GTM Gets a Glow-Up
A Practical Guide to AI in GTM for Executives and Founders
I. Introduction
Feeling the pressure to unlock the next level of growth for your company? As an executive or early-stage founder, you're likely exploring every avenue to gain a competitive edge. At a recent industry marketing meetup I attended, the buzz around AI in Go-To-Market was electric – some were already diving in headfirst, while others were understandably cautious. The reality is clear: marketing, sales, and customer success teams are increasingly leveraging AI to transform everything from crafting compelling content to forecasting and retention.
<“In 2024, marketing and sales teams more than doubled their use of GenAI.” — McKinsey, 2024
If you're wondering where to even begin, you're in the right place. This article aims to provide you, as a busy leader, with a pragmatic starting point and key questions to ask your teams. And let's be clear: I'm not offering a definitive, final answer in this rapidly evolving landscape. Instead, think of this as a guide of considerations.
Read on, you'll discover:
The compelling reasons behind AI adoption in GTM.
Practical use cases and the tools making them possible.
Essential questions every executive should be considering with their team.
(Coming up next: we'll explore the tailored AI strategies for early-stage ventures versus larger enterprises. —- In the meantime, feel free to jump to the GTM function most relevant to you.)
Market Intelligence
Content Creation
Product Marketing
Demand Generation
Sales Enablement
Customer Success
II. GTM Functions Being Transformed
1. Market Research
From Manual to Machine-Augmented
Market research has evolved. We’re no longer waiting weeks for survey results or digging through 80-slide PowerPoint decks for insights. AI has changed the tempo. Today’s tools scan customer reviews, forums, and competitor updates in real time—surfacing trends and helping teams pivot fast.
Why the Shift?
2–3x faster insights (Gartner)
30% better sentiment analysis (Forrester)
40% less manual data work (McKinsey)*
These gains explain the rising adoption of AI, especially among competitive intelligence (CI) and strategy teams.
Strategic Questions to Ask Your Team
Are we analyzing unstructured feedback—or just structured forms?
Can we automate competitive tracking and alerts?
Are we incorporating win/loss insights into GTM strategy?
The research may point to trends—but it’s what you say next that matters. That’s where content comes in.
2. Content Creation:
Volume Is Easy—Voice Is Everything
Let’s be honest: posting every day doesn’t make you a thought leader. It just makes you noisier. Have you seen what is happening to LinkedIn posts and responses?
AI can churn out content fast—but it still can’t think for you. Thought leadership requires real thought: a sharp perspective on a stale topic, a new take on familiar data, or a well-argued prediction. In that sense, AI is the accelerator—but humans are still the spark.
AI Is Ubiquitous—but Not Autonomous
Today, over 80% of marketers use tools like Jasper, Copy.ai, and ChatGPT to draft blogs, emails, and ad copy. It’s a huge unlock for teams stuck at the blank page. But let’s not confuse speed with originality. AI handles volume—but voice, nuance, and emotional resonance? That still takes a human hand.
>“When deciding between AI and human input in communications, I consider the 4 Rs: research, reach, relevance, and relationships. AI is great at accelerating insights and scaling reach — but relevance and relationships? Those still need human judgment and connection.”
— Michele Landry, President, Tanis Communications
You can see the impact most clearly on LinkedIn:
189% spike in AI-generated posts post-ChatGPT launch (Originality.AI, 2023)
54% of long-form LinkedIn posts may be AI-generated by late 2024
Average post length up 107%—but not always better thinking
Ask Yourself (And Your Team)
Where can AI handle the first pass—without sacrificing quality?
Are we adapting content for persona, funnel stage, and industry nuance?
How are we protecting tone, authenticity, and brand voice?
Content doesn’t exist in a vacuum. The best messaging is grounded in sharp product positioning, and that’s exactly where AI is starting to play a more strategic role.
Let’s look at how AI is reshaping the way we position and bring products to market.
3. Product Marketing
From Gut Instinct to Evidence-Backed Positioning
It’s easy to think of AI as a tool for content generation or campaign automation—but it’s quietly transforming one of the most strategic functions in the GTM stack: product marketing.
From market segmentation to win/loss analysis to real-time competitive tracking, AI is making product marketing faster, sharper, and more evidence-based. No more digging through spreadsheets or waiting on static reports.
>“Today’s tools can surface patterns in buyer behavior, test messaging hypotheses, and identify gaps in positioning before your next launch goes sideways,” Vasu Madabushi, Product Marketing Executive.
Why Use AI in Product Marketing
82% of marketers say AI speeds up workflows like research and positioning (CMI, 2024)
2–3x faster insight synthesis using AI vs. manual research (Gartner, 2023)
25% faster message validation cycles (Gartner, 2023)
Ask Yourself (and your team)
Are we using AI to analyze win/loss trends and fine-tune our positioning?
Can AI help us build or refine personas faster?
Are we tracking competitor activity in real time—or waiting for reports?
Where are we relying on gut instinct when AI could give us actual signals?
Can we simulate buyer journeys to validate messaging or positioning?
From positioning to pipeline—once your product story is nailed, demand gen turns it into momentum.
