Define the business need: What are you trying to improve? Growth? Retention? Speed? Cost savings?
Diagnose the root cause: Is the problem jordan cell phone database about data? Process? Technology? Resources? People?
Evaluate possible solutions (AI or not): Would AI help? Or is there a more straightforward fix?
Pilot with purpose: Pick a narrow use case with a clear KPI. Start small, learn fast.
Measure, refine and scale: Prove it works. Then build on the momentum.
This approach is deliberate and agile. Rather than locking into a 12-month AI roadmap, you’re testing assumptions and learning in the context of real business needs.
Dig deeper: How marketers can go beyond random acts of AI and why they should
Beware of FOMO
One of the most significant risks of AI adoption is the fear of missing out. It looks like this:
Trying a tool for the novelty.
Launching a flashy pilot with no owner.
bPlaying with outputs that never get used.
Moving on when the next shiny thing appears.
This doesn’t just waste time. It sets your team back, creates confusion and breeds cynicism. Instead, treat AI like any other strategic investment. Ask what business outcome you’re looking to achieve. Starting with something small and valuable builds trust.
AI won’t fix a broken process, clarify an unclear strategy or drive results if it’s adopted just to say you’ve adopted it. The next time someone asks, “What’s our AI strategy?” Try reframing it: “What are the biggest problems we need to solve?” Then you can determine if AI is the right tool to help.