TL;DR: New research reveals that ChatGPT performs searches 31% of the time users query it—and for enterprise software, that number jumps significantly higher. If your company isn’t optimizing for AI-driven search, you’re invisible to a rapidly growing segment of B2B decision-makers researching solutions.
The Search Revolution Happening Behind Closed Doors
While your marketing team obsesses over Google rankings and ad spend, a seismic shift in B2B research behavior is unfolding. Enterprise buyers are increasingly turning to ChatGPT and other large language models (LLMs) for vendor research, competitive analysis, and solution discovery. But here’s what most SaaS leaders don’t realize: these AI tools aren’t just pulling from their training data—they’re actively searching the web, just like your prospects used to do on Google.
Recent data analysis of over 8,500 prompts reveals exactly how ChatGPT searches behind the scenes, and the implications for enterprise software companies are profound.
The Data: What We Now Know About AI Search Behavior
A comprehensive study by Nectiv Digital analyzed ChatGPT’s search patterns across multiple industries, including a specific focus on enterprise software. The findings should be required reading for every SaaS CEO building a go-to-market strategy in 2025 and beyond.
Key Findings for Enterprise Software Leaders
Search Frequency: ChatGPT performs searches in 31% of all user prompts when it lacks native knowledge. For enterprise software queries specifically, this behavior becomes even more aggressive, as the LLM recognizes it needs current information about rapidly evolving products, pricing, and features.
Query Fan-Out Patterns: When ChatGPT searches, it doesn’t stop at one query. The average across all industries is 2.17 searches, but software queries average 2.68 searches per prompt—meaning the AI is cross-referencing multiple sources to answer a single user question about your category.
Query Length and Specificity: ChatGPT’s searches average 5.48 words—61% longer than typical Google searches. Instead of searching “project management software,” it’s querying “enterprise project management software features comparison 2025.” This specificity matters tremendously for your content strategy.
Temporal Obsession: The year “2025” appeared as one of the most common terms across all searches. ChatGPT aggressively prioritizes fresh, current information. If your website content, documentation, or comparison pages aren’t updated with current dates, you’re effectively invisible to AI search.
The Enterprise Software Opportunity
For B2B software companies, several critical patterns emerge:
- Features dominate: “Features” appeared as a top-5 search term across the dataset, including prominently in software queries. When a buyer asks ChatGPT about solutions in your category, the AI is specifically seeking detailed feature information.
- Comparison content wins: “Comparison” consistently appeared in fan-out queries. Buyers use ChatGPT to evaluate alternatives, and the AI actively searches for content that compares solutions side-by-side.
- Reviews drive credibility: With 702 instances across the dataset, “reviews” was the most common search term. ChatGPT grounds its recommendations in third-party validation.
- Pricing transparency matters: “Pricing” emerged as a key term specifically in software searches. The AI actively seeks pricing information to provide complete answers to buyer questions.
- Tools and integrations: “Tools” appeared frequently, suggesting queries about ecosystem compatibility and integration capabilities.
Why This Matters More Than You Think
The B2B Buyer Journey Has Evolved
Your potential customers aren’t just using ChatGPT for casual queries. They’re conducting serious vendor research through conversational AI. Consider these scenarios:
- A VP of Sales asks: “What CRM platforms have the best Salesforce integration for enterprise teams?”
- A CFO queries: “Compare enterprise expense management software pricing and features”
- An IT Director prompts: “Best cybersecurity tools for 500-person remote teams in 2025”
In each case, ChatGPT is likely performing 2-3 searches behind the scenes, pulling information from multiple sources to formulate a response. If your company’s content isn’t optimized for these AI search patterns, you’re not part of the conversation.
First-Mover Advantage Is Still Available
Here’s the opportunity: most enterprise software companies are still focused exclusively on traditional SEO. While Google optimization remains important, the companies that establish strong AI visibility now will capture mindshare as adoption accelerates. Your competitors likely haven’t seen this data. You have a window.
