Where to Start with AI: A Practical Roadmap for Real Business Impact
By Fabien Cros, Chief Data & AI Officer at Ducker Carlisle | SparkWise Solutions Lead
Artificial Intelligence is everywhere—from boardroom agendas to news headlines, and yes, even your LinkedIn feed. But amidst all the buzz, one of the most common questions I hear from leaders is:
“Where do we even begin?”
It’s a fair question. Many organizations overthink their first steps with AI and stall before they’ve even started. The good news? You don’t need to boil the ocean. With the right approach, you can start small, demonstrate value quickly, and scale with confidence.
Here’s a proven three-step framework—based on real client examples—that can help your organization launch AI the right way.
Step 1: Start with Sales – The Ideal Entry Point for AI
When companies ask me where to begin, my answer is nearly always the same: start with sales and top-line growth. Sales teams are often eager adopters of AI, viewing it as a tool to accelerate revenue—not as a threat to their jobs.
Take one example: a client whose sales reps were spending hours manually sorting leads. We introduced an AI-powered lead scoring tool that ranked prospects based on historical conversion data and enriched those leads with insights from publicly available sources. The outcome? A 20% increase in closed deals—without hiring a single new rep.
The takeaway here is simple:
Start with a high-impact, low-barrier use case to prove AI’s value while giving your organization time to learn and adapt.
💡 Pro Tip: Form a small cross-functional AI squad and assign a “Learning Agenda Product Owner” to track results, document insights, and refine your internal AI playbook.
⚠️ Avoid This: Trying to automate everything at once. Focus on one team, one problem, one solution.
Step 2: Educate and Share What Works
One of the biggest mistakes I see? Companies succeed with a pilot AI initiative—but never communicate the results. The technology stays siloed, and momentum fizzles.
For example, I worked with a firm that deployed an AI chatbot for customer service. It reduced ticket volume by 40%, yet the rest of the company continued to operate as if nothing had changed. The reason? No one shared the story.
If you want AI adoption to scale, you need to bring everyone along.
🎯 Best Practice: Launch an AI education initiative after your first successful use case. Host internal sessions where teams can interact with the solution owners, ask questions, and see real outcomes. Transparency builds trust—and trust drives adoption.
Step 3: Scale Strategically with Parallel Use Cases
Once you’ve proven value and built organizational support, it’s time to expand. But don’t fall into the trap of scaling too quickly. Instead, run two AI projects in parallel to demonstrate that AI is a repeatable, scalable capability.
One manufacturing client started with AI in sales. Once successful, they introduced AI-driven inventory predictions. The result? A 30% reduction in stockouts—an impact that resonated across the business.
💡 Why Two Projects? It reinforces that AI isn’t a one-off—it’s a strategic capability that can be replicated across departments.
⚠️ Avoid This: Scaling without a foundation. AI success depends on continuous learning, iteration, and refinement.
In Summary: A Simple, Repeatable Approach
To recap, here’s your roadmap to getting started with AI:
✅ Step 1: Start small—Sales is a great entry point with fast, measurable impact
✅ Step 2: Educate—Share learnings to build trust and broaden support
✅ Step 3: Scale smart—Prove scalability with two strategic projects
Remember, AI isn’t just about automation—it’s about acceleration. And real transformation starts with testing, learning, and growing from there.
Curious where your AI journey should begin? Let’s figure it out together. At SparkWise Solutions by Ducker Carlisle, we’re helping organizations just like yours turn AI from a buzzword into business value. Learn more here!