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 Emerging Providers
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), machine learning (ML) and agentic AI are being applied to all aspects of data analytics software to make the products easier to use. ML models deliver more advanced analytics and automated insights, and agentic AI is helping simplify many aspects of software usage. It should come as no surprise that ISG Market Lens research shows that 87 percent of participants are now AI-enabling analytics and BI applications.
Emerging AI analytics providers have gained traction as enterprises look for platforms that can deliver GenAI and agentic AI capabilities more quickly than large, established software providers.
As these expectations accelerate, enterprises are increasingly exploring emerging providers that bring new AI capabilities to market faster than established platforms. Emerging AI analytics providers have gained traction as enterprises look for platforms that can deliver GenAI and agentic AI capabilities more quickly than large, established software providers. These providers often differentiate through advanced cloud-native architectures, more flexible pricing models and faster development cycles that introduce new AI-driven features sooner. Many specialize in areas such as automated insight generation, conversational interfaces or AI-augmented data preparation, offering lightweight implementations that appeal to organizations seeking rapid time to value. At the same time, enterprises must consider factors such as product maturity, integration breadth and long-term roadmap clarity when evaluating these emerging AI-focused platforms.
ISG Research defines AI analytics as the use of generative AI, agentic AI and other AI and 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 and 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 and ML has proven more complicated than many expected. Ideally, BI software products could simply be extended to include a complete set of AI and ML capabilities, but that has not yet fully happened. AI and ML require 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 and ML more accessible by augmenting system capabilities. With the advent of GenAI, elements of AI and ML can be more easily incorporated into analytics experiences. For example, AI and ML can drive automated insights that identify and explain relationships in data and recommend which actions to take.
AI and ML can augment analytics in various ways. One of the most common and beneficial uses of GenAI is natural language processing to support conversational analytics with natural language queries and narrative responses. Automated machine learning 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 and ML can augment data quality processes by identifying outliers and anomalies and recommending potential corrections for those data points.
While efforts to apply AI and ML have been underway for some time, the rapid expansion of GenAI capabilities has fueled greater interest in copilots and assistants. GenAI is used to generate SQL to access data sources and, in some cases, to produce documentation of data pipelines used in analytics processes, enhancing understanding and lineage. In many ways, the market is evolving quickly, with providers racing to differentiate the application of GenAI. The technology holds much promise, and we expect it will have a significant impact on the analytics market, but it is still early in its evolution.
AI Analytics will continue to evolve. Many features are still under development or in early release. GenAI is making conversational analytics more common and more capable than it is today. It will enable better support for multilingual capabilities, which has been lacking in many analytics products. And it will likely increase automation in data preparation and create initial analyses that improve analyst productivity. 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 produce models with improved accuracy. The exact intersection between AutoML in GenAI analytics products and the models produced through more sophisticated AI and ML tools remains to be seen. Today, some AI analytics products can work with these models, but it is still a loosely coupled process.
GenAI can only enhance what already exists. If foundational analytics capabilities are weak, the value of generative AI will be limited.
Enterprises should be aware of changes occurring in the market and understand capabilities offered by existing providers, comparing them with capabilities other providers offer. In evaluating AI analytics, one must consider the strength of underlying analytics capabilities. GenAI can only enhance what already exists. If foundational analytics capabilities are weak, the value of generative AI will be limited. Consequently, this Buyers Guide combines an assessment of AI analytics capabilities with core analytics capabilities to determine each provider’s overall ranking. Organizations can use this report not only to guide purchasing decisions but also to guide conversations with providers about AI roadmaps. Although the market is evolving rapidly, organizations can realize value today that improves analytics processes.
The 2025 ISG Buyers Guide™ for AI Analytics Emerging Providers 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 such as discover analytics, integrate analytics, predict analytics, act analytics, collaborate analytics, inform analytics, manage analytics, access data and data models. This research assessed the following providers: Cube, GoodData, Hex, IDERA, Incorta, Klipfolio, Kyligence, Kyvos, Phocas, Pyramid Analytics, Sigma and Toucan.
