6/18/25: The Velocity Mandate: How AI is Rewriting the Go-To-Market Playbook
This is sponsored by the AI Business Network and GTM AI Academy
Today we get to deep dive into some REALLY good AI and GTM/business topics and specifically what it means for YOU.
We get to talk with my guy Mike Allton on the GTM AI Podcast and have 8+ Articles to go through!
Lets get into it:
You can go to Youtube, Apple, Spotify as well as a whole other host of locations to hear the podcast or see the video interview.
"From Prompting Secrets to AI Agents: How This Marketing Expert Saves 18% of Work Time with Simple AI Tricks"
Just wrapped up an incredible conversation with Mike Alton, Chief Storyteller at Agorapulse, and my mind is still buzzing from all the AI gold he dropped. If you're feeling overwhelmed by AI or wondering how to actually use it in your day-to-day work, this one's for you.
The Bridge Between Tech and Reality
Here's what struck me most about Mike: he's a coder who speaks human. After 20+ years in digital marketing and a computer science background, he's become what I call a "translator" - someone who can take complex AI concepts and make them click for regular folks like us.
Mike discovered something fascinating when he asked AI to analyze him based on their conversations. It identified his superpower: bridging the gap between highly technical concepts and simple, practical applications. And honestly? That's exactly what we need more of in the AI space.
The RICC Framework That Changes Everything
One of the biggest takeaways was Mike's RICC prompting framework. Here's the breakdown:
R - Role: Tell the AI who it needs to be
I - Instructions: What you want to accomplish
C - Context: All the relevant background info
C - Constraints: Any limitations or specific requirements
But here's the kicker - Mike always adds "Take your time. Ask me whatever questions you need before we move on." This simple addition transforms AI from a one-way output machine into an actual collaborative partner.
The 20% Game-Changers
During our chat, I asked Mike about the small tweaks that make big differences. Beyond just using a framework, here's what moves the needle:
Chain Prompting: Instead of asking for a finished product, break it down. For a blog post, start with topic ideas, then outline, then headline, then content. Each step builds on the last.
Let AI Ask Questions: Most people don't realize AI won't push back unless you tell it to. Give it permission to clarify, and watch your outputs improve dramatically.
Specific Use Cases: The magic happens when you show someone exactly how AI solves THEIR specific problem, not generic examples.
Real-World Magic in Action
Mike shared how Agorapulse built a RAG (Retrieval Augmented Generation) system that connects their documentation to Slack. Now sales reps can ask questions like "Does Agorapulse support video sharing to LinkedIn?" and get instant, accurate answers while on calls with prospects. No more diving through documentation or pinging colleagues.
He's also automating his podcast production workflow - taking interview transcripts, creating customer success stories, translating them into French and German, and automatically posting to their international blogs. What used to take hours now happens with a few prompts.
The Creativity Factor That Blew My Mind
Here's something unexpected: Mike uses AI for creative projects too. He had AI write lyrics for his podcast theme song, then used Suno (an AI music generator) to create the actual music. I've been doing the same thing - my podcast intro was generated by Suno, and now I listen to my AI-generated playlist more than iTunes!
The Agent Revolution Is Here
We dove deep into the world of AI agents, and Mike's take was refreshing. Instead of getting caught up in the hype, he's focused on practical applications. He's looking forward to AI that can set up entire automation workflows through conversation - imagine telling AI to create a content distribution system and having it build the whole thing in Make.com or Zapier for you.
I shared my experiments with OpenAI's Operator, which can now log into Slack and post research updates automatically. The days of complex API integrations might be numbered.
The Mindset Shift for Leaders and Doers
Mike's advice for navigating AI comes in two flavors:
For Leaders/Business Owners:
Look 12-24 months ahead at how AI will impact your business
Think beyond the time savings - if everyone saves 18% of their time, what happens next?
Start planning how to redeploy that human genius elsewhere
For Individual Contributors:
Focus on how AI can eliminate your mundane tasks
Think about what you could do with the time saved
Remember: you're not being replaced, you're being amplified
My Personal Takeaways
After 314 AI tool demos last year (yes, I counted), conversations like this remind me why I love this space. It's not about the tools - it's about the transformation. Mike's approach of making AI accessible to SMBs who can't afford massive enterprise solutions? That's democratization in action.
