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Enterprise Architect

Review and govern technology decisions

Enhances✓ Available Now

What You Do Today

You participate in architecture review boards, evaluate project proposals for architectural fit, and ensure new solutions align with enterprise standards and strategy.

AI That Applies

AI pre-screens proposals against architecture standards, identifies potential conflicts with existing systems, and generates assessment reports before review board meetings.

Technologies

How It Works

For review and govern technology decisions, the system identifies potential conflicts with existing systems. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — assessment reports before review board meetings — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Review preparation becomes faster when AI pre-screens proposals and identifies the key architectural questions to address.

What Stays

The governance judgment — knowing when to enforce standards strictly and when to grant exceptions, and maintaining credibility as a helpful guide rather than a gate.

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 review and govern technology decisions, understand your current state.

Map your current process: Document how review and govern technology decisions works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The governance judgment — knowing when to enforce standards strictly and when to grant exceptions, and maintaining credibility as a helpful guide rather than a gate. 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 Architecture Review AI 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 review and govern technology decisions 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 review and govern technology decisions?

They set the strategic priority for transformation initiatives

your CTO or CIO

Who on our team has the deepest experience with review and govern technology decisions, 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 review and govern technology decisions, 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.