A Market Guide for Independent Software Vendors and Data Providers

Business Demand for Intelligent Applications

Increased demand for intelligent applications presents opportunities for Independent Software Vendors (ISVs) and data providers to create applications with embedded intelligence to improve engagement and fuel revenue expansion. As demand switches to SaaS applications and subscription licensing, working with embedded analytics providers enables ISVs to respond rapidly to evolving customer requirements and the latest AI capabilities to deliver AI-powered analytics applications that provide non-technical users with contextual access to data-driven insights.

Leading organizations recognize that the use of data and analytics in decision-making processes is essential to establish and maintain competitive advantage. The use of analytics is foundational in helping business users shape strategic decision-making, with artificial intelligence (AI) and generative AI (GenAI) increasingly being adopted to deliver advanced predictive analytics and automated assistance. Analytics and AI are also critical in enabling organizations to run the business. The delivery of contextually relevant data, recommendations, predictions and forecasting increases efficiency and improves the customer experience by delivering actionable insight to decision-makers in their preferred applications—on desktop or mobile.

AI-powered applications are increasingly being adopted by the most data-driven organizations to deliver instant, relevant information to accelerate business decision-making. Data visualizations, descriptive explanations and prescriptive recommendations are now not just commonplace but expected by users who have become accustomed to interactive and personalized apps. Independent software vendors (ISVs) that incorporate analytics and AI capabilities into their applications have the opportunity to deliver differentiation and innovation. Those who fail to do so run the risk of being left behind and losing customers to more innovative providers delivering data-driven applications.

Incorporating analytics into operational applications democratizes data access, encourages engagement and accelerates decision-making. By embedding analytics into operational applications, ISVs enable users to benefit from increased engagement with AI and analytics, overcoming perennial challenges related to self-service access to traditional analytics tools. Less than one-half (48%) of organizations report they are comfortable allowing business users to work with data that has not been integrated or prepared for them. Incorporating AI and analytics into applications enhances engagement with non-technical users by providing them with the information they need without having to look elsewhere. 

73% of organizations consider embedded analytics important to their analytics and data efforts, and 49% say their business is most likely to take advantage of embedding AI capabilities into existing applications and processes.

Infusing personalization and AI-powered recommendations into operational applications brings intelligence to business users in the applications they use every day, reducing skills and efficiency challenges that result from them having to try and find the data they need in separate reports and dashboards. This improved access enables the organization to operate more efficiently and be more responsive to evolving customer demands and competitive threats. Through 2026, more than two-thirds of line-of-business personnel will have immediate access to cross-functional analytics embedded in activities and processes, helping to make operational decision-making more efficient and effective.

Embedded analytics reduce the cost and complexity of developing intelligent applications. According to ISG’s Analytics and Data Benchmark Research, almost three-quarters (73%) of organizations consider embedded analytics important to their analytics and data efforts, and nearly one-half (49%) of participants in our Application Development and Maintenance survey say their business is most likely to take advantage of embedding AI capabilities into existing applications and processes. While many organizations are investing in data science and AI projects to develop intelligent applications, less than one-quarter (23%) of businesses have skilled resources in place to take advantage of AI and machine learning.

Investing in the skills and resources required to develop intelligent operational applications can be costly and complex, with no guarantee of success. Many organizations are therefore looking to software providers to embed analytics and AI capabilities into their applications to enable them to gain the benefits of enhanced intelligence without the risks and challenges associated with internal development projects.

The Opportunity for ISVs

Independent software vendors have a critical role in helping organizations take advantage of increased use of analytics and AI. ISVs are the trusted source of applications currently used by organizations to run the business because they understand customers’ business processes and have the expertise required to address industry-specific requirements. Since their applications are used to run the business for their customers, ISVs also have ready access to the data that needs to be processed and analyzed to understand the business, make predictions and formulate recommendations as to how the business can improve efficiency and responsiveness.

Organizations are willing to pay a premium of between 6% and 10% for applications that incorporate AI features.

Embedding AI can enhance ISV revenue opportunities, and as a result, businesses are willing to pay more for applications that incorporate AI. ISG’s 2024 AI study indicates that average spending on AI is expected to almost triple from 2% of technology budgets in 2023 to 5.9% in 2025. The study also shows organizations are willing to pay a premium of between 6% and 10% for applications that incorporate AI features. The price premium businesses are prepared to pay varies by application area. At the lower end of the spectrum, businesses expressed willingness to pay, on average, a 6.1% premium for ERP financial management applications that incorporate AI. Meanwhile businesses are willing to pay, on average, 7.6% more for customer relationship management applications that incorporate AI, and 9.4% extra for sales performance management applications that incorporate AI.

ISVs looking to incorporate analytics and AI into their applications can either develop the functionality themselves or embed analytics capabilities from a specialist analytics software provider. Investing in the skills and resources required to develop intelligent operational applications can also be costly and complex for ISVs, with little guarantee of success. Utilizing specialist analytics and AI software providers enables ISVs to accelerate development and reduce the risks involved in developing analytics capabilities in-house.

The Advantages of Embedded Analytics

Embedding analytics into business applications and processes addresses several key issues that can become barriers to success. When analytics are embedded in business processes and applications, analyses are easier to perform and more accessible to line-of-business personnel. Analysis is easier to consume because there is no need to switch context between the business application and an analytics tool. Embedded analytics also accelerate time to action. For instance, if an analysis suggests a change in pricing, the appropriate logic can be included in the application to make those changes. If an analysis suggests a marketing campaign for a specific customer segment, that segmentation can be performed, and the campaign launched. In our research, 69% of organizations cite preparing data for analysis as the most time-consuming aspect of analytics. When analytics are embedded into applications, data-preparation processes can be automated to avoid an impact on application customers. 

