Visit Mesa CEO Launches New DMO AI Road Map

Welcome to the Destination AI newsletter from Matador Network, creator of the GuideGeek AI chat for DMOs.

Greg Oates, Director of AI Advocacy, Matador Network

Visit Mesa in Arizona kicked off a new internal initiative this week to integrate AI processes and platforms across the organization. I worked with Visit Mesa to help develop their new DMO AI Road Map, which provides a 5-phase strategic framework that most any visitor organization can use as a template.

The project officially began this week during Visit Mesa’s spring board meeting to communicate why the organization is moving forward with AI to optimize the DMO’s value for Mesa’s visitor industry and the local community.

Marc Garcia, CATP , president and CEO of Visit Mesa, has been eager to start this initiative. He was one of the dozen CEOs who contributed to the first GuideGeek Destination AI story a few months back, What Tourism CEOs Want to Know About AI.

For that deep dive into a CEO's mindset regarding AI, Garcia asked:

• “How do we create value with AI for the organization, our community and our residents? Everything we do has to benefit our residents.”
• “How do we explain that value to our team, our board, our partners and the community, and what are the KPIs? Those are the basics: How do we explain AI and how do we measure it?"
• “What do we need to develop and adopt in terms of ethics and policy?”

The Visit Mesa DMO AI Road Map answers those questions.

“I've recognized that AI is extremely important not only for the world moving forward, but specifically for our industry,” Garcia explained at the beginning of this project. “We at Visit Mesa need to get out in front of it. Even as a small to medium-sized DMO with our budget, we've always taken pride in the fact that we’re typically at the forefront of the latest and greatest in terms of technology, and so this is just an extension of that.”

The following is a high-level overview of the DMO AI Road Map and each of the five phases.

However, like all strategic planning, there is a big jump between developing a plan and executing it effectively. This is especially true with internal AI training. In any given organization today, there is often a wide breadth of how people perceive AI and how proficient they are with AI models. It will take time to educate and align everyone around shared benefits for the organizations and staff, expected processes, intended outcomes and success metrics.

Visit Mesa DMO AI Road Map

The DMO AI Road Map is a structured, phased approach for educating staff and integrating AI across DMO departments. The goals for the road map are to optimize internal operations, enhance sales and marketing outcomes, inspire staff self-learning, future-proof the organization, and contribute to how well local industry and community leaders adopt AI best practices and benefit from them.

There are two imperatives that should be established and agreed upon among leadership at the very beginning.

• Integrating AI processes and platforms is an imperative for the DMO to ensure its relevancy and long-term impact in the local visitor economy. If leadership doesn’t fully believe that, this strategic framework won’t work.
• AI proficiency is an imperative for staff members for their future growth and marketability. That said, you can’t really force people to embrace, learn and use AI intentionally, but the job market will.

The following phases are highly fluid and some of the processes can be rolled out concurrently. Ultimately, these phases provide structure for effectively and responsibly implementing a new technology that has no precedent. Because of generative AI, machines can now do three things they haven’t been able to do before. They have the ability to reason, the ability to communicate, and the ability to generate stuff based on that reasoning and communication.

Meaning, AI is messy. Integrating it into our daily workflows is also messy because it's challenging us to rethink our relationship with technology.

But as Kate Yordi, director of marketing at Visit Mesa, said during the board meeting this week, “Finally, we have some direction.”

Phase 1: Leadership Alignment & Staff Communication

As discussed at length in previous Destination AI posts, the DMO CEO must make it clear to all staff that the organization prioritizes AI because the organization prioritizes being relevant.

It certainly helps, as is the case with Visit Mesa, when the CEO is fully backed by the board to invest in AI integration. During the board meeting, one member asked, “What do you need from us, Marc?” That’s a good sign.

For the DMO AI Road Map to work, all leadership needs to not only have a basic understanding of AI tools, they need to actually use them as well on a regular basis. That goes a long way in driving agency across the organization. However, making that happen typically requires some C-suite training to explain the primary AI models more indepth: ChatGPT, Claude, Perplexity, and Microsoft Copilot and/or Google Gemini depending on the specific workplace platform(s) the organization uses.

Identifying and prioritizing just a handful of AI models like that brings a lot of clarity to the process right off the bat. You see people's shoulders relax when they're confronted with 5-6 AIs versus dozens. (Yes, VEED, Gamma, Manus, Sora, Granola, Slack AI, etc., are all really cool but let's not start there.) A common lament among DMO leadership related to AI is, “Where do we even start?”

