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Enterprise Architect

Manage technical debt and modernization

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What You Do Today

You identify, quantify, and prioritize technical debt across the portfolio — making the case for modernization investments and designing the path from legacy to modern systems.

AI That Applies

AI analyzes code repositories, infrastructure configurations, and dependency graphs to quantify technical debt and prioritize modernization by business impact and risk.

Technologies

How It Works

The system ingests code repositories as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Technical debt becomes quantifiable and prioritizable when AI analyzes the full codebase and infrastructure portfolio.

What Stays

Making the business case for modernization, choosing the right modernization approach for each system, and managing the politics of replacing systems people are attached to.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for manage technical debt and modernization, understand your current state.

Map your current process: Document how manage technical debt and modernization works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Making the business case for modernization, choosing the right modernization approach for each system, and managing the politics of replacing systems people are attached to. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Technical Debt Analysis tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long manage technical debt and modernization takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your CEO or executive sponsor

What data do we already have that could improve how we handle manage technical debt and modernization?

They set the strategic priority for transformation initiatives

your CTO or CIO

Who on our team has the deepest experience with manage technical debt and modernization, and what tools are they already using?

They own the technology capability that enables your strategy

the leaders of the business units you're transforming

If we brought in AI tools for manage technical debt and modernization, what would we measure before and after to know it actually helped?

Their buy-in determines whether your strategy actually gets implemented

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.