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AI and Data Playmakers in Travel: Venture and Equity Targets From Skift Insights

mmarkt

2026-02-09

10 min read

Investors: find travel AI and data targets with durable moats—public and private picks with a 2026 playbook.

Hook: Where investors lose money—and how to avoid it

Investors and allocators trying to capture growth in travel tech face two parallel problems: a noisy market of startups making bold AI claims, and a handful of incumbent public companies quietly building the real data engines that will decide winners in 2026. If you chase press releases rather than underlying data moats, you risk overpaying for hype. This piece synthesizes Skift's 2026 industry signal with market events from late 2025 and early 2026 to identify high-potential venture, PE and long-equity targets in travel AI and data — and gives you a practical due diligence checklist to turn information into actionable investment decisions.

Top-line thesis

Travel recovery is mature: demand volatility is lower than 2022–23, and commercial teams have reallocated budgets to data and AI systems. Winners in 2026 will be companies that combine three ingredients: first-party traveler intent, defensible data aggregation (scale + coverage), and automation that meaningfully improves unit economics for suppliers and distributors. That spells opportunity across three investor categories:

Venture: Early-stage startups building proprietary intent signals, AI-powered revenue management, and vertical SaaS for hotels and experiences.
Private Equity / Roll-ups: Consolidation targets in fragmented hotel tech (PMS, RMS, channel managers) and business-travel SaaS with predictable ARR.
Long-equity: Public travel platforms and GDS/GDS-adjacent companies that convert scale into monetizable data products.

Context: What changed in late 2025 and early 2026

Skift Megatrends 2026 (London sold out; NYC follow-up) made one thing clear: executives want clarity on data strategy before budgets close. Two structural developments accelerated that shift:

Regulation and compliance pressure: EU privacy rules and the EU AI Act increased the cost of building generative and predictive systems without robust governance — creating advantages for incumbents with mature compliance frameworks.
NDC and distribution evolution: Airlines and GDSs continued incremental NDC adoption in 2025, creating richer offer data for those who can ingest and normalize it at scale. Monitoring the downstream ecosystem (including consumer flight scanning and aggregation tools) matters when modeling where intent signals originate — see practical tooling and reviews for flight data apps.

Put another way: data is now both a revenue line and a compliance risk. Investors who underweight governance will face unexpected liabilities; those who overweight it will find lower competition for assets with enterprise-grade controls.

Where value accrues in travel AI & data — 7 priority domains

Demand and intent data marketplaces

First-party signals (searches, fare watches, corporate booking intent) are gold. Platforms that aggregate and anonymize intent data for advertisers and suppliers can monetize at CPM-like rates. Expect advertising-style margins once privacy compliance is baked in. When pricing data products, teams should factor in potential cloud per-query cost caps and the economics of serving high-cardinality queries to buyers.
Revenue management & dynamic pricing

AI-driven RMS tools that lift RevPAR (hotels) or ticket yield (airlines) by 1–3% unlock recurring, value-based pricing. This is a mature use-case with defensible ROI if the model consistently outperforms revenue managers. For teams building RMS, paying attention to sandboxing and auditability of LLM-driven pricing agents reduces enterprise risk and accelerates procurement.
Distribution and merchandising

OTAs and metasearch that personalize offers and packaging on the fly increase conversion and take-rates. Companies that own both demand and decisioning (e.g., search + personalization engines) get leverage over suppliers. Operationalizing this edge requires robust edge observability and telemetry to ensure decisioning services meet SLAs across regions.
Corporate travel automation (T&E closure)

Business-travel platforms that reduce T&E leakage and automate policy compliance save large enterprises millions. The SaaS economics and stickiness here make for attractive PE targets. Integrations with enterprise identity and anti-fraud tooling (see notes on credential stuffing and rate-limiting strategies) are often a procurement checklist item.
Hotel operations and guest experience (PMS + CRM + upsell)

Vertical SaaS that ties bookings to guest CRM and automated upsell flows increases ancillary revenue per stay — a direct lever for margins. Consider best practices from CRM selection guides when modeling integration timelines and customer economics.
Identity, fraud and payments

Travel's cross-border flows create fraud risk. AI-enabled identity and payments orchestration with global AML/KYC compliance is strategically important and monetizable. Teams should benchmark against security playbooks and incident-response case studies and evaluate the cost of building vs. buying these controls.
ESG and carbon intelligence

Travel suppliers face growing pressure to measure and price carbon. Companies that provide trusted, auditable emissions scoring and offset marketplaces will find corporate buyers willing to pay premium prices. The ability to integrate carbon flows into booking and payment rails becomes a differentiator for enterprise buyers.

