights and natural language generation. AI analytics includes predictive, descriptive and prescriptive analytics. It also incorporates agentic AI to help implement decisions made using analytics software.
Adopting AI/ML has proven more complicated than many had expected. Ideally, BI software products could simply be extended to include a complete set of AI/ML capabilities, but that has not yet fully happened. AI/ML requires skills that are beyond the reach of many analysts, and organizations have had difficulty finding skilled resources. As a result, we expect that through 2027, more than one-half of enterprises will realize their AI competencies and skills are insufficient and will require new investments to avoid being at a competitive disadvantage.
Faced with this gap, BI software providers have invested in ways to make AI/ML more accessible by augmenting system capabilities. With the advent of GenAI, elements of AI/ML can be more easily incorporated into analytics experiences. For example, AI/ML can drive automated insights to identify and explain relationships in the data as well as recommend which actions to take.
AI/ML can augment analytics in various ways. One of the most common and beneficial uses of GenAI is natural language processing (NLP) to support conversational analytics with natural language queries and narrative responses. Automated machine learning (AutoML) automates the process of creating ML models, making more sophisticated analytics, such as customer segmentation using clustering techniques, accessible to more individuals. GenAI can be applied to many tasks in analytics and data processes to make those actions easier to design and perform.
In addition to conversational analytics, one of the biggest opportunities for GenAI is to assist with data preparation. Data preparation continues to be the area where organizations spend the most time in the analytics processes. GenAI can be used to suggest which tables of data to combine and how to merge those tables. It can automatically construct a logical data model from a physical data model. AI/ML can augment data quality processes, identifying outliers and anomalies in the data and even recommending potential corrections for those data points.
While efforts to apply AI/ML have been underway for some time, the sudden explosion of GenAI capabilities has fueled more interest in how to augment BI, such as co-pilots and assistants. GenAI is also used to generate SQL to access data sources and, in some cases, GenAI produces documentation of data pipelines for analytics processes, enhancing the understanding and lineage of the data. In some ways, it is the wild, wild west, with providers racing to outdo each other to apply GenAI. The technology holds much promise, and we expect it will have a significant impact on the analytics market, but it is still early days.
Generative AI is making conversational analytics more common and more capable than it is today.
AI analytics will continue to evolve. Many features are still under development or in initial release. Generative AI is making conversational analytics more common and more capable than it is today. It will enable better support for multilingual capabilities, which have been lacking in many analytics products. And it will likely lead to increased automation in data preparation processes and in creating initial analyses, making analysts much more productive.
More products will offer AutoML capabilities. Among the providers we evaluated, AutoML is most often used to generate forecasts and perform customer segmentation analyses. Over time, AutoML capabilities will expand to support more types of analyses and will produce models with improved accuracy. The exact intersection between AutoML in GenAI analytics products and the models produced from more sophisticated AI/ML products remains to be seen. Today, some AI analytics products can work with these models, but it is still a loosely coupled process.
Organizations should be aware of changes going on in the market and understand what capabilities providers currently offer. Enterprises should also be comparing offerings from current providers with what other providers have to offer. In evaluating AI analytics, one must consider the underlying analytics capabilities. GenAI can only do so much if the foundation of the underlying analytics is weak. Consequently, this Buyers Guide combines an assessment of AI analytics capabilities with core analytics capabilities to determine the provider’s overall rankings. Organizations can then use this report not only to help guide purchasing decisions but to guide conversations with providers about the roadmap for AI analytics. The market is still evolving rapidly, but organizations can realize value today that will improve the analytics processes.
The 2025 ISG Buyers Guide™ for AI Analytics evaluates software providers and products in three key areas of data, analytics and communications. It also includes capability requirements used in our overall Analytics Buyers Guide, spanning analytics-specific areas: discover, integrate and predict analytics; act, collaborate, inform and manage analytics; access; and models of data.
This research assessed the following providers: Alibaba Cloud, AWS, Cloud Software Group, Databricks, Domo, Epicor, Google, IBM, Infor, insightsoftware, Microsoft, OpenText, Oracle, Qlik, Salesforce, SAP, SAS, Sisense, Strategy, ThoughtSpot and Zoho.
