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Chief Marketing Officer

Content & Creative Strategy

Enhances✓ Available Now

What You Do Today

Set the content strategy — what stories to tell, how to tell them, and where to distribute them. Content is the fuel of modern marketing.

AI That Applies

AI content analytics that identify content gaps, predict which topics will drive engagement, and generate content variations for testing.

Technologies

How It Works

The system takes the content brief — topic, audience, constraints, and style guidelines — as its starting input. A language model generates initial drafts by synthesizing the input context with learned patterns, producing text that follows the specified tone, format, and domain conventions. The output — content variations for testing — surfaces in the existing workflow where the practitioner can review and act on it. The creative vision.

What Changes

Content strategy becomes data-driven. The AI identifies that your audience engages 3x more with case studies than whitepapers, and that a specific topic cluster is trending in your industry.

What Stays

The creative vision. The stories worth telling, the voice that defines the brand, and the creative risks that break through — these come from human creativity.

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 content & creative strategy, understand your current state.

Map your current process: Document how content & creative strategy 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 creative 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 Generative 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 content & creative strategy 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 content do we produce the most of that follows a repeatable structure?

They shape expectations for how AI appears in governance

your CTO or CIO

What's our current review and approval process, and would AI-generated first drafts change the bottleneck?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.