AI bots are no longer on the fringes of the internet. They are integral actors that are reshaping traffic patterns and digital discovery. Far beyond simple crawlers, today’s bots can execute complex, high-volume interactions at scale.
In the retail sector, AI bots are increasingly interacting with e-commerce websites as large language model providers expand their use of crawlers to gather and retrieve information.
These bots can access product descriptions, pricing and availability data that may later be surfaced and prioritized through AI-powered search and assistant tools.
EMEA Director of Security Technology and Strategy at Akamai.
As AI-driven discovery becomes more common, retailers are beginning to consider how this automated access could directly shape product visibility, customer journeys and competitive positioning.
However, with automated agents increasingly sitting between brands and consumers, the implications for performance, security and visibility cannot be ignored. These agents often mimic real users, making it harder to distinguish legitimate automated traffic from malicious activity.
As the volume of AI-driven requests grows, organizations must also manage the strain this places on IT infrastructure while improving visibility into machine-to-machine interactions.
The shift towards an agentic commercial landscape
As the public grows more aware of AI’s potential, bot traffic patterns have shifted from simple data scraping to sophisticated interaction. In the second half of 2025, a surge in AI fetchers and search crawlers signaled a shift toward everyday AI reliance.
By Q4, momentum had already pivoted toward AI training crawlers, highlighting a rapid ecosystem evolution as providers optimized model architectures to meet shifting consumer demands.
This shift reveals the next phase of competition: the race to build autonomous agents capable of executing entire transactions. For enterprises, this creates immense operational pressure, as they inadvertently provide the “data fuel” for models that could ultimately disintermediate them.
To manage this, businesses are increasingly adopting bot mitigation strategies, weighing the trade-offs between blocking bots to protect intellectual property and allowing them access to drive discovery. Effective mitigation now requires deep visibility into bot behavior.
The right approach depends on whether an agent is driving commerce or siphoning away engagement.
However, this transition will not be seamless. AI agents are already disengaging from retailers that fail to provide high-precision, structured data. Moving forward, agentic commerce will only flourish where clear frameworks of trust and permission are established.
Businesses must treat these agents as a new class of participant, requiring explicit verification, differentiated access, and specific rules of engagement to safely usher in the era of autonomous transactions.
Visibility in the agentic age
As the presence of AI bots accelerated throughout 2025, many organizations responded cautiously, limiting or blocking access while they assessed the implications.
But as AI assistants and agentic tools increasingly shape how users discover information, products, and services, the conversation is shifting from simple control to strategic visibility.
Rather than navigating directly to websites, users are increasingly relying on AI systems to summarize options, compare products, and surface recommendations on their behalf.
This changes the mechanics of discovery. It is no longer enough for organizations to be indexed. They must also be interpretable, prioritized, and usable within AI-generated outputs.
As a result, visibility is becoming less about driving traffic and more about influencing how and whether a brand appears in machine-mediated decisions.
This introduces a new tension between control and access, as organizations weigh the risks of exposing content to automated agents against the commercial reality that, without that access, they may simply not appear at all.
In this environment, competition is no longer confined to search rankings, but takes place within the answers and actions generated by AI systems themselves.
The next phase of digital discovery will be defined by how effectively organizations make their content accessible to AI systems.
Generative Engine Optimisation (GEO) focuses on ensuring content can be interpreted and used by generative AI models, while Answer Engine Optimisation (AEO) ensures key information is structured so it can be surfaced directly within AI-generated responses.
Together, these approaches represent the evolution of search optimization for an AI-mediated web.
At the same time, organizations will need clearer governance over automated traffic. Rather than blanket policies that either allow or block bots entirely, businesses must develop more nuanced approaches that distinguish trusted AI crawlers from other forms of automated activity while maintaining control over valuable digital assets.
As 2026 continues to unfold, organizations that treat AI bot traffic as strategic infrastructure, rather than background noise, will be best positioned to compete and be chosen in an internet increasingly navigated by machines.
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