By Lisa Singer and Beth Caplow
For years, B2B software companies obsessed over discoverability. Could buyers find their website? Could they rank in search? Could analysts, review sites, and influencers tell the right story?
But a new challenge is emerging: Can AI buying agents find and understand your pricing?
A recent benchmark study from Siteline found that AI agents frequently struggle to retrieve pricing and packaging information from B2B software websites. When agents encountered inaccessible pages, hidden pricing, or JavaScript-rendered pricing tables, they often abandoned the vendor’s website and sought information from third-party sources or directed their humans to a competitive offering instead.
This finding is much more critical than a technical SEO issue. Organizations that don’t address the information needs of their new agent buyers will find it’s a revenue issue.
The New Buyer Isn’t Always Human
As AI agents become integrated into buyer workflows, companies will increasingly delegate software research, vendor comparisons, and pricing analysis to agents acting on their behalf. Instead of visiting ten vendor websites, a buyer may ask an AI agent:
“Compare the pricing, features, and ROI of the top customer support platforms.”
The agent then attempts to gather information from vendor websites and other sources before presenting recommendations. The problem? Many supplier pricing pages were designed for humans as opposed to machine consumption.
According to Siteline’s research, roughly 30% of agent runs encountered at least one search or retrieval error. In some cases, agents couldn’t access pricing information at all and instead relied on analyst reviews, marketplace listings, G2 pages, comparison blogs, or other third-party content. In other words, if AI can’t understand your pricing, someone else may end up defining it for you.
The Hidden Cost Of “Contact Sales”
For years, software companies intentionally obscured pricing to ensure control of the sales narrative and eventual price negotiation and importantly, to maximize sales engagement. The intent was for prospects to engage in conversations about their needs and potential solutions as opposed to only considering price.
However, AI agents are seeking to answer the buyer’s question as efficiently as possible. They are not going to submit a form. When they encounter,“contact us,” AI buying agents are going to look elsewhere for the information. At best, they may deliver rough third-party estimates; at worst, they may surface inaccurate or misleading claims from forums, blog posts, or even fabricated customer complaints.
Packaging Is Becoming A Machine-Readable Asset
The Siteline study highlights another critical issue: many pricing tables rely on client-side JavaScript rendering, making plan details difficult for AI agents to parse. Package details are critical because AI buying agents – and customers themselves – need to understand which offerings meet their requirements. As buyers increasingly delegate research and vendor evaluation to AI agents, package details become the machine-readable information that enables agents to compare offerings, assess fit, estimate value, and create shortlists. Additionally, packaging – and the overall packaging hierarchy – is also a tool for agents (and buyers) to understand who the ideal customer is, their maturity level, and the upgrade path. These details help the agent determine fit before they even evaluate the price.
Treat Packaging And Pricing Information As Strategic Content Assets
Portfolio and product marketers should treat packaging and pricing information as strategic content assets designed for both human buyers and AI agents. Practitioners should audit pricing pages to ensure that package structures, feature entitlements, consumption metrics, pricing units, and value propositions are presented so AI agents can easily retrieve and understand.
They also should expand their optimization efforts beyond traditional SEO and focus on what could be called “agent-ready commercialization.” Pricing pages should be accessible without login requirements, readable without JavaScript dependencies, structured with clear package hierarchies, and explicit about pricing metrics, usage limits, and feature differentiation. As enterprises place greater emphasis on AI governance, ROI, transparency, and predictable economics, the same practices that build buyer trust also improve AI comprehension: clear packaging, transparent pricing, defined usage boundaries, and explicit value communication. By making commercial information easier for AI agents to interpret, vendors increase the likelihood that their products will not only be discovered but also accurately evaluated, compared, and recommended during increasingly automated buying journeys.
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