The Future of Digital Discovery or How AI Agents Will Replace Websites

Date : January 23, 2026 By Andrei Iunisov

AI Is Becoming the New Buyer

Enterprise software purchase & procurement has entered a fundamental transformation. AI agents now perform the research tasks that human buyers traditionally executed through manual website visits and vendor comparisons. The shift represents more than workflow automation. It marks a complete restructuring of how B2B software buying decisions occur at the enterprise level.

Modern enterprise buyers no longer browse vendor websites the way previous generations did. AI agents are used for these purposes now – reading product pages and documentation, analysing whitepapers, solution briefs, and technical specifications, and even scoring vendors against procurement requirements. The AI agents operate at speeds and with the consistency that human researchers cannot match. These systems process hundreds of pages of technical content, extract relevant capabilities, and generate comparison matrices without human intervention.

A recent enterprise procurement process demonstrated this evolution in practice, as a result of my personal experience with Bacula Systems. The prospective buyer needed backup and recovery software to replace existing systems and support Nutanix virtual machines. 

Instead of visiting vendor websites themselves, the buyer used the AI agent fed with the RFP requirements, competitor names, and integration specifications. The instruction was straightforward: review the vendor website, analyse documentation and whitepapers, map capabilities to RFP sections, and produce a scoring report. The entire initial research phase occurred through automated analysis. The vendor discovered this approach only after the AI agent had already completed its evaluation.

From Human Browsing to Agent-Driven Evaluation

The traditional B2B research workflow required weeks of manual effort across multiple stakeholders: from vendor identification through demos to final proposals.

AI-driven procurement workflows dramatically compress this timeline. Buyers now deploy their own AI agents or hire external AI agents like ChatGPT Agent to execute research tasks autonomously. Agents might be tasked with specific vendors or may begin with a fan-out search query to identify relevant vendors. They analyse vendor websites, technical documentation, and other publicly available resources, extract product specifications, and map capabilities to procurement requirements. They can generate scoring matrices that compare vendors against defined criteria. What’s notable is that the entire evaluation phase occurs before any human contact with the vendor sales teams.

The preference for AI agents for such tasks comes from three operational advantages. Firstly, speed allows buyers to evaluate dozens of vendors in hours rather than weeks. Agents process content volumes that would require multiple full-time employees to review manually. Secondly, objectivity eliminates subjective bias from initial vendor screening, because agents apply consistent evaluation criteria across all candidates without favoring established relationships or brand recognition. Lastly, analytical capacity enables a comprehensive assessment of complex technical requirements. AI agents can cross-reference integration specifications, compliance documentation, and architecture details across massive content repositories.

The workflow fundamentally transforms vendor selection, as shortlists emerge from algorithmic evaluation rather than human judgment. Vendor interaction begins only after AI agents complete initial qualification. As such, organisations that optimise their websites and other marketing or technical content only for human browsing patterns, rather than for AI systems’ retrieval, will not even reach the consideration stage in procurement workflows.

An AI Agent Shortlisting Solutions

The Procurement Approach

The theoretical shift becomes concrete through a real enterprise procurement case. A prospect evaluating backup and recovery solutions never browsed the vendor website through traditional means. Instead, the buyer instructed an AI agent to conduct the entire vendor evaluation autonomously. They provided specific instructions that detailed every aspect of the research to their AI agent. Their prompt specified the RFP sections that required mapping, identified existing competitors by name, and outlined the prospect’s IT environment. The buyer requested an analysis of how the software would integrate with specific server infrastructure, network storage systems, and Nutanix virtual machines. The AI agent received explicit instructions to review the vendor’s website, technical documentation, and white papers to retrieve capabilities and produce a scoring report.

The IT Procurement Prompt 

“I wanted to review the [company name] RFP. The current backup and recovery system is [competitor 1]. The competitor is [competitor 2]. We want to take sections 4, 5, and 6 of the RFP and map those to our solution around [server vendor] servers, Bacula Enterprise, and [storage vendor] acting as our network storage system. This storage system will be the destination for backups, a recovery location and place where replication of the backup media can take place. The storage unit is only for the backup and recovery system. We will rely heavily on Bacula to perform the entire backup and recovery capabilities for the [company name]. Bacula is being positioned to replace [competitor 1] and [competitor 2]. The system will need to support Nutanix virtual machines. We anticipate that we will need to write some API calls to Nutanix to create a seamless automated disaster recovery. Please review the requirements, the Bacula Systems website, documentation and attached whitepapers for an analysis (scoring report) of how our solution will satisfy [company name].”

