The relentless pace of updates and large-scale programs within complex enterprise environments has created a significant challenge for modern organizations trying to maintain operational stability while innovating. For companies relying on sprawling systems like SAP S/4HANA, Oracle, and Salesforce, traditional testing processes have increasingly become a major impediment to progress, bogged down by fragmented knowledge silos, a persistent shortage of deep expertise, and overwhelmingly labor-intensive manual work. These bottlenecks not only slow down the delivery of critical changes but also introduce substantial business risk. In response to this industry-wide pain point, Panaya has introduced Seemore, a sophisticated AI agentic layer meticulously designed to revolutionize testing and change delivery. This launch signals a strategic shift from reactive problem-solving to proactive, intelligent management of the entire application lifecycle, aiming to equip enterprise teams with the tools to navigate complexity with unprecedented efficiency and confidence.
A New Paradigm for Change Delivery
Seemore is positioned as far more than a simple tool; it functions as an intelligent project specialist that provides expert-level context and guidance across diverse teams, including IT, QA, development, and business units. Addressing the core challenge of fragmented institutional knowledge, the AI agent leverages a natural-language interface and a network of specialized agents to analyze risk, streamline testing workflows, and resolve issues before they escalate. This system is grounded in Panaya’s two decades of domain expertise, allowing it to continuously learn from customer-specific business processes. By building a comprehensive, context-aware understanding of the entire change lifecycle—from initial requirements gathering all the way through to final deployment—Seemore effectively acts as a central nervous system for change management. This represents a significant evolution in enterprise software, moving away from passive, user-driven tools toward active, autonomous systems designed to intelligently assist and augment human capabilities.
The integration of such an intelligent layer is engineered to deliver tangible benefits that directly impact project velocity and overall efficiency. A key objective is to multiply team capacity, enabling existing personnel to manage a greater volume of changes without a corresponding increase in manual effort or headcount. Furthermore, Seemore aims to dramatically reduce onboarding friction for new team members, who can lean on the AI’s contextual guidance to become productive much faster. This acceleration of an organization’s time to value for critical IT projects is achieved by overcoming the traditional hurdles of manual test creation and knowledge transfer. By providing a unified, end-to-end solution that bridges the gaps between different stages of the development lifecycle, the platform ensures that every stakeholder is operating from a single source of truth. This holistic view minimizes the risks associated with miscommunication and incomplete information, fostering a more collaborative and effective project environment.
Redefining Testing with Intelligent Automation
The core capabilities of the Seemore AI layer are designed to provide a unified and deeply analytical approach to quality assurance. The system performs precise, deep analysis across multiple, disparate environments to generate highly accurate reports on the most critical changes, allowing teams to focus their efforts where the risk is greatest. For testing itself, the platform orchestrates smart, codeless automation, which is a transformative feature for both technical and non-technical users. It enables the creation, execution, and autonomous self-healing of tests based on simple natural language commands. This capability effectively removes the bottleneck of manual scriptwriting and maintenance. When an underlying application feature is altered, the AI’s self-healing function can intelligently adapt the corresponding test case, a task that has historically consumed countless hours of QA resources. This shift empowers teams to move from tactical script management to a more strategic oversight of quality.
This new AI layer extends its intelligence to proactively detect and fix anomalies before they ever reach a production environment, fundamentally changing the role of quality assurance from a reactive gatekeeper to a proactive partner in the development process. The system is designed to identify everything from subtle code defects to broken test scripts and initiate corrective action, thereby preventing downstream disruptions. To enhance the overall user experience, Seemore offers conversational, step-by-step guidance throughout the platform, making its powerful features accessible to a broader range of users. According to Panaya’s CEO, David Binny, this launch represents a significant advancement that shifts an enterprise’s focus from navigating complexity to achieving desired business outcomes. By fully integrating change impact analysis, test management, and test automation, the platform helps organizations handle change faster, improve test coverage, and significantly reduce the business disruption associated with traditional testing methods.
The Future of Enterprise Application Management
The introduction of this advanced AI agentic layer represented a pivotal moment for the management of enterprise applications. It presented a comprehensive solution that moved well beyond the confines of conventional automation by offering an intelligent framework capable of understanding business context, managing intricate dependencies, and proactively guiding teams toward more effective outcomes. This development directly challenged long-held notions about the distinct roles within IT and QA departments, pointing toward a future where human expertise was seamlessly augmented by AI-driven collaborators. The launch of such a sophisticated system signaled a definitive industry trend, where the primary objective shifted from the mere execution of procedural tasks to the strategic achievement of business goals with enhanced speed, accuracy, and confidence. This innovation prompted organizations to reconsider their entire approach to the software lifecycle, encouraging them to explore how intelligent systems could not only mitigate risk but also unlock new levels of agility and innovation within their core operational processes.
