We’ve been thinking about search intent wrong.
For years, the framework was simple: informational, navigational, transactional. A user searches for something, and you categorize what they want based on the query. Then you write content to match that category.
That framework is dead. It was already struggling in 2023. By 2026, it’s completely obsolete.
The reason: search intent isn’t a function of keywords. It’s a function of decisions. And those decisions have become far more complex and context-dependent than any keyword category can capture.
I’ve been running companies and investing in early-stage businesses for 15 years. I know how buying decisions actually work. They’re not linear. They’re not single-query. A prospect doesn’t search “how to choose a sales intelligence platform” and immediately become a customer. They search 40 different things across three months. Some queries are educational. Some are comparative. Some are implementation-focused. Some are doubt-driven (“is this worth it?”). Each query serves a different moment in their decision process, and a one-size-fits-all keyword category system completely misses that.
Modern SEO requires mapping content to decision stages, not search categories. And if you’re still using the informational/transactional framework, you’re losing deals to competitors who understand buyer intent.
Why the Old Framework Broke
The informational/transactional model was useful when Google gave you 10 blue links and users had limited ways to explore a topic. It forced simplification, and that worked.
But several shifts have destroyed that model:
1. Search results are no longer homogeneous
A query like “sales intelligence platform” now returns: ads, videos, Reddit threads, AI summaries, knowledge panels, people also ask sections, featured snippets, review sites, and organic listings. Users aren’t following a linear path from “what is X?” to “how do I buy X?” They’re jumping between formats, exploring tangentially, and reconstructing their understanding based on multiple sources.
Your content can’t compete in this landscape by trying to be everything. It has to be specific to a particular decision moment.
2. AI search flattened keyword intent
When ChatGPT and other AI search tools became mainstream, users realized they could ask nuanced questions and get comprehensive answers. A search query is no longer constrained by “what fits in 50 characters.”
Users can now search “I’m evaluating sales intelligence tools and I already have Salesforce. What integrations should I prioritize?” That’s a specific decision-stage query that’s nothing like a simple “sales intelligence tools” informational search. But both might be tagged as “transactional” under the old framework.
The specificity of queries has increased exponentially, and intent categories haven’t kept pace.
3. The buyer journey has become non-linear
B2B buying in particular has become a committee-driven, multi-stakeholder, multi-month process. A CFO is searching for “AI ROI metrics.” An operations manager is searching for “implementation timeline for sales tools.” A sales VP is searching for “competitive comparison: Gong vs. Chorus.” All three are part of the same buying decision, but none of these queries fit neatly into informational/transactional buckets.
You need content for each of those decision moments, and traditional intent categories don’t help you build it strategically.
The New Framework: Decision-Stage Intent Mapping
Here’s how I think about intent in 2026: every search query represents one of five decision stages. Your content strategy should map to all five.
Stage 1: Problem Recognition
The prospect doesn’t know they have a problem yet, or they’re just starting to recognize it. Their queries are about symptoms, frustrations, and “is this normal?”
Examples for a sales intelligence platform:
- “Why are my sales cycles getting longer?”
- “How do I know what my reps are doing in calls?”
- “What metrics indicate poor sales performance?”
- “How much visibility should a sales leader have?”
Content strategy: educational, empathetic, problem-focused. You’re not selling yet. You’re helping prospects articulate what’s wrong. Use content marketing frameworks that focus on pain acknowledgment.
Stage 2: Solution Exploration
The prospect knows they have a problem. Now they’re exploring what types of solutions exist. Their queries are about categories, not specific products.
Examples:
- “What is sales conversation intelligence?”
- “How does AI help sales teams?”
- “What features do sales enablement platforms have?”
- “How do you measure sales rep coaching effectiveness?”
Content strategy: educational, framework-focused, building authority. You’re educating the market about solutions that exist. Your pillar pages live here. This is where comprehensive SEO and answer engine optimization matter most.
Stage 3: Product Evaluation
The prospect has narrowed down the solution category. Now they’re comparing specific products or vendors. Queries are competitive, specific, and detail-oriented.
Examples:
- “Gong vs. Chorus vs. Athena”
- “Best sales intelligence tools for remote teams”
- “Sales intelligence platform pricing comparison”
- “What integrations does [Product] support?”
- “[Product] case studies and results”
Content strategy: comparative, specific, proof-focused. You need comparison content, feature breakdowns, case studies, and pricing information. This is where most companies fail—they don’t have enough specific, evaluative content.
Stage 4: Validation and Risk Mitigation
The prospect is close to a decision but looking for reassurance. Queries are about implementation risk, success rates, and “does this actually work for someone like me?”
Examples:
- “How long does sales intelligence tool implementation take?”
- “What’s the learning curve for [Product]?”
