Selling to Agents
Ross Sylvester, Co-Founder & CEO, Adrata | Feb 2026 | ~10 min read
Your next buyer might not have a LinkedIn profile.
Gartner's latest forecast is stark: by 2028, 90% of B2B purchases will be intermediated by AI agents, pushing over $15 trillion in annual spend through automated exchanges.^1^ Not assisted by agents. Intermediated by them. The algorithm will decide which vendors make the shortlist before a human ever sees your pitch deck.
This is not a distant future. Gartner also projects that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025.^2^ Half of all procurement contract management will be AI-enabled by 2027.^3^ And according to Responsive's October 2025 report, two-thirds of B2B buyers already rely on AI agents and chatbots as much as or more than Google when evaluating vendors — a number that jumps to 80% in tech and software.^4^
The shift is not theoretical. It is happening in pipeline right now. And most CROs are not ready for it.
What Agents Optimize For
Here is the uncomfortable truth about AI buying agents: they are ruthlessly rational in ways that human buyers never were.
A human buyer is influenced by a compelling narrative, a charismatic AE, a well-timed dinner, a reference from a trusted peer. Humans satisfice — they find something "good enough" and stop looking. They anchor on first impressions. They are swayed by recency bias and social proof from people they know personally.
Agents do none of this.
An AI procurement agent evaluates vendors by ingesting and weighting thousands of data points simultaneously: published pricing against stated requirements, technical documentation against integration specifications, third-party review sentiment across G2 and Gartner Peer Insights, security certifications against compliance frameworks, support SLA history, API response times, community forum activity, contract terms against organizational policies. It processes all of this in minutes and produces a ranked shortlist with confidence scores.
This means the criteria for making a shortlist are fundamentally changing. The qualities that got you into deals — a strong brand impression, a memorable demo, a champion who loved your UX — still matter. But they matter later. Before any human forms an opinion, the agent has already filtered you in or out based on machine-readable signals you may not even know you are emitting.
G2's research shows 87% of B2B software buyers say AI chatbots are changing how they research, with ChatGPT leading at 47% preference among AI research tools.^5^ When a prospect's procurement agent asks "What are the best revenue intelligence platforms for mid-market SaaS companies with Salesforce integration?" your fate depends on what the model has ingested about you — not what your AE would say in a discovery call.
The AI Attention Problem
In the old world, the scarce resource was human attention. You competed for mindshare through brand marketing, outbound sequences, event sponsorships, and content. The entire demand generation apparatus was built to earn a human's consideration.
In the agent-intermediated world, the scarce resource is algorithmic inclusion. Your product needs to be comprehensible, comparable, and credible to a machine — before it ever needs to be compelling to a person.
This creates what I call the AI Attention Problem. It is structurally different from the human attention problem in three ways:
1. Agents don't browse — they query. A human might stumble across your brand at a conference or through a colleague's recommendation. An agent executes a structured search against defined criteria. If your product data is not structured in a way the agent can parse, you do not exist.
2. Agents don't forget — they weight. A human buyer might forgive a bad G2 review because they had a great demo experience. An agent aggregates every signal with mathematical precision. One hundred five-star reviews and ten one-star reviews about "terrible onboarding" produce a weighted score that reflects the onboarding problem — every time, without exception.
3. Agents don't get sold — they compare. The entire art of sales has historically been about controlling the narrative. Agents demolish narrative control. They pull data from sources you do not curate, compare you against competitors you did not choose, and evaluate you on dimensions you may not have prioritized.
As SaaStr's analysis puts it bluntly: "You can't keyword-stuff your way into an AI's good graces. You have to actually be worth recommending."^6^
From SEO to AEO
For two decades, B2B marketers optimized for Google. Keyword research, backlink strategies, domain authority, SERP rankings — the entire playbook was designed to win in a world where buyers typed queries into a search bar and clicked blue links.
That world is ending.
Answer Engine Optimization (AEO) is the emerging discipline of structuring content so that AI systems — ChatGPT, Google AI Overviews, Perplexity, Copilot, and the procurement agents built on top of them — select, summarize, and reference your content when generating answers.^7^ The strategic question is no longer "How do we rank?" It is: When buyers ask AI about our category, are we included? And if so, are we described the way we would describe ourselves?
