If you run a venue or attraction, you know the stakes. Pricing dictates revenue, attendance, and your brand’s reputation. Naturally, the industry is buzzing about AI, and the conversation generates both excitement and concern. The real question isn’t whether AI belongs in pricing – it’s how it should be used.
Think of dynamic pricing like flying a plane. AI isn’t the autopilot, per se; it’s more like an instrument panel. It can track more gauges, sensors, and data points than any human ever could. However, at the end, you still need a pilot in the cockpit to decide where the plane is going, and not completely hand over the keys to a machine.
At Digonex, our Economist team moved quickly to adopt agentic workflows. We didn’t do it to replace our staff, but to clear the runway for them. In dynamic pricing, teams usually lose time on three things: fixing data, studying trends, and managing exceptions. That is exactly where we put the AI to work.
Dynamic pricing works with clean data, yet as we work with multiple ticketing platforms and clients, datasets have a wide variety of formats. We built agents to absorb that complexity by:
Our economists still validate the final setup, but AI dramatically reduces manual translation and migration time.
Staying competitive means keeping up with a moving target: new techniques, academic research, and shifting industry trends. Agents are excellent at this kind of "knowledge ops." We use them to scan and summarize:
AI summarizes and extracts what matters most, which keeps our experts focused and efficient. We treat the AI’s output as a starting point and verify its credibility and relevance.
Dynamic pricing is full of exceptions like stakeholder requests regarding operational constraints or specific market signals. Addressing these requires deep context, not just a calculator. AI acts as a prep layer by:
The complex analysis and final pricing decisions remain human-led, but the AI ensures economists have exactly what they need without all the digging.
AI is great at summarizing and drafting, but "autopilot pricing" is dangerous. Pricing requires precision and repeatability. General-purpose AI models have a few distinct flaws that make them risky for direct revenue management.
Large Language Models (LLMs) are designed to predict the next word in a sentence, not to be a source of truth. If they don't know the answer, they often guess. That is fine for writing an email; it is unacceptable when a decimal point determines your revenue. We never let an AI hallucinate a price tag.
AI doesn’t always give the same answer twice, which is risky for pricing. To test, audit, and trust decisions, the same data should produce the same result. That’s why AI helps with analysis, but econometric and machine learning algorithms do the actual pricing.
You can’t just ask ChatGPT, “What should I charge tomorrow?” It doesn’t know your history, your business constraints, or your strategic goals. Real pricing engines rely on massive operational datasets that exceed the memory of a chat bot. AI is a tool within the system, not the system itself.
As of January 2026, the model that works is clearly a co-pilot system:
This human‑in‑the‑loop approach ensures accountability, precision, and trust.
We treat publishing a price as a serious, accountable action. High-impact changes never go live without human eyes on them. When signals conflict, the system doesn't guess—it escalates to a person.
We are using AI to deliver better decisions and faster cycles, not to chase autonomy for the sake of novelty.
If you’d like to see what a co-pilot pricing model looks like in practice, we’d be happy to show you. Book a Digonex demo to see AI-assisted dynamic pricing done right.