Buyers Guide Overview
ISG Research has conducted market research for over two decades across vertical industries, business applications, AI and IT. We have designed the ISG Buyers Guide™ to provide a balanced perspective of software providers and products that is rooted in an understanding of business and IT requirements. Utilization of our research methodology and decades of experience enables our Buyers Guide to be an effective method to assess and select software providers and products. The findings of this research provide a comprehensive approach to rating software providers and rank their ability to meet specific product and customer experience requirements.
ISG Research has designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of business and IT requirements.
The 2025 ISG Buyers Guide™ for AI Analytics is the distillation of continuous market and product research. It is an assessment of how well software providers’ offerings address enterprises’ requirements for AI analytics software. The Value Index methodology is structured to support a request for information (RFI) for a request for proposal (RFP) process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with software providers. The ISG Buyers Guide evaluates customer experience and the product experience in its capability and platform.
The structure of the research reflects our understanding that the effective evaluation of software providers and products involves far more than just examining product features, potential revenue or customers generated from a provider’s marketing and sales efforts. It can ensure the best long-term relationship and value achieved from a resource and financial investment We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of AI analytics software can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its potential. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on opinions or historical biases.
ISG Research believes that an objective review of existing and potential new software providers and products is a critical strategy for the adoption and implementation of AI analytics software. An enterprise’s review should include an analysis of both what is possible and what is relevant. We urge enterprises to do a thorough job of evaluating AI analytics software and offer this Buyers Guide as both the results of our in-depth analysis of these providers and as an evaluation methodology.
Key Takeaways
AI analytics has evolved from traditional BI into a discipline that layers conversational interfaces, automated insights and assisted data preparation onto core analytic workflows. Enterprises now look to these platforms to simplify the use of AI/ML, reduce reliance on scarce specialist skills and accelerate interpretation of increasingly complex data. The category reflects rapid innovation driven by GenAI and agentic assistance, even as underlying analytics capabilities remain essential for value realization. These shifts highlight the need for solutions that balance advanced automation with robustness, scalability and operational clarity.
Software Provider Summary
The ISG Buyers Guide™ for AI Analytics evaluates 21 software providers offering products supporting AI-enhanced analytics across data, analytics and communication capabilities. The research ranked the top three overall leaders as Oracle, Databricks and Domo. Providers were classified using weighted performance in Product Experience and Customer Experience for ISG quadrant placement. AWS, Databricks, Domo, IBM, Microsoft, Oracle, Qlik, Salesforce, SAP and Zoho were rated as Exemplary, with Strategy rated as Innovative. Google, Infor, OpenText, SAS and ThoughtSpot were rated as Assurance, and Alibaba Cloud, Cloud Software Group, Epicor, insightsoftware and Sisense were rated as Merit.
Product Experience Insights
Product Experience, representing 80% of the evaluation, focuses on Capability (50%) and Platform (30%), which includes adaptability, manageability, reliability and usability. Oracle, Domo and IBM achieved the highest performance as Leaders in this category, supported by the breadth and depth across AI analytics capabilities and strong underlying platform foundations enabling secure operations, scalable performance and intuitive user experiences. Leaders demonstrated enterprise-grade platform capabilities across varied roles and contexts.
Customer Experience Value
Customer Experience, representing 20% of the evaluation, focuses on validation and TCO/ROI. Oracle, Domo, Salesforce and ThoughtSpot were the Leaders in this category, showing strong customer advocacy and clear investment in success outcomes. Providers with lower performance often lacked publicly available customer validation or failed to demonstrate structured ROI measurement and proactive lifecycle engagement.
Strategic Recommendations
Enterprises should treat AI analytics platforms as strategic investments that unify core analytics functionality with expanding AI-driven automation and assistance. Buyers should prioritize providers that combine strong foundational analytics, clear AI roadmaps and demonstrable customer value. Solutions that streamline data preparation, enhance interpretability and support guided analytics experiences will enable more consistent adoption across business roles. Using these criteria, organizations can align provider capabilities with long-term needs for scalability, operational clarity and AI readiness.
How To Use This Buyers Guide
Evaluating Software Providers: The Process
We recommend using the Buyers Guide to assess and evaluate new or existing software providers for your enterprise. The market research can be used as an evaluation framework to assess existing approaches and software providers or establish a formal request for information from providers on products and customer experience and will shorten the cycle time when creating an RFI. The steps listed below provide a process that can facilitate best possible outcomes in the most efficient manner.