Leadership should then discuss and begin to answer the five big questions all CEOs face when navigating AI integration, as vetted in the aforementioned Destination AI post about what CEOs want regarding AI education:

1. How do we channel our staff's enthusiasm for AI (who all have wildly different perspectives and capabilities related to AI) to elevate our organization’s impact in our industry and community?
2. How do we create AI policy that serves and protects our organization but doesn't suppress wonder and experimentation among staff?
3. How does AI benefit all our departments, versus just marketing, and how do we measure improvements in productivity, creativity and quality of work?
4. How do we redevelop our content and channels to rank higher in AI search?
5. How do we capitalize on conversational AI for business purposes to influence travel purchase decisions and in-destination visitor behavior?

The next step in this phase, at a basic level, is communicating to staff why they’re being asked to embrace AI. The following is not meant to be comprehensive, but a CEO would likely want to articulate:

• The purpose of the AI Road Map, guiding principles, and the phases and processes involved
• How AI integration will take time and people will evolve at different paces
• It’s ok if everyone feels like they’re building the plane while flying it
• Everyone is encouraged to use AI and will be supported, but there will also be guardrails in place
• AI adoption and use cases will become part of employee reviews, but traditional roles, responsibilities and key success metrics are still the priority

"How do we create value with AI for the organization, our community and our residents? Everything we do has to benefit our residents." — Marc Garcia, president and CEO, Visit Mesa

Phase 2: Staff Assessment & AI Task Force

Sitting down and asking staff how they feel about AI and how/if they’re using it is a good way to begin. Depending on the size of the organization, this can be done with every staff member individually or a representative group from each department.

Generally people fall into three camps when it comes to AI: 1) Those who are already using AI on a regular basis; 2) those who are curious and have experimented a bit, but they don’t actively engage with AI tools; and 3) those who don’t see much value in AI. This last group can also include people who outright oppose AI, but they make themselves apparent pretty quickly.

Data also shows there is often a significant percentage of staff who use AI in secret because they’re not sure they’re allowed to use it for work purposes.

The second way to collect staff sentiment and AI capacity is with a blind online survey asking everyone what they like about AI, what concerns them, how they feel about job security, what they want to learn, what they hope to achieve, etc. Everything is anonymous so you can expect a wide variety of feedback ranging from constructive/optimistic to fears of an impending robot apocalypse.

Once complete, all the data should be synthesized into key takeaways for leadership and shared with staff to show a high level of transparency right from the beginning of this process.

The second part of this phase is developing an AI task force. The value of this cannot be overemphasized. Choose a small group of AI champions from various departments to do three things:

• Provide direction for stewarding the various processes in each phase as effectively as possible from beginning to end
• Share constructive and thoughtful input they’re hearing from other staff members about how things are proceeding well and where there are challenges
• Help scale AI adoption across teams by showing how they’re using AI effectively to everyone else in the organization

An internal AI task force or council is invaluable because the members are motivated and they want to learn. Mostly, their enthusiasm and success with AI will begin to rub off on many of their colleagues and inspire self-learning and greater adoption among the teams.

But not all. There will be those who disengage, and there can be a significant number of them. Over time, they should be consulted to understand their trepidation, which should then be addressed. This is a challenge I'm having with one organization where a significant cohort are saying they're too busy to learn AI. Meanwhile, the majority of the staff are reporting daily usage with effective results, which definitely wasn't the case a few months ago.

And even with those leaning in, the level of improvement and AI Iearning will be slow at times. This is why it’s critical for the CEO and the rest of management to be all-in and consistently participate in staff training sessions to show direction, intention and support. It’s very easy for the novelty of AI training to wear off after the first few months. This is where the AI task force can be instrumental to help maintain overall staff interest and keep engagement levels as high as possible.

"Finally, we have some direction." — Kate Yordi, director of marketing, Visit Mesa

Phase 3: AI Policy & Investments

Developing an official AI policy and governance manual accomplishes two primary objectives:

• It protects the organization against legal and financial liability by establishing how all employees are required to use AI responsibly.
• It encourages and gives license for staff to proactively experiment with AI because there are clear guidelines in place about how to do so.

There are many DMOs who have created AI policies and are willing to share with the rest of the industry. These are typically not lengthy documents (3-4 pages) nor full of legalese. Generally they’re written so every single employee can understand everything in them.

