GTM in The Age of AI: The Top 10 Learnings from ICONIQ’s 2025 B2B SaaS Report – SaaStr

Team ZYT Web3 / 1 week
June 26, 2025
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8 min read

by Jason Lemkin | Blog Posts
We’re living through the biggest transformation in B2B sales since the birth of SaaS itself. ICONIQ’s latest report, surveying 205 GTM executives in April 2025, reveals a market splitting in two: AI-forward companies pulling dramatically ahead while traditional SaaS companies struggle with flat growth, longer sales cycles, and declining conversion rates.
The latest from 2025 survey data by ICONIQ from 205 GTM executives across leading B2B SaaS companies

The data tells a stark story. While overall SaaS growth has stagnated for two years, AI-native companies are achieving 56% trial-to-paid conversion rates versus just 32% for traditional SaaS—a 24-percentage-point chasm that’s widening fast. Companies with strong AI adoption across their GTM organizations are outperforming peers on virtually every metric: higher quota attainment (61% vs 56%), shorter sales cycles (20 vs 25 weeks), lower cost per opportunity ($8.3K vs $8.7K), and dramatically leaner operations.
This isn’t about sprinkling ChatGPT into your sales process. It’s about fundamental organizational redesign—from pricing models (hybrid consumption/subscription) to team structures (forward-deployed engineers vs traditional CSMs) to investment priorities (94% AI spend increases among high-growth companies).
The message is clear: The Age of AI isn’t coming—it’s here. And the companies that don’t adapt risk being left behind in what may be the most consequential shift in B2B software history.
Here are the 10 most critical findings every SaaS leader needs to understand:
The Reality Check: If you’ve felt like your growth has plateaued, you’re not imagining it. Top quartile YoY ARR growth rates have remained essentially flat across most segments since 2023. But there’s one bright spot that should give mid-stage founders hope.
The Mid-Stage Breakout: Companies in the $25M-$100M ARR range are finally breaking free from what many call the “growth plateau.” These companies saw their top quartile growth rates jump from 78% in 2023 H1 to 93% in 2025 YTD—the only segment showing meaningful improvement.
Why This Matters: If you’re approaching or in this range, you’re hitting the market at an inflection point. These companies have likely figured out repeatable sales processes, achieved some product-market fit refinement, and are benefiting from increased market maturity around their solutions. For earlier-stage companies, this suggests the path through the plateau is real—you just need to execute through it.
What’s Not Working: Larger companies ($200M+ ARR) actually saw growth rates decline from 39% to 27%, suggesting that scale advantages aren’t what they used to be in this market environment.

The Conversion Gap: This isn’t about having AI features—it’s about being fundamentally built around AI. At the $100M+ ARR level, AI-native companies convert free trials to paid customers at 56% vs just 32% for traditional SaaS companies. That’s not a rounding error; it’s a systematic advantage.
Why AI-Native Wins: The report suggests these companies have “more immediately clear and valuable” ROI propositions. When prospects can see tangible productivity gains or cost savings within days or weeks of using an AI product, the decision to buy becomes obvious rather than theoretical.
The Smaller Company Story: Even at smaller scale (<$100M ARR), AI-native companies still outperform at 43% vs 37% conversion rates. The advantage exists across all stages, but becomes more pronounced as companies scale.
Strategic Implications: If you’re building a traditional SaaS product, ask yourself: Can prospects immediately see and measure value during a trial? If not, you might be fighting an uphill battle against AI-native competitors who can demonstrate ROI in real-time.

The Funnel Breakdown: Marketing teams can breathe a sigh of relief—lead generation and early-stage conversion rates have remained relatively stable. The problem is happening much later in the funnel where sales teams are supposed to close deals.
Where It’s Breaking:
The Sales Problem: This suggests the issue isn’t lead quality or initial interest—it’s execution. Companies are getting prospects interested and even into the demo stage, but something fundamental is failing in the closing process.
Potential Causes: Longer evaluation cycles, more complex buying committees, increased scrutiny on ROI, or simply that traditional sales approaches aren’t working in the current environment. Sales teams need to rethink their closing methodologies, not their lead gen strategies.
The Universal Slowdown: This isn’t sector-specific—every industry is seeing meaningful extensions in how long it takes to close deals. But some sectors are getting hit harder than others.
Sector Breakdown:
The Fintech Story: The 12-week extension in Fintech likely reflects increased regulatory scrutiny, economic uncertainty affecting financial services, and more cautious evaluation of new financial tools.
Operational Impact: Longer sales cycles directly translate to higher cost per opportunity and lower sales team productivity. Companies need to adjust their pipeline management, forecasting, and cash flow planning accordingly. What used to be a quarterly sales cycle might now span two quarters.