According to the research, only a minority of companies are actively tracking their AI visibility. Those that do gain critical insights into how their brand appears in AI-generated responses—insights that directly inform product positioning, content strategy, and competitive messaging.
Strategic Imperatives for SaaS CEOs
1. Audit Your AI Visibility Immediately
You can’t optimize what you don’t measure. Begin tracking how your brand appears in responses across ChatGPT, Claude, and other major LLMs. Ask questions your buyers would ask and document the results. Are you mentioned? Are competitors mentioned instead? What information is the AI pulling, and from where?
This isn’t a marketing task—this is strategic intelligence that should inform board-level discussions about market positioning.
2. Restructure Your Content for AI Consumption
Traditional SEO content won’t fully capture AI search traffic. Based on the research findings, optimize for these patterns:
Create Comprehensive Comparison Content: Since “comparison” ranks as a top search term and software prompts trigger 2.68 searches on average, develop detailed comparison pages that position your solution against alternatives. Be fair, factual, and thorough. AI models reward comprehensive, unbiased content.
Emphasize Features Explicitly: Don’t bury your feature set in marketing fluff. Create dedicated, scannable feature documentation that clearly articulates capabilities. Use structured data markup to help AI models extract this information efficiently.
Update Everything for 2025: Add current year dates to titles, headers, and throughout your content. The research shows ChatGPT is “obsessed with freshness.” Your Q4 2024 pricing page should become your 2025 pricing page. Your 2024 buyer’s guide should be updated for 2025.
Build a Review Acquisition Strategy: With “reviews” appearing 702 times in the dataset, third-party validation is critical. Actively cultivate reviews on G2, Capterra, TrustRadius, and other platforms where AI models can find credible user feedback.
Make Pricing Transparent: “Pricing” emerged specifically in software searches. While many enterprise software companies hide pricing behind form walls, AI optimization favors transparency. Consider publishing at least starting prices or transparent pricing frameworks.
3. Optimize for Longer-Tail Queries
Remember, ChatGPT searches are 61% longer than typical Google queries. This means traditional keyword optimization around short terms like “CRM software” misses the mark. Instead, optimize for the specific, detailed queries AI models actually perform:
- “enterprise CRM platforms with advanced API integrations 2025”
- “project management software features comparison for remote teams”
- “expense management tools pricing mid-market companies”
These longer phrases should appear naturally throughout your content, particularly in headers, feature descriptions, and comparison sections.
4. Structure Data for Machine Readability
AI models don’t just read text—they extract structured information. Implement schema markup for:
- Software application metadata
- Pricing information
- Feature lists
- Review aggregations
- Integration capabilities
- Use cases
This structured data helps AI models quickly identify and extract the specific information they’re seeking in their fan-out queries.
5. Create “AI-First” Content Assets
Consider developing content specifically designed to answer the complex, multi-faceted questions buyers ask AI tools:
- Detailed solution guides that compare your platform across multiple dimensions
- Feature matrices that map capabilities to use cases
- Integration documentation that explains ecosystem compatibility
- ROI calculators and pricing models
- Implementation timelines and requirements
This content serves dual purposes: it captures AI search traffic while providing valuable resources for direct visitors.
The Competitive Intelligence Angle
Beyond optimizing your own presence, understanding AI search patterns provides competitive intelligence. You now know:
- What information ChatGPT seeks when buyers research your category
- Which terms and phrases consistently appear in software searches
- The length and specificity of queries the AI performs
- The importance of recency in AI responses
This intelligence should inform not just marketing, but product development, positioning, and competitive strategy. If ChatGPT consistently searches for specific features when users inquire about your category, consider whether your product roadmap addresses those capabilities.
