The 95/5 rule, why bottom-funnel spend is hitting a wall, and how to fix your mix
This week: rewiring your GTM team for AI, the data cleanup you can’t skip, marketing to the 95% of buyers not in-market, and the new benchmarks for efficient growth. And what the rapid pace of AI model releases means for your strategy.
Rewiring GTM Teams for the AI Era
The mandate to "do more with less" is driving a significant restructuring of go-to-market teams. Instead of simply layering AI onto existing workflows, companies are redesigning their GTM engines for efficiency, leading to smaller, more agile, and more technical teams.
According to the ICONIQ Growth 2025 GTM report, the era of large, siloed sales and marketing departments is giving way to integrated "pods" that own a piece of the customer journey. This shift is mirrored in sales team composition, where many companies are reducing headcount while increasing quotas for the remaining, more effective reps. The focus is on productivity per employee, not just raw team size.
For marketing leaders, this trend requires a new hiring and development focus. The most valuable marketers will be T-shaped individuals who combine deep domain expertise with broad technical literacy. Your team needs to understand the data, the product, and the technology to effectively partner with their leaner sales counterparts and drive efficient growth.
BIG IDEA: AI's primary impact on GTM isn't replacing jobs, but forcing a move toward smaller, more integrated, and technically skilled teams focused on capital efficiency.
WHY IT MATTERS: As a CMO, you must evolve your hiring profile and team structure to align with a more consolidated and productive GTM model, or risk becoming misaligned with the CEO and CRO.
The Data Prerequisite for AI Success
Is your organization trying to build an AI-powered engine on a foundation of messy data? Many companies are rushing to deploy AI without first addressing the quality of their most critical asset.
Salesforce CEO Marc Benioff has repeatedly stated that trust is the foundation of AI, and you can’t trust AI if you can’t trust your data. This sentiment is backed by ICONIQ’s 2025 State of AI report, which identifies data quality and infrastructure as top barriers to AI adoption. The market is reinforcing this view, with Salesforce’s recent acquisition of data-management firm Informatica signaling a massive bet on the data layer as the critical enabler for enterprise AI.
For CMOs, this means the unglamorous work of data governance is now a strategic imperative. Before you can leverage AI for advanced personalization or attribution, you need a "single source of truth" for customer data. This involves auditing your CRM, standardizing data fields, and ensuring seamless integration across your tech stack. Prioritizing data hygiene today is the only way to unlock the transformative potential of AI tomorrow.
BIG IDEA: You cannot have an AI strategy without a data strategy; garbage in, garbage out is amplified at the scale of modern AI.
WHY IT MATTERS: CMOs who champion data infrastructure projects now will build a durable competitive advantage, while those who chase shiny AI tools without a solid data foundation will see their initiatives fail.
Rethinking Acquisition for the 95%
Are your marketing campaigns and budgets overwhelmingly focused on the 5% of your target market that is actively buying right now? While essential, this intense focus on demand capture often neglects the other 95% of potential customers, leaving significant long-term growth on the table.
This "95/5 rule," a core concept from the B2B Institute, is gaining traction as leaders recognize the limits of a purely bottom-funnel strategy. The majority of your ideal customers are not in a buying cycle. Yet most B2B ad budgets are spent trying to convert that tiny, in-market slice, leading to intense competition and diminishing returns.
This presents a clear mandate for CMOs: balance your portfolio. While you must continue to capture existing demand, you also need to invest in creating future demand by engaging the 95%. This means funding activities like creating insightful content, building a community, and investing in brand advertising that may not have a direct, immediate ROI. It’s a strategic shift from short-term lead generation to long-term audience building, ensuring a steady stream of future customers who think of you first when they finally enter a buying cycle.
BIG IDEA: Focusing only on in-market buyers is a defensive, low-growth strategy; true market leadership comes from building affinity with the 95% of your TAM that isn't buying today.
WHY IT MATTERS: CMOs who can articulate and justify a balanced "capture and create" strategy will build a more resilient and predictable revenue engine, protecting their brands from the volatility of short-term performance marketing.
The New Benchmarks for Efficient SaaS Growth
The "growth at all costs" playbook is officially retired. In today’s market, the health of a SaaS business is judged by its capital efficiency and ability to retain and expand customer revenue.
Recent benchmark reports paint a clear picture of the new expectations. The High Alpha 2024 SaaS Benchmarks survey shows a relentless focus on the "Rule of 40" (where growth rate + profit margin should exceed 40%). Meanwhile, investors emphasize that Net Revenue Retention (NRR) has become the single most important metric, with top-tier companies achieving 120% or more. This focus on existing customers is a direct path to efficient growth, as highlighted in ICONIQ’s analysis of top-performing "Enterprise Five" companies.
As a CMO, you must align your strategy and reporting with these financial realities. The conversation in the boardroom is about CAC payback, LTV, and NRR—not just MQLs or website traffic. This requires a deeper partnership with finance and customer success to demonstrate how marketing activities influence not just initial acquisition but also customer retention and expansion. Your budget and influence depend on your ability to connect your team’s work directly to these measures of efficient growth.
BIG IDEA: In the current SaaS landscape, how you grow is just as important as how much you grow, with NRR and capital efficiency supplanting raw growth as the primary indicators of success.
WHY IT MATTERS: Marketing leaders who cannot speak the language of financial efficiency and demonstrate their impact on metrics like NRR will struggle to secure budget and a strategic seat at the table.
Keeping Pace with Foundational Model Mayhem
Just as teams were getting comfortable with GPT-4, OpenAI released GPT-5.1, followed days later by Google’s launch of its powerful Gemini 3 model. This relentless pace of innovation from foundational model providers presents both a massive opportunity and a significant strategic challenge for B2B tech companies.
The capabilities of these models are advancing at a staggering rate, with each new version offering larger context windows, improved reasoning, and enhanced multimodality. Leaders are constantly tracking these releases, highlighting how quickly the state-of-the-art is changing. While enterprise AI adoption is growing, ICONIQ’s 2025 AI report shows many companies are still in the early stages, making it difficult to commit to a single platform.
This creates a critical architectural decision for marketing leaders overseeing AI-powered features or internal tools. Building your entire strategy on a single model (e.g., "all-in on OpenAI") is increasingly risky. The emerging best practice is to build an abstraction layer that allows you to easily swap underlying models. This approach lets you leverage the best model for a specific task—whether it’s the most powerful, the fastest, or the most cost-effective—and prevents vendor lock-in. It’s about building for the AI wave, not just the current ripple.
BIG IDEA: The rapid, competitive release cycle of foundational AI models makes a model-agnostic, abstraction-layer strategy essential for future-proofing your tech stack.
WHY IT MATTERS: Committing too deeply to a single AI model provider today could leave your product or marketing stack at a competitive disadvantage tomorrow when a better, cheaper model emerges.
The throughline this week is clear: AI is forcing a return to fundamentals. Stronger teams, cleaner data, and more disciplined financial strategies are no longer optional. They are the bedrock upon which any successful AI initiative will be built.
What’s one unglamorous, foundational project you could champion this quarter to set your team up for future AI wins?
