How AI is changing the face of travel marketing (and why you can’t ignore it)

AI is changing the entire travel experience – from the moment travelers begin their search till the start of their journey. Intelligent assistants can anticipate travel needs and predict how to proceed. According to forecasts, the AI in Tourism market is estimated at USD 2.95 billion in 2024 and USD 13.38 billion in 2030 at a CAGR of 28.7%. 

Advanced algorithms in AI are able to optimize business policies based on real time analysis of market trends, competitor pricing, and customer demand, help to develop more targeted marketing campaigns and customized experiences. It constructs entire user journeys, predicts future behaviours, and even crafts original content with remarkable precision. Ajit Desai, Director of Marketing at Deltek exploring how AI is reshaping marketing tools in the travel segment and the steps companies must take to stay competitive in this dynamic landscape.

AI’s strategic impact on travel infrastructure

There’re plenty of AI-powered tools that can book any travel product available promising hyper-personalisation, predictive analytics, and automated campaign optimisation. Marketers today have more AI-driven solutions than ever, from AI-agents to integrated tools such as OpenAI’s “Operator’”.

However, there’s a catch—none of this works unless the IT infrastructure is designed to support it. The reality? Most travel platforms are not equipped with a MarTech stack optimised for AI. Legacy systems, fragmented data silos, and a lack of interoperability are some of the biggest hurdles companies face when trying to implement AI. Companies have to redefine their entire technological approach, instead of just adding AI features to outdated systems.

Traditionally, industry players relied on CRM, automation, analytics, and content management systems. Now, real-time AI-driven decisions, predictive modelling, and automated customer interactions are driving performance. Is your organisation ready for this shift?

Key changes in the MarTech Stack:

• From Static CRMs to AI-Powered CDPs: AI-powered CDPs, such as Segment and Adobe Experience Platform, are able to synchronise and activate data across multiple channels. At the same time companies require IT infrastructure supporting real-time data pipelines.
• From automating to orchestrating campaigns: AI doesn’t just execute automated tasks—it coordinates campaigns across paid, owned, and earned media. For instance, Persado’s AI-driven content system generates and A/B tests messaging in real-time. Yet, many MarTech stacks still rely on outdated batch-processing automation tools.
• AI-Driven Attribution: Platforms like Neustar and Fospha offer ways to track ROI using AI-driven attribution models. The only drawback: these require unified real-time data from marketing teams.
• AI-Powered Content Creation: Tools like Jasper, Writer, and Copy.ai generate SEO-optimised content tailored to specific audiences.

Marketing professionals rarely put at the forefront data architecture, middleware, or API orchestration. However, without a solid IT infrastructure, AI-driven marketing risks being nothing more than a collection of disconnected tools.

AI-Driven consumer behaviour modelling

Traditional marketing segmentation methods relied heavily on demographics—outdated, static snapshots of consumers. AI allows real-time behavioural segmentation, showing global shifts in consumer sentiment and purchase intent. OTAs were always great in performance marketing, analysing every click, every booking and every abandoned cart to make their next bid smarter, meanwhile agentic ad platforms are bringing this science to a new level.

Machine Learning (ML) models analyse micro-interactions, and Natural Language Processing (NLP) can process contextual sentiment data. This way marketing teams can create dynamic consumer profiles updated in real-time. Pricing and promotions are adjusted depending on actual user intent rather than historical trends. However, we still need to understand if this level of AI-driven personalisation blurs privacy boundaries.

How we can use AI ethically (without breaking GDPR & CCPA Laws)

AI raises critical privacy concerns. Regulations such as GDPR and CCPA demand that businesses implement ethical AI practices and ensure personalised marketing does not come at the cost of consumer trust.

Finding the balance between personalisation and data ethics is one of the biggest challenges marketers face today. How can businesses have trust using AI for deeper consumer insights?

Privacy-first measures include:

• Differential Privacy: Uses dataset randomisation to anonymise users and maintains data utility with minimal compromise.
• Federated Learning: Enables on-device analytics, reducing dependance on central servers and lowering compliance risks.
• Consent Management Platforms (CMPs): Solutions like OneTrust integrate real-time user consent management within marketing workflows.

How to develop a unified data platform: real-world case study

As part of my professional experience I led the development of a unified data platform. This solution is designed to enhance content, sentiment detection, and personalised user interactions, and ensures compliance with ethical AI practices. This system integrated three core components:

• MarketMuse for AI-driven content optimisation.
• A customised lead qualification and predictive lead scoring system using Salesforce Einstein.
• Brandwatch’s machine learning capabilities for sentiment tracking.

Through a full data audit, we traced information flow between CMS platforms, HubSpot marketing automation, and Salesforce CRM. This clean data foundation was essential for AI-powered marketing.

Salesforce Data Cloud helped to consolidate customer profiles from multiple sources and straighten predictive analytics. Using Einstein Predictive Lead Scoring, we ranked leads based on engagement, demographics, and purchase history. AI-powered predictive analytics definitely allowed more precise budget allocation and dynamic decision-making.

MarketMuse’s AI-powered recommendations were very useful to optimise content, boost search rankings and enhance domain authority. The integration between WordPress CMS and MarketMuse improved engagement metrics thanks to real-time content updates and metadata enrichment.

Brandwatch’s sentiment analysis tools processed social media data, distinguishing useful insights from noise. Machine learning-based classification models accurately analysed industry-specific language trends.

AI-first marketing in the travel industry

AI is no longer a futuristic concept. Nowadays AI agents provide truly tailored experiences: they will check every travel product bookable on the internet and provide a personalized selection of all possible options. However, it’s not about blindly adopting new tools. Travel industry players must restructure their IT infrastructure, provide ethical AI implementation, and foster trust through transparency.