Part 1 of 2:

"How to stop wasting time on AI pilots - and deliver results your team will use."

The Reality You’re Living

Artificial Intelligence is everywhere forecasting demand, automating tasks, chatting with customers, and now coordinating its own actions through Agentic AI. The excitement is real, yet many organizations still struggle to turn that promise into business results.

If your organization has experimented with AI, you already know how easy it is to start a pilot and how hard it is to turn that pilot into something that lasts. It’s rarely a technology issue. Often, the challenge is linking AI to what matters most to your business and helping your people trust and adopt it.

AI is simply the next wave of digital transformation. But this wave moves faster and touches every corner of your operations, so you need a stronger foundation to ride it including shared goals, trusted data, and the right governance to guide decisions.

Why do AI Pilots Miss the Mark?

When AI pilots stall, the reasons are familiar: unclear purpose, data you can’t trust, and teams working in silos. Add in a bit of change fatigue, and you get enthusiasm without execution.

You’ve launched an AI pilot. It started with a demo. Three months later, it’s dead in a shared drive.

You’re not alone. 70% of AI pilots vanish within 90 days - not because the tech failed, but because no one owned the outcome (HBR, 2024).

Linking AI to Business Outcomes

To succeed, AI must connect to your strategy. Clarify your vision and define the business outcomes that matter most. Set goals that show progress and establish governance, so AI efforts stay accountable and aligned with your values. When these elements work together, AI moves from side project to business driver.

This guide gives you a 90-minute process to fix that - starting next week.

Link AI to Your Team’s Goals with a Use Case Canvas

Forget “digital transformation.” Ask: “What would make your week easier?”

Examples:

• Cut weekly reporting from 6 hrs → 30 mins
• Reduce planning errors by 20%
• Free 2 hrs/day for customer calls
  
  

90-Minute Workshop Agenda

Run this with your team and one IT partner next week:

Assessing Readiness

AI success depends on readiness, not perfection.
Ask yourself: is your data ready and trusted enough to support AI?
Are your teams confident using analytics?
A simple maturity check across vision, people, process, technology, and governance helps you see where to start. If several answers are “not yet,” strengthen data and governance first. If you’re further along, start small and prove value, build trust, and scale deliberately.

AI doesn’t wait for perfect data. It waits for trusted data.

Assess Your Team’s Readiness (Self-Score)

The above questions can be rated on a scale of 1 to 5.
Is your total <12?

Fix data first.

From First Win to Flywheel

Each successful project should do three things for you: show measurable value, build trust in data, and prepare your people for the next step. Over time, those wins create a cycle of progress better data enables better AI, and effective AI justifies further investment in data and skills.

Every AI project must do three things:

1. Save time or reduce errors (measurable)
2. Build trust (“This actually works”)
3. Unlock the next step (data improves → better AI)
   
   

One ops team cut reporting from 6 hrs → 30 mins.
Six months later, they automated three more processes.

Conclusion: Start Before You Scale

Every lasting AI success starts with alignment between your strategy, business outcomes, and the people turning data into action. Get that right first, and AI will grow naturally into your organization’s daily rhythm.

But clarity is only the first step.

In Part 2, you’ll see how to turn that foundation into momentum with roadmaps, leadership support, and structured execution that turns plans into results.

AI success begins with connection between data and decisions, and between your ambition and results.

You don’t need perfect data.

You need one trusted source and one clear win.

Run the AI Impact Canvas workshop next week.

Walk out with a pilot your team owns.

In Part 2: How to turn that win into a roadmap - and get leadership to fund it.

About the Author

With over 28 years of leadership experience in digital strategy, data analysis, and guiding strategy through to execution, Jay-Lynne Leppky is a trusted advisor for organizations navigating transformation. A Certified Management Consultant (CMC) and CBAP-certified Business Analyst, she combines advisory expertise with hands-on execution, helping clients move from vision to measurable results.

She has worked across industries to shape strategies, build roadmaps, and design data-driven solutions that enhance performance, strengthen decision-making, and deliver lasting value. Through structured facilitation, co-creation, innovation, and leadership, Jay-Lynne helps bring alignment across business and IT, integrating people, process, and technology with governance and vision as the foundation for success. Her approach ensures organizations don’t just experiment with AI, but adopt it in ways that create sustainable impact.