AI in Dynamic Pricing: What It Can Do (and What Still Needs Guardrails)

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.

Key Takeaways: AI’s Role in Pricing Decisions

• Where AI wins: It handles the messy, high-volume grunt work—cleaning data, scanning research, and prepping decisions for review.
• Where AI fails: It lacks accountability. Without guardrails, it can hallucinate numbers or make inconsistent decisions.
  
• Our solution: A "Co-pilot" model. AI does the prep, the solution runs the math, and humans make the call.
  

Where AI Does the Heavy Lifting for Dynamic Pricing

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.

1. Integration acceleration

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:

• Quickly assessing new datasets
• Proposing mappings to our standardized pricing model
• Generating the code for validation checks

Our economists still validate the final setup, but AI dramatically reduces manual translation and migration time.

2. Mass information processing

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:

• White papers
• Product docs
• Industry coverage

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.

3. Exception triage and escalation

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: 

• Pulling the recent history and historical precedents
  
• Checking against current constraints
• Packaging the data for review
  

The complex analysis and final pricing decisions remain human-led, but the AI ensures economists have exactly what they need without all the digging.

Where AI Still Falls Short Without Guardrails

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.

1. AI Can Be Confidently Wrong

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.

2. You Need Consistency, Not Creativity

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. 

3. Context is King

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.

The Co-Pilot Model: Human-Led, AI-Assisted Pricing

As of January 2026, the model that works is clearly a co-pilot system: 

• AI Agents do the prep work (integrations, data checks, research)
• Our pricing solution, a deterministic system, enforces the rules and calculates the optimal prices
• Economists, humans, own the strategy and approve the exceptions

This human‑in‑the‑loop approach ensures accountability, precision, and trust.

That’s why we’re confident Digonex delivers strong dynamic pricing performance in production.

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.