Latest Thinking

This is not a revolution in itself, as many people already use ChatGPT and similar services in exactly this way to research their next trip. What is new is that IHG is integrating this search behaviour into its own direct-sales channels, where it can draw on proprietary product data, prices and loyalty information. The more specific these data are, the more relevant the recommendations become. Conversational search could therefore become a genuine conversion booster when users do not yet have a specific search intent and might otherwise leave.

Nevertheless, a critical perspective is worthwhile: it remains to be seen whether guests will adopt IHG’s offering on its direct booking channels and whether it will generate more direct bookings.

There is also a structural problem. Direct sales have been under pressure for years. More and more customer journeys begin in AI assistants, on platforms such as Expedia or in increasingly prevalent zero-click environments such as Google Search, and frequently never reach the company’s website.

Conversational search on a company’s own channels may increase the conversion rate, but it does not solve the problem of declining direct-traffic shares. Large hotel groups such as IHG at least have brand awareness, reach and, above all, strong loyalty programmes to counter this trend.

The reality is different for smaller providers. They should prepare early for a future in which visibility is created at an increasing number of touchpoints outside their own websites and generally comes with a commission. AI is changing the entry point into the customer journey – that entry simply happens earlier.

Source: IHG Hotels & Resorts, press release dated 28 July 2026.

For many years, CRM was primarily a system of record. It documented customer data, activities, opportunities and transactions. It should still fulfil this role today as the leading system for customer data.

Around 15 years ago, the term system of engagement, coined by Geoffrey Moore, became established in enterprise IT. CRM systems evolved into platforms through which companies also interact with customers across digital channels.

AI now marks the next stage of this evolution. Salesforce, Microsoft, HubSpot and Oracle are ultimately pursuing the same direction: through the use of AI agents, CRM is developing into a system of action. It does more than document information; it prioritises leads, recommends the next action, automates processes and ideally supports operational decisions.

For CRM leaders and teams, this means that customer data quality becomes even more important because AI prepares decisions on this basis. CRM processes also gain significance because companies must define which tasks AI may perform and which remain under human responsibility. The ability to guide AI effectively, establish guardrails and make decisions understandable therefore becomes more important.

The value of an AI agent in CRM depends not least on whether the CRM professional understands which recommendations the agent makes, why it makes them, which data they are based on and under which conditions they should be implemented. This is precisely where I see the next stage of CRM evolution.

Sources: Geoffrey Moore, “Systems of Engagement and the Future of Enterprise IT” (2011); Salesforce Agentforce; Microsoft Dynamics 365 AI & Copilot; HubSpot Breeze; Oracle AI for Fusion Applications.

A recently published scientific preprint offers an interesting perspective. The two authors, researchers from Canada and France, compare two identical e-commerce websites in an experiment, including the same product catalogue, prices, inventory and checkout.

The only difference was that one website had been specifically optimised for AI agents using a framework with clearly structured information, unambiguous actions and machine-readable product data. Browser agents based on GPT-4.1, Gemini 2.5 Flash and Grok 4 Fast then had to complete typical shopping tasks such as product searches and comparisons.

A total of 300 test runs were evaluated. The agent-optimised website increased the browser agents’ success rate from 49.3% to 89.3%, an improvement of 40 percentage points. It should be noted that this was a proof of concept and the results cannot be transferred directly to real-world websites.

If AI agents take over research, comparison or parts of the buying process in the future, a website must provide information in a way that agents can reliably understand and use.

SEO and GEO help companies get found. Usability, accessibility and conversion rate optimisation help turn visitors into customers. Agent readiness will determine whether AI agents can successfully interact with a website. I consider this scenario particularly realistic for low-involvement products and standardised purchases.

Websites are only the beginning. Native apps will face the same question: how easily can AI agents retrieve information and execute transactions directly without having to operate the user interface like a human?

Source: “Designing Agent-Ready Websites for AI Web Agents: A Framework for Machine Readability, Actionability, and Decision Reliability” (Said Elnaffar, Farzad Rashidi), preprint published on arXiv on 13 July 2026.