- Define the business case and goals.
Define the mission and business case for investment and the expected outcomes from your organizational and technological efforts.
- Specify the business and IT needs.
Defining the business and IT requirements helps identify what specific capabilities are required with respect to people, processes, information and technology.
- Assess the required roles and responsibilities.
Identify the individuals required for success at every level of the enterprise from executives to frontline workers and determine the needs of each.
- Outline the project’s critical path.
What needs to be done, in what order and who will do it? This outline should make clear the prior dependencies at each step of the project plan.
- Ascertain the technology approach.
Determine the business and technology approach that most closely aligns to your enterprise’s requirements.
- Establish software provider evaluation criteria.
Utilize the product experience: capability and platform with support for adaptability, manageability, reliability and usability, and the customer experience in TCO/ROI and Validation.
- Evaluate and select the software provider and products properly.
Apply a weighting the evaluation categories in the evaluation criteria to reflect your enterprise’s priorities to determine the short list of software providers and products.
- Establish the business initiative team to start the project.
Identify who will lead the project and the members of the team needed to plan and execute it with timelines, priorities and resources.
Using the ISG Buyers Guide and process provides enterprises a clear, structured approach to making smarter software and business investment decisions. It ensures alignment between strategy, people, processes and technology while reducing risk, saving time and improving outcomes. The ISG approach promotes data-driven decision-making and collaboration, helping choose the right software providers for maximum value and return on investment.
The Findings
The software providers and products evaluated in the research provide product and customer experiences, but not everything offered is equally valuable to every enterprise or is needed to operate in business processes and use cases. Moreover, the existence of too many capabilities in products may be a negative factor for an enterprise if it introduces unnecessary complexity. Nonetheless, you may decide that a more comprehensive set of capabilities in the product is important, and where they match your enterprise’s requirements.
An effective customer relationship with a software provider is vital to the success of any investment. The overall customer experience and the full lifecycle of engagement play a key role in ensuring satisfaction and long-term success. Providers with dedicated customer leadership, such as chief customer officers, tend to invest more deeply in these relationships and prioritize customer outcomes to TCO and ROI expectations. It is equally important that this commitment to customer success is clearly demonstrated throughout the provider’s website, buying process and customer journey.
Overall Scoring of Software Providers Across Categories
The research finds Oracle atop the list, followed by Databricks and Domo. Providers that place in the top three of a category earn the designation of Leader. Domo and Oracle have done so in five categories, Databricks and IBM in two and Salesforce and ThoughtSpot in one category.
The overall representation of the research below places the rating of the Product Experience and Customer Experience on the x and y axes, respectively, to provide a visual representation and classification of the software providers. Those providers whose Product Experience have above median weighted performance to the axis in aggregate of the two product categories place farther to the right, while the performance and weighting for the Customer Experience category determines placement on the vertical axis. In short, software providers that place closer to the upper-right on this chart performed better than those closer to the lower-left.
The research categorizes and rates software providers into one of four categories: Assurance, Exemplary, Merit or Innovative. This representation of software providers’ weighted performance in meeting the requirements in product and customer experience.
Exemplary: This rating (upper right) represents those that performed above median in Product and Customer Experience requirements. The providers rated Exemplary are: AWS, Databricks, Domo, IBM, Microsoft, Oracle, Qlik, Salesforce, SAP and Zoho.
Innovative: This rating (lower right) represents those that performed above median in Product Experience but not in Customer Experience. The provider rated Innovative is: Strategy.
Assurance: This rating (upper left) represents those that performed above median in Customer Experience but not in Product Experience. The providers rated Assurance are: Google, Infor, OpenText, SAS and ThoughtSpot.
Merit: This rating (lower left) represents those that did not surpass the median in Customer or Product Experience. The providers rated Merit are: Alibaba Cloud, Cloud Software Group, Epicor, insightsoftware and Sisense.
We advise enterprises to use this research as a supplement to their own evaluations, recognizing that ratings or rankings do not solely represent the value of a provider nor indicate universal suitability of a set of products.
Product Experience
The process of researching products to address an enterprise’s needs should be comprehensive and evaluate specific capabilities and the underlying platform to the product experience. Our evaluation of the Product Experience examines the lifecycle of onboarding, configuration, operations, usage and maintenance. Too often, software providers are not evaluated for the entirety of the product; instead, they are evaluated on market execution and vision of the future.
