AI agents offer a new way to rationalize application portfolios

<p><b>AI agents can turn application portfolio rationalization into a strategic, agile, real-time program.</b></p>

<p>Application portfolio rationalization is hardly a new phenomenon. However, various factors—legacy modernization pressures and rising technical debt among them—have increased the urgency with which businesses approach it.</p> <p>By integrating AI agents, organizations can transform application portfolio rationalization from a reactive and time-consuming pursuit into an ongoing program that continuously accelerates decision-making, reduces costs, fosters cross-enterprise collaboration and builds stakeholder trust.</p> <p>Based on an analysis of our APR engagements in the past year with dozens of global businesses, an AI-powered approach can cut application assessment time in half and potentially result in substantial annual IT savings. Here’s a detailed look at how businesses can leverage AI agents to turn their application portfolio rationalization process into a strategic program aligned with business goals.</p>

<p><a><span class="small">Figure 1</span></a></p> <h4>Optimizing application portfolio rationalization with AI</h4> <p>Businesses with complex IT ecosystems face persistent portfolio rationalization challenges. Siloed data, manual assessments and fragmented stakeholder engagement result in slow decision-making and suboptimal resource allocation.</p> <p>Here’s how we advise clients on integrating AI agents into each stage of the application portfolio rationalization process.</p> <h5><span style="font-weight: normal;"><span class="text-bold-italic">1</span>.&nbsp; &nbsp; Data aggregation and enrichment</span></h5> <p>AI agents can be deployed to interface with the organization’s key data repositories: the enterprise resource planning systems, configuration management databases, cloud management platforms and vendor databases. Leveraging advanced natural language processing and data harmonization algorithms, the AI agents can extract and standardize relevant information with contextual metadata. This eliminates the need for manual cross-referencing and ensures that decision-makers work from a single source of truth.</p> <h5><span style="font-weight: normal;"><span class="text-bold-italic">2</span>.&nbsp; &nbsp; Automated analysis and scenario planning</span></h5> <p>Once the data foundation is established, AI agents can run automated analyses to identify trends, bottlenecks and opportunities for optimizing the application portfolio. Machine learning models simulate various investment scenarios; weigh the risks and benefits of retaining, replacing or retiring an application; and generate recommendations tailored to the client’s strategic objectives (notably cost reduction and portfolio optimization). These insights can be visualized in interactive dashboards, enabling leaders to compare options and make informed choices.</p> <p>Scenario planning is particularly powerful. AI agents can model the effects of different application funding allocations, infrastructure technology adoptions and operational changes, projecting outcomes across financial, operational and compliance dimensions. This capability supports dynamic, evidence-based decision-making and helps stakeholders anticipate downstream impacts.</p> <h5><span style="font-weight: normal;"><span class="text-bold-italic">3</span>.&nbsp; &nbsp; Continuous monitoring and compliance tracking</span></h5> <p>Unlike traditional application portfolio rationalization processes that rely on a “once in a while” project with periodic reporting, an AI-driven approach can introduce real-time monitoring of the application portfolio.&nbsp;&nbsp;&nbsp;&nbsp;<br> <br> AI agents continuously scan system logs, policy updates and external regulatory feeds, alerting teams to emerging risks and compliance gaps as they pertain to the various applications running in the enterprise processing mix. This proactive oversight not only reduces the likelihood of regulatory violations but also positions IT as a strategic partner with the business in enterprise risk management.</p> <h5><span style="font-weight: normal;"><span class="text-bold-italic">4</span>.&nbsp; &nbsp; Feedback loop and stakeholder engagement</span></h5> <p>Perhaps one of the most significant shift is in stakeholder engagement. AI agents can facilitate collaborative review cycles, collecting feedback from business units, surfacing concerns and updating recommendations. This iterative approach promotes transparency and ensures that portfolio rationalization outcomes reflect the collective wisdom of the organization.</p> <h4>Measurable results: A leaner, smarter IT organization</h4> <p>One of the most immediate benefits of this AI-powered approach is reducing the duration of the assessment phase. With a manual process, application portfolio assessments can commonly consume 12 weeks of resources and effort. This extended timeline is driven by the need for data extraction from disparate sources, manual reconciliation of conflicting reports and laborious stakeholder interviews. Each step introduces delays and opportunities for error.</p> <p>With AI agents, this phase can be reduced to just six weeks—a remarkable 50% acceleration. AI agents automatically aggregate and harmonize data from core systems, generate preliminary analyses and flag inconsistencies for rapid resolution. The time savings not only speeds up the application portfolio rationalization cycle but also frees valuable human resources to focus on higher-value strategic tasks.</p> <p>Figure 2 shows how an organization with over 600 applications could reduce annual IT spending by $13 million once AI agents surface redundant applications, identify underutilized assets and highlight opportunities for consolidation, enabling smarter investment decisions.</p>

<p><span class="small">Figure 2</span></p> <p>Overall, businesses can expect their IT application portfolio to become leaner, more agile and better aligned with business goals. The cost savings are realized not only through direct reductions in spending but also through productivity gains and improved resource utilization. Decision-making would accelerate, reducing time-to-market for critical projects and enabling the business to seize new opportunities. Data-backed insights foster a shared understanding of priorities across IT, finance, operations and the broader business, enabling better collaboration.</p> <p>Bottom line: The application portfolio rationalization process is no longer a burdensome annual (or occasional) ritual but an intelligent, ongoing strategy for continuous improvement.&nbsp;</p> <h4>Key benefits of AI-driven application portfolio rationalization</h4> <p>Here’s what businesses can expect from an AI-powered approach to application portfolio rationalization:</p> <ul> <li>Improved decision-making: AI agents can analyze vast amounts of data to provide actionable insights, helping organizations make informed decisions.<br> <br> </li> <li>Enhanced efficiency: Because AI-driven automation can streamline processes, it reduces the time and effort required for various tasks.<br> <br> </li> <li>Better resource allocation: AI can identify areas where resources are most needed and predict future requirements.<br> <br> </li> <li>Cost savings: AI can surface redundant applications, underutilized assets and opportunities for consolidation, enabling smarter investment decisions.<br> <br> </li> <li>Improved agility: With its proactive approach to application portfolio rationalization, AI can enable IT to anticipate needs, respond to market dynamics and support innovation initiatives more effectively.<br> <br> </li> <li>Better governance: Real-time visibility is a hallmark of AI-powered application portfolio rationalization. AI-powered dashboards that track compliance, risk and performance metrics strengthen governance, reduce audit risk and ensure continuous alignment with corporate policies and regulatory requirements.<br> <br> </li> <li>Stakeholder alignment: AI-driven recommendations build trust, minimize conflicts and foster collaboration across the business.</li> </ul> <h4>Rethinking application portfolio rationalization for the future</h4> <p>As technology continues to advance, the lessons from this application portfolio rationalization transformation study will resonate across industries. Enterprises willing to embrace AI-driven change will position themselves not only to survive but to thrive in the next era of digital business.<br> &nbsp;</p>