Galway restaurants and AI Search

When someone queries for the “best restaurant in Galway,” most people picture a tourist asking Google or TripAdvisor. But that question is increasingly going into an AI conversational model like ChatGPT. The result is not a ranked list of 10; it’s one or two names. The stakes are higher than you think.

AI is creeping into how people plan travel. One report suggests that 30 % of people already use AI to build itineraries and route out their travel plan. As AI is woven into chatbots, travel apps, and planning tools, it’s not just the “search engine” that restaurants, hotels and attractions must worry about. Now it’s the AI model that recommends places with authority. The best will get stronger and others will simply become invisible to tourists planning their trips to your destination.

Now, he says, Irish businesses face a new challenge: being found in a world that’s quickly moving away from Google search.

Galway restaurants and AI.  

Let’s take a real example: someone asks a travel AI “best restaurant in Galway.” The output isn’t a broad list. The model will pick one or two names, based on training data, past user interactions, prominence, and signals of legitimacy.

If your restaurant isn’t among those, you’re now invisible to that user, no matter if you’re fantastic. Right now, Aniar is in pole position in AI citations for best restaurant in Galway. 

However the real power of AI comes from the back and forth conversations users have with it to refine exactly what they are looking for. It learns what people want in a way that search or social media never could. Here are some refined answers currently by AI.  

A user will ask follow up questions specific to their needs so expect factors like dietary requirements, costs, views from restaurant, trust factors such as reviews and local intel and best places for different scenarios. Each one can lead to different answers and all the refinement happens inside a chat that your business has no access to and won’t know what's happening until you get full or empty tables which will compound aggressively over time. 

Here is a table of Galway restaurants and which businesses are currently in front and why

Those suggestions aren’t random. They come from what the model has “learned” about which restaurants satisfy those criteria (based on data, reviews, websites, online mentions). If your business doesn’t have signals in those dimensions i.e. if nobody has codified you as “great for veggie, sea view, kid friendly”, you simply won’t be recommended.

In practical terms:

• A single conversational step could disqualify you (e.g. “open late Sunday,” “parking available,” “outdoor seating”)
  
  
• You don’t just compete for the top of Google anymore. You’ll compete inside the AI model’s memory and recommendation logic
  
  
• The “ranking” becomes less about being #1 in search results, and more about being among the few the model knows about
  
  

To illustrate: suppose twenty people ask (over a week) “best restaurant in Galway,” on average. If your restaurant is already frequently cited in travel blogs, review sites, local press, visitor guides, and AI training datasets, the model is more likely to pick you. If not, you won’t come up and you just lost out on potentially 40-100 diners potentially per week .

What being cited “inside the model” looks like in real life

Let’s unpack what it means “to be listed inside the model.” This is more than just being talked about inside an AI model. It translates to real traffic, bookings, and revenue potential.

1. Discovery traffic (zero ad cost) Many restaurant owners already pour money into print, local radio, digital ads, and PR. But those are outbound pushes reaching people who might or might not see your message or be thinking about eating with you. Being inside AI recommendations is an inbound pull of qualified potential customers.  People already asking “where should I eat” get pointed to you.
   
   
2. Higher intent leads When someone is using AI to structure an itinerary, they are far along in planning. If you appear in that step, you're not selling cold, you're now potentially a key part of their plan.
   
   
3. Improved conversion on secondary queries Once a user asks “Is this restaurant good for kids?”, “Do they accept bookings?”, “Is there parking?”, “Are they open late?”, those successive queries can funnel them toward your business if the model knows those attributes.
   
   
4. Domino effect — review, social, bookings Once you are being recommended more, you get more visits, more reviews, more online content. That feedback loops into more “signal” for future AI recommendations.
   
   

As for marketing budgets and ROI: those vary widely by scale, city, positioning. But consider this rough sketch:

• Small to medium restaurants may spend a few thousand euros per month on digital ads, social media, local radio, PR.
  
  
• For many, ROI is marginal — they chase seasonal demand, promotions, and specials to fill slow nights.
  
  
• Getting even a few extra covers through AI-driven discovery (without incremental ad spend) can swing a month from breakeven to profit.
  
  

If your restaurant can capture, say, 5–10 extra tables a week from AI discovery (especially higher-spend tourists), the return on those few hours of visibility work could outstrip months of traditional campaigns.

Where to start from scratch your AI discovery journey

Obviously this is where I would tout hire AiSe.ie to take care of it and work with you every step of the way.

If you want to understand what you need to do from scratch though. Let’s break it down. 

1. Audit your visibility footprint: What mentions, listings, reviews, structured data already exist about your restaurant
   
   
2. Define attributes that matter: Is your restaurant good for vegetarians, kids, sea view, group bookings, etc. It is better for digital footprint to hone on one or no more than a couple of niches rather than trying to be all things to all people.
   
   
3. Optimize content and data: Make sure your website, description pages, menus, structured data (schema.org), review sites, and local directories clearly reflect those attributes and are consistent across the digital landscape.
   
   
4. Conversations + alignment: Your operations, service style, hours, booking process must back up what you declare. The key ingredient is reviews. Don’t be afraid to nudge happy customers to rate you. We can be shy about this and try to rely on great service and great word of mouth. And great word of mouth will always trump other forms of marketing but remember, Word of mouth is great. Algorithms have bigger mouths.
   
   
5. Monitoring and training . Start tracking things offline better. Forget about tracking website hits, social media reach, brand recall or other factors as the key performance indicators. You are entering a world where it’s almost impossible to properly track metrics and right now AI has no proper analytics for company mentions. Go to the basics. How many tables a week. If you want to find out the source of your marketing, ask your customers. ‘How did you hear about us?’ and have your front of us record it accurately.
   
   

Risks, caveats, and what to watch

• AI models are not perfect. They learn from biases, noisy data, and sometimes propagate errors.
  
  
• If your competitor manipulates signals (e.g. pushes “vegetarian friendly” claims, gets PR, generates content) faster than you, they may outrank you inside the AI.
  
  
• The user’s first few questions heavily influence the path. If you’re not present in those early “broad” categories, you may never get a chance.
  
  
• Visibility today doesn’t guarantee visibility tomorrow. AI models get retrained, sources change. Continuous maintenance is required.

Conclusion

We are in a transition phase. If you wait for AI to mature, you’ll be playing catch-up. If you treat this as the crucial new channel, one that already determines travel decisions, you can reposition your restaurant from “local contender” to “global option.”

AI discovery is no longer theoretical.  It is quietly becoming the first place prospective visitors ask where to eat. For restaurants, that means the fight for visibility is shifting from Google SERPs or Instagram reach, to the memory and recommendation logic of conversational models.

The future of restaurant discovery may happen in AI first. If you don’t shape your signals now, you risk being invisible in the single place people ask: “Where do I eat tonight?” If you do, you can reap that funnel of bookings without having to pay for them.