Add TechGraph on Google
In an interaction with TechGraph, Dr. Anshu Jalora, Founder and Managing Director of Sciative Solutions, outlined how AI-driven pricing has become a core component of business strategy as retailers and travel operators work to balance revenue optimization with customer trust, and how the company’s technology enables data-informed pricing decisions that are transparent to consumers and sustainable for long-term growth.
He also spoke about the continued importance of human oversight in Sciative’s approach, where experts collaborate with the AI engine to validate insights, refine recommendations, and ensure that every decision aligns with business objectives and customer expectations in rapidly evolving markets.
Read the interview in detail:
TechGraph: Pricing in retail, e-commerce, and travel has always been a delicate balance between maximizing revenue and keeping customers loyal. What specific shifts in consumer behavior are you seeing today that make AI-powered pricing intelligence more critical than ever, and how does Sciative address that complexity without making prices feel manipulative?
Dr Anshu Jalora: Consumers nowadays are faster, smarter, and demand prices that make sense at face value in an instant. They shop by comparing offers, searching for bargains, and reacting in real time. This means traditional pricing methods, based on intuition or static rules, simply won’t cut it. At Sciative, we have built AI that ingests millions of real-time signals, from competitor prices and demand shifts to customer sentiment and booking patterns, to recommend prices.
We want to make it fair and equitable, but mostly about trust, and I think of it simply like this: we want to get the customer to say, “this price makes sense”, while also helping the business maximize revenues in a shifting world.
TechGraph: Many companies claim to deliver dynamic pricing, yet they often face criticism for being opaque or unfair. How does Sciative differentiate itself in ensuring that the prices it helps generate are perceived as contextual and fair by consumers while still meeting the aggressive revenue targets of your clients?
Dr Anshu Jalora: I’ve observed businesses having difficulties when dynamic pricing appears to be arbitrary or unjust. At Sciative, we ensure that our AI has awareness of the context: past purchases, local demand, inventory, competitor activity, etc. When we consider all of that, every price feels reasonable and fair to the customer.
While our AI delivers powerful recommendations, we always leave room for human judgment to refine and contextualize pricing decisions. This is about creating a system that will achieve the desired objective of the client, and, in turn, making certain that customers feel respected and valued.
For me, this balance is important: technology can support insight and action, but empathy is the difference between manipulative pricing and meaningful pricing. When people have trust in the price, they become loyal – and that’s how actual growth occurs.
TechGraph: Both the travel and retail industries have faced enormous disruption in recent years, from supply chain shocks to changes in digital shopping habits. How has Sciative adapted its models to account for sudden volatility, and how do you reassure clients that AI-driven systems can respond with agility rather than simply amplifying the uncertainty?
Dr Anshu Jalora: In the travel & retail industries, change happens in the blink of an eye: supply chains get disrupted, demand shifts, and consumer behavior evolves overnight. The AI models built at Sciative adapt to these changes by continuously monitoring market dynamics, identifying anomalous signals, and suggesting actions before the situation goes haywire.
I always remind my team that volatility is not the enemy but the opportunity to show it the smart way. By combining the insights generated by the AI engine with our experience, pricing can remain competitive without much disruption. Clients know they can trust the system to respond with precision, not panic. This is my favorite part of the work; it is part science, part intuition, and requires a little bit of courage.
TechGraph: There is often a cultural hesitation within traditional companies when it comes to trusting algorithms with decisions that directly impact their brand reputation. What have been the toughest challenges in convincing leadership teams to embrace AI-led pricing, and how do you bridge the gap between the science of your technology and the intuition of experienced business managers?
Dr Anshu Jalora: It is understandable that leaders are reluctant to allow anything as sensitive as pricing to be influenced by AI. It is an issue of trust, responsibility, and reputation as a brand. At Sciative, we demystify how our AI works, explain every recommendation, and actively involve them in the decision-making process.
As the system continues providing consistent, positive results, their trust and confidence build. I like to say, AI doesn’t replace intuition; it factors into it. As a result, leaders will begin to feel empowered, rather than threatened.
To me, it has always been about open dialogue and continuous collaboration. Technology and human judgment combined lead to smarter results. And when you see that “aha” moment in a leader’s eye, you know that journey was worthwhile.
TechGraph: AI systems depend heavily on data quality, yet retailers and travel companies often deal with fragmented, incomplete, or biased datasets. How do you ensure that Sciative’s solutions remain robust and accurate in environments where the underlying data may not be perfect, and what safeguards are in place to avoid systemic pricing errors?
Dr Anshu Jalora: We don’t have perfect data to work with every day – far from it. Retailers and travel businesses tend to have fragmented, messy datasets. We at Sciative embrace and accept the imperfect state of data as a puzzle to be solved. We are always cleaning, cross-checking, and validating data.
Our AI solution is trained to find outliers before any problems arise. But we do not just rely on machines: our people engage with and check the outputs and lead the recommendation process to make sure everything fits and makes sense. I have come to believe that technology can accomplish incredible things, but human judgment and concern can never be replaced.
Altogether, we end up with pricing and insights to work with that are accurate, fair, and robust – even in the face of imperfect data.
TechGraph: The conversation around AI today often revolves around ethics and accountability. In the context of pricing, that responsibility feels particularly sensitive because it directly affects what a customer pays. How does Sciative build accountability into its technology so that pricing decisions can withstand both regulatory scrutiny and consumer trust?
Dr Anshu Jalora: Pricing is deeply personal – what any customer pays matters. That is why accountability is non-negotiable for us. Sciative’s AI logs every decision it makes, explains how it was made, and ensures ethical guardrails are in place. If a regulator or customer asks why a price was set at a certain level, we can defend it with confidence.
For me, fairness is not simply a box to check – it is part of the foundation of trust. And trust, a moral obligation, drives loyalty, repeat business, and ultimately long-term success. I often think of it as being a steward: we are entrusted with both revenue and reputation and take that trust very seriously.
TechGraph: Looking ahead, the role of AI in commerce seems to be expanding beyond efficiency into shaping entirely new consumer experiences. Where do you see the next frontier for AI-driven pricing, and how might Sciative’s approach evolve to influence not just how prices are set but how customers emotionally engage with brands over the long term?
Dr Anshu Jalora: AI is evolving from efficiency to empathy. The next frontier is not just about setting the price right – it’s about knowing your customer, understanding their needs, and creating a memorable experience.
Sciative is exploring ways to tie pricing to emotional engagement, loyalty, and personal relevance. Imagine a system that could not only know what someone should pay, but also make them feel confident and respected about it.
This is where commerce becomes human again. I think the most exciting aspect is that brands can use AI to connect with people, not just to optimize transactions but to make every interaction meaningful while creating business outcomes.