The Systematic Advantage: Companies with AI “fully embedded across all GTM teams” aren’t just slightly better—they’re systematically outperforming across every metric that matters to sales leaders.
Complete Performance Scorecard:
Why This Matters: This isn’t about having a few AI tools—it’s about systematically embedding AI into sales processes. High adopters are likely using AI for lead scoring, call analysis, proposal generation, forecasting, and deal coaching in integrated ways that compound into massive advantages.
The Compounding Effect: Each individual improvement might seem modest, but when you’re closing deals 5 weeks faster, at higher rates, with better quota attainment, the cumulative business impact is enormous.
The Early-Stage Leverage: While larger companies are still figuring out how to get efficiency gains from AI, smaller companies are already seeing dramatic operational leverage. Companies under $25M ARR with high AI adoption are running GTM organizations that are 38% leaner.
Headcount Distribution Changes:
What’s Being Automated: The report suggests AI tooling may be automating parts of customer onboarding and enablement, allowing smaller teams to handle more customers effectively. This could include automated customer communications, self-service onboarding flows, and AI-powered support.
Scaling Implications: If you can achieve efficient growth with leaner teams early, you have more runway and higher margins as you scale. This creates a compounding advantage over time.

The Pricing Evolution: The pure SaaS subscription model is becoming less common, especially among AI-native companies. More than one-third of AI-native companies have adopted hybrid models that blend subscription and usage-based components.
Revenue Mix in Hybrid Models:
Why Hybrid Makes Sense: As Neha Narkhede (Oscilar CEO/former Confluent CPTO) explains: “Hybrid models can offer a balanced alternative: a platform fee combined with usage-based credits. This setup suits lower-volume products and sales-led motions, while also unlocking NRR upside through consumption.”
AI Feature Monetization: For traditional SaaS companies adding AI features, 38% are bundling them into premium tiers and 32% are offering them at no extra cost—suggesting many are still experimenting with AI monetization strategies.
The Scale Advantage: Channel partnerships become increasingly critical as companies grow. The largest companies derive nearly 30% of their revenue from partnerships—almost double the rate of early-stage companies.
Partnership Adoption by Scale:
The Long Game: As Rob Bernshteyn (former Coupa CEO) notes: “Partnerships are an incredibly efficient strategic lever for scalable growth. The earlier companies lay the foundation (ideally well before $25M ARR) the more likely they are to see channel revenue become a meaningful contributor down the line.”
Infrastructure Exception: Infrastructure companies rely even more heavily on partnerships, typically deriving 30%+ of revenue from channels across all growth stages, reflecting the ecosystem nature of infrastructure solutions.

The Investment Reality: This isn’t experimental budget—companies are making massive bets on AI for GTM. High-growth companies are planning to nearly double their AI spend for internal GTM use cases.
Investment by Segment:
What They’re Buying: The top AI use cases getting investment are lead generation (61% adoption), automated content creation (58%), call transcription/analysis (71%), and AI-powered personalization (46%).
Implementation Challenges: The biggest barriers are cost of AI tools, deploying AI at scale, and privacy/security concerns—but companies are investing through these challenges rather than waiting for them to be solved.
The Organizational Split: Perhaps the most fascinating finding is that AI-native and traditional SaaS companies are evolving their GTM organizations in completely opposite directions.
AI-Native Approach:
Traditional SaaS Evolution:
The Expert Take: Dennis Lyandres (former Procore CRO): “We’re seeing the emergence of roles like the ‘forward-deployed engineer’—particularly as AI-Native companies expand into multiproduct offerings more aggressively.”
Nick Cochran (former Databricks VP Customer Success): “The traditional CSM role no longer made sense for us… The core responsibilities of Customer Success are now distributed across all teams that engage with the customer.”
Strategic Implications: Your organizational structure should match your product complexity and customer onboarding needs. AI products may require more technical hand-holding, while mature SaaS products can rely on distributed customer success models.
The Bottom Line: These aren’t just statistical variations—they represent fundamental shifts in how successful B2B companies operate. The winners are embracing AI systematically, rethinking pricing models, building partnerships early, and adapting their organizational structures to match their product realities. The performance gaps are widening, and catching up will only get harder.
Founder SaaStr