At G2 Reach this week, Tim Sanders, Chief Innovation Officer at G2, made two statements that stuck with me: "Agents are loyal to the prompts" and "Agents are ungrateful."
Both challenge something content marketers have relied on for years. Publishing useful content helps build trust, familiarity and, eventually, consideration. Someone reads your articles, attends your webinars and starts recognising your organisation as a credible source. When the time comes to buy, that relationship can influence the decision.
AI agents don't build relationships in quite the same way. They can use your content to answer a question without giving your organisation any preference when recommending a product. Your article might be the best explanation of a problem, while your competitor is still the better answer to the buyer's requirements.
That distinction has consequences beyond marketing. It changes what information we publish, how we structure it and how our digital platforms make it available.
Being helpful doesn't make you the right choice
Sanders illustrated this with an interesting distinction. An AI model can encounter CrowdStrike as a publisher of security content and CrowdStrike as a vendor of security products. Being excellent at explaining cybersecurity doesn't automatically prove that your product is the right choice.
He described a recommendation process consisting of three stages: candidacy, validation and selection.
During candidacy, a model identifies potential suppliers that meet the buyer's requirements. Validation involves looking for evidence that supports their claims, including independent sources. Finally, the model selects a recommendation based on the available information and its confidence in that evidence.
This is a useful way to think about AI-assisted purchasing, although I wouldn't mistake it for a documented internal algorithm shared by every AI platform. Different models, retrieval systems and agent workflows can behave quite differently.
What matters is the underlying principle: your claims need to be discoverable, understandable and supported by evidence.
One example from the session made this particularly clear. A security vendor had a competitive advantage around agent latency, but its customer reviews mainly discussed ease of use, support and value for money. Those reviews were positive, yet they didn't validate the specific performance claim that mattered to potential buyers.
It's a good reminder that collecting more testimonials isn't necessarily the answer. The evidence needs to support the reasons customers actually choose your product.
Your content architecture is now part of the buying process
This is where I think the conversation becomes particularly interesting. Much of the discussion around Answer Engine Optimisation (AEO) focuses on mentions, citations and visibility. Those are useful measurements, but they don't tell us whether an AI system can properly evaluate what an organisation offers.
Consider a typical enterprise website. Product capabilities might be described across dozens of pages. Pricing information could be hidden inside downloadable documents, technical specifications might sit in separate systems, and essential content may only appear after client-side JavaScript has executed.
A human visitor can often navigate around these limitations. An AI system attempting to compare fourteen suppliers against a detailed list of requirements may have a much harder time.
The solution isn't simply to publish more content or add schema markup to every page. It starts with the content model itself. Product capabilities, specifications, eligibility criteria and commercial terms should be represented as explicit, maintainable information rather than being buried inside presentation components.
From there, the delivery architecture needs to make that information accessible. Semantic HTML, reliable server-side rendering, predictable URLs and appropriate structured data all help. So does ensuring that relevant crawlers can actually retrieve the content.
This is also why I don't believe organisations should immediately start building separate websites for AI agents. We've spent years advocating structured content and headless architectures precisely because content shouldn't be inseparable from its presentation. If we've implemented that separation properly, serving another type of consumer should be an extension of the architecture rather than another content management problem.
There may be value in providing agents with a more concise representation of a page, such as Markdown. But that is a delivery optimisation, not a replacement for a well-structured content model or independently verifiable information.
And it is worth remembering that not every AI system retrieves or interprets content in the same way. Optimising for one crawler or one response format is not an agent-readiness strategy.
Being recommended is different from being actionable
Sanders also discussed the next evolution: agents that move beyond recommending products and start completing tasks or purchases on behalf of users. I agree that the work organisations do today on content quality and evidence will remain relevant. But this is where the architectural requirements change significantly.
An agent researching a product primarily needs reliable information. An agent requesting a quote, checking availability or placing an order needs access to business capabilities. Those capabilities might be exposed through existing APIs, supported commerce integrations or protocols such as the Model Context Protocol (MCP). The appropriate interface depends on the task and the systems involved.
More importantly, agents need to operate within clear boundaries. Authentication, authorisation, approval workflows, transaction validation and auditability become essential once an agent can do more than retrieve information.
We shouldn't assume that making a website readable to agents automatically makes the underlying business ready to transact with them.
Being discoverable, being recommendable and being actionable are three related, but distinct, architectural challenges.
And while autonomous purchasing attracts plenty of attention, it is worth keeping the current reality in perspective. G2's 2026 buyer research suggests that organisations are far more comfortable using AI for research and evaluation than allowing agents to execute purchases independently.
The opportunity is real, but the transition will be gradual.
Don't confuse visibility with recommendation
One point I appreciated in Sanders' presentation was his distinction between being mentioned, being cited, and actually being recommended. These aren't equivalent outcomes. A company might appear frequently in AI-generated answers because it publishes excellent educational content. That doesn't necessarily mean it is being shortlisted when buyers ask for solutions to specific problems.
For me, this changes how we should approach measurement. Rather than asking only whether our brand appears in AI search results, we should examine the questions potential customers are asking. Does our organisation appear when those questions include real requirements, constraints and buying criteria? If not, is the problem missing information, inaccessible content, insufficient supporting evidence or simply a product that doesn't meet the requirements?
Those are very different problems, and buying another AEO dashboard won't solve all of them. The same critical thinking applies to G2's own recommendations. As a review platform, G2 naturally emphasises third-party validation. That doesn't invalidate its research, but independent evidence extends well beyond review platforms. Technical benchmarks, certifications, customer case studies and authoritative documentation can be equally important, depending on the claim being evaluated.
Start with what actually wins you business
If I were advising an organisation where to start, I wouldn't begin with an ambitious agentic commerce initiative or a complete website rebuild. I'd start with three questions.
- What are the two or three capabilities or differentiators that genuinely influence customers to choose us?
- Can an AI system find and understand those claims on our website, without navigating a maze of presentation components, PDFs and vague marketing language?
- Is there credible evidence available to substantiate those claims?
If the answers reveal gaps, you already have a practical starting point. Some improvements will be editorial. Others will require changes to content modelling, rendering or integration architecture.
The bigger lesson I took from Sanders' session is that AI-assisted discovery rewards organisations that can make their value explicit and verifiable. But I would take the argument one step further: the same architectural foundations will determine whether those organisations can eventually let agents interact with their services.
Agents may be ungrateful, but that isn't necessarily a problem. They force us to confront something we should probably have addressed years ago.
Is our digital experience actually designed to communicate what we offer, or have we become too dependent on humans figuring it out for themselves?