The 3 Buyer Jobs Framework™
A buyer-centric framework for understanding how B2B companies get discovered, evaluated, and shortlisted when AI does the research.
How AI changed B2B buying
B2B buying behavior changed fundamentally when ChatGPT launched in November 2022.
Buyers stopped Googling keywords and clicking through blue links. Instead, they started using AI as a research assistant—describing their problem, explaining their requirements, and asking for vendor recommendations.
By 2024, tier-1 research firms documented the scale of this shift:
Gartner (2025): 61% of B2B buyers now prefer to complete their research without speaking to a sales representative
G2 (August 2024): 50% of B2B buyers now start their research in AI chatbots instead of search engines
Forrester (2024): 89% of B2B buyers are using AI tools throughout their purchasing process, from researching vendors to creating RFPs to comparing options to validating decisions
Responsive (October 2025): One in four buyers now use generative AI more than conventional search engines, and two-thirds rely on AI chatbots as much as or more than Google
The shift is real and documented.
Buyers aren't reading your website anymore. At least, not at first. They're asking AI to do the research for them. They use AI to find vendors and create shortlists. Only after they have their shortlist in hand do they visit your website to validate what AI told them.
This requires a different way of thinking. In the attention economy, you could control the buyer's journey through your funnel. You could track them, nurture them, and guide them through awareness, consideration, and decision stages.
In the age of AI, buyers control their own journey. Most of that journey happens invisibly, inside conversations with AI that you cannot see, track, or influence directly. By the time they visit your website, they've already decided whether you're worth considering.
Why AI citations don’t drive pipeline
If you've talked to marketing agencies or consultants in the last year, you've heard the pitch: optimize for AI search. Target long-tail keywords that AI responds to. Get mentioned in AI Overviews. Increase your AI visibility and share of voice. Show up when ChatGPT answers questions. Structure your content for featured snippets and answer boxes.
These tactics sound logical. More visibility should equal more pipeline, right? That's how it worked in the attention economy. And agencies can show you monthly reports tracking your citation count and AI visibility scores across platforms. Measurable progress you can see.
But here's what most companies and most agencies don't understand yet: Citations get you visibility, but they don't get you on the AI buyer's shortlist.
The Critical Distinction
Getting cited means AI mentioned your brand somewhere in a public AI Overview or response. You're visible. That's good for brand awareness. It might drive some traffic. This is what most AI optimization services focus on because it's measurable and uses the same optimization patterns agencies learned from SEO.
Being shortlisted means AI recommended you as a qualified vendor who matches a buyer's specific requirements. This happens in private conversations between buyers and their AI research assistant. The buyer describes their situation, explains their constraints, and asks for recommendations. AI synthesizes information from your website and third-party sources to determine if you're a fit.
The difference matters because buyers aren't searching with keywords anymore. They're having conversations.
A CEO asks ChatGPT:
"I run a $8M professional services firm with 45 employees. We've grown fast but operations are breaking down. Projects run over budget, client onboarding takes too long, and my team is constantly firefighting. I need someone who can build systems and processes without slowing us down or requiring a massive implementation project. What are my options?"
That’s not a keyword or even a long tailed keyword.
The buyer is describing a complex, specific situation with context and constraints.
AI needs to understand who you serve, what specific problems you solve, what your process is, and what results you deliver. Then it determines if you match this buyer's unique requirements. This requires fundamentally different information architecture than what drives citations.
Why Everyone Is Applying the Old Mental Model
Most companies and most agencies are still applying search engine mental models to a different type of system.
AI citations work like SEO worked.
Optimize for predictable patterns, target specific queries, get mentioned in results, track what's measurable. These are valuable skills that translate directly from search engine optimization to AI Overview optimization.
The problem isn't that agencies are wrong or incompetent. The problem is that citations and shortlisting operate on different mechanisms.
Citations respond to queries. Buyers ask a question, AI provides an answer, your brand gets mentioned. This is pattern matching against known queries. The same skill agencies have been practicing for years.
Shortlisting matches requirements.