High-potential public equity targets (long ideas)

Public companies with scale, improving unit economics and clear data monetization strategies are core long-equity plays. Below are names to research for portfolio allocation (not investment advice):

Booking Holdings (BKNG) — Scale in consumer intent and accommodation supply gives Booking leverage to expand advertising and direct-merchant margins through intelligent packaging and AI-powered search relevancy.
Airbnb (ABNB) — Community-sourced inventory and granular stay-data provide unique signals for personalization and downstream services (experiences, extended stays).
Expedia Group (EXPE) — OTA distribution scale plus a growing push into ads and AI merchandising make Expedia a play on data-driven take-rate expansion.
Amadeus (AMS) — A strategic platform for distribution and airline/hotel ops; its move into cloud-native data services and compliance-ready tooling positions it as a provider to modernized suppliers.
Trip.com / Trip.com Group (TCOM) — Strong data on Asian travel flows and a fast-moving AI stack focused on conversion and cover rates.

Private & venture targets to watch (high-conviction startups)

Here are categories and representative names that have the attributes investors should prize: strong first-party data, model ownership, enterprise distribution, and compliance posture.

Price- and demand-forecasting specialists

Examples: Hopper (predictive pricing/packaging). Why: proven consumer examples of price prediction + potential to white-label models to OTAs and airlines.
Business travel platforms

Examples: TravelPerk (expense + booking automation). Why: corporate adoption, predictable ARR, low churn and clear roll-up opportunities for adjacent services (payments, carbon).
Hotel ops and revenue platforms

Examples: Mews (PMS + guest experience), Duetto (revenue strategy). Why: vertical SaaS that combines bookings, pricing and CRM — prime targets for PE consolidation.
Intent data aggregators & ad platforms

Examples: Sojern, ADARA, Arrivalist. Why: they turn anonymized travel intent into targeted advertising; with privacy-safe pipelines they can scale DaaS revenues.
Identity, payments and fraud prevention

Examples: Specialist fintechs that tie global payment rails to travel workflows; these businesses have high switching costs for enterprises and attractive margins.

Why these picks — a quick investment logic

Data moat: Names above collect signals in day-to-day transactions (searches, bookings, corporate flows) that are hard to replicate.
Monetizable workflows: The product improves supplier economics (higher RevPAR, yield or lower leakage), enabling value-based pricing.
Regulatory readiness: Early investment in compliance (privacy, AI governance) becomes a competitive edge in Europe and enterprise sales cycles. Operational playbooks often include technical guidance like architecting consent flows for hybrid apps and regional controls.
Exit optionality: For venture/PE, strategic acquirers include OTAs, GDSs, large hospitality groups, or consolidation by PE roll-ups.

Practical due diligence checklist — what to inspect

Use this checklist when evaluating a travel AI/data investment. It separates marketing from durable value.

First-party data share: What percentage of the firm's intent signals are proprietary vs. purchased? First-party >50% is ideal.
Model performance: Request back-tested KPIs (MAE for price forecasts, conversion uplift % for personalization, impact on RevPAR). Insist on third-party audits for predictive claims.
Customer economics: CAC payback period, NRR (net revenue retention), churn. For SaaS/PE targets, NRR >110% is a strong positive signal.
Compliance posture: Evidence of GDPR, AI Act and local data residency controls. Ask for privacy impact assessments and incident history. Teams building LLM-based features should demonstrate sandboxing, isolation and auditability for agents and assistants.
Integration risk: For roll-up targets, evaluate tech debt and API maturity. How many engineering hours to integrate into a platform?
Supplier economics: Are suppliers (hotels, airlines) using the product because it materially improves their margins? Supplier willingness to pay is decisive. Consider how tariffs and supply chain pressures affect supplier pricing — broader macro research is available on tariffs and supply chains.
Competitive positioning: Does the company defend against large cloud providers (e.g., Google) or GDS/OTA encroachment?