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 Emerging Providers 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 is advancing from traditional BI toward platforms that blend core analytical capabilities with generative, machine learning and agentic assistance. Emerging providers are accelerating this shift by delivering streamlined data preparation, conversational interaction and automated insights. These changes are expanding access to analytics while increasing the importance of strong, underlying capabilities. As expectations rise, enterprises require solutions that balance rapid innovation with operational clarity and integration breadth.
Software Provider Summary
The ISG Buyers Guide™ for AI Analytics Emerging Providers evaluates 12 software providers offering products supporting AI-enhanced analytics across data, analytics and communication. The research ranked the top three overall leaders as Pyramid Analytics, Kyvos and Sigma. Providers were classified using weighted performance in Product Experience and Customer Experience for ISG quadrant placement. Hex, Kyvos, Pyramid Analytics and Sigma were rated as Exemplary, with IDERA and Klipfolio rated as Innovative. Cube and Toucan were rated as Assurance; and GoodData, Incorta, Kyligence and Phocas were rated as Merit.
Product Experience Insights
Product Experience, representing 80% of the evaluation, focuses on Capability (50%) and Platform (30%), including adaptability, manageability, reliability and usability. Pyramid Analytics, Kyvos and Sigma achieved the highest performance as Leaders in this category, supported by breadth and depth across AI analytics capabilities and robust platform foundations that provide adaptability, manageability and reliable performance across roles and workloads. 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. Sigma, Hex and Kyvos 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 emerging solutions as strategic investments that unify core analytics with expanding, AI-driven automation and assistance. Buyers should prioritize providers that combine strong foundational capabilities, clear AI roadmaps and evidence of customer value. Platforms that streamline preparation, enable conversational and automated insights and integrate effectively into data environments will better support agile decision-making. Using these considerations, enterprises can align provider selection with long-term needs for scalability, usability and AI maturity.
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 Pyramid Analytics atop the list, followed by Kyvos and Sigma. Providers that place in the top three of a category earn the designation of Leader. Pyramid Analytics, Kyvos and Sigma did so in four categories, Hex in two and IDERA 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: Hex, Kyvos, Pyramid Analytics and Sigma.
Innovative: This rating (lower right) represents those that performed above median in Product Experience but not in Customer Experience. The providers rated Innovative are: IDERA and Klipfolio.
Assurance: This rating (upper left) represents those that performed above median in Customer Experience but not in Product Experience. The providers rated Assurance are: Cube and Toucan.
Merit: This rating (lower left) represents those that did not surpass the median in Customer or Product Experience. The providers rated Merit are: GoodData, Incorta, Kyligence and Phocas.
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%). Pyramid Analytics, Kyvos and Sigma 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 Sigma, Hex and Kyvos. These category leaders best communicate commitment and dedication to customer needs.
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 Emerging Providers, 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 |
|---|---|---|---|
| Cube | Cube | 1.5 | November 2025 |
| GoodData | GoodData Platform | N/A | November 2025 |
| Hex | Hex Platform | N/A | November 2025 |
| IDERA | Yellowfin Platform | 9.16.1.1 | November 2025 |
| Incorta | Incorta Platform | 2025.7.1 | October 2025 |
| Klipfolio | Klips | N/A | November 2025 |
| Kyligence | Kyligence Enterprise | N/A | November 2025 |
| Kyvos | Kyvos Semantic Layer | N/A | November 2025 |
| Phocas | Phocas Platform | N/A | November 2025 |
| Pyramid Analytics | Pyramid | N/A | November 2025 |
| Sigma | Sigma | N/A | November 2025 |
| Toucan | Toucan | 3.0 | November 2025 |
Providers of Promise
We did not include software providers that, as a result of our research and analysis, did not satisfy the criteria for inclusion in this Buyers Guide. These are listed below as “Providers of Promise.”
| Provider | Product | Capability | Revenue | Geography | Customers |
|---|---|---|---|---|---|
| Corraldata | Corraldata | Yes | No | Yes | Yes |
| Deepnote | Deepnote | No | No | Yes | Yes |
| Discern | Discern | No | No | No | No |
| Panintelligence | piDashboard, piReports, piAnalytics | Yes | No | Yes | Yes |
Executive Summary
AI Analytics Emerging Providers
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), machine learning (ML) and agentic AI are being applied to all aspects of data analytics software to make the products easier to use. ML models deliver more advanced analytics and automated insights, and agentic AI is helping simplify many aspects of software usage. It should come as no surprise that ISG Market Lens research shows that 87 percent of participants are now AI-enabling analytics and BI applications.
