The gap between AI experiments and real business impact is where most teams get stuck. They've got scattered pilots and individual wins. What they don't have is a strategy to drive adoption across the team or prove ROI to leadership. The teams that close this gap do one, two, or all of these: ► Anchor to strategic initiatives Map your AI use cases to initiatives that already have owners, budgets, KPIs, timelines, and executive visibility. This increases your probability of adoption and success because you're working with built-in momentum and resources. One CMO had "Accelerate Customer Growth" as a top priority with a goal to increase net revenue retention by X percent. The team mapped AI use cases directly to it: spotting upsell opportunities through behavior analysis, personalizing nurture campaigns, and flagging at-risk accounts early. Because these supported a board-level initiative with clear metrics, they got immediate resources and fast adoption. Sometimes one initiative needs multiple AI use cases working together. Other times, a single use case moves the needle on several initiatives. Either way, you turn AI from a side experiment into a strategic asset. ► Solve biggest pain fast Focus on work that's eating up hours, blocking progress, or burning budget. The bigger the pain, the faster you'll see value. A global marketing team I worked with was spending tens of thousands of dollars monthly on agency fees to translate and localize every customer-facing asset into eight languages. The process took weeks. A small group of field marketers who were local language speakers built custom GPTs with brand guidelines, market expertise, and localized examples. Within a week, they had a working solution. AI handled 80-90 percent of the work, with the team checking it and handling the final edits. Translation time dropped from weeks to days, saving thousands in agency fees every month. Big pain, fast ROI, and proof that's hard to ignore. The win inspired the team to quickly tackle other big problems. ► Let your trailblazers inspire what's possible Your AI trailblazers have been experimenting, building workflows, and learning what AI can and can't do. They understand AI's potential and limitations. They're pushing it to its full capacity. Find them. Give them space to prove what's possible. Then let them mentor the team and show others what they've built. At one B2B tech company I guided, a small group of trailblazers quickly grew to 75 across marketing who became the engine for transformation. They created 211 custom AI teammates during experimentation. Today, 57 are integrated into regular workflows across the team. Their wins moved the skeptics faster than any exec mandate could. The bottom line: Tie AI use cases to strategic initiatives with momentum. Prioritize high-pain, high-value problems. Let your trailblazers inspire what's possible. That's how you close the gap between experiments and real business impact.

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