So, your company wants to get serious about AI. Maybe you’ve seen a few pilots go well. Maybe your competitors are starting to pull ahead. Or maybe your exec team just gave you the green light to ​“figure out the AI thing.”

Now what?

The first 100 days are critical. This is when you build the foundation that will either unlock momentum — or quietly collapse under its own weight six months in.

Here’s a straightforward, no-nonsense playbook for how to spend those first 100 days so your AI strategy doesn’t just sound good — it actually works.

Day 1 – 30: Set the Groundwork

1. Align on Why You’re Doing This

Don’t start with tech. Start with business value.

Ask:

• What do we need AI to do?

• Where are we bleeding time, money, or opportunity?

• What could we do better if we had faster insights or fewer manual steps?

Frame AI as a tool for solving problems — not as the goal itself.

Tip: Don’t aim for transformation on day one. Aim for traction. Look for ​“boring” problems AI could quietly improve.

2. Inventory Your Existing Data and Tools

You can’t build anything useful if your data is scattered, dirty, or inaccessible. This isn’t the flashy part, but it’s where most AI projects go off the rails.

Checklist:

• Where is your data stored?

• Who owns it?

• Is it structured, unstructured, or both?

• Is it accessible via APIs or trapped in PDFs and spreadsheets?

Reality check: You probably won’t need all your data. But you do need the right data for the use cases you care about.

3. Get the Right People in the Room

AI is a team sport.

You’ll need:

• Business leaders who can define value

• Data engineers who know the plumbing

• Legal and compliance folks to flag risks

• Someone who’s fluent in machine learning (hire or borrow if needed)

Set expectations early. This isn’t an IT project or a one-person experiment. It’s cross-functional from day one.

Day 31 – 60: Pick Your First Plays

4. Choose Use Cases That Are Small, Clear, and Valuable

Think of these as your first ​“AI reps.” You’re looking for problems that are:

• Painful enough to matter

• Narrow enough to scope

• Solvable with the data and tools you already have

Examples:

• Auto-tagging support tickets to reduce triage time

• Summarizing internal documents to speed up onboarding

• Predicting low-stock inventory items before they run out

Avoid: Moonshots, long timelines, or anything too vague (“improve customer experience” won’t cut it).

5. Define Success in Advance

What does ​“good” look like?

It could be:

• X% reduction in time to complete a task

• Y% improvement in accuracy

• Z fewer manual handoffs

Set a clear baseline so you can measure impact later. And communicate that baseline with stakeholders — don’t assume they know what’s realistic.

6. Plan for Integration, Not Just a Demo

Pilots that live in a sandbox are fine to start. But don’t stop there.

Ask:

• Where will this AI system plug into the existing workflow?

• Who will use it — and how?

• What systems does it need to talk to (ERP, CRM, etc.)?

Reminder: If no one ends up using it, it doesn’t matter how accurate or advanced it is.

Day 61 – 100: Build Momentum

7. Launch, Learn, and Iterate

Roll out your first project. Keep it lean. Don’t aim for perfect — aim for learnable.

Track:

• Usage: Are people actually engaging with it?

• Output: Is it giving useful, reliable results?

• Feedback: Where are people getting stuck or losing trust?

Expect to make changes. That’s not failure — it’s exactly how early adopters create systems that actually stick.

8. Start Documenting the Stack

As you build, you’re going to start forming a reusable toolkit.

Capture:

• Which model(s) you used

• What data pipelines fed them

• How you handled prompts, logic, and evaluation

• What governance steps were in place

This will be your foundation for future projects — and it’ll help avoid reinventing the wheel each time.

9. Evangelize Inside (But Keep It Real)

As your first wins take shape, start sharing them. Internally, not as hype, but as proof of value.

Create:

• A 2‑minute demo or walkthrough

• A short case study with results

• A list of ​“next use cases” others can pitch in on

Keep the tone grounded: ​“Here’s what worked, here’s what we’re still improving, here’s where we’re going next.”

Final Thought: Don’t Let the First 100 Days Happen to You

AI strategy doesn’t fail because of bad intentions. It fails because people dive in without a plan. Or they get stuck in strategy docs and never launch anything. Or they ship something clever — but no one uses it.

Your first 100 days are about avoiding those traps. Not by moving recklessly, but by moving with purpose.

Start small. Measure everything. Get the right people involved. And build in public — so others can learn with you, not just from you.

That’s how early adopters turn experiments into momentum. And momentum into transformation.