Why Modernizing Code The Old Way Risks Obsolescence

​In this segment of Joseph Ours’ Forbes Technology Council column, Joseph talks about why companies need to understand what their systems actually do before modernizing.

If you’re modernizing software the same way you always have, you’re not truly modernizing.

Yes, many CTOs have equipped their teams with AI coding tools. Developers are generating code faster than ever. But most organizations are still treating AI agents as narrow code generators—peering through a keyhole instead of seeing the full picture.

The real opportunity isn’t just to speed up coding. With innovations like AI agents, the entire legacy modernization lifecycle can be reshaped: recovering missing requirements, mapping system behavior, designing modern architectures, generating code aligned with current best practices and validating quality.

Right now, organizations are modernizing legacy systems the same old way, only with shinier tools. But if your approach stays the same, your outcomes won’t change. The fixation on code generation misses the bigger challenge at the heart of modernization: understanding what your systems actually do.

The Legacy Modernization Trap

Legacy app modernization has long been a challenge for organizations. Old applications often suffer from outdated libraries, deteriorating architecture and lost institutional knowledge about the system’s functionality. At Centric, we’ve found that after roughly three years, documentation quality drops precipitously, and by five years, it’s almost nonexistent.

Organizations traditionally tackle this in two ways:

1. Business analysts audit processes and redocument everything. This tends to capture a majority of regular operations, but misses edge cases such as quarterly exceptions, annual processes and intermittent scenarios that people often forget.

2. Organizations deploy technical analysts who read code to extract system behavior; however, they can’t capture how users interact with the system or what the business truly needs.

Both approaches are limiting when you’re tasked with making buy-versus-build decisions, as you can’t choose a solution without knowing what your system does. As a result, more organizations are using AI agents to help solve these challenges, which requires strategic deployment to ensure success.

The Four Tiers Of AI Integration

While many CTOs think they’re ahead of the curve because they’ve adopted AI coding tools, they’re often stuck in the early stages of a four-tier maturity model:

1. Intelligent Autocomplete: AI-enhanced versions of traditional tools auto-complete entire small functions of 10 to 12 lines. Humans write about 90% of the code; AI handles 10%.

2. AI-Assisted Development: Tools write multiple functions and entire classes at once within one or two files. This is where I see most organizations today. The split shifts to 60% to 70% human-written code and 30% to 40% AI-generated code.

3. Current-State AI-Augmented Development: Agents understand your existing codebase well enough to refactor code or add features following established patterns. The ratio inverts here to 60% to 70% AI-generated and 30% to 40% human-written code.

4. Mature AI-Augmented Development: Agents write entire ecosystems of code conforming to your organization’s standards. Architects and senior developers review output rather than writing code themselves. The split becomes 90% AI, 10% human.

What separates tier 4 from everything else is that mature organizations can deploy teams of 20 to 30 agents across the entire software development life cycle rather than just the coding phase.

Beyond Code Generation

True AI-augmented development operates across every stage of the modernization process. Code generation represents the most visible piece, but it is not the majority of the work. 

Through this approach, business analysts can use agents to extract and reconstruct requirements directly from existing code, recovering system flows, inheritance and data relationships without relying on institutional memory. Architects can leverage agents to propose modern architectures and patterns based on a deep understanding of current systems, rather than starting from scratch, while developers and QA teams can then use agents to generate consistent code and comprehensive tests aligned to those patterns.

AI agents help capture the edge cases and synthesize system behavior and business intent, filling gaps that have always plagued modernization efforts.

Getting Started With AI Agent Augmented Development

To ensure success with your AI agent initiative, here’s where I suggest you begin.

Develop significant agentic competencies first.

Deploying agents effectively across a development life cycle requires understanding how to architect agent teams, manage their interactions and validate their outputs. This might include retraining, hiring for these competencies or partnering with a firm that has them.

Audit your current life cycle processes.

Identify where agents can compress time and generate savings at each development stage, including requirements gathering, architecture design, testing, documentation and coding.

Account for human review time in your planning.

Organizations that fail with AI-augmented development typically expect to achieve 100% of theoretical time savings without adjusting for human validation needs. Effective human review of large volumes takes time and deep subject matter expertise, so be sure to build rigorous review processes into your workflows.

Start with your next modernization project.

Legacy application modernization requires exactly the kind of comprehensive system understanding that agents excel at providing, including reverse-engineering requirements, understanding complex code relationships and maintaining consistency across large ecosystems.

Closing Thoughts

Organizations that approach legacy modernization with the same old processes — just with AI bolted on — are leaving major value on the table.

Using AI solely for code generation is like using a Swiss Army knife as a can opener. Sure, it works, but it ignores the far more capable tools right in front of you. To unlock real impact, I believe we need to stop treating AI as a single-purpose accessory and start leveraging its full potential across the modernization lifecycle.

This article was originally published on Forbes.com.

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