The differences between SEO and AEO are fundamental:
| Dimension | SEO | AEO |
|---|---|---|
| Goal | Rank on page one | Be included in the AI-generated answer |
| Signal | Keywords, backlinks, domain authority | Structured data, factual accuracy, source credibility |
| Content format | Long-form blog posts, landing pages | Hierarchical, extractable answer blocks with verifiable claims |
| Measurement | Organic traffic, click-through rate | AI citation rate, inclusion in agent shortlists |
| Timeframe | Weeks to months | Near-real-time (agents re-query frequently) |
Bing has confirmed that their LLM models use structured data, and Google has followed suit.^7^ Schema markup, clean taxonomy, and machine-readable product specifications are no longer nice-to-haves for your website. They are the new table stakes for being discoverable by the agents that increasingly mediate your buyers' research.
The Agent-Readable Enterprise
If agents are the new gatekeepers, what does it mean to be "agent-readable"? Based on what we are seeing across our customer base and in the broader market, I believe there are six dimensions that matter:
1. Structured Product Data
Agents cannot evaluate what they cannot parse. Your product capabilities, pricing tiers, integration specifications, security certifications, and SLA commitments need to exist in structured, machine-readable formats — not buried in PDF whitepapers or locked behind demo request forms.
This means publishing comprehensive schema markup on your website, maintaining up-to-date product comparison data on review platforms, and ensuring your technical documentation is organized hierarchically with clear metadata.
2. API-First Proof of Value
The traditional demo is a human-to-human performance. An agent cannot sit through a 45-minute screen share. What an agent can do is query an API, evaluate response times, test integration endpoints, and assess data quality programmatically.
Forward-thinking vendors are already creating sandbox environments and API-accessible trial experiences that procurement agents can evaluate autonomously. If your product requires a human to demonstrate its value, you have a structural disadvantage in an agent-mediated buying process.
3. Machine-Readable Case Studies
"We helped Company X achieve a 40% improvement in Y" is compelling to a human. To an agent, it is one unverifiable claim among thousands. Machine-readable case studies include structured outcome data: baseline metrics, implementation timeline, measured results, methodology, industry vertical, company size, and specific use case — all tagged with schema markup that an agent can extract and compare.
4. Transparent and Comparable Pricing
Agents excel at comparison. Opaque pricing — "contact us for a quote" — is a signal to an agent that your product is either expensive, non-standard, or trying to hide something. None of those interpretations help you. Companies that publish clear, comparable pricing structures give agents the data they need to include them in evaluations where price is a factor.
5. Review and Sentiment Hygiene
Agents aggregate review data across platforms with a thoroughness no human buyer would match. They weight recency, volume, specificity, and sentiment. A systematic approach to soliciting, responding to, and learning from reviews — across G2, Gartner Peer Insights, TrustRadius, Reddit, and community forums — becomes a core revenue function, not a marketing afterthought.
6. Verifiable Claims and Third-Party Validation
AI systems are increasingly trained to distinguish between self-reported claims and independently verified facts. SOC 2 reports, independent benchmark results, analyst evaluations, and peer-reviewed integrations carry more weight with agents than your own marketing copy. Invest in third-party validation not for the badge on your website, but for the structured data it feeds into the systems evaluating you.
What Changes in the Buyer Journey
When agents pre-filter, the buyer journey compresses and bifurcates. BCG's research on AI in B2B sales describes a world where buyers now spend 80% of their journey in "shadow mode" — using private AI agents to research solutions and verify claims before ever visiting a vendor website.^8^ By the time a human reaches out to your sales team, the real evaluation is largely complete.
This has profound implications for pipeline:
Discovery shifts from outbound to inbound-via-agent. Your SDR's cold email competes against the procurement agent's pre-built shortlist. If you were not on the agent's list, the SDR's email is noise. If you were, the email is confirmation of a decision already trending your direction.
The first human interaction happens later and at higher stakes. When a buyer's agent has already filtered the market to three vendors, the human conversation is not exploratory — it is validating. Your AE is not selling; they are defending a position the agent already established. This requires a fundamentally different skill set: deep technical credibility, rapid trust-building, and the ability to address concerns the agent flagged rather than delivering a standard pitch.
