Keeping up with what’s happening in GTM can feel overwhelming when you’re juggling a full schedule. That’s why this monthly recap is here. Each month, we pull together the key insights from the latest GTMonday newsletters from GTM Partners and highlight what’s worth noticing.
Now grab a coffee, take a breath, and enjoy October’s GTM highlights.
Companies everywhere in the world faces the same reality today: cuts to budgets, RIFs, and decreased staff but still tries to get the same results. Leaders are forced into making hasty decisions, AI vendors promise efficiency overnight, and teams are stretched thin trying to fill gaps. But here's a fact most organizations overlook:
When a company isn’t aligned – on its markets, products, priorities, or metrics – budget cuts only amplify the problems. Teams lose focus, execution slows, customer trust wavers, and leaders end up guessing what to cut or protect. A RIF doesn’t create alignment; it exposes the lack of it.
This is why strategy becomes even more important when budgets shrink. The GTM Operating System (GTM OS) gives companies a structured way to reconnect strategy with execution so every remaining resource is pointed toward the work that actually moves the business forward. Rather than reacting out of urgency, companies can recalibrate with intention.
The GTM OS helps leaders answer the questions that matter most in a lean environment – questions that often get ignored during a RIF:
Where can we still win with the capacity we have?
Which products truly deserve protection right now?
What GTM motions are still realistic with a smaller team?
How do we maintain customer trust when internal resources change?
And which metrics actually matter in this new reality?
With this system in place, companies stop cutting blindly. They make smarter choices, protect the functions that matter most, and rebuild alignment around a shared, predictable way of operating.
Total Relevant Market: Focus on the segments that bring the highest ROI.
Market Investment Map: Protect the products that matter; hit pause on the rest.
Brand & Demand: Stay visible – repurpose content and keep a consistent POV.
Pipeline Velocity: Run fewer GTM motions but execute them well.
Customer Time-to-Value: Safeguard onboarding so customers still see fast results.
Customer Expansion: Use data to spot upsell opportunities and give clear ownership.
Revenue Operations: Zero in on the 3–5 KPIs that define business health today.
Leadership: Lead with clarity, transparency, and shared language across teams.
Businesses everywhere are investing in AI, launching pilots, testing tools, and hoping for fast wins. But the data tells a different story: most AI pilots fail, many projects never scale, and companies spend millions without seeing meaningful ROI. The problem isn’t the tech – it’s the way organizations approach it.
Teams too often start with the tool, not the problem. Pilots remain isolated, workflows are not updated, and adoption is spotty. Leadership treats AI as an experiment, not part of the operating model. Without clear goals, integration, training, or governance, AI is something people "try" instead of something they rely on.
This is where the 3P Framework helps. Most companies remain stuck in Problem Market Fit-scattered pilots and no real prioritization. Product Market Fit happens when AI becomes part of daily workflows. Platform Market Fit is when AI turns into shared infrastructure used across the business. Few companies reach that stage, because scaling AI requires alignment, ownership, and leadership-not just buying more tools.
The C.A.T. framework and Pillar 8 of the GTM OS provide that structure.
Clarity: This means defining what AI is for and what outcomes it should drive.
Alignment: Build playbooks, guardrails, and feedback loops.
Team: assign roles, incentivize usage and build habits.
Leadership cadence: Make AI part of planning, reviews, and accountability.
AI doesn't fail because it's not powerful; it fails because organizations don't build the system around it. When leaders create clarity, alignment, and consistent rhythm, AI moves from interesting pilot to real business impact.
Product-led growth has always been appealing: lower GTM costs, faster adoption, and the promise that the product could "sell itself." For companies like Slack or Calendly, it worked brilliantly. But today, fewer than half of the SaaS companies report consistent revenue efficiency from PLG-even though nearly all plan to invest more in it. And AI is now reshaping the PLG conversation in ways most teams aren't prepared for.
The original idea with PLG was simple: let the users experience the value for themselves. If the product was intuitive and the customer was the right fit, they'd onboard themselves, they'd convert quickly, and they'd expand naturally. But AI is changing the equation by making products easier to demo, easier to onboard, and in some cases, capable of generating new workflows or features automatically. It's no longer a question of "Should we try PLG?" but "Are we ready for what happens when our product starts to sell-and build-itself?"
PLG still brings huge advantages: shorter sales cycles, reduced friction, and a motion that matches how many modern buyers want to go about evaluating software. But PLG only works when two things are true: the product is really easy to use, and the buyer is comfortable self-serving. When either of those breaks down, the model naturally shifts toward Sales-Assisted PLG or even a full sales-led approach. Even strong PLG companies rely heavily on community, support, and guided help to turn free users into real revenue.
AI adds both opportunity and risk. It can dramatically shorten time-to-value and make experiences feel smoother – but it can also create feature overload, make products look more intuitive than they actually are, and drive shallow adoption that doesn't stick. AI doesn't remove PLG challenges; it amplifies them. You can bring in more users faster, but you can also lose them faster if the product isn't truly differentiated or habit-forming.
That's why the most successful GTM teams move toward Sales-Assisted PLG: combining a self-serve entry point with human guidance on activation, value realization, and expansion. It maintains low friction at the top of the funnel and provides depth and retention where it matters.
The bottom line: PLG still works, but it isn't a magic shortcut-especially in the AI era. It works when the product is intuitive, the buyer is empowered, and the company invests in support and community. It fails when teams confuse sign-ups with success, or assume AI-driven automation equals real adoption. The critical question before going all-in on PLG is, is your product truly able to sell itself, or does AI just make it look that way?
And year after year, companies leap into planning with new ideas, new campaigns, and ambitious targets-but skip the one exercise that would make everything else easier: defining their Total Relevant Market. TRM isn't a messaging workshop or a brainstorming session. It's the foundation of your entire GTM strategy. And when teams get it right, the impact is dramatic: higher win rates, more efficient pipeline, and stronger revenue performance-without needing new tools or new headcount.
Most teams rely on TAM - the broad set of companies they could sell to - but TAM is theoretical. TRM is practical. It's the segment of the market that is actually likely to buy, stay, and grow. Yet many companies still operate with vague sketches of who their best customers are, using surface-level criteria like size or industry. High-performing GTM teams go deeper. They use firmographics, technographics, intent signals, buying triggers, ecosystem context, and readiness-to-buy indicators. They know exactly who gets value fast - and who doesn't.
A clear TRM sits atop the GTM Operating System for a reason: everything downstream depends on it. Product prioritization, demand generation, pipeline efficiency, onboarding quality, expansion potential, forecasting accuracy, and even leadership alignment all improve when the company shares a precise understanding of its best-fit market. When TRM is fuzzy, everything else suffers-messaging becomes generic, sales wastes cycles, customers struggle to see value, and teams lose confidence in the plan.
This is where high-performing teams separate themselves. They don't just define TRM once; they operationalize it. They build plays around their best segments, tailor campaigns to the right audiences, measure time-to-value by segment, route deals intelligently, and constantly refine based on real data. They plan with discipline, communicate with clarity, and make decisions based on who is most likely to buy and succeed-not wishful thinking. The bottom line: heading into 2026, the companies that win won't be the ones trying to sell to everyone. They'll be the ones aiming with precision-focusing their time, budget, and energy on the customers who are most likely to convert, retain, and expand. If you don't know exactly who you build for, sell to, and support, you aren't planning-you're guessing. And high-performing teams don't guess.
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