Chris Nassetta, Hilton’s iconic CEO who has held the role since 2007, described two practical AI use cases for hotels this week on “The Angle”, a podcast by asset manager T. Rowe Price. First, AI should identify service issues during a stay, for example by analysing guest messages or social media posts, and help employees resolve them while the guest is still at the hotel. Second, Hilton wants to use booking history, search behaviour and preferences to present guests with significantly more relevant offers. Nothing revolutionary at first glance.

These may initially sound like separate projects, but both use cases are based on the same foundation: high-quality first-party data.

AI models are becoming more capable and accessible to everyone, while access to high-quality customer data remains company-specific. For digital sales and CRM leaders, the implication should be clear: companies need to build and prepare the data foundation across the entire customer journey that enables AI to deliver better service, more relevant offers and stronger customer loyalty, whether in hospitality or other industries.

First-party data is not everything, but without it many AI applications cannot realise their true value for customers. First-party data is the strategic competitive advantage.

Source: “The Angle” podcast (T. Rowe Price), interview with Hilton CEO Chris Nassetta, 15 July 2026

Kollective, an Athens-based agency specialising in hospitality, analysed almost 14,000 responses from ChatGPT, Copilot, Gemini, Google AI Overviews and Google AI Mode about hotels in 100 destinations for its AI Visibility Index. A fixed set of search queries was used, such as “Recommend a boutique hotel in Lisbon”. According to the study’s preliminary findings, the AI systems agreed on the same “top hotel” in only around 4% of cases for an identical query, meaning the hotel mentioned first or ranked highest in each system’s response. In an intraday test in which the same prompt was submitted twice on the same day, one hour apart, the top hotel recommendation changed in an astonishing 45% of cases.

This fundamental observation is consistent with what we know about AI systems: AI responses are not deterministic. They depend, among other things, on the model, context and timing. Many hotels therefore try to appear directly in the AI response for “the one” typical traveller query, which is hardly feasible. This also transfers the logic of conventional keyword rankings to a system that simply works differently.

My experience confirms this: properties with current, structured data and a presence across many channels are also more visible in AI systems. Recurring presence across multiple systems matters more than a single top position on one particular day.

In practice, a hotel website that is already strong in conventional off-page and on-page SEO, technically well structured and equipped with machine-readable data, current prices and availability will be more consistently discoverable across multiple AI systems. That is the work that actually makes a difference.

Source: Kollective, Boutique Hotel AI Visibility Index, preliminary findings reported by Hospitality Net, 8 July 2026.

At the end of June, Salesforce released its largest agentic commerce update to date. Its centrepiece is the Shopper Agent, which supports customers on the merchant’s website from product discovery through to checkout.

Salesforce supports the value proposition of its Shopper Agent with data from the 2025 holiday season in November and December. Companies with their own Shopper Agent, including Pandora and SharkNinja, grew by 6.2%, compared with 3.9% for companies without agents – an increase of 59%. This is, of course, a Salesforce analysis, but the figures are a useful signal. In this model, the merchant’s own website remains the place where the purchasing decision is made. The customer relationship and the final step before purchase remain with the merchant.

Google is taking a different approach. Through the new Universal Commerce Protocol (UCP), Google plans to enable checkout directly within Google AI Mode and Gemini, without customers leaving the Google interface. The positive aspect is that the merchant remains the merchant of record. Although the customer purchases in Gemini, the merchant receives the full data for its CRM system and subsequent marketing activities – but it still needs to use that data effectively.

On the one hand, I see a new platform risk emerging, comparable to what merchants know from Amazon Marketplace or travel providers from Booking and Check24: less website traffic, fewer cross-selling and upselling opportunities, less first-party data and weaker brand loyalty.

Instead of avoiding AI channels, however, companies should shape their customer relationships so that customers buy directly from them next time. Loyalty programmes, personalised added value and exclusive benefits can deliberately guide customers back towards direct purchasing or booking.

Initial reports about UCP from the United States still focus primarily on technical integration and the growing number of AI search queries. It will therefore be interesting to see when the first reliable data becomes available on how merchant revenues develop through UCP. I will continue to watch this closely.

Sources: Salesforce press release, “Salesforce Introduces Agentforce Commerce” (25 June 2026); Salesforce Shopping Index, “2025 Holiday Shopping Data”.