The part that really hit home was when Mike talked about testing 100 ad variations instead of 2, because AI makes it instant. That's not just efficiency - that's a fundamental shift in what's possible.
Your Next Steps
Try the RICC framework on your next prompt - especially the "take your time" addition
Start chain prompting - break big tasks into conversational steps
Give AI permission to ask questions - it wants to help but needs the freedom
Think about your 18% - what would you do with that time back?
Mike's philosophy resonated deeply: AI isn't about replacing us, it's about amplifying what makes us uniquely human. The creative breakthroughs, the strategic thinking, the relationship building - that's where we get to focus when AI handles the rest.
Want to dive deeper? Check out Mike's work at theaihat.com and his podcast "The AI Hat." Trust me, if you want someone who can translate AI into actual business value, Mike's your guy.
What's been your biggest AI breakthrough lately?
Video made by Gemini ^^
The Velocity Mandate: How AI is Rewriting the Go-To-Market Playbook
The New Reality - Speed, Hype, and the Hard Truth
I’ve spent the last week buried in the latest research on AI's impact—from VC funding benchmarks to academic papers and internal engineering blogs from places like Anthropic and I've come away with one, unifying conclusion: we are all thinking about GTM in the AI era with an outdated map. The game has fundamentally changed, and it's defined by a massive paradox that every single one of us in a sales, marketing, or leadership role needs to understand intimately. We're seeing numbers that feel like typos next to performance stats that are frankly, a little terrifying. This new reality is about navigating unprecedented speed, cutting through the hype, and facing some hard truths head-on.
The $4 Million ARR Elephant in the Room
Let's start with the headline that’s rewriting the rulebook for growth. A new revenue benchmark report from Andreessen Horowitz (a16z) just dropped, and it effectively renders the old SaaS growth playbooks obsolete. For years, the gold standard for a top-tier enterprise startup was hitting $1 million in Annual Recurring Revenue (ARR) in your first year. It was the magic number that signaled product-market fit and a high-growth trajectory. According to a16z's data, that's now the low end of average. The median enterprise AI company is now hitting over $2 million in ARR in its first 12 months. And for consumer-facing AI apps, it's even more staggering: a median of $4.2 million in ARR in year one. This isn't just an incremental shift; it's a phase change. The willingness of both businesses and consumers to pay for valuable AI products from day one is off the charts.
The timeline from monetization to a Series A round has compressed dramatically. The data shows companies are raising their A-round just eight to nine months after they start charging, fueled by this hyper-growth.
This creates what a16z calls a "velocity story." Speed is no longer just a competitive advantage; it is rapidly becoming the primary moat. The gap between a "good" company and an "exceptional" one is widening at a shocking pace, and the market is rewarding pure, unadulterated speed.
But Can It Do the Job? A Sobering Look at Enterprise AI
Just as you're recalibrating your entire concept of growth, here comes the cold water. While the financial metrics are stratospheric, the actual, on-the-ground performance of AI in complex business roles is, to put it mildly, a work in progress. Researchers at Salesforce AI just published a groundbreaking paper called CRMArena-Pro, where they built a new benchmark to test how well LLM agents can perform real-world Customer Relationship Management (CRM) tasks. These aren't abstract puzzles; this is about routing sales leads, identifying policy violations, and mining call transcripts for insights. The results were sobering. Even the most advanced models from Google and OpenAI struggled mightily when the workflow required more than a single step.
In simple, single-turn tasks, the best agents succeeded about 58% of the time. While not great, it's a starting point.
However, when the task required a multi-turn conversation—the agent having to ask for clarification, process new information, and then act, which is how most real business gets done—the success rate plummeted to a jaw-dropping 35%. Let that sink in: in a realistic, interactive business scenario, the best AI agents fail almost two-thirds of the time.
Furthermore, the study found that agents have "near-zero" inherent awareness of data confidentiality, a non-starter for almost any serious enterprise use case.
Inside the Minds of the Builders
So how do we reconcile this? How can we have companies hitting $4M in ARR while the underlying tech fails a basic business conversation? Part of the answer lies in understanding the culture of the people building this technology. A recent article in Futurism shed light on the almost religious fervor inside labs like OpenAI. It details how key figures are driven by the conviction that they are on the cusp of creating Artificial General Intelligence (AGI), a belief so strong it includes casual mentions of needing "bunkers" for when