From Free Utility to Monetised Journey
We are becoming the product once again.
We have seen this playbook before.
Search engines first positioned themselves as neutral gateways to the world's information before they started monetising users' searches with keyword-based ads.
Social media platforms used the same approach but with our attention, first building massive audiences around free connection before inserting ads into our feeds.
Conversational AI chats are following the same playbook.
Today, ChatGPT, Gemini, and Perplexity are all accessible for free. They have proven helpful and feel like almost invisible assistants embedded in our daily workflows.
But as with previous platforms, the underlying economic logic is clear: at some point, these interactions will be monetised.
The question is no longer if (recent leaks suggest OpenAI is already testing ads inside ChatGPT conversations) but when this will be implemented and what consequences it will have for your business.
To better anticipate these changes, we need a framework to guide our thinking.
From Intent and Attention to Immersive Interactions
Recent research by Puntoni and colleagues provides valuable insight into this shift.
Their primary focus is on establishing the PACE Framework (Privacy, Autonomy, Fair Competition, Explicability) as an ethical foundation for conversational AI monetisation, an important contribution for policymakers and platform designers.
However, their research also provides practical insight for marketers: conversational AI brings a fundamentally different economic logic to digital platforms. Instead of monetising intent or attention, these platforms monetise interactions at the centre of the customer journey.
Thus, if we use monetisation and the customer journey as our analytical lenses, we see that their research becomes highly relevant for marketing leaders trying to prepare for this shift.
Search engines make money by monetising intent at the bottom of the funnel (BoFu), at least that is where they work best. You express a need ("best CRM for SMBs") and they sell ad space around that intent.
Social platforms monetise attention at the top of the funnel (ToFu). You spend time scrolling on Instagram or TikTok and they insert ads into that stream.
Conversational AI (CAI), however, will do something different. It'll monetise immersive interactions in the middle of the journey (MoFu, or the "Messy Middle" as Google calls it), where people are comparing options, asking follow-up questions, and trying to make sense of trade-offs.
Three particularities of CAI make this commercially powerful:
- Native integration: Ads are not next to the content; they are embedded into the answer. Think "contextual native ads" that appear as part of the conversation.
- Adaptive targeting: The message can change in real time as the conversation unfolds, based on your questions, history, and context. This is more than segmentation, it's personalised persuasion at the level of the individual interaction, or "hypersuasion" as the authors call it. (Whilst this level of personalisation raises legitimate ethical concerns about manipulation and autonomy, it's the commercial reality platforms are moving towards.)
- Social presence: The assistant speaks with a human-like voice, can play the role of expert or adviser, and frames commercial messages as advice rather than advertising.
Combined, these elements turn the conversation itself into a monetised asset.
Instead of fighting for a click or an impression, brands will compete for a place in a trusted and highly contextual answer at the very moment when customers are evaluating their options and making decisions.
From Mid-Funnel to Full-Funnel: Conversational AI's Reach
As we have just seen, conversational AI inserts itself at a critical decision point in the middle of the funnel.
But it doesn't stop there!
CAI, more than other acquisition channels, has the ability to reach up and down the funnel more elegantly.
In the middle of the journey, CAI acts as a trusted adviser that helps users make better and faster buying decisions.
From there, it could easily reach downward towards the bottom of the funnel with features like in-chat checkout or direct purchase links, turning advice into transaction right from the conversation.
On the other hand, it could just as easily reach upward towards the top of the funnel by helping identify problems or needs, find alternative solutions, or explore related categories the user hadn't yet considered, effectively creating new interest and demand whilst it answers questions.
For companies, this doesn't mean the funnel is dead (as many influencers want you to believe), but that it is being rewired around an AI-mediated interaction layer.
The consequence is simple: there will be a point in many journeys where the customer no longer compares offers on your website or on a search results page, but asks a CAI to do the comparison for them.
That's precisely where AI ads will appear, and where you'll need to be present to be considered.
In practical terms, this creates a new gatekeeper at the consideration stage of the customer journey.
Brands will need to think not only about how they cover the different stages of the funnel, but also how they remain visible and competitive inside AI-driven conversations that feel personal, are highly trusted, and are dynamically tailored to each user.
You won't be able to treat AI chats as a neutral "helper" in your customer journey. They will be monetised gatekeepers.
If you want to know more about the impact LLMs already have on online search behaviour and the impact it has on various types of website traffic, there is interesting research done by Padilla et al. (2025).
Why This Matters for Marketers and Business Leaders
Until now, customers typically compared solutions on brand websites, comparison sites, or forums.
When more and more customers start asking an AI assistant to "do the work" for them, and they become ad-funded products, those decisive decisions are no longer fully under your control.
They are mediated, ranked, and priced by a platform you don't own!
Furthermore, the format of advertising changes fundamentally.
AI ads won't be banner boxes or sponsored links; they will appear as part of a helpful answer in a conversational environment, with adaptive targeting that adjusts in real time.
That combination of immersion and personalisation is something we haven't seen at this scale in previous channels.
