The era of “AI Gatekeepers”: when assistants become the interface between consumers and brands

I’m deliberately using this term here with a dual meaning:
On the one hand, platform gatekeepers in the sense defined by the European DMA, major players such as Microsoft, Apple, Google, or Amazon that control access to interfaces, data, and markets.
On the other hand, algorithmic gatekeepers, namely AI assistants, agents, and orchestration systems capable of filtering, prioritising, recommending, and increasingly acting on behalf of users.
And it is likely at the intersection of these two dimensions that a major shift is taking place. Because tomorrow, in many cases, it will no longer be customers who interact directly with brands… but their AI assistants.

From customer experience… to experience for AI agents

Personal AI assistants are gradually becoming capable of understanding intent and taking action:

• Search for a product
• Compare offers
• Negotiate a subscription
• Book a service
• Place an order

Brands will now need to learn how to be visible and understandable not only to humans or search engines… but also to AI agents that filter access to consumers.
Customer experience is entering a new phase: the experience for agents.

This almost opens up a new relational logic: after B2C and B2B, B2A may emerge: Business to Agent.
In this model, it is no longer just about appealing to a consumer, but also about being readable, interpretable and actionable by their assistant.

The new protocols of AI commerce

To enable this interaction between agents and companies, several emerging building blocks and protocols are taking shape:

• UCP – Universal Context Protocol: to expose user context: preferences, history, location, permissions.
• MCP – Model Context Protocol: to expose business context to AI models and agents: catalog, pricing, availability, rules.
• ACP – Agent Commerce Protocol: to enable AI agents to execute transactions with enterprise systems.

In other words, interfaces will no longer be just websites or applications… but also systems and APIs designed for autonomous AI agents.

New visibility challenges: AEO, GEO, GSO

This evolution is giving rise to new disciplines. For twenty years, brands have optimized their presence for search engines through SEO. Today, new logics are emerging:

• AEO – Answer Engine Optimization: optimizing content for generative answer engines.
• GEO – Generative Engine Optimization: optimizing visibility within generative AI systems.
• GSO – Generative Search Optimization: structuring data and content for AI-driven search.

In this paradigm, product data, metadata, APIs, reviews or trust signals potentially become recommendation levers.
Tomorrow, part of brand preference may also be determined by the criteria used by agents to arbitrate between multiple options.

AI Gatekeepers: China already offers a glimpse of this future

While this may seem forward-looking in Europe, some signals are already visible elsewhere. One often-cited example is Alibaba and its assistant based on Qwen. During a campaign run over the Chinese New Year, an exceptional and particularly favorable context, users could simply say: “Order me a bubble tea.”

The AI agent then handled the rest: geolocation, merchant selection, coupon application, ordering, payment and delivery.
The result: more than 10 million orders were placed via the AI agent in just nine hours.

Beyond the volumes announced during this campaign, the real interest lies elsewhere: this scenario is no longer fictional. It shows that a conversational transaction orchestrated by an agent can operate at scale.
And it also reveals new challenges:

• logistical resilience
• execution quality
• customer satisfaction
• operational steering when decisions become automated

The challenge is therefore not only technological. It becomes systemic.

How will an AI choose one brand over another?

In a world of AI agents, this question becomes central. Determining criteria could include:

• Quality and structuring of product data
• API availability
• Algorithmic reputation
• Reviews and trust signals
• Real-time pricing
• Agent compatibility
• Access to the DPP (Digital Product Passport)

Branding and emotional marketing will remain essential. But a growing share of decisions could be mediated by algorithmic systems. The new battleground may therefore be as much human preference… as machine preference.

The emerging risk: dependency on new intermediaries

But this evolution also raises a critical question. If tomorrow OpenAI, Microsoft Copilot, Apple Siri or Google Gemini become major entry points to consumers, new dependencies could emerge:

• on ranking rules
• on recommendation logics
• on access protocols
• on the platforms that control these agents

The question then becomes less: how to be visible?
And more: how to be chosen by AI gatekeepers?
And more fundamentally still: who controls the AI gatekeepers?

AI Gatekeepers and responsible AI

This shift is not only about business. It also affects governance. In the era of AI Gatekeepers, transparency, auditability and traceability of interactions become regulatory as well as commercial issues.

This raises major topics:

• algorithmic transparency
• explainability of recommendations
• neutrality of arbitration
• compliance (DMA, AI Act)
• fair access to agent environments

Optimization for AI agents will likely not be separable from a responsible AI approach.

The new battleground: AI agents

We may be entering a new phase of digital. After:

• the era of websites
• the era of apps
• the era of platforms

… the era of AI agents may now be opening.
The brands that succeed may be those that quickly understand that their true interface is no longer just a website or an app… but an ecosystem capable of interacting with artificial intelligences.

AI Gatekeepers: anticipating the coming years

The transformations linked to AI agents, context protocols and new search models are only just beginning.
Understanding them becomes essential to:

• Anticipate new customer journeys
• Adapt platforms
• Structure data
• Prepare visibility in AI environments
• Design “agent-ready” architectures

Because the issue may not only be the arrival of smarter assistants. But a deeper shift:
the transition from a web navigated by humans… to a web increasingly negotiated by agents. And this could profoundly change the relationship between brands and consumers.

If these topics interest you, I regularly support organizations in deciphering these major technological shifts and their business impacts over the next 3 to 5 years.
With the SQLI teams, we can also help on very operational topics:

• Strategic and forward-looking intelligence
• Data and platform architecture
• Artificial intelligence
• Agent-ready architectures
• GEO / AEO / GSO optimization

Feel free to contact me if you would like to anticipate these transformations rather than undergo them.

Sources

• Joyce Gordon — Brands Will Need to Adapt in the Era of AI Gatekeepers, Amperity
• AI Business — The Rise of Personal AI: Disrupting Customer Experience Through Automation, Jason Maynard
• Boston Consulting Group — When Brands Meet AI Bots
• Elizabeth Kartini — Alibaba's AI shopping chatbot overwhelmed by 10 million orders in nine hours, Technobezz
• IndexBox — Alibaba's Qwen AI App Tops Charts After Free Order Campaign