The prompt demonstrates how enterprise buyers now delegate entire research workflows to AI systems. They have never planned to read the website themselves; instead, they have delegated the research to an AI agent. The vendor entered the later consideration process in the buying cycle through machine analysis rather than human browsing.

Your Website Is Still Critical, But Not Only Humans Are Not Reading It

This example reveals a fundamental truth about modern B2B buying (B2C as well, but I don’t have any case studies for it at the moment). Websites are still essential for the marketing process, but the primary audience is shifting from human browsers to AI agents and LLM crawlers that chunk, retrieve, and synthesise answers taken from multiple content sources using semantic similarity. 

The website, your technical papers, documentation, and help sections, as well as third-party review sites, Wikipedia, and Reddit-like online communities – they all function as the definitive sources of truth for AI-driven vendor evaluation. AI agents analyse all this content to extract product specifications, technical capabilities, and integration details. They can also parse documentation to verify technical claims made in pure marketing materials and even cross-reference white papers with RFP requirements to assess if the solution fits. 

The accuracy, completeness, and retrievability of your entire content set directly influences whether you reach the shortlist or not. However, if you can optimise for marketing rhetoric without chunking and retrieval in mind – you will fail the initial AI screening, irrespective of the product’s actual capabilities.

The critical elements of this discovery process are:

• Structured product information to allow AI systems to easily extract technical capabilities without unverifiable marketing claims
• Clear technical documentation optimised for chunking and retrieval to serve later during AI answer synthesis
• Accessible whitepapers and other technical guides to improve the depth and context that AI systems are looking for
• Consistent claims across all assets to avoid potential credibility penalties (if there are contradictory specifications to be found)

AI systems can easily identify discrepancies between website claims, documentation specifications, and technical whitepaper details. As such, organisations have to maintain unified messaging across the entirety of their content portfolio.

Optimising for AI Systems, Not Search Engines 

Search engine optimisation has already evolved to provide better help for websites to rank for human search queries. The new approach is focused on keyword density optimisation, backlink profiles, and content that would satisfy human intent. The current reality of AI-driven discovery requires a fundamental reconception of optimisation strategy. Brands must now be able to “rank” in agent reasoning processes and search engine results pages at the same time. The evaluation itself happens after discovery, once AI agents have finished analysing vendor content to generate procurement recommendations.

Key Optimisation Requirements

Machine-readable documentation – from product pages to technical documentation – creates the foundation of AI optimisation. Product pages must be able to present capabilities in a very structured, chunkable format. Technical specifications need to be presented in a format or formats that AI agents would be able to parse systematically. These systems rely on consistent terminology for entity structuring, while ambiguous descriptions and marketing hyperbole are sure to reduce AI retrieval accuracy – thus harming vendor scoring.

Structured data and schema markup create the much-needed semantic context that AI agents require to assess software capabilities systematically. Schema implementations that define product specifications, technical requirements, and integration capabilities allow agents to map vendor offerings to RFP criteria while avoiding inferential errors in the meantime. This includes examples such as using schema.org’s SoftwareApplication markup to highlight supported operating systems, compliance certifications, or API versions in a JSON-LD format that is easily readable by AI methods.

RFP-ready technical value propositions remove the translation work that AI agents would otherwise need to conduct (like stating “Supports HIPAA compliance with BAA available” instead of “Enterprise-grade security”). Precise feature and compatibility lists make it possible for AI to conduct automated verification against buyer specifications. With that in mind, I have created a custom GPT that helps optimise for such retrieval-friendly language – https://chatgpt.com/g/g-685a0c05d4bc81918313e39037d47ea2-geo-optimisation. 

Transparent performance and comparison data would allow AI agents to perform objective vendor assessments. Benchmark results, solution briefs, and architecture diagrams are all treated as verifiable evidence that agents go through when it comes to scoring matrices. One example of such would be to publish a TPC-C benchmark score with information such as the test methodology, SOC 2 Type II certification dates, and auditor details – or to provide network topology diagrams that show failover mechanisms/redundancy paths.

Trust Data, Not Slogans

Product marketing used to put a heavy emphasis on differentiation via messaging and brand positioning. Marketing teams created all kinds of value propositions that could resonate emotionally with human decision-makers specifically. Unfortunately, AI agents are already known for “penalising” such an approach, necessitating a different tactic to be used. 