Emerging AI analytics providers have gained traction as enterprises look for platforms that can deliver GenAI and agentic AI capabilities more quickly than large, established software providers.
As these expectations accelerate, enterprises are increasingly exploring emerging providers that bring new AI capabilities to market faster than established platforms. Emerging AI analytics providers have gained traction as enterprises look for platforms that can deliver GenAI and agentic AI capabilities more quickly than large, established software providers. These providers often differentiate through advanced cloud-native architectures, more flexible pricing models and faster development cycles that introduce new AI-driven features sooner. Many specialize in areas such as automated insight generation, conversational interfaces or AI-augmented data preparation, offering lightweight implementations that appeal to organizations seeking rapid time to value. At the same time, enterprises must consider factors such as product maturity, integration breadth and long-term roadmap clarity when evaluating these emerging AI-focused platforms.
ISG Research defines AI analytics as the use of generative AI, agentic AI and other AI and 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 and 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 and ML has proven more complicated than many expected. Ideally, BI software products could simply be extended to include a complete set of AI and ML capabilities, but that has not yet fully happened. AI and ML require 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 and ML more accessible by augmenting system capabilities. With the advent of GenAI, elements of AI and ML can be more easily incorporated into analytics experiences. For example, AI and ML can drive automated insights that identify and explain relationships in data and recommend which actions to take.
AI and ML can augment analytics in various ways. One of the most common and beneficial uses of GenAI is natural language processing to support conversational analytics with natural language queries and narrative responses. Automated machine learning 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 and ML can augment data quality processes by identifying outliers and anomalies and recommending potential corrections for those data points.
While efforts to apply AI and ML have been underway for some time, the rapid expansion of GenAI capabilities has fueled greater interest in copilots and assistants. GenAI is used to generate SQL to access data sources and, in some cases, to produce documentation of data pipelines used in analytics processes, enhancing understanding and lineage. In many ways, the market is evolving quickly, with providers racing to differentiate the application of GenAI. The technology holds much promise, and we expect it will have a significant impact on the analytics market, but it is still early in its evolution.
AI Analytics will continue to evolve. Many features are still under development or in early release. GenAI is making conversational analytics more common and more capable than it is today. It will enable better support for multilingual capabilities, which has been lacking in many analytics products. And it will likely increase automation in data preparation and create initial analyses that improve analyst productivity. 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 produce models with improved accuracy. The exact intersection between AutoML in GenAI analytics products and the models produced through more sophisticated AI and ML tools remains to be seen. Today, some AI analytics products can work with these models, but it is still a loosely coupled process.
GenAI can only enhance what already exists. If foundational analytics capabilities are weak, the value of generative AI will be limited.
Enterprises should be aware of changes occurring in the market and understand capabilities offered by existing providers, comparing them with capabilities other providers offer. In evaluating AI analytics, one must consider the strength of underlying analytics capabilities. GenAI can only enhance what already exists. If foundational analytics capabilities are weak, the value of generative AI will be limited. Consequently, this Buyers Guide combines an assessment of AI analytics capabilities with core analytics capabilities to determine each provider’s overall ranking. Organizations can use this report not only to guide purchasing decisions but also to guide conversations with providers about AI roadmaps. Although the market is evolving rapidly, organizations can realize value today that improves analytics processes.
The 2025 ISG Buyers Guide™ for AI Analytics Emerging Providers 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 such as discover analytics, integrate analytics, predict analytics, act analytics, collaborate analytics, inform analytics, manage analytics, access data and data models. This research assessed the following providers: Cube, GoodData, Hex, IDERA, Incorta, Klipfolio, Kyligence, Kyvos, Phocas, Pyramid Analytics, Sigma and Toucan.
