What problem are we trying to solve?
Be specific. Is it lead conversion? Churn reduction? Content production speed?
Why does this problem matter to the kazakhstan cell phone database business? Tie it to tangible outcomes like revenue, customer satisfaction or efficiency.
What’s your current approach? What’s broken or slow? Understand the existing process to help clarify the opportunity.
Do you have the correct data to support a solution? Where is that data? Is it accurate, accessible and structured? What else do we need?
Who will use the solution and how? You need buy-in from those impacted. Tools that don’t fit into workflows won’t get used.
How will we measure success? Define KPIs early. Otherwise, how will you know it’s working?
Is AI even the answer?
Sometimes the best solution isn’t AI. Would you be better served by training or better processes?
These questions will force you to clarify your intent, which is your most valuable asset when exploring AI.
Dig deeper: Is your marketing team AI-ready? 8 steps to strategic AI adoption
Common problems where AI can help
Once you’ve grounded your thinking in business needs, you might discover areas where AI could unlock real value. Here are some common marketing and operations challenges that lend themselves well to AI-powered solutions:
Conversion gaps
Leads are coming in but not closing. Why? AI can help score leads, personalize touchpoints or identify drop-off points in the funnel.
Content bottlenecks: Your team is drowning in content requests. AI can assist with first drafts, translations, repurposing and even tagging.
Customer churn: You’re losing customers, but don’t know why. Predictive models can flag at-risk users earlier, giving teams a chance to act.
Lack of personalization: You know customers want relevance. AI can help create micro-segments or even real-time personalization.