The research results in Product Experience are ranked at 80%, or four-fifths, using the underlying weighted performance. Importance was placed on the categories as follows: Capability (50%) and Platform (30%). Oracle, Domo and IBM were designated Product Experience Leaders.
Customer Experience
The importance of a customer relationship with a software provider is essential to the actual success of the products and technology. The evaluation of the Customer Experience and the entire lifecycle an enterprise has with its software provider is critical for ensuring satisfaction in working with that provider. The ISG Buyers Guide examines a software provider’s customer commitment, viability, customer success, sales and onboarding, product roadmap and services with partners and support. The customer experience category also investigates the TCO/ROI and how well a software provider demonstrates the product’s overall value, cost and benefits, including the tools and resources to evaluate these factors.
The research results in Customer Experience are ranked at 20%, or one-fifth of the 100% index, and represent the underlying provider validation and TCO/ROI requirements as they relate to the framework of commitment and value to the software provider-customer relationship.
The software providers that evaluated the highest in the Customer Experience category are Oracle, Domo, Salesforce and ThoughtSpot. These category leaders best communicate commitment and dedication to customer needs. While not a Leader, SAP was also found to meet a broad range of enterprise customer experience requirements.
Software providers that did not perform well in this category were unable to provide or make sufficient information readily available to demonstrate success or articulate their commitment to customer experience. The use of a software provider requires continuous investment, so a holistic evaluation must include examination of how they support their customer experience.
Appendix: Software Provider Inclusion
For inclusion in the 2025 ISG Buyers Guide™ for AI Analytics, a software provider must be in good standing financially and ethically, have at least $150 million in annual or projected revenue verified using independent sources, sell products and provide support on at least two continents, and have at least 50 customers. The principal source of the relevant business unit’s revenue must be software-related and there must have been at least one major software release in the past 12 months.
The product must be actively marketed as an analytics product that includes generative AI, agentic AI or machine learning capabilities to support the analytics processes with an organization including assisting with data access and preparation, automated analyses and insights, and natural language query or chat interfaces.
The research is designed to be independent of the specifics of software provider packaging and pricing. To represent the real-world environment in which businesses operate, we include providers that offer suites or packages of products that may include relevant individual modules or applications. If a software provider is actively marketing, selling and developing a product for the general market and it is reflected on the provider’s website that the product is within the scope of the research, that provider is automatically evaluated for inclusion.
All software providers that offer relevant AI analytics products and meet the inclusion requirements were invited to participate in the evaluation process at no cost to them.
Software providers that meet our inclusion criteria but did not completely participate in our Buyers Guide were assessed solely on publicly available information. As this could have a significant impact on classification and ratings, we recommend additional scrutiny when evaluating those providers.
Products Evaluated
| Provider | Product Names | Version | Release Month/Year |
|---|---|---|---|
| Alibaba Cloud | Quick BI | 6.0 | October 2025 |
| AWS | Amazon Quick Suite | NA | November 2025 |
| Cloud Software Group | Spotfire | 14.6 LTS | October 2025 |
| Databricks | Databricks Platform | NA | November 2025 |
| Domo | Domo | NA | November 2025 |
| Epicor | Grow | 15.0.0 | October 2025 |
| Looker | 25.2.0 | November 2025 | |
| IBM | IBM Cognos Analytics | 12.1.1 | October 2025 |
| Infor | Infor Birst | NA | November 2025 |
| insightsoftware | Logi Symphony | NA | November 2025 |
| Microsoft | Power BI | 2.148.1477.0 | November 2025 |
| OpenText | Analytics Cloud | NA | November 2025 |
| Oracle | Oracle Analytics Cloud | NA | November 2025 |
| Qlik | Qlik Cloud Analytics | NA | November 2025 |
| Salesforce | Tableau | NA | November 2025 |
| SAP | SAP Business Data Cloud | NA | November 2025 |
| SAS | SAS Viya | 2025.11 | November 2025 |
| Sisense | Sisense Platform | NA | November 2025 |
| Strategy | Strategy One Platform | NA | November 2025 |
| ThoughtSpot | ThoughtSpot Cloud | 10.14.0.cl | November 2025 |
| Zoho | Zoho Analytics | NA | October 2025 |
Fill out the form to continue reading.