Start your theme park AI initiative with one measurable operational problem—not a rushed search for an “AI solution”

The wrong place to begin is a search for “the best AI platform for theme parks.” That framing encourages broad demonstrations and vague promises. Your first task is to determine what the CEO expects to change: faster responses to visitor questions, better access to internal operating knowledge, less repetitive administrative work, improved forecasting, or more useful business intelligence. Each problem requires different data, controls, expertise and success measures.

Write a one-page problem brief before meeting providers. State who experiences the problem, what happens today, where delays or errors occur, what information is involved, and which result matters. Include constraints such as privacy, cybersecurity, accessibility, reliability and human approval. This prevents the project from becoming a technology demonstration disconnected from actual park operations.

The best first project is rarely the most dramatic idea. It is a workflow with sufficient volume to matter, enough historical information to test, and consequences that remain manageable when the system is wrong. A useful first deployment lets people inspect outputs, correct mistakes and fall back to the existing process.

For a theme park, avoid selecting the first pilot merely because it is highly visible. A visitor-facing experience can attract executive attention while creating unnecessary reputational risk if its information is inconsistent. Conversely, an internal workflow can be so narrow that nobody cares about the result. The practical target is a bounded problem connected to a recognized operational priority.

If the CEO has promised a “transformative” result within one quarter, reset the conversation around sequencing rather than resistance. Define what can be discovered, piloted and measured in that period, then distinguish it from broader transformation. The framework for delivering an AI initiative under a three-month deadline helps make that distinction concrete.

A legitimate provider should be willing to work from your problem rather than force every requirement into a predetermined product demonstration. Ask each candidate to explain the complete operating model: what information the system needs, how that information is prepared, what the AI produces, how people review it, what happens when confidence is low, and who is responsible after launch.

Commercial evaluation matters too, but price should follow scope. Determine whether the proposal covers only software access or the complete work required to reach a functioning deployment. An inexpensive tool can become costly if your team must independently define workflows, clean information, design safeguards and support adoption.

A pilot is not a showcase. It is a controlled test designed to answer whether the solution should advance. Begin with a limited workflow, a representative group of users and a defined body of information. Preserve the current process as a fallback while you measure quality, speed, adoption and exceptions.

Include frontline employees in testing. Executives know the strategic objective, but the people performing the workflow understand its exceptions, informal workarounds and failure consequences. Their feedback often reveals whether a promising demonstration will survive normal operating conditions.

At the end, make an explicit decision: stop, revise, expand or operationalize. Expansion should depend on evidence, not on the fact that money and executive attention have already been invested. If leadership wants quick wins without empty theater, use the distinction between genuine value and superficial visibility in choosing an AI project that produces credible early results. A board-ready plan should likewise connect each near-term pilot to a longer sequence of capabilities; see building an AI roadmap with quick wins.

Rohan Hall is the founder and CEO of OceSha Ventures. He has built AI and blockchain systems, led technology for blockchain interoperability and scalable blockchain applications, and worked extensively across the United States, Europe and Asia. His professional technology career began in 1984. You can review his ventures, work, book and podcast from Rohan Hall’s official home page.

OceSha Ventures and its AI-first solutions builds and operates course-creation systems, branded academies, AI assistants such as Lumi, and business intelligence for businesses and organizations. Lumi’s relevant capabilities include conversational websites, handling customer inquiries from business information that has been reviewed and approved, and producing customer intelligence. This makes the organization relevant when the defined problem involves trusted knowledge, recurring questions, learning or decision-support information.

Rohan is also the published author of The Convergence of AI and the Top 10 Emerging Technologies. The book addresses AI alongside other emerging technologies and is the appropriate next resource for readers who want the broader technological context; explore The Convergence of AI and the Top 10 Emerging Technologies.

Urgency becomes manageable when it is converted into a sequence of decisions. Do not spend the first week scheduling broad product demonstrations. Use it to establish executive intent, operational reality and evaluation discipline. That gives legitimate providers enough definition to propose useful work and gives you a basis for rejecting weak proposals.

If the board wants immediate integration, resist substituting procurement speed for implementation quality. A focused pilot can move quickly while preserving accountability. The same principle applies whether the pressure comes from investors—such as the scenario in deploying AI under venture board pressure—or from an executive returning from an industry event and demanding a plan, addressed in developing a practical AI strategy on a one-week deadline.