On this episode of Suite Success, host Katie Cline sits down with André Boersma, Chief Operating Officer of Paradero Hotels, whose three-decade career includes leadership roles at Rosewood, One&Only, Viceroy, and Discovery Land Company. Together, they explore how Paradero is rewriting the rules of luxury hospitality by putting the destination first, prioritizing experiences over amenities, and redefining what it means to truly take care of guests.

From an innovative all-inclusive model designed to eliminate decision fatigue to regenerative farming practices that bring depleted land back to life, André shares why doing less can sometimes deliver more. He also reveals how Paradero is expanding into family-friendly villas and branded residences, and why he believes the future of luxury travel will be defined not by what hotels give their guests, but by how those guests feel when they leave.

DEEP DIVE

The rapid adoption of artificial intelligence in hotel revenue management has created a new generation of automated pricing tools promising faster decisions, greater efficiency, and improved revenue performance. However, automation does not necessarily translate into better pricing intelligence. Traditional revenue management systems, such as IDeaS G3, have been built around structured forecasting, historical performance analysis, demand modelling, and clearly defined pricing rules. These systems provide revenue managers with a framework for understanding not only what rate to charge, but why that rate is commercially appropriate. In contrast, many newer AI-driven autopilot solutions rely heavily on short-term market signals and automated algorithmic responses, sometimes overlooking broader demand patterns, seasonal behaviour, competitive positioning, and individual hotel brand strategies. The result can be pricing decisions that appear intelligent in real time but fail to support long-term revenue performance.

One of the fundamental weaknesses of unsupervised AI pricing is its dependence on reliable data and meaningful historical context. Established hotels in mature markets may have years of booking patterns, seasonal trends, event demand, cancellation behaviour, and customer segmentation data to inform pricing decisions. However, newer properties, boutique hotels, independent operators, and hotels entering emerging destinations often lack these established baselines. Without sufficient context, algorithms can misinterpret limited booking activity, respond disproportionately to isolated demand signals, or establish inappropriate rate floors and ceilings. This becomes particularly problematic during shoulder seasons, when demand is less predictable and historical patterns require careful interpretation rather than automatic extrapolation. A sophisticated pricing engine should recognise the difference between a temporary fluctuation and a genuine shift in market demand. Without structured forecasting and human oversight, AI can mistake short-term noise for a meaningful commercial opportunity.

Algorithmic volatility presents another significant challenge. When competing hotels use similar automated pricing engines, their systems may continuously react to one another's rate movements, creating a feedback loop in which prices fluctuate without a corresponding change in underlying demand. Rather than reflecting each property's unique positioning, value proposition, and revenue strategy, pricing can become a reactive exercise driven by competitor movements. This creates the risk of unnecessary discounting during periods when a hotel should be protecting its rates, or excessive price increases that undermine conversion and guest value perceptions. The problem is especially relevant during transitional seasons, where demand signals can be inconsistent and competitive rate changes may not accurately represent market conditions. Effective revenue management requires more than tracking what competitors charge; it requires understanding when to compete, when to hold pricing, and when a property's brand positioning justifies a premium.

Transparency and commercial control are equally important considerations. Revenue managers need to understand the reasoning behind pricing recommendations, particularly when decisions have implications for profitability, distribution strategy, and brand reputation. Yet some full-autopilot solutions offer limited visibility into the factors influencing individual rate changes. When managers cannot easily establish why an algorithm has recommended a particular price, they are left repeatedly overriding automated decisions or accepting recommendations without sufficient commercial justification. Both outcomes undermine the purpose of automation. Hotel pricing is not simply a mathematical exercise: it must account for positioning, customer expectations, channel strategy, market conditions, and the long-term value of the guest relationship. A pricing system that optimises short-term revenue without considering these factors can create unintended consequences, including inconsistent rate positioning and reduced guest confidence. The strongest technology should therefore enhance managerial decision-making rather than remove meaningful human control.

Ultimately, the future of hotel revenue management is unlikely to be defined by complete automation or a choice between traditional systems and artificial intelligence. It will be defined by the quality of the intelligence, transparency, and control behind pricing decisions. Established enterprise platforms such as IDeaS G3 demonstrate the value of structured forecasting and disciplined revenue management, while newer solutions such as RoomPriceGenie offer alternative approaches to automated pricing and operational efficiency. The important distinction is not simply whether a system uses AI, but whether its recommendations are grounded in reliable data, explainable logic, appropriate pricing constraints, and the commercial realities of each property. A hybrid approach, combining predictive technology with strategic rules and experienced revenue management oversight, offers a more balanced model for sustainable performance. Hotels do not need pricing systems that simply react faster; they need systems that interpret demand intelligently, protect their positioning, and give revenue managers the confidence to make better decisions.