Alongside China Speed and Creator Commerce, agentic commerce was the dominant topic at K5 – Future Retail 2026 in Berlin. It is not limited to product-based e-commerce. It is equally relevant to travel and ticketing, subscriptions and digital services, although these areas receive comparatively little attention.

For the travel industry, this represents a differentiated opportunity. Not every travel booking is the same. Planning a dream holiday in the B2C segment can be an emotional experience. In this situation, users are less likely to want an agent to make the decision for them. Commoditised bookings, such as business travel, are different: price, availability, location and speed are the primary criteria. This is the realistic entry point for agentic commerce in travel distribution. In every case, success depends on data quality, API accessibility and machine-readable offer structures. Yet providers will not want to become mere product feeds in the long term.

Based on my many years of successfully managing profitable direct bookings against third-party channels, I can say that the counterstrategy includes strong brand management, relevant CRM and quality on property that generates repeat bookings and recommendations. Brands that remain top of mind are more likely to be booked directly: commission-free, with control over pricing and customer data – and therefore less often through agents.

Agentic commerce is still at an early stage. OpenAI’s Instant Checkout failed in March 2026 because of insufficient real-time data synchronisation, fraud prevention, tax infrastructure and merchant adoption. In the EU, regulatory questions involving liability, data protection and payment authorisation remain unresolved. Broad user adoption is also still to come.

This makes it even more important to monitor developments closely and move into implementation as a fast follower as soon as reliable evidence emerges about what works.

Sources: IBM Think 2026; K5 conference, June 2026; Google Cloud, January 2026; Forrester Research 2026.

Les Binet and Peter Field, the two godfathers of marketing effectiveness research associated with the UK advertising industry body IPA, established the concepts of brand building and brand activation. Their study “The Long and the Short of It” (2013) covers almost 1,000 campaigns across 30 years. Its rule of thumb for maximising total returns is to allocate 60% of the budget to brand building and 40% to brand activation. Brand building fills the funnel from the top; activation empties it at the bottom.

In 2026, a new argument for brand building has emerged. According to Seer Interactive, organic click-through rates have fallen by 61% since the arrival of Google AI Overviews, which are still shown somewhat less frequently for purely commercial searches, while paid click-through rates have fallen by as much as 68%. Brands cited in AI responses, by contrast, see 35% more organic clicks and 91% more paid clicks.

A brand is not cited solely because of its awareness. What is becoming visible, however, is that brands with high authority and a strong presence across the digital ecosystem are used as references disproportionately often. Brand building influences the signals that matter to Google in this context.

My many years of experience in digital sales lead me to one clear conclusion: performance campaigns work better when brand awareness and trust have prepared the ground.

Activation determines today’s revenue; brand building determines tomorrow’s revenue. Companies that pursue only one optimise at the expense of the other.

Sources: Les Binet, IPA Effectiveness Conference, October 2025; Binet & Field, “The Long and the Short of It”, IPA 2013/2018; Seer Interactive AI Overviews study, September 2025.

Industry data shows where most companies stand today: 61% use their CRM primarily for email marketing and automated communication. Only 34% use its analytical capabilities to support decisions. From my own experience, customer data is generally available in sufficient quantities. The greater challenge is analysing it properly, deriving better decisions from it and consistently translating those decisions into operational processes.

Salesforce, HubSpot and Microsoft are systematically developing their CRM platforms towards AI-supported decision-making. Analytical CRM – including scoring, segmentation, churn prediction, customer lifetime value optimisation and next best action – has traditionally been the domain of professionally organised teams with dedicated specialists.

AI-powered modules in the major CRM suites are now making these capabilities more accessible and lightweight for regular business teams. Nevertheless, an understanding of the core concepts of analytical CRM remains essential. Judgement and a human in the loop still matter.

Companies that continue to use CRM primarily as a campaign tool leave a great deal of potential untapped. The future of CRM will be shaped more by the quality of decisions prepared by AI and validated by people than by the number of campaigns deployed.

Sources: HubSpot Breeze AI Guide 2026; DesignRush/Salesforce CRM Statistics, 2026.