Buyers describe a situation, AI evaluates which vendors can solve it, and recommends options that fit. This requires AI to understand your value proposition well enough to make contextual recommendations.
Nobody was taught how to optimize for requirements-based discovery because it didn't exist until buyers started using AI as a research assistant instead of a search engine. We're all learning this together.
The Honest Reality
Nobody can guarantee you'll appear when a specific buyer asks AI for recommendations. Buyer prompts are too variable, the context is too unique, and LLM behavior is too unpredictable for anyone to make that promise.
What you can control is whether you have the information AI needs to understand how you match buyer requirements. When buyers describe problems you solve, can AI find clear answers about who you serve, what specific problems you solve, how you work, and what results you deliver?
If AI can't find this information clearly articulated on your website and validated by third-party sources, it won't recommend you. Not because of bias, but because it doesn't have the information it needs to determine fit.
That's what the AI Shortlist Audit measures.
The 3 Buyer Famework™
Buyers use AI to accomplish three distinct jobs when searching for vendors. Two of these jobs happen entirely inside AI, invisible to you and your CRM. By the time buyers reach Job 3, validating their choice through demos and pricing discussions, the shortlist is already set.
Job 1: Find Vendors
When a buyer has a problem to solve, their first job is to find vendors who can solve it. Before AI, this meant hours of Googling, clicking through websites, and piecing together options.
Now, buyers have a conversation with AI. They describe who they are, what's frustrating them, what they've tried, and what they need. AI asks clarifying questions. Once it understands the buyer's situation and requirements, it searches for vendors that match.
AI doesn't return blue links. It returns a curated list of vendors with explanations of why each might be a fit.
But AI can only recommend vendors it can find clear information about. Your website is now a database for AI. When buyers ask AI for recommendations, AI uses Retrieval Augmented Generation (RAG). It searches your website and extracts information to answer the buyer's questions.
If your website is filled with vague marketing language like "we help businesses succeed" or "innovative solutions for modern challenges," AI won't find what it needs. You won't make the initial list. Not because AI is biased against you, but because AI couldn't extract clear answers to basic questions about who you serve and what problems you solve.
To win Job 1, you need to make it easy for AI to understand your value proposition. Who you serve (specific industries, company sizes, situations). What problems you solve (named in the language buyers use to describe them). What your approach is (your methodology, process, or framework). What evidence you have (results, case studies, proof points).
In the analog days, the gatekeeper was the secretary. In the digital age, it was the attention economy. In the age of AI, it's your website. If your website doesn't answer buyer questions in clear, specific language, you'll never get on the AI shortlist.
Job 2: Create Shortlist
Once a buyer has a list of potential vendors, their next job is to narrow it down to two or three finalists. Gartner's research shows that buyers are creating smaller shortlists and engaging fewer vendors than ever before. Buyers now interact with 22% fewer vendors compared to last year.
AI makes this easier. Buyers ask AI to compare vendors, evaluate fit, and flag concerns. AI does deep research to validate fit. Do you serve their industry? Do you work with companies their size? Can you integrate with their existing systems? What results have you delivered for companies like them? What do third-party sources say about you?
AI analyzes your website, reviews, case studies, and any other information it can find. If it can't find clear answers, you don't make the shortlist.
To win Job 2, you need specific information about who you serve and how you work. Case studies showing results for companies similar to the buyer. Third-party validation through directories, reviews, and media mentions. Evidence that proves you can deliver what you claim.
Getting discovered by AI is Job 1. Making the AI shortlist is Job 2. Both happen before buyers ever contact you.
Job 3: Validate Vendors
Once buyers have their shortlist, their final job is to validate their choice. Demos and product walkthroughs. Reference calls with current clients. Site visits or team meetings. Pricing discussions. Final evaluation with stakeholders.
This is when buyers leave their AI research assistant and engage with your sales team. This is the only job that's visible in your CRM.
Most companies focus all their attention on Job 3 because it's the only part they can see. But if you fail Job 1 and Job 2, the invisible jobs that happen inside AI, you never get the opportunity to compete in Job 3.
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