- “Common implementation mistakes with [Product]”
- “[Product] reviews and user feedback”
- “ROI for sales intelligence tools”
Content strategy: reassurance-focused, risk-mitigation, user-voice. Reviews, implementation guides, ROI calculators, customer testimonials. Social proof matters here.
Stage 5: Post-Purchase Enablement
The prospect has bought (or is in final contract negotiation) and now they’re searching for how to actually succeed with the product. Queries are implementation-focused, best-practices-oriented, and troubleshooting.
Examples:
- “How to set up [Product] integrations”
- “[Product] best practices for remote sales teams”
- “How to track call metrics in [Product]”
- “[Product] training and onboarding guide”
Content strategy: tactical, detailed, enablement-focused. Your customer success and technical SEO teams should own much of this content. It seems less relevant to acquisition, but it actually drives expansion revenue and reduces churn.
How AI Search Changes Intent Mapping
Here’s what most people miss: AI search engines like ChatGPT and Perplexity have fundamentally changed how intent manifests in queries.
With Google, users are constrained by keywords. With AI search, users ask natural language questions. This means two things:
1. Specificity has increased
Users can now ask multi-dimensional questions that capture nuance: “I have a team of 12 sales reps spread across EST and PST. I need visibility into pipeline. Our CRM is Salesforce but our comms happen in Slack. What should I look for in a sales intelligence tool?”
That query captures decision-stage specificity that traditional keyword intent categories completely miss. But it’s a valid search query that deserves relevant content.
Your content needs to address these specific, nuanced decision moments. Generic comparisons aren’t enough.
2. Search has moved from discovery to synthesis
Users are no longer searching to find one authoritative answer. They’re searching to synthesize multiple perspectives. AI search tools scrape content from multiple sources and provide synthesized answers. This means your single article needs to be so authoritative and comprehensive that AI tools prioritize it as a source.
That’s a higher bar than ranking #1 on Google. It means your content needs to be the definitive source that AI systems reach for first.
Building Your Decision-Stage Content Map
Here’s the practical framework I use with clients:
Step 1: Identify your decision stages
Map your actual buyer journey. Not the theoretical one. The one your sales team sees. How do prospects typically come in? What questions do they ask? What objections emerge? What research do they do before talking to sales?
For a SaaS product, the five stages I outlined usually fit. But you might have different stages. The framework is the process, not the exact labels.
Step 2: Map queries to decision stages
Take your keyword list (use Google Search Console, competitor analysis, prompt engineering for SEO research) and organize every keyword by decision stage. You’ll probably find:
- You have 50+ keywords in one or two stages
- You have almost nothing in other stages
- Your content strategy has huge gaps
Step 3: Audit content coverage
For each decision stage, ask: what content do I have? Is it comprehensive? Is it authoritative? Does it actually address what prospects are searching for at this stage?
Most companies have solid awareness content (problem recognition, solution exploration) and weak evaluation content (product evaluation, validation). This gap costs deals.
Step 4: Build content for missing stages
Start with the stages where you’re weakest but prospects are most likely to search. Usually that’s product evaluation and validation stages.
For product evaluation, you need:
- Comprehensive competitive comparisons
- Feature breakdowns and capability matrices
- Integration and compatibility guides
- Pricing and packaging analysis
For validation, you need:
- Implementation guides and timelines
- ROI calculators and success metrics
- Customer case studies and testimonials
- Common pitfalls and how to avoid them
Step 5: Optimize for AI search
As AI search becomes increasingly important (and it will), your content needs to be:
- Comprehensive and primary-source quality (not summarizing others)
- Factually precise with proper citations
- Covering multiple perspectives and counterarguments
- Optimized for natural language queries, not keywords
This is different from Google SEO. You’re not optimizing for keyword density. You’re building authoritative, comprehensive resources that AI systems will prioritize as sources.
Why Decision-Stage Mapping Wins
I’ve watched companies transform their results by moving from keyword-based intent to decision-stage intent mapping. Here’s what changes:
Higher conversion rates: When you have content that specifically addresses each decision moment, prospects find the exact resource they need when they need it. This accelerates decisions and increases conversion rates.
Better content strategy: Instead of randomized content calendar, you have a strategic framework. You know which stages need content investment. You know which queries matter most.
Competitive advantage: Most of your competitors are still optimizing for keywords. You’re optimizing for decisions. You’ll have content they don’t have, addressing decision moments they’re completely missing.
Expansion opportunity: Once you’ve built a strong content foundation for decision stages, creating adjacent content becomes faster. You understand the framework. You can apply it to adjacent products or customer segments quickly.
AI search readiness: As search continues to evolve toward AI-powered synthesis, your comprehensive, authoritative content will perform better because you’ve built it to be the primary source, not the aggregated source.
The market has shifted. Intent is no longer about keywords. It’s about decisions. The companies that understand this and build their content strategy around decision stages will dominate search in 2026 and beyond.