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Chief Information Officer

IT Strategy & Roadmap

Enhances◐ 1–3 years

What You Do Today

Define and maintain the technology strategy — application portfolio, infrastructure direction, cloud migration, legacy modernization, and emerging technology evaluation. You're planning 3 years out while delivering this quarter.

AI That Applies

AI-powered technology portfolio analysis that evaluates application health, technical debt, and modernization ROI. Automated technology landscape monitoring that surfaces relevant innovations.

Technologies

How It Works

For it strategy & roadmap, the system evaluates application health. Machine learning models identify the patterns in historical data that most strongly predict the target outcome, then apply those patterns to score new inputs. The output — relevant innovations — surfaces in the existing workflow where the practitioner can review and act on it. The strategic vision.

What Changes

Technology portfolio assessment becomes data-driven — the AI scores every application on technical debt, business criticality, and modernization urgency. Innovation scanning happens continuously.

What Stays

The strategic vision. Choosing which technologies to bet on, which to sunset, and how to sequence the transformation without breaking the business requires experience and organizational awareness.

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 it strategy & roadmap, understand your current state.

Map your current process: Document how it strategy & roadmap 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 strategic vision. 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 Business Intelligence 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 it strategy & roadmap 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

What data do we already have that could improve how we handle it strategy & roadmap?

They shape expectations for how AI appears in governance

your CTO or CIO

Who on our team has the deepest experience with it strategy & roadmap, and what tools are they already using?

They own the technology infrastructure that enables AI adoption

a peer executive at a company further along on AI adoption

If we brought in AI tools for it strategy & roadmap, what would we measure before and after to know it actually helped?

Their lessons learned are worth more than any consultant's framework

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.