Deal structures & value-creation playbook for PE

PE buyers should focus on three levers when designing deals in 2026:

Buy-and-build: Consolidate fragmented hotel tech (PMS, channel managers, RMS) to create cross-sell motions for direct bookings and ancillaries.
Monetize data: Create a centralized travel-intent marketplace where anonymized demand is normalized and sold to advertisers and suppliers under strict compliance. Modeling the unit cost of queries (and potential cloud per-query caps) should be part of the financial plan.
Enterprise GTM: Move SMB SaaS products upmarket with stronger account management, compliance packaging and integrations to ERP/payments stacks.

Risks and red flags

No thesis is complete without risk calibration. Watch these closely:

Model drift: Travel patterns can change (geo risk, macro), degrading ML performance. Insist on retraining cadence and governance.
Channel disintermediation: Large platforms (Google, Meta) may extract demand or re-bundle services, pressuring margins.
Regulatory shocks: New privacy rules or AI liabilities can impose unexpected compliance costs and slow enterprise sales.
Supplier pushback: Hotels and airlines sometimes resist pay-for-data models; supplier economics must clearly improve.

“Executives want a shared baseline before budgets harden.” — Skift Megatrends 2026 framing; apply it to investment due diligence: clarity first, capital second.

Case studies: small, illustrative examples

These hypothetical, experience-based examples show how the thesis plays out in practice.

Venture: AI pricing specialist

Stage: Series B. Thesis: Proprietary fare-intent model trained on millions of search events reduces price volatility and increases conversions for OTAs. Exit path: strategic sale to a large OTA or RMS provider. Key diligence: validate uplift with a blinded A/B test and check whether the model uses first-party signals or relies on scraped public fare data. Also verify whether consumer signal sources include popular flight tools and aggregators referenced in independent flight scanner reviews.
PE roll-up: European hotel tech platform

Stage: Consolidation of 3 regional PMS and a channel manager. Thesis: Cross-sell a unified RMS and convert direct bookings via smarter on-site merchandising. Value creation: margin expansion, lower churn, and eventual strategic sale to a global distribution provider. Part of the playbook is ensuring integrations are resilient and ready for enterprise identity flows and anti-fraud controls.
Public equity: OTA with data-as-a-service path

Stage: Public. Thesis: OTA converts a portion of its search traffic into anonymized intent signals sold to travel advertisers at high margins. Monitoring: growth in DaaS revenue as percent of total and improvements in take-rates. Be alert to regulatory gaps — teams that have built out robust consent and residency controls stand to benefit in EU markets (see guidance on consent architectures).

2026 predictions investors should price in

AI-driven RMS becomes table stakes; incremental gains shrink as models diffuse — investors must pay for distribution and data scale, not just algorithms.
Data monetization moves from pilot to material revenue lines for the largest OTAs and several aggregated hotel tech platforms.
Compliance-ready platforms will trade at a premium in Europe; expect valuation separation between compliant and non-compliant players. Startup teams should study the developer playbooks for adapting to Europe's rules to avoid last-minute rewrites (startup EU AI adaptation guide).
Corporate travel consolidation accelerates as buyers seek full-stack automation to control T&E leakage and emissions reporting.

Actionable takeaways — a checklist for investors

Focus on data origin: Prefer firms with >50% first-party signals or exclusive supplier integrations.
Demand proof: Ask for randomized controlled results, not just uplift anecdotes.
Prioritize governance: Treat compliance and model auditability as primary value drivers, especially in EU exposures.
Target SaaS with NRR >110%: It signals product-led expansion and pricing power.
Plan exits: Model potential acquirers now — OTAs, GDSs, hospitality groups and large cloud providers are the likeliest buyers.

Final verdict

Late 2025 and early 2026 crystallized a simple investment reality: travel is no longer a recovery story only — it is a data and AI economy where scale, compliance and supplier value determine winners. For allocators, the highest-conviction opportunities sit at the intersection of first-party intent, enterprise distribution and compliance-ready AI. That favors a mix of long equities in scaled platforms, targeted venture bets on specialized models, and PE roll-ups of fragmented hotel and corporate travel SaaS.

Call to action

If you want a practical tool to act on this thesis, we prepared a downloadable diligence workbook and a monthly watchlist of prioritized targets updated after Skift Megatrends NYC sessions. Subscribe to markt.news briefings or request a private briefing to receive the workbook, model templates for RMS performance validation, and a curated list of VC/PE targets with sensitivity analyses for 2026 regulatory scenarios.

markt

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