How AI-Driven Dynamic Pricing is Disrupting Business Models

In an exclusive interaction with Asia Business Outlook, Dr. Anshu Jalora, Founder & MD, Sciative Solutions, shares his insights on how AI and real-time data analytics are driving dynamic pricing across sectors.

He throws light on real-world examples of how hotel chains, fuel retail networks, and travel brands have successfully implemented AI-driven dynamic pricing to improve key business metrics such as revenue, margins, or conversion rates.

Also, Anshu Jalora highlights the evolution of ethical AI shaping the future of pricing strategies.

How AI and real-time data analytics are driving dynamic pricing across sectors like hospitality, fuel retail, ecommerce, logistics, and fashion

We’re living in a world where pricing decisions can no longer be made once a week in a spreadsheet. The pace of change is simply too fast - demand, competition, and customer behaviour are shifting by the hour. What’s also changed is the sheer volume of data available today. Every customer action, competitor move, or market signal adds to a stream of insights that traditional tools just can’t keep up with. That’s where AI and real-time analytics in pricing step in.

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Across sectors like hospitality, fuel retail, ecommerce, and fashion, AI helps businesses respond dynamically to all these shifts. In hospitality, for example, we can adjust room rates based on upcoming events or sudden demand surges. In fuel retail, AI monitors competitor price moves in real time and reacts instantly. Ecommerce platforms now personalize prices based on browsing behavior and purchase history, thereby boosting conversion rates without hurting margins.

The beauty of it is that AI doesn’t just automate - it learns. Over time, it gets better at spotting patterns, predicting trends, and recommending price actions that drive both revenue and customer satisfaction. It’s almost like having a pricing optimization engine that never sleeps.

Could you elaborate on how Sciative’s platforms—ZettaRMS, BRIO, and Viaje.AI—are enabling pricing intelligence and personalization?

At Sciative, we understand pricing is not a one-size-fits-all situation - every industry has its quirks. That is why we have developed unique AI pricing tools for each industry. Each of our platforms ZettaRMS, BRIO, and Viaje.AI has been purpose-built to solve the pricing challenges of its respective industry. But they all share one philosophy: to make pricing intelligent, fast, and fair.

ZettaRMS is redefining revenue intelligence for hotels. It brings precision to pricing, replacing outdated rulebooks with dynamic, data-led strategies. From single properties to large chains, it empowers revenue managers to act with clarity, speed, and confidence, every single day.

BRIO is retail’s pricing command center. Whether its fashion, electronics, or ecommerce, BRIO helps brands move at the speed of the customer, adapting prices across thousands of SKUs, instantly and intelligently. It’s not just about automation; it’s about orchestrating profitability at scale.

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Viaje.AI is redefining fare intelligence in travel. For intercity buses and mobility operators, it brings clarity to chaos by decoding route-level demand in real time and setting fares that fill seats and grow revenue. It’s pricing on autopilot, with foresight.

With all three platforms, the ultimate goal is simply to bring businesses back from manual and guesswork pricing to data-driven pricing solutions, personalized, real-time pricing decisions that maximize revenue, and hence margin, and increase customer conversion rates.

Can you share case studies or real-world examples of how hotel chains, fuel retail networks, and travel brands have successfully implemented AI-driven dynamic pricing to improve key business metrics such as revenue, margins, or conversation rates.

Absolutely, we have seen very tangible business outcomes in every industry we support.

In hospitality, we saw a mid-sized hotel group that was relying on manual pricing and dependent on OTAs for all of their direct bookings. After switching to ZettaRMS, in just three months they realised a 14 percent increase in RevPAR, were spending 37 percent less time on manual rate updates, and were receiving 18 percent more direct bookings as a result of better parity controls. This impacted both revenue and operational efficiencies in a meaningful way.

In retail, using BRIO, the previous inconsistent margins that outlets faced could finally be addressed. In markets where pricing could be adjusted in real-time, they achieved a 5 percent uplift in gross margins and a 22 percent uplift in sales of high-margin products, and managers could respond to market conditions more rapidly to make more profitable decisions, which is generally very difficult to do in a manual world.

In travel, and in particular bus transport, Viaje.AI allowed operators to move beyond flat rates to smarter, demand-driven pricing. This resulted in an increase of 30 percent YOY revenue per bus, 50 percent higher seat occupancy during traditional off-peak times, and 15 percent higher ticket rates during traditional peak times.

For all of these industries, what is clear is that real-time dynamic pricing doesn't just optimize pricing, it is capable of enhancing profitability, operational efficiency, and proactive customer engagement in a scalable, sustainable manner.

What kind of data infrastructure or high-velocity data pipelines are required to support instantaneous pricing decisions?

To support instantaneous pricing decisions, the underlying infrastructure must be capable of processing high-velocity, high-volume data streams from both internal and external sources, with minimal latency and maximum reliability.

Our platform currently parses over 83 unique demand signals. These include a wide range of real-time and historical indicators like spanning market activity, competitor movements, customer behavior, inventory dynamics, and macro-environmental factors such as weather or events. Each of these signals contributes to a multi-dimensional understanding of price sensitivity and demand fluctuations.

Internally, we enable bi-directional data synchronization between our platform and clients’ operational ecosystems. This architecture allows us to ingest historical and live data streams that capture demand patterns, behavioral signals, and performance metrics, while simultaneously pushing optimized prices back into the client’s systems in real time. This closed-loop data flow ensures that pricing decisions are both data-driven and instantly actionable within the business's day-to-day workflows.

For external data, our infrastructure is designed to ingest and process large-scale, frequently changing datasets using real-time data pipelines. To maintain performance at scale, we implement pre-processing layers and model pre-compilation, allowing the platform to handle complex computations with near-zero latency. This enables the system to reflect market changes in pricing decisions almost instantaneously, ensuring both responsiveness and stability in execution.

In essence, we’ve built a data infrastructure that’s not only scalable and intelligent but also engineered for speed, interoperability, and precision - all critical to delivering instantaneous pricing decisions at an industrial scale.

How do you see the evolution of ethical AI shaping the future of pricing strategies?

Ethical AI pricing is indeed the future in terms of pricing. AI is a powerful tool, but with that power comes responsibility. If not handled carefully, it can lead to pricing that feels unfair or even discriminatory, like charging different prices to customers based on personal traits or vulnerabilities.

At Sciative, we’ve built in ethical guardrails within our systems. We also make our pricing logic explainable, so businesses can understand how a price was arrived at, and customers don’t feel like they’re being treated arbitrarily.

In the future, I believe the most successful pricing strategies will not just be profitable, they’ll be trustworthy. Consumers are becoming more aware of how technology shapes their choices, and brands that use AI with transparency and fairness will win in the long run by adopting transparent pricing algorithms.