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 Emerging Providers 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 is advancing from traditional BI toward platforms that blend core analytical capabilities with generative, machine learning and agentic assistance. Emerging providers are accelerating this shift by delivering streamlined data preparation, conversational interaction and automated insights. These changes are expanding access to analytics while increasing the importance of strong, underlying capabilities. As expectations rise, enterprises require solutions that balance rapid innovation with operational clarity and integration breadth.
Software Provider Summary
The ISG Buyers Guide™ for AI Analytics Emerging Providers evaluates 12 software providers offering products supporting AI-enhanced analytics across data, analytics and communication. The research ranked the top three overall leaders as Pyramid Analytics, Kyvos and Sigma. Providers were classified using weighted performance in Product Experience and Customer Experience for ISG quadrant placement. Hex, Kyvos, Pyramid Analytics and Sigma were rated as Exemplary, with IDERA and Klipfolio rated as Innovative. Cube and Toucan were rated as Assurance; and GoodData, Incorta, Kyligence and Phocas were rated as Merit.
Product Experience Insights
Product Experience, representing 80% of the evaluation, focuses on Capability (50%) and Platform (30%), including adaptability, manageability, reliability and usability. Pyramid Analytics, Kyvos and Sigma achieved the highest performance as Leaders in this category, supported by breadth and depth across AI analytics capabilities and robust platform foundations that provide adaptability, manageability and reliable performance across roles and workloads. 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. Sigma, Hex and Kyvos 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 emerging solutions as strategic investments that unify core analytics with expanding, AI-driven automation and assistance. Buyers should prioritize providers that combine strong foundational capabilities, clear AI roadmaps and evidence of customer value. Platforms that streamline preparation, enable conversational and automated insights and integrate effectively into data environments will better support agile decision-making. Using these considerations, enterprises can align provider selection with long-term needs for scalability, usability and AI maturity.
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 Pyramid Analytics atop the list, followed by Kyvos and Sigma. Providers that place in the top three of a category earn the designation of Leader. Pyramid Analytics, Kyvos and Sigma did so in four categories, Hex in two and IDERA 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: Hex, Kyvos, Pyramid Analytics and Sigma.
Innovative: This rating (lower right) represents those that performed above median in Product Experience but not in Customer Experience. The providers rated Innovative are: IDERA and Klipfolio.
Assurance: This rating (upper left) represents those that performed above median in Customer Experience but not in Product Experience. The providers rated Assurance are: Cube and Toucan.
Merit: This rating (lower left) represents those that did not surpass the median in Customer or Product Experience. The providers rated Merit are: GoodData, Incorta, Kyligence and Phocas.
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%). Pyramid Analytics, Kyvos and Sigma 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 Sigma, Hex and Kyvos. These category leaders best communicate commitment and dedication to customer needs.
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 Emerging Providers, 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 |
|---|---|---|---|
| Cube | Cube | 1.5 | November 2025 |
| GoodData | GoodData Platform | N/A | November 2025 |
| Hex | Hex Platform | N/A | November 2025 |
| IDERA | Yellowfin Platform | 9.16.1.1 | November 2025 |
| Incorta | Incorta Platform | 2025.7.1 | October 2025 |
| Klipfolio | Klips | N/A | November 2025 |
| Kyligence | Kyligence Enterprise | N/A | November 2025 |
| Kyvos | Kyvos Semantic Layer | N/A | November 2025 |
| Phocas | Phocas Platform | N/A | November 2025 |
| Pyramid Analytics | Pyramid | N/A | November 2025 |
| Sigma | Sigma | N/A | November 2025 |
| Toucan | Toucan | 3.0 | November 2025 |
Providers of Promise
We did not include software providers that, as a result of our research and analysis, did not satisfy the criteria for inclusion in this Buyers Guide. These are listed below as “Providers of Promise.”
| Provider | Product | Capability | Revenue | Geography | Customers |
|---|---|---|---|---|---|
| Corraldata | Corraldata | Yes | No | Yes | Yes |
| Deepnote | Deepnote | No | No | Yes | Yes |
| Discern | Discern | No | No | No | No |
| Panintelligence | piDashboard, piReports, piAnalytics | Yes | No | Yes | Yes |
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