4. Demand Generation:
From Campaigns to Co-Pilots
Once your positioning is dialed in, the pressure shifts to growth—and that’s where demand gen teams are finding new power in AI. Instead of guessing which campaigns will hit or which accounts might convert, marketers now have tools that help them see what's working and why, in real time. With intent signals, predictive scoring, and automated testing, teams can focus less on setup and more on scaling what drives pipeline. It’s like having thermal imaging for your funnel—AI reveals who’s truly warming up to buy, not just who filled out a form.
>AI is a game-changer for demand gen. It takes care of the repetitive tasks, automates workflows, and scales operations, giving teams the freedom to focus on strategy while AI handles the heavy lifting behind the scenes.
-Deepa Caveney, B2B SaaS MarketingImprovements You Can Expect
Lift in MQL-to-SQL conversion rate after implementing AI scoring (often 10–25% improvement)
70% faster time-to-market for campaign content using AI Content Automation & Experience Platforms
Source: TechRadar, Adobe Summit 202437% increase in campaign response rate using AI Real-Time Analytics & A/B Testing Tools to optimize in-flight campaigns.
Source: McKinsey & Co.
Ask Yourself (and your team)
Are we using AI to prioritize leads based on intent, behavior, and fit — or still relying on basic scoring?
How often are we refreshing segmentation based on AI insights or new signals
Which parts of our campaign workflows are still manual and could be automated?
While demand generation fills the funnel with qualified interest, sales enablement ensures those leads are converted—arming sellers with the right insights, content, and tools to close the deal.
5. Sales Enablement
Sales Enablement: Smarter Reps, Faster Wins
Sales enablement has always been about giving teams the tools, content, and training they need to close more deals — but AI is taking it to the next level. Today, top teams are using AI to surface the right content at the right time, coach reps based on real call data, automate follow-up tasks, and give managers clear visibility into what's working. From onboarding and readiness to conversation intelligence, deal reviews, and content optimization, AI is helping sales teams move faster, improve performance, and stay focused on selling.
>"The real value of AI in sales enablement is its ability to drive performance at scale to deliver the right insights at the right time, personalizing coaching to each rep’s strengths, and continuously learning what accelerates deals. It’s not about replacing the human element, it’s about amplifying it to boost productivity, consistency, and results across the team."
– Louisa Morassutti, Enablement ConsultantImprovements with AI:
20–40% faster rep ramp time (Gong internal benchmark)
25% more productive calls via real-time recommendations (Chorus)
15% higher deal close rates using AI-assisted coaching (Salesloft)
Ask Yourself (and your team):
Are we capturing and analyzing rep-customer conversations?
Can AI suggest the right enablement content based on deal stage?
How can AI help new reps ramp faster?
The AI-driven insights and tools that equip sales teams are just the beginning; the journey continues with Customer Success, where AI offers equally powerful ways to nurture relationships and maximize customer lifetime value.
6. Customer Success
Transitioning from Reactive Support to Proactive Engagement
In the early stages of building a company, retaining and expanding your customer base is paramount. AI is revolutionizing Customer Success by shifting it from a reactive support role to a proactive, data-driven function. By leveraging AI, startups can anticipate customer needs, personalize experiences, and scale support without proportionally increasing headcount. This proactive approach not only enhances customer satisfaction but also drives retention and growth.
Why Customer Success Functions are looking at AI:
20% reduction in churn for companies using AI-driven customer success strategies (Gainsight, The State of Customer Churn in 2024)
30–50% of support tickets are now resolved by AI chatbots (Zendesk CX Trends 2024)
18% higher Net Promoter Score (NPS) for AI-personalized customer engagements (Intercom, 2024)
By integrating AI into your Customer Success strategy, you can proactively address customer needs, enhance satisfaction, and drive sustainable growth—all crucial factors for early-stage companies aiming to scale efficiently.
Ask Yourself (and Your Team)
Are we using AI to detect early signs of customer churn?
Can we automate routine customer support interactions?
How can AI personalize the CS journey by segment?
With AI in their corner, customer success teams can stop playing defense and start driving proactive, personalized experiences that keep customers happy—and coming back for more.
III. Where the Human Still Leads
Despite AI’s speed and scale, humans remain essential for:
Strategic judgment
Emotional intelligence
Brand storytelling and creative inspiration
AI doesn’t replace these functions — it supports them. Human-AI collaboration will become the defining competitive edge of modern GTM teams.
What Leaders Should Do Today
Ask the questions of your GTM functional leaders within this article
Embrace a test-and-learn culture with AI tools
Reskill teams for AI collaboration (prompting, auditing, training models)
Set KPIs that reflect both human-led and AI-supported success
Final Thought
The most successful GTM organizations of the future will be those who treat AI not as a threat but as a teammate. The winners won't be the fastest to automate, but the fastest to collaborate — blending human creativity with machine precision to drive growth at scale.
Watch for my next article in a few weeks that will comparing an early-stage to a large enterprise approach to AI GTM tools.
This article is great for understanding how AI can enhance marketing strategies!
A must read for all marketers!