Kris Glabinski
VP Strategy and Business Development, Aggregate Intelligence, Inc.
Walking through the technology pavilions at Messe Berlin for ITB 2026, conversations emerging from exhibition stands centred on a consistent, cross-sector theme across aviation, hospitality, and mobility:
The structural limitations of legacy data infrastructure in meeting the demands of dynamic pricing, evolving distribution channels, and artificial intelligence integration.
After two days of discussions with revenue managers, pricing directors, and travel technology innovators, a clear picture emerged. Traditional approaches to business intelligence are increasingly misaligned with the pace and complexity of modern market conditions.
What follows is a synthesis of the core data challenges that customers and prospects across the industry are actively working to resolve in 2026.
From Scheduled Data Delivery to On-Demand Intelligence: The AI Readiness Gap
Across all sectors, one of the most widely discussed limitations was the continued reliance on scheduled, batch-based data delivery.
Revenue Management Systems (RMS) and Business Intelligence (BI) platforms drawing on internal data are now designed to refresh multiple times per day. Yet competitive intelligence feeds sourced externally frequently remain on once-daily delivery cycles, creating a meaningful lag between market conditions and decision-making inputs.
This gap has become particularly acute in 2026 as organisations across the industry move toward AI agent deployment.
When AI systems are provided with static, infrequently updated pricing data, their analytical outputs are correspondingly limited in reliability.
As a result, the industry is increasingly moving away from legacy batch-transfer approaches — such as static files delivered via cloud storage — toward:
- On-demand APIs
- Semantically structured data feeds
- Real-time competitive intelligence
These formats allow machine learning models to parse and interpret market data with greater precision.
Aviation: Distribution Fragmentation and the Ancillary Intelligence Gap
For years, airlines constructed their pricing strategies around fares filed through Global Distribution Systems (GDS).
At ITB, airline executives acknowledged that this approach no longer provides a complete picture of the competitive landscape.
The expansion of New Distribution Capability (NDC) and the growth of Low-Cost Carriers (LCCs) operating outside traditional GDS channels mean that filed fares frequently diverge from the prices customers actually encounter.
As a result, competitive monitoring relying solely on GDS data misses a meaningful share of market activity, including:
- OTA-specific pricing
- Meta-search aggregator pricing
- Direct-channel promotional fares
The Ancillary Intelligence Gap
Ancillary revenue presents a further intelligence gap.
Industry-wide, ancillary revenues — encompassing baggage fees, priority boarding, seat selection, and related services — now represent approximately 15% to 25% of airline revenue and continue to grow.
Despite this, granular competitive data on ancillary pricing remains difficult to obtain through conventional data suppliers.
Several pricing teams noted that manual monitoring of competitor websites remains a common practice for gathering this information.
The Emerging Rail Competition Factor
European carriers raised an additional dimension of competitive exposure: limited visibility into high-speed rail pricing.
On many short-haul routes, airlines increasingly compete with services such as:
- Eurostar
- TGV
- ICE
As rail operators expand capacity and refine their own yield management strategies, the absence of cross-modal pricing intelligence represents a growing blind spot for airline revenue teams.
Hospitality: Anti-Scraping Technologies and Distribution Fragmentation
In the hotel sector, two interconnected issues dominated discussions:
- Increasingly sophisticated OTA-side data protection measures
- The rapid fragmentation of distribution channels
OTA platforms have deployed advanced technical countermeasures, including:
- AI-driven bot detection systems
- Deliberate injection of misleading data to detect automated extraction
These developments have made in-house competitive data collection significantly more resource-intensive.
Even established third-party data providers are experiencing increased friction in extraction processes — precisely as hotels require more frequent and granular competitive data.
LLM-Driven Search Is Changing Booking Behaviour
At the same time, the growing adoption of LLM-powered search is fundamentally altering how travellers discover accommodation.
Customers increasingly access hotel inventory through a diverse and expanding set of niche and localised OTAs and meta-search platforms.
Properties that previously monitored a small set of core distribution channels now face a far more complex competitive environment.
Tasks such as rate parity monitoring and direct price guarantee validation now require visibility across a much broader channel landscape.
Mobility: Customer-Facing Price Visibility and Fleet Classification Complexity
Car rental operators identified challenges that closely parallel those faced by airlines.
Rates sourced through GDS feeds frequently fail to reflect the total prices customers actually see on aggregator platforms.
Additional charges often include:
- OTA insurance products
- Fuel policies
- Location-specific taxes
- Optional service add-ons
Revenue managers working from GDS-derived feeds may therefore be operating with an incomplete view of competitive pricing.
The Fleet Classification Problem
Another operational challenge involves vehicle category alignment.
Competitors frequently use different naming conventions and category structures across locations and markets, making direct fleet comparisons difficult.
Effective benchmarking requires specialised mapping frameworks that align competitor vehicle categories with an organisation’s internal fleet taxonomy — a capability that many traditional data providers have not yet implemented.
Algorithm Initialisation and the Need for Historical Data
As organisations across travel verticals upgrade or replace their RMS platforms, a common operational constraint has emerged.
New pricing algorithms require substantial volumes of historical competitive data to calibrate effectively.
Whether for:
- a newly launched airline route
- a new hotel pricing model
- a mobility market entry
Revenue management systems typically require a minimum of twelve months of historical competitive pricing data before algorithms can operate at intended performance levels.
Without this context, optimisation periods become significantly longer.
The Shift Toward Flexible Data Delivery
The broader takeaway from ITB Berlin 2026 is that the industry is seeking greater flexibility and specificity in how competitive intelligence is sourced and delivered.
There is clear demand for consumption-based models that allow clients to define precisely:
- which routes to monitor
- which channels to track
- which locations to cover
- which monitoring frequencies to apply
Increasingly, organisations also want self-service interfaces that allow direct analytical access to data without requiring intermediary processing.
Looking Ahead
Some providers (guess which one 🙂) are already advancing toward semantically structured data feeds, on-demand APIs, and flexible historical archives that address these emerging requirements.
Organisations that move beyond single-source data strategies and adopt comprehensive, on-demand intelligence — spanning GDS, NDC, OTA, meta-search, and increasingly cross-modal competitors — will be far better positioned as AI continues reshaping how competitive intelligence is generated and applied across the travel sector.
