To really come to grips with the end of "business as usual" in an AI world, we need to be curious and courageous enough to get outside our comfort zones. Because it's "out there" where opportunity awaits and relevance is assured.
But here's what most DMO professionals are doing instead: attending industry conferences where everyone discusses the same challenges, reading trade publications that recycle the same insights, and networking with colleagues who share the same knowledge gaps about AI.
What if the smartest solutions to your current frustrations are already being used in industries you've never studied? While you've been focused on perfecting traditional DMO approaches, companies in completely different sectors have solved similar challenges around personalization, efficiency, and client service. The fastest way to get your time back just might be learning from how other industries handle the same problems you face every day.
Here's the next step in how to make your AI adoption journey an amazing opportunity for yourself, your clients and your organization: Launch three specific AI experiments in the next 30 days paired with systematic learning from a completely different industry.
Experiment 1: The "Outside Eyes" Content Test
What You Do: Take your three most important pieces of sales and marketing content and run them through ChatGPT with this prompt: "Rewrite this from the perspective of someone planning their first corporate/association/incentive event in this destination."
Why It Matters: This isn't about AI replacing your content creation. It's about developing the discipline to see your destination through completely fresh eyes. This is the perspective-taking skill that's essential for effective AI collaboration.
Takeaway: Most DMOs describe their destinations like real estate agents from 1995, focusing on features instead of experiences. This exercise forces you to think like your actual clients, which is precisely the mindset shift that separates AI collaborators from those who struggle to adapt.
Experiment 2: The "Pattern Recognition" Challenge
What You Do: Every week, spend 30 minutes studying how a non-business events industry brand solves a problem similar to yours. How does Airbnb match guests with hosts? How does LinkedIn suggest connections? How does Netflix decide what to recommend?
Then, document three insights that could apply to your work.
The Payoff: You'll develop the cross-industry intelligence that enables you to spot opportunities and threats before your competitors who only look within the legacy business events ecosystem.
Takeaway: The research shows that breakthrough innovations happen when insights from one domain or industry get applied to completely different problems. But most DMO professionals have never systematically studied how other industries solve similar challenges.
Experiment 3: The "Friction Discovery" Audit
What You Do: Identify the three most annoying, repetitive tasks you/your team does every week (expense reports perhaps?). Document exactly how much time each task takes. Then research: How have other industries automated similar tasks?
Don't try to implement anything yet, just understand what's possible.
The Payoff: You'll develop realistic expectations about what AI can solve immediately versus what requires better preparation—essential knowledge for making smart AI collaboration decisions.
Takeaway: Most organizations approach AI backwards. They think about sophisticated applications and use cases first and never address basic inefficiencies that are actually solvable today. This exercise builds bottom-up understanding of where AI creates immediate value.
What You're Really Building: Job Security Through Adaptability
These experiments seem simple, but they're developing critical capabilities that separate professionals who thrive with AI from those who get replaced by it. While your competitors are either ignoring AI or waiting for perfect conditions to engage with it, you're building the intellectual capabilities that will make you and your team effective AI collaborators whenever you're ready to implement more sophisticated systems.
The 30-Day Reality: Small Steps, Big Difference
Week 1: Launch all three experiments. Assign ownership. Set up 15-minute weekly check-ins to share insights.
Week 2: Start documenting patterns. What assumptions about your work are you discovering? What solutions from other industries seem most applicable?
Week 3: Begin connecting insights across experiments. How do the content insights relate to pattern recognition discoveries? How do friction points connect to solutions you've researched?
Week 4: Assess what you've learned about AI readiness—not your technology readiness, but your intellectual readiness to work with AI when opportunities arise.
The goal isn't to become an AI expert in 30 days. The goal is to stop being systematically ignorant about AI applications while competitors build AI collaboration skills.
Your Choice: Curiosity or Displacement
The beautiful thing about these experiments is they cost nothing except intellectual effort. You don't need budget approval, vendor selection, or technology upgrades.
But every day you delay building systematic curiosity about AI applications, you fall further behind professionals who are already thinking like AI collaborators.
You just need the discipline to look beyond your industry comfort zone and start learning from organizations that are already winning the AI transformation.
Time to get curious. Your job security depends on it.
How is your DMO currently thinking about AI experimentation? What barriers are you encountering? Share your thoughts in the comments below or contact us directly.
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