For marketers, CMOs, and business leaders, the consequence is straightforward: you will need to think about how your brand shows up inside AI-driven conversations.
How to Prepare Your Brand for AI Chats
This begs the question: "So how do we prepare?"
If this feels overwhelming, you're not alone. But the good news is you can start preparing now with focused actions in three key areas.
When I work with clients navigating this shift, I help them bring structure and clarity by focusing on where they can actually act.
There are three interconnected areas where companies need to focus and assert control: Content, Conversation, and Commerce—let's call it the 3C Framework for AI Readiness.
1. Make Your Content "Conversation-Ready"
Is your content ready to be used by a machine that acts like an adviser?
For CAI chats to recommend your products and services, your content needs to be usable and readable by them. That means moving towards more modular content blocks. For example:
- Create clear, concise statements of your key selling points framed as advice ("If you're in situation X, consider option Y because…") on your digital platforms.
- Make your content machine-readable and comparable so it's easy to put you on a shortlist: price ranges, key features, use cases, typical outcomes (who it's for, when it's best, key trade-offs).
Here's a concrete example of reframing content for AI readiness:
Before: "We offer flexible pricing options to suit businesses of all sizes."
After: "If you're a startup with fewer than 10 employees, our Starter plan at £99/month typically works best because it includes essential CRM features without the complexity of enterprise tools. Growing teams of 10-50 usually benefit from our Growth plan at £299/month, which adds automation and advanced reporting."
Think of this as preparing snippets of arguments and proof points that an AI assistant could realistically use to justify including you in its answer when a user asks, "What should I choose?" or "What is the best solution for…"
2. Build Your Own Conversational Touchpoints
If AI chats become the new way to discover, compare, and buy products, relying only on third-party platforms is risky.
One way to maintain influence over critical decision moments, according to Puntoni and colleagues, is to build your own conversational touchpoints along the journey:
- Build branded AI assistants on your website or app that help users find and choose the right product, plan, or configuration.
- Use post-purchase bots that handle onboarding, FAQs, and part of the support process, reinforcing customer loyalty whilst gathering insight on friction points.
The point is not to "compete" with ChatGPT, but to capitalise on a new customer behaviour to ensure that some of these critical mid-funnel conversations happen in environments you control.
This not only allows you to shape the narrative but also to capture first-party data and better understand what really drives buying decisions.
3. Prepare Your Stack for "In-Conversation" Commerce
Finally, if AI chats can pull users down the funnel and close the loop inside the chat, your infrastructure has to be ready. Concretely, that means:
- Ensure your product and pricing data is structured and machine-readable, so AI assistants can easily and accurately use it.
- Be ready for frictionless hand-offs from the AI to your checkout, booking system, or sales team.
You don't need to implement all of this tomorrow.
But if you agree that AI chats will increasingly shape your customer journey (up and down the funnel), then Content, Conversation, and Commerce are the areas where you can start preparing now.
Now what?
The uncomfortable truth is that you won't control how platforms monetise their conversational AI.
Infrastructure costs and business models will push them towards advertising, whether we like it or not.
What you can control is how exposed you are to that shift by:
- Making your brand and offers readable to AI systems.
- Ensuring some of the key conversations happen in spaces you own and control.
- Preparing your tech stack for seamless AI-to-commerce handoffs.
Start with small initiatives to improve on each area (Content, Conversation, and Commerce) instead of waiting for the next big announcement from OpenAI, Anthropic, or Google.
You don't need a perfect roadmap before you start. You need a specific use case or touchpoint you can improve that will make your customer journey more resilient to this shift.
Action item: Audit one key product page with your team and rewrite three feature descriptions as conversation-ready statements that an AI could cite when recommending you.
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I'm curious how you see this playing out in your industry?
- Do you already notice customers leaning on AI in their decision process?
- Where do you see the biggest risk (or opportunity) in your funnel?
Share your thoughts in the comments below.
Sources
Hermann, Erik and Hermann, Erik and Puntoni, Stefano and Schweidel, David A., Conversational AI: The Next Frontier of Digital Platform Monetization (October 08, 2025). The Wharton School Research Paper , Available at SSRN: https://ssrn.com/abstract=5634270 or http://dx.doi.org/10.2139/ssrn.5634270
Chen, M., Wang, X., Chen, K., & Koudas, N. (2025). Generative Engine Optimization: How to Dominate AI Search. ArXiv, abs/2509.08919.
Padilla, Nicolas and Lam, H. Tai and Lambrecht, Anja and Hollenbeck, Brett, The Impact of LLM Adoption on Online User Behavior (August 15, 2025). Available at SSRN: https://ssrn.com/abstract=5393256 or http://dx.doi.org/10.2139/ssrn.5393256
Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, and Ameet Deshpande. 2024. GEO: Generative Engine Optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24). Association for Computing Machinery, New York, NY, USA, 5–16. https://doi.org/10.1145/3637528.3671900
https://www.beebyclarkmeyler.com/what-we-think/guide-to-content-optimzation-for-ai-search