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VP of IT

Lead application development and modernization

Enhances✓ Available Now

What You Do Today

Manage the portfolio of business applications — ERP, CRM, core systems. Decide what to build, buy, or modernize. Oversee development teams and system integrators.

AI That Applies

AI-assisted code generation, testing, and code review that accelerates development velocity. Low-code/no-code platforms with AI capabilities for simpler applications.

Technologies

How It Works

The system ingests that accelerates development velocity 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

Developers become more productive with AI coding assistants. Simple applications can be built by business users on AI-enhanced low-code platforms.

What Stays

Architecture decisions, build-vs-buy strategy, and managing the complexity of enterprise integrations — those require experienced technologists who understand both the technology and the business.

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 lead application development and modernization, understand your current state.

Map your current process: Document how lead application development 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: Architecture decisions, build-vs-buy strategy, and managing the complexity of enterprise integrations — those require experienced technologists who understand both the technology and the business. 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 GitHub Copilot 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 lead application development 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 board chair or lead independent director

Which training programs have the highest completion rates, and which have the lowest — what's different?

They shape expectations for how AI appears in governance

your CTO or CIO

How do we currently assess whether training actually changed behavior on the job?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.