Implementation: Getting Started This Quarter
For early-stage enterprise software companies, implementing an AI optimization strategy doesn’t require massive resources. Here’s a pragmatic 90-day roadmap:
Month 1 – Assessment
- Conduct AI visibility audit across major platforms
- Document how your brand currently appears in AI responses
- Identify gaps in existing content against the research findings
- Benchmark against top competitors
Month 2 – Optimization
- Update all content with 2025 dates where relevant
- Create or enhance comparison pages
- Implement structured data markup
- Develop comprehensive feature documentation
- Initiate review cultivation program
Month 3 – Expansion
- Develop AI-first content assets
- Optimize for longer-tail software queries
- Set up ongoing AI visibility monitoring
- Create dashboards to track AI mention share
The Bottom Line
The research is clear: AI-driven search is happening now, it’s happening at scale, and it’s particularly relevant for complex B2B purchases like enterprise software. ChatGPT searches 31% of the time, performs multiple queries per prompt, and actively seeks specific types of information—reviews, features, comparisons, pricing, and tools.
As a SaaS CEO, you’re either building for this reality or conceding ground to competitors who are. The companies that establish strong AI visibility in 2025 will be better positioned as adoption continues to accelerate. The cost of inaction isn’t just missed opportunities—it’s systematic exclusion from an increasingly important channel of buyer research.
Traditional search optimization focused on ranking positions. AI optimization focuses on being part of the conversation. When a potential customer asks ChatGPT about solutions in your category, is your company mentioned? Is the information accurate? Are you positioned favorably against alternatives?
These aren’t marketing questions. They’re existential questions about market positioning in an AI-first future.
The data exists. The patterns are clear. The question is whether you’ll act on them before your competitors do.
About AI Optimization: Enterprise software companies looking to understand their AI visibility can track their presence across major language models, analyze how their brand appears in AI-generated responses, and identify optimization opportunities. As the research demonstrates, the companies with the best data win—and that applies to AI optimization as much as traditional marketing channels.
FAQ: AI Optimization for SaaS CEOs
What is AI-driven search and how is it different from traditional SEO?
AI-driven search refers to answers generated by large language models (LLMs) like ChatGPT that often include live web lookups and multi-source synthesis. Traditional SEO targets ranked links on Google; AI optimization targets being cited, summarized, and accurately represented inside the LLM’s answer itself.
Does ChatGPT actually search the web?
Yes. When its native knowledge is insufficient or time-sensitive, ChatGPT performs multiple targeted queries, cross-checks sources, and composes a grounded response. Your content must be discoverable, current, and structured for extraction.
Why does content freshness (e.g., “2025”) matter so much?
LLMs prioritize recency for fast-changing categories like enterprise software. Explicit year cues in titles, H1/H2s, and pricing/comparison pages help models select your pages as up-to-date authorities.
What content types perform best for AI visibility?
Feature documentation, transparent pricing, side-by-side comparisons, credible third-party reviews, and integration guides. These align with what LLMs actively seek and quote in answers.
Should we publish pricing if we sell enterprise plans?
At minimum, publish starting prices or pricing frameworks. Transparent anchors improve inclusion in AI answers and reduce friction for buyers researching budgets.
How do we audit our AI visibility?
List 20–30 buyer-intent prompts (features, integrations, pricing, comparisons). Ask major LLMs those questions, capture mentions, sources, and accuracy, then track “share of inclusion” versus competitors monthly.
What schema or structure helps LLMs extract our content?
Use schema for SoftwareApplication, pricing, FAQs, review aggregations, and how-to content. Employ clean H2/H3s, feature tables, comparison matrices, and machine-readable bullets.
How often should we update core pages?
Quarterly for pricing, features, and comparisons; monthly for fast-moving categories. Stamp the current year in key assets and maintain a visible changelog.
What’s a pragmatic 90-day AI optimization plan?
Month 1: Visibility audit, gap analysis, competitor benchmark. Month 2: Update dates, add comparisons, publish pricing framework, implement schema. Month 3: Build AI-first assets, track inclusion share, and iterate.