Sections in these policies often include:

• Positioning/Purpose Statement: Expresses how and why the organization needs to be intentional about implementing AI processes
• Primary Objectives and Intended Outcomes: Defines the business case for how the organization is attempting to deliver on its core mandate of driving revenue to local businesses in alignment with community priorities
• Guiding Principles: Establishes the principles of responsible AI use in alignment with the values of the organizations related to themes such as inclusivity, bias, authenticity, veracity, collaboration, knowledge sharing, etc.
• AI Definitions: Provides clear explanations of AI terminology used in the document and workplace
• AI Tools: Identifies specific AI models that the organization provides access to
• Data Input: Dictates clear rules about what employees can upload into AI models and what they can’t upload, such as personal information of any person
• Monitoring: Defines how the organization reserves the right to monitor AI practices
• Training & Support: Defines opportunities for professional growth and learning AI on an ongoing basis

There are other items that DMOs might include in the above list, and they vary depending on how HR, financial and legal departments interpret AI governance, but it serves as a foundation.

One key consideration for DMO leadership when it comes to policy is identifying which pro AI models to invest in.

Paid AI tools provide higher levels of privacy and security because AI companies don’t train on them, or at least there’s the choice to select that option. Many DMOs, for example, purchase annual subscriptions for paid ChatGPT Team accounts ($25 per employee monthly) for staff to use for work, which is a first step for protecting the IP and other interests of the organization.

Other paid AI models are then provided to specific departments based on need and the specific attributes of the tools, such as Claude for marketing and social media staff because many consider it the best AI for content creation.

Phase 4: AI Education & Implementation

This is the most important thing in training teams to embrace and use AI effectively. You want to foster an environment where structured learning evolves into self-learning. That’s the only way this scales.

When staff members with little experience in AI start exploring how to use AI on their own, and they start helping their colleagues see new ways to use AI, that’s a pretty special thing.

From an education standpoint, the DMO AI Road Map begins at a high level defining AI’s place in online history. AI is the third transformational shift since the PC was invented, including: The mainstream web/internet in the 1990s; the evolution of mobile/social/cloud and apps in the 2000s; and gen AI in the 2020s. These are the only three times when big tech went all-in and pivoted their business models around a new technology.

I then break AI training into the following buckets, and again, this is all very surface level and not meant to be comprehensive. Also, creating engaging demos are everything, and that's not also easy because AI demos can be a real slog sometimes. But, showing how different AIs provide real value is key for people to internalize their learning and use it more intuitively over time:

• Explain how pre-gen AI tech is deterministic and gen AI is probabilistic, which messes with a lot of people’s minds. Gen AI outputs are based on statistical patterns and probabilities, just like the human brain processes ideas and information. Traditional tech is based on absolutes, delivering the same output based on the same input. Old tech is about information. AI is about knowledge. Spend a lot of time on this with workshops and demos.
• Highlight the capabilities of only three AI models to start: ChatGPT for brainstorming and planning; Claude for writing and data analysis; and Perplexity for conversational search. The other two, Microsoft Copilot and Google Gemini, are more general purpose. They also do amazing things (Gemini 2.5 Pro is great for reasoning), but focusing on the first three will provide direction at the beginning so people don’t get overwhelmed. At the very minimum, leadership should have a basic understanding of those three.
• Explain how AI search isn't 100% perfect but it's a 1,000% better than old search. Define how DMOs and all brnads are redesigning their websites and content strategy to optimize for AI search, and the basics of AI SEO.
• I would argue the most important thing for using AI is really understanding how generative AI = conversational AI. It's a dialogue. It's called "chat" for a reason. This is where I’ve heard the most positive feedback from teams I’ve worked with. It happens when people move beyond the Google mindset—where search is one and done—and they see the real value of iterative conversations with AI models that evolve with each exchange in the discussion, just like a human conversation. This also removes some misperceptions about how to prompt. Yes, there are some basic rules about how to prompt well. But beyond that, it’s vastly more beneficial to explore AI conversations further with thoughtful follow-up prompts that flow logically and organically to achieve the best results.
• Explore how the real value of AI extends beyond just efficiency and productivity gains. That's a dangerous dialogue with staff who can construe that as leading to more work or being terminated over time. The bigger value and better narrative is how AI increases creativity and knowledge, and how those both influence overall quality of work, marketing effectiveness, sales productivity, staff culture and pride, job satisfaction and growth opportunities, etc.
• Really dig into the advanced features of the paid models: ChatGPT Plus, Claude Pro and Perplexity Pro. Aside from the added security and privacy they provide, they offer functions not available in the free models and their outputs are significantly better. ChatGPT Projects and custom GPTs, Claude Projects and Perplexity Spaces are where the magic happens.
• Create an environment where staff members are eager to share their successes with AI with the rest of the organization. This feedback loop is critical for steering education and driving adoption, and it’s one of the most important roles for the internal AI task force.
• AI-training meetings can be highly effective during video calls but they often have the greatest impact during in-person workshops. Also, make sure to give staff a specific amount of hours during the week to develop their AI skills.
• Explore options for AI chat platforms and the benefits they provide for visitors and local industry partners. I work with Matador Network, which developed the industry leading GuideGeek AI chat platform. The feedback from 35+ DMO clients is impressive and irrefutable, but there is still much work to do educating visitors and industry about AI chat strategy and their benefits to continually increase adoption and business outcomes.  