AI systems retrieve and transform all the aspirational language and subjective claims into their vector representations, then compare them against RFP requirements using primarily semantic similarity scoring. Generic phrases such as “industry-leading solution” tend to achieve low similarity scores when matched against highly-specific criteria such as “support for MySQL 8.0 replication.” The significant vector distance between vague marketing statements and concrete technical information often results in poor evaluation scores for the company in question.

Technical accuracy becomes the primary marketing tool here. AI systems cross-reference vendor claims against existing documentation, as well as industry standards and competitive offerings. Exaggerations in capability statements are sure to harm vendor scores. As such, marketing content needs to learn how to align precisely with the product’s actual functionality in order to survive these completely automated fact-checking procedures.

The “proof over promises” approach dramatically improves vendor credibility in the eyes of AI evaluation frameworks. Case studies with quantified results are great evidence that agents would be able to incorporate into their scoring matrices. Implementation architecture examples, on the other hand, demonstrate the technical competence of the solution that generic benefit statements would never be able to convey. Customer references with highly-specific use cases tend to carry a lot more weight to them compared with testimonial quotes without any kind of measurable outcomes.

Critical Documentation Requirements

Interoperability clarity addresses an essential component of the enterprise software evaluation process. AI systems analyse integration requirements extensively when conducting vendor fit assessments. Organisations are now required to document API capabilities, supported protocols, and certified integrations with specificity in order to pass these kinds of checks and processes. Vague claims about “seamless integration” and such are guaranteed to fail when it comes to satisfying AI agent analysis requirements. Detailed compatibility matrices and technical integration guides offer the much-needed evidence that automated systems require.

Security and compliance details receive even higher degrees of scrutiny during AI-driven vendor assessment. Agents verify all kinds of information like certifications, audit reports, and security frameworks against their procurement requirements. Organisations that are used to bury compliance information in dense legal documents or require human inquiry to access those specifications are not going to pass initial agent screening in the first place. Marketing materials have to highlight these details prominently, with explicit documentation references, if they want to keep being evaluated at all.

The marketing evolution practically destroys any room for fluff or uncertainty. Generic statements about innovation, leadership, and customer satisfaction are worth nothing to AI agents and their evaluation scores. With that in mind, organisations need to rebuild their current marketing content around 100% verifiable technical facts, as well as quantified performance data and specific capability evidence. 

Preparing Your Brand for AI-First Buying

Building Content Infrastructure

Organisations cannot afford to remain passive with their observations as AI systems reshape enterprise procurement. Building agent-retrievable content infrastructure necessitates systematic investment across multiple organisational functions. The transition in question demands a high level of coordination between product marketing, technical documentation, web development, and sales enablement teams.

The foundation of such a symbiotic relationship begins with unifying product documentation and website claims. AI systems are known for easily identifying various inconsistencies across content sources, penalising vendors accordingly. Organisations have to audit their existing content in order to eliminate contradictions between their marketing pages, technical specifications, knowledge base articles, and whitepaper claims.

Publishing architecture and technical details serve as clear separators between qualified vendors and eliminated candidates in the eyes of AI evaluation. Organisations have to move beyond high-level feature descriptions to also provide the specificity that agents require for proper RFP mapping. Implementation architecture diagrams, system requirements, and integration specifications all have to be placed in prominent spots instead of being buried in downloadable PDFs (which require human navigation to access).

Structured compatibility matrices help address the integration analysis that AI agents perform extensively. Organisations need to document the following:

• Certified integrations with specific platform versions and API specifications
• Supported protocols and standards to enable interoperability assessment
• Hardware and infrastructure requirements for the sake of deployment planning
• Security and compliance certifications with references for audit report 
• Performance benchmarks combined with methodology documentation for further verification

Maintaining Competitive Advantage

Maintaining up-to-date whitepapers and benchmarks is necessary to guarantee that AI agents have access to the most up-to-date information during vendor evaluation processes. Outdated technical content creates doubt when it comes to product relevance and organisational competence of the product’s developer. Regular content audits have to also verify that published materials fully reflect current product capabilities and industry standards.

Enabling API specifications, integration guides, and configuration resources creates the technical depth that enterprise AI evaluation requires. Organisations have to treat these resources as primary marketing assets, not support documentation. The more technical audience now even includes AI systems that require machine-parsable formats and structured information architecture.

The competitive advantage is moving over to organisations that recognise AI systems as primary customers of technical content. Vendors that are capable of adapting their content strategy to serve automated evaluation are sure to dominate shortlists. Meanwhile, those who continue to optimise their content for human browsing patterns only will discover their exclusion once it is already too late. The future of B2B software purchasing has arrived, and it reads through all the public documentation before scheduling the first call.