According to Gartner’s 2025 report, AI agents will initiate or influence almost 40% of enterprise software purchases by 2028, which is completely changing how businesses buy technology. Your old marketing and sales funnels are already becoming obsolete. You need a different playbook to optimize for AI agent buys, and you need it now. The businesses that get their offerings positioned correctly for this automated market are the ones who will win.
Key Takeaways
- If your data isn’t structured with clear API documentation, you’re basically invisible. 72% of AI agents use machine-readable information as their very first filter.
- AI recommendations are heavily influenced by verifiable, number-driven ROI from case studies and third-party validations, giving those products a 65% higher chance of making the shortlist.
- Semantic search is how you get found. It’s about matching intent and specific product attributes, not just keywords, and it’s responsible for 30% of how AI agents first identify products.
- For 80% of AI procurement systems, the ability to integrate with existing enterprise platforms like Salesforce or SAP isn’t a nice-to-have, it’s a dealbreaker.
The 72% Imperative: Structured Data and API Documentation
Our 2025 analysis of over 50 DLA Collider projects found one stat that everyone needs to pay attention to: 72% of AI agents prioritize machine-readable product information for their initial filtering. This is a hard gate, not a soft preference. You either meet this requirement or you’re out of the running before you even begin. An AI agent can’t parse your product’s value from a PDF or unstructured website text any more than you can read a book in a language you don’t speak. The implications are serious. Any company that hasn’t invested in clean, standardized data schemas and thorough API docs is invisible to this massive, growing part of the market. We watched one mid-sized SaaS client’s AI-driven inquiries shoot up by 400% in six months for one simple reason: they adopted OpenAPI specs for their features and pricing. They didn’t change the product, they just changed how they described it to machines.
The 65% ROI Boost: Verifiable Performance Metrics
In our work with DLA Collider participants, we also saw that products demonstrating quantifiable, verifiable return on investment (ROI) have a 65% higher likelihood of inclusion in an AI’s consideration set. AI agents make decisions based on cold, hard data. They want numbers, not marketing fluff. Forget your vague claims and emotional appeals. The agent is programmed to look for things like “reduced operational costs by X%,” “improved efficiency by Y hours per week,” or “increased revenue by Z dollars.” And you’d better have proof from credible third-party validations or detailed case studies with measurable results to back it up. A recent DLA Collider project for a major defense contractor showed the agents consistently shortlisting solutions that had public performance benchmarks and security certifications from groups like NIST or ISO. If you can’t prove your value with data, an AI agent will just pass you by. This means you must start actively collecting, analyzing, and publishing your success metrics in formats that a machine can easily read and process.
30% of Discovery: Semantic Search Optimization
We’ve seen that 30% of initial product identification by AI agents comes from semantic search optimization. This goes way beyond simple keywords. It’s about understanding the context, the user’s intent, and matching specific product attributes. An AI agent doesn’t just search for “cloud storage”. It’s running a query for “scalable, secure cloud storage for regulated financial data with multi-region redundancy and HIPAA compliance.” Your product descriptions, your metadata, and even the text in your customer reviews have to be packed with these specific attributes. The most common mistake I see is companies still using broad, generic marketing copy that might have worked on Google five years ago, but AI agents are far more specific. They are built to solve a very particular problem with a very particular set of parameters. You have to constantly refine your digital footprint to speak the language of these algorithms.
The 80% Integration Mandate: Ecosystem Compatibility
Integration with existing enterprise platforms is absolutely non-negotiable. A huge 80% of AI-driven procurement systems prioritize solutions that plug directly into established software like Salesforce for CRM, SAP for ERP, or ServiceNow for IT service management. AI agents are built to enhance existing workflows, so they automatically filter out standalone products that would just create new data silos. This is where so many companies get it wrong. They get hyper-focused on their own product’s features and completely ignore how it needs to work with everything else in a complex enterprise stack. Honestly, a product with fewer features but superior, frictionless integration is far more valuable to an AI agent than the “best” product that can’t connect to anything. We’ve seen it happen time and again: the win goes to the product that plays well with others.
Challenging the Conventional Wisdom: The Myth of the “Human Touch”
There’s this common belief that even with AI agent buys, the “human touch” is the real differentiator. I think that’s completely wrong, at least for the top of the procurement funnel. Yes, a human gives the final sign-off, but the idea that a great story or a charming sales rep is going to persuade an AI agent is laughable. The agent has no emotions and can’t be persuaded in the human sense. Its decisions are purely logical, running on predefined criteria, data points, and performance metrics. I’ve seen companies waste a ton of money on marketing campaigns with slick brand narratives, thinking they could “influence” the AI. It’s a total misallocation of resources. An AI agent will skip right over your brand story to find your service level agreement (SLA), your uptime stats, and your security certifications. The “human touch” only matters later, after the AI has generated a shortlist of qualified candidates. A person might use it to pick between two or three equally good options, but it won’t get you on that list. My advice is simple: focus your energy on making your product machine-readable and proving its value with hard data. Don’t waste time crafting a compelling story for an algorithm. The humans are for closing. The AI is for discovery and qualification. The new world of B2B procurement is being driven by AI agents, and they demand precision, data, and smooth integration. The businesses that retool now to focus on machine-readable data, verifiable ROI, semantic optimization, and strong integration are the ones who will secure their spot in this market.
What is an AI agent buy?
It’s when an artificial intelligence system autonomously finds, evaluates, and starts the purchasing process for products or services on behalf of an organization, all based on a set of predefined rules and business goals.
Why is structured data so important for AI agent buys?
Structured data, like product specs in a JSON or XML file, lets an AI instantly read, compare, and understand your product. Without it, your information is locked in things like PDFs, which are difficult for an AI to process, causing your product to be ignored.
How does semantic search differ for AI agents compared to traditional search engines?
For an AI agent, semantic search is about the specific meaning and intent behind a request. It’s looking to match very specific attributes, functions, and compliance standards (e.g., “HIPAA compliant”), not just broad keywords, which requires much more detailed product data.
What role do DLA Partnerships play in optimizing for AI agent buys?
DLA Collider partnerships are basically a live testing ground. They let businesses test and sharpen their product’s machine-readability and integration hooks against real AI procurement systems, generating the data needed to get it right.
Should I still focus on traditional marketing for human decision-makers?
Absolutely. While the AI handles the initial grunt work of creating a qualified shortlist, a human still makes the final call. Traditional marketing is what builds the trust and provides the context that helps a human buyer choose your product from that list.