The DMO AI Road Map also provides guidance for developing various learning and engagement formats:

• Schedule weekly or bi-weekly meetings specifically, and only, for AI training with both internal and external people sharing insights
• Create a library of AI educational materials for staff to reference
• Start a weekly internal AI newsletter to share how teams are using AI
• Start a Slack or similar channel dedicated for AI education, AI news updates, knowledge sharing, etc
• Start a weekly and optional one-hour AMA meeting for everyone to ask everyone else questions (highly effective)
• Create a regular schedule for staff to report back to leadership or department heads about how they’re using AI and what they’re achieving with it

Phase 5: Success Tracking

I kick off every AI presentation identifying the three AI models mentioned above—ChatGPT, Claude and Perplexity—followed by the assertion that, "AI is the death of 'I don't know.'"

That is arguably the biggest ROI for integrating AI in a DMO or any other type of organization.

There are few questions that are too complex or too intimidating for DMO staff to approach with confidence and conviction if they know how to use those three AI models effectively. I shared an example previously where I used Perplexity Pro to educate myself about how board governance is evolving. That for me was something I knew was out of my realm of expertise, but now I feel I can speak with some semblance of authority about that to any board because of AI.

Before gen AI, I dismissed board governance as one of those things I'd just have to leave in the dark.

That said, trying to explain how AI helps staff become more self-reliant, where they can always build a foundation of understanding for anything to empower themselves, only goes so far with a board. They're looking for more quantitative KPIs.

Here are just some examples of that in a DMO context. In 2025, defining KPIs for AI integration is an evolving science, and we as an industry are still figuring a lot of this out.

Therefore, this list is nowhere near comprehensive and it will evolve, but we have ample data today to validate the following:

• Using AI for search and content development saves significant time and increases opportunities for higher output and increased creativity for producing blog posts, social media, videos, marketing campaigns, etc.
• Developing custom GPTs (in ChatGPT Plus) for group sales is a proven strategy for creating better, more customized proposals in much less time. That gives sales staff more time for brainstorming and prospecting leading to higher production. That's a fact. In this case, group sales people are actually pumped to try a new technology.
• Using AI for regression analysis and scenario modeling increases the accuracy of how DMOs predict visitor volumes year-round, event attendance, economic impact, etc.
• One of the biggest conversations in DMOs today is how to develop content and websites to rank higher in AI search. Developing best practices in AI SEO leads to higher traffic. That is a core mandate for any DMO, and no DMO can afford to ignore that over time.
• AI chat platforms for DMOs like GuideGeek AI are driving real business. As described in PhocusWire in March, New York City Tourism + Conventions launched its GuideGeek-powered "Ellis" meeting-specific AI chat in January. ROI to date: 100% increase in traffic to the meeting planner website, 800+ AI-driven queries in the first month, 50% growth in newsletter sign-ups, and 10% increase in time spent on the site.

Summary

The AI train has left the station and it's gaining steam. The DMO AI Road Map is the next logical step for helping DMO CEOs and their teams integrate AI processes and platforms.

As Visit Mesa's Kate Yordi said, "Finally, we now have direction." That's a big deal for DMO staff to feel that way. Generative AI has been with us for more than two years now, but a structured and phased approach for integrating AI across DMO departments has been the missing piece.

Visit Mesa's DMO AI Road Map is going to evolve as the team works through the process, but at least now there's a vision and scope. Visit Mesa CEO Marc Garcia and the board are fully committed—a critical first step for embarking on this journey.

Also, this strategic framework for how DMOs approach AI is going to underpin the interactive AI Playground activation at U.S. Travel's upcoming ESTO conference in Phoenix, August 17-19. U.S. Travel and GuideGeek are partnering to develop educational programming for the hands-on AI training experience and series of thought leadership.

For information about Matador Network's industry leading GuideGeek AI chat platform, visit guidegeek.com/destinations.