Embedding analytics supports and enhances existing business processes. Embedding analytics can avoid the most common complaint organizations report about their current analytics technology: it is hard to integrate with their business processes. Embedded analytics facilitates real-time decision making. Traditional BI dashboards are not very useful when processing data in real time. The metrics produced by analytics processes need to drive automated recommendations and actions. Embedding analytics in business applications brings analytic insight to business users in real time, and if the system can produce recommendations on the best next course of action, then the customer benefits from a better experience and can improve its bottom line.

Embedding analytics also reduces governance challenges. When analytics are embedded into business applications and processes, there is less chance for violations of policies or regulations. The data remains within the domain of the business application. The manipulations of the data are controlled by the application or process in which they are embedded.

Working with External Specialists

By working with embedded analytics providers to incorporate functionality into applications, ISVs can reduce the cost and complexity associated with analytics and AI development projects. ISVs have domain and application expertise, which can be combined with the skills and expertise of analytics specialists to deliver analytic sophistication without the up-front investment in skills, time and resources.

Working with embedded analytics providers gives ISVs access to functionality that would take months (if not years) to develop in-house.

Working with embedded analytics providers gives ISVs access to functionality that would take months (if not years) to develop in-house. Developing SQL-based query functionality is relatively straightforward, but analytics specialists have invested in areas such as advanced search and exploratory analytics, advanced visualization capabilities that adapt as data sources and query requirements evolve, and intuitive personalized interfaces that deliver natural language query and processing capabilities, including explanations and recommendations. Analytics providers have already established the functionality required to connect and transform data from multiple sources, reducing the burden on ISVs as they seek to meet the needs of multiple individual customers. Analytics specialists have also established the capabilities required to meet the security, privacy and regulatory requirements involved in analytics and AI.

The pace of change in analytics and AI is accelerating rapidly and analytics specialists deliver continuous improvement. Attempting to keep up with the latest advances in GenAI models, automated machine learning and natural language processing could be potentially exhausting and expensive. Through 2026, more than one-half of embedded analytics processes will include AI and ML algorithms to improve line-of-business decision-making. For ISVs, the cost of investing in analytics and AI skills and development projects will quickly eat up any potential revenue benefits that might be generated from applications that incorporate analytics and AI. By working with embedded analytics specialists, ISVs can keep up with the latest advances in analytics and AI without losing focus on the core application functionality that is integral to their value proposition. 

Key Considerations for ISVs Selecting an Analytics Partner

It can be challenging for providers to package and deliver their capabilities in a way that makes them easy to embed into other applications and processes.

Organizations must evaluate analytics specialists carefully. Analytics providers understand the need to deliver embedded analytics; most have been developing and enhancing APIs and other mechanisms to integrate analytics more tightly into business processes and applications. However, it can be challenging for analytics and business intelligence providers to package and deliver their capabilities in a way that makes them easy to embed into other applications and processes. While interactive charts are important in enhancing the user experience, any visualization is only as good as the data that supports it. Analytics providers also need to be capable in terms of the grunt work—combining data from multiple sources and ensuring that it meets the quality and validity thresholds required by the business.

Our Embedded Analytics Buyers Guide research highlights differences among key providers and their offerings. For example, while all providers offer API access to their products, not all products provide API access to every administrative and management feature. Another area of differentiation is the extent to which providers offer the capability to “white label” the resulting applications so that they have alternate branding as opposed to the provider’s branding. ISVs must also consider the underlying analytics capabilities. Great APIs and SDKs can only do so much if the foundation of underlying analytics is weak. Additionally, the data must be trusted to be of high quality.

There are several key considerations when selecting embedded analytics providers.

• Above all, ISVs need to be wary of introducing anything that will negatively impact the performance of their existing functionality. Embedded analytics should add value and enable actionable insight.
• The delivery of analytics capabilities via cloud-based services provides confidence in the ability of ISVs to deliver functionality in SaaS applications that can scale as adoption grows.
• Actionable insight is not enabled simply by the presentation of tables and charts, which decision-makers need to spend time evaluating before taking action. Embedded analytics should not be static. Decision-makers should be able to click-through to explore more detail and interact with the analytics capabilities.
• Embedded analytics providers can provide additional value and accelerate decision-making through descriptive narratives that explain the data being presented, as well as predictions and prescriptive recommendations.
• Explainability is also essential for AI-powered predictions and recommendations. If decision-makers are to trust the information that they are provided with they need to be able to understand the basis for the predictions and recommendations.

In Summary

Data and analytics in decision-making processes is essential to establish and maintain competitive advantage, democratize data access and reduce the cost and complexity of intelligent applications. Businesses are willing to pay more for applications that incorporate AI, thereby enhancing ISV revenue opportunities, but keeping pace with changes and growth within analytics and AI is critical. ISVs that fail to incorporate analytics and AI capabilities into their applications run the risk of being left behind and losing customers to more innovative application providers. Working with embedded analytics providers gives ISVs access to functionality that would take months if not years to develop in-house. But not all analytics providers are created equal, nor do APIs and SDKs typically offer the complete picture of a provider’s capabilities, so organizations must evaluate potential relationships carefully. During these evaluations, ISVs should consider not just the ease of development and integration, but also the breadth and depth of analytics functionality provided, and also the experience of working with the software analytics provider. Consult research, such as ISG’s Embedded Analytics Buyers Guide, to examine the differences between the options available on the market to make the best-informed decision.