Is Your Application Portfolio Ready for AI?

Enterprise applications are entering a period of rapid change as organizations look for new ways to embed AI into everyday workflows. Gartner predicts that 40% of enterprise applications will include task-specific AI agents by the end of 2026, which is up from below 5% in 2025. That is a significant amount of change for application portfolios to absorb in a short period.

Organizations are already launching AI pilots, introducing AI-assisted development tools, and connecting intelligent capabilities to core workflows. Yet those investments often run into application architectures, integrations, and data environments that were built for an earlier set of business needs.

Let’s take a closer look at how AI is changing application development and the portfolio decisions organizations should make before trying to scale it.

AI Is Changing the Application Development Landscape

AI is beginning to influence both the applications organizations build and the processes teams use to build them.

Development teams can use AI to support parts of coding, testing, documentation, and analysis. Meanwhile, businesses are looking for applications that can support more intelligent workflows, personalized experiences, automation, and AI agents. Gartner describes AI as changing the purpose, form, and function of enterprise applications, with implications for application roadmaps through 2030.

These tools can help teams move faster. But producing new code or features more quickly does not automatically make an application ready for AI. The application still has to operate within the realities of the wider portfolio, including its data sources, integrations, dependencies, security requirements, and business owners.

That is where many organizations are getting stuck. In our Messy Middle of AI survey, 89% of business leaders said an AI initiative had stalled at their company. Among the reasons cited:

29% pointed to deploying AI into systems or workflows.
28% cited building or engineering the solution.
23% identified data readiness.

AI may be accelerating development, but it is also exposing application weaknesses that have existed for years.

Modernizing an Application Portfolio for AI

Technical debt, fragmented data, aging architectures, redundant systems, and poorly documented dependencies did not begin with AI. Still, they can become much harder to work around when an organization wants applications to exchange data, support AI-enabled workflows, or connect with emerging tools.

That creates a two-way relationship. AI can support parts of modernization, while modernization can better prepare applications for approved AI use cases. Before investing, though, leaders need a clear view of what is already running across the business.

Application Portfolio Management Brings AI Readiness into Focus

Application portfolio management is the ongoing process of evaluating an organization’s applications based on factors such as business value, cost, risk, technical health, dependencies, and strategic fit. In the context of AI readiness, it can help leaders decide:

Which applications could support a priority AI use case
Which systems require modernization first
Which applications serve overlapping purposes
Which dependencies or data limitations could create obstacles
Which systems no longer justify further investment

These decisions often involve application rationalization—the process of determining which applications should be retained, modernized, consolidated, replaced, or retired.

Rationalization keeps organizations from treating every aging application as a candidate for a costly rebuild. A business-critical application with a strong future use case may warrant a significant modernization investment. A redundant or low-value system may be better suited for consolidation or retirement. Another application may need only targeted updates to its architecture, data access, or integrations.

The goal should be a portfolio that supports the organization’s business and AI priorities without carrying unnecessary cost or complexity.

6 Best Practices for Preparing Your Application Portfolio

Application readiness should come before scaling AI. That work does not require leaders to predict every future use case. But it does require a disciplined application portfolio management strategy.

1. Start With the Business Outcome

Before investing in modernization, identify:

The business problem being solved
The users affected
The data required
The expected value
The success metrics

This distinction matters because organizations are measuring AI success in several ways. Insight Global found that leaders most commonly pointed to time saved or productivity gains and quality improvement, both at 37%. Improved customer experience and cycle-time reduction followed at 35% each.

A clear outcome helps the organization decide which applications deserve investment and which modernization work directly supports that goal.

2. Build an Accurate View of the Portfolio

Document application owners, business functions, costs, integrations, dependencies, data flows, technical health, and known risks. This assessment should show more than which applications exist. It should clarify how they work together and where one modernization decision could affect other systems.

That visibility is increasingly important as adoption moves beyond isolated experimentation. IBM found in a study that 70% of surveyed technology executives said teams across their businesses were deploying technology faster than IT could track.

3. Prioritize Instead of Modernizing Everything

Use consistent criteria to compare business value, AI relevance, cost, risk, maintainability, and strategic fit. Then place each application on the appropriate path: retain, enhance, rehost, re-platform, refactor, consolidate, replace, or retire.

This makes modernization an intentional portfolio decision rather than a series of disconnected technical projects.

4. Connect Application and Data Readiness

An application cannot support an AI use case effectively if it relies on inaccessible, inconsistent, or poorly governed data. Review where the necessary data lives, how applications access it, who owns it, and whether its quality supports the intended outcome.

This does not mean every data issue must be solved before development begins. It does mean data readiness should be assessed alongside architecture and application readiness rather than discovered late in deployment.

5. Align the Operating Model and Talent

Application modernization brings together development, architecture, data, product, security, governance, and business leadership. Teams need clear ownership over modernization decisions, AI use cases, connected data, ongoing application support, and the results the organization expects.

The workforce will need to adapt alongside the portfolio. The World Economic Forum reports that employers expect 39% of workers’ core skills to change by 2030.

For application leaders, that makes workforce planning part of readiness. The organization may need to develop existing employees, add specialized talent, engage external consulting support, or combine all three to close gaps across application development, modernization, data, and AI.

6. Create a Roadmap Before Scaling

Organizational readiness has not necessarily kept pace with AI adoption. Only 11% of technology executives surveyed by IBM said they were fully prepared for the scale of AI-agent deployment they expected during the following year. IBM also found that surveyed technology leaders anticipated a 38% increase in the number of deployed AI agents by 2027.

A practical roadmap connects near-term use cases with portfolio priorities, modernization work, talent needs, and operating-model changes. It also gives leaders a clearer basis for determining when an application or workflow is ready to move from experimentation into broader use.

Turn AI Readiness Into an Actionable Roadmap

An AI strategy can only move as confidently as the applications, data, decisions, and people supporting it. For many organizations, the first step toward scaling AI is determining whether their current modernization strategy supports where the business wants to go.

Insight Global brings talent, consulting, and AI capabilities together to help organizations assess application environments, prioritize modernization opportunities, and build the teams needed to execute. From application portfolio management and strategy to specialized AI expertise and ongoing support, we help businesses turn AI readiness into a practical roadmap for long-term growth.