The Smartest Way to Start With AI

If you're exploring AI but aren’t sure where to begin, you're not alone. The flood of tools, bold claims, and constant hype makes it easy to feel like you're already behind, or that the right tool will magically solve it all.

The smartest approach isn’t to chase the trendiest option. It’s to identify one specific area of your business that’s ready to be reimagined from the ground up. This isn’t about layering AI on top of something that’s already broken. It’s about finding a focused slice of your operation where a targeted improvement could unlock real performance gains. That might mean faster service, fewer support tickets, or more time for strategic work. When you start small but intentionally, each win builds momentum. That momentum turns AI from a buzzword into something that moves your business forward. In this post, we’ll walk through how to identify your entry point, set yourself up for success, and build a foundation that makes every next step with AI easier and smarter.

One of the most useful questions you can ask is: If I could rebuild this part of the business with AI from day one, what would it look like? When you take a systems-level view, you unlock more than efficiency. You create a fundamentally better way of working. This mindset not only leads to stronger outcomes, but it also builds a culture of continuous improvement, where each success makes the next one easier to achieve. After all, adopting AI isn’t just a technology decision. It’s an operational one. To succeed, organizations often need to rethink how roles are structured, how responsibilities shift, and how learning happens on the job.

Start Smart with AI: A Simple Framework

1. Spot the Friction
Look for parts of your workflow where things get stuck, repeated, or delayed. Think: member support tickets, repeated internal questions, onboarding bottlenecks, or time-consuming data lookups.

2. Pick a Focused Win
Choose one measurable outcome to improve. Maybe it's faster response times, fewer tickets, or reduced staff workload.

3. Support the Shift
Restructure roles, align incentives, and encourage learning-by-doing. Your team is more likely to embrace AI if they’re part of the improvement, not just subject to it.

AI often takes over repetitive tasks, like answering common support questions or compiling reports, which frees up your team to focus on strategy, creativity, or deeper member relationships. If you don't redefine responsibilities, the time-saving potential often goes untapped.

Learning matters now more than ever. A McKinsey study found that only 21% of employees have learned a new skill through formal training in the past five years, even though generative AI could automate up to 30% of tasks in many roles by 2030. On-the-job learning is becoming rare, yet AI is changing how we work faster than most training programs can keep up. If we want our teams to thrive, we have to build environments where people are rewarded not just for performance, but for adaptability, experimentation, and growth.

That might look like:

• Letting staff lead an AI pilot or onboarding phase

• Rewarding employees who improve prompts or refine workflows

• Giving teams dedicated time to explore new use cases and share what they learn

When AI takes over repetitive or time-consuming tasks, it's not about replacing people, it’s about repositioning them. The employees whose roles are evolving are often the ones who feel most uncertain about AI. However, with the proper support, that fear can turn into empowerment. Formal training tied to new responsibilities gives them clarity, confidence, and a clear path forward. It transforms AI adoption into a development opportunity, not just an efficiency play.

The bottom line: precision beats scale. Trying to overhaul everything at once is risky, expensive, and usually ineffective. The smartest way to start with AI is to focus on one area where it can drive real, measurable impact. A well-executed win gives you faster feedback, clearer ROI, and stronger internal buy-in. That’s how momentum builds: not by doing everything, but by doing one thing well. You won’t just be testing AI. You’ll be building a smarter, more adaptive way of working.

Ready to find your first win? Start with the problem that slows you down the most and let AI make it lighter.