Enterprise Applications are the lifeblood and framework for accomplishing work in the modern organization. We examine 12 categories of applications used in the enterprise, including Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Workplace Collaboration, Human Resources, Supply Chain & Logistics, Analytics & Business Intelligence (BI), Project & Portfolio Management (PPM), Industry/Vertical-Specific Applications, and Communication Services, and delve into how they shape the broader enterprise information architecture. We also focus on the underlying technologies and systems that power these applications, including artificial intelligence and automation, and assess how trends in employee engagement and experience impact the market.
Key Issues for 2026
The Increasing Use of Agentic AI to Manage More Complex Workflows and Processes
In just over a year, agentic AI has evolved from a nascent technology with limited use cases and capabilities to a core technology embedded in a wide range of enterprise applications and platforms. And while agentic AI delivers results in relatively simple scenarios or tasks, the substantial ROI promised by vendors is unlikely to materialize until agentic technology can be applied across more complex workflows that incorporate near-real-time data, multi-step reasoning, and self-optimization capabilities. It is these more complex processes that consume significant time, effort, and resources to address and often have the most significant impact on customer, employee, and partner metrics, including experience, effort, and satisfaction, which directly affect a business’s overall health and success:
- Vendors that can help their customers deploy agentic AI to address these complex scenarios likely will see the most success in monetizing agentic AI.
- Ensuring the accuracy and efficiency of these agents, as well as building trust among decision-makers, workers, and customers, will be top of mind throughout the year as the technology continues to mature.
Shifting Pricing and Business Models
As the types and complexities of AI workflows and use cases continue to expand, vendors are still struggling to effectively monetize AI. While the traditional, seat-license-based approach appeared to be on the way out in 2025, the challenges of generating a solid ROI from AI led to increased vendor flexibility, with some offering a choice of pricing models, ranging from seat-based to consumption-based to outcome-based. As technology improves and the diversity of use cases continues to expand, customers may adopt a variety of pricing approaches tailored to specific usage patterns and risk tolerances. Vendors that can provide this flexibility will be best positioned to attract new business across a broader range of scenarios. Notably, vendors that are able to successfully automate agentic workflows and tasks that are highly repeatable and scalable will likely shift to an outcome-based pricing model.
The Integration of Data and Applications Across the Technology Stack
Vendors will continue to support the integration of data from disparate apps and systems into the front end of customers’ choosing, while also highlighting the benefits of a unified platform approach:
- Instead of being forced to work with data in a specific application, vendors are increasingly making it easy to pull in and manipulate data and initiate workflows from within the application of their choosing, a technique designed to make enterprise software adapt to the user, rather than the other way around, thereby driving more efficiency and productivity while reducing friction.
- The challenge will lie in strengthening and delivering the right message (the power of using a unified platform vs. application and data flexibility) to the right customer at the right time, in a way that does not dilute other messaging.
This will also lead to a growing convergence of disparate functional areas, such as contact center operations, customer service and support, marketing, sales, and fulfillment, into a more unified customer experience that delivers the right messaging, actions, and process flows across the entire customer journey, fed by a unified and real-time data-driven strategy.
The Era of SaaS Platforms as Orchestrators
As the era of agentic AI continues to evolve and mature, SaaS players have realized the value of not only managing their own data, AI agents, and workflows but also serving as an enterprise-wide orchestration layer capable of monitoring and managing third-party workflows and AI agents. These market participants are realizing that control over enterprise data and workflows – both AI- and human-augmented – drives platform utilization, revenue, and, perhaps most importantly, stickiness, which increases the likelihood of contract renewals and value expansion.
In 2026, expect to see both major SaaS platform vendors, as well as third-party integration vendors, consultants, and application-management platforms, enter the market and fight to control this important function. In fact, it is also likely that a new class of vendors will emerge as agnostic (or mostly agnostic) arbiters that serve as a master control plane for managing agents, humans, and workflows, regardless of the company or organization that built or provided the agents. These vendors may become increasingly important as agentic workflows span disparate systems, organizations, and jurisdictions.
A Renewed Focus on Contextual Assistance and Training
While agentic AI may become the front door for basic horizontal functionality, the predictions of the demise of SaaS applications are premature, due to the large backlog of software implementations already on the books, the complexity of domain-specific workflows and processes, and the desire of many organizations to extract value from their existing technology investments.
As humans and AI increasingly work together in the enterprise application space, there will be a growing need for contextual assistance and training to ensure customers derive the maximum value from their software investments. The increasing use of AI and agentic AI technology portends the deployment of relevant, easily digestible, and context-based assistance and training features on top of or within enterprise applications.
The new and emerging generation of workers, who have little or no patience for reading documentation, combined with the rapid pace of innovation, has rendered obsolete most traditional learning and training resources. Organizations will fail to quickly realize value from their software investments if their workers and customers are unable to adopt and utilize these new learning tools and capabilities within the flow of normal work.