Competitive displacement becomes harder. In a human-mediated process, a skilled rep could unseat an incumbent through relationship-building and creative positioning. Against an agent-mediated shortlist, the data is the data. If the incumbent's structured signals are stronger than yours, no amount of executive dinner is going to change the algorithm's recommendation.
Win rates will polarize. Companies that are agent-readable will see higher conversion rates on fewer, higher-quality opportunities. Companies that are agent-invisible will see pipeline shrink as they get filtered out of evaluations they never knew were happening. Gartner estimates that by 2028, AI agents will outnumber sellers by 10x.^9^ The math is unforgiving.
The CRO Playbook for an Agent-Mediated World
Here is what I would prioritize if I were running revenue at an enterprise software company today:
Audit your agent readability. Ask ChatGPT, Claude, Perplexity, and Gemini about your product category. See where you show up, how you are described, and what data they cite. Do the same for your top three competitors. The gaps you find are your most urgent marketing priorities.
Invest in structured data infrastructure. Hire or assign someone to own your schema markup, product taxonomy, and machine-readable content strategy. This is not an SEO project — it is a revenue project. Treat it with the same urgency you would treat a broken CRM integration.
Rebuild your content strategy around extractability. Every piece of content should be designed to be quoted, summarized, and compared by an AI system. This means clear hierarchical structure, specific and verifiable claims, tagged metadata, and answer-formatted sections that an LLM can extract without hallucinating.
Create API-accessible proof of value. Build sandbox environments, public API documentation, and programmatic trial experiences. Make it possible for a procurement agent to evaluate your product without a human in the loop. This does not replace the demo — it precedes it.
Systematize your review strategy. Treat G2, Gartner Peer Insights, and TrustRadius as pipeline channels, not marketing vanity metrics. Build automated workflows to solicit reviews from successful customers, respond to negative reviews substantively, and track your sentiment scores with the same rigor you track pipeline coverage.
Retrain your sellers for the post-filter conversation. When the buyer has already been pre-qualified by an agent, the sales conversation changes. Train your AEs to ask "What did your evaluation surface as concerns?" rather than "Let me walk you through our platform." The agent already walked them through it. Your job is to address what the agent could not: trust, partnership, vision, and the intangibles that still require a human.
Measure what matters. Add "AI citation rate" and "agent shortlist inclusion" to your marketing metrics. Track how often your product appears in AI-generated responses for key buying queries. This is the new pipeline leading indicator.
The Human Premium
I want to be clear about what this article is not arguing. I am not arguing that human sellers become irrelevant. The data actually suggests the opposite.
While 61% of B2B buyers now prefer a rep-free experience for straightforward evaluations, they still seek human engagement when stakes are high and ambiguity increases.^8^ The agent handles the commodity work — filtering, comparing, shortlisting. The human handles the premium work — building trust, navigating organizational complexity, co-creating solutions to ambiguous problems.
In an agent-mediated world, the human seller becomes more valuable per interaction, not less. But they interact later, less frequently, and at higher stakes. The CRO who prepares for this reality — investing in agent readability on the front end and human excellence on the back end — will build a durable advantage.
The companies that win the next five years will be the ones that figure out how to sell to the algorithm and the human. The algorithm gets you to the table. The human closes the deal. Neither works without the other.
The question is whether your go-to-market is ready for both.
Footnotes
^1^ Gartner, "AI Agents Will Command $15 Trillion in B2B Purchases by 2028," Digital Commerce 360, November 2025.
^2^ Gartner, "40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025," Gartner Newsroom, August 2025.
^3^ Gartner, "Half of Procurement Contract Management Will Be AI-Enabled by 2027," Gartner Newsroom, May 2024.
^4^ Responsive, "B2B Buyers Now Rely on AI Agents as Much as or More Than Google When Evaluating Vendors," October 2025.
^5^ G2, "87% of B2B Software Buyers Say AI Chatbots Are Changing How They Research," 2025.
^6^ SaaStr, "The Future of B2B Marketing is AI Agent Recommendations. And AI Agents Play Favorites," 2025.
^7^ BOL Agency, "What Is GEO and AEO? How AI Is Changing B2B SEO in 2026," 2026; Bing and Google structured data confirmations.
^8^ BCG, "How AI Agents Will Transform B2B Sales," 2025.
^9^ Gartner, "By 2028 AI Agents Will Outnumber Sellers by 10X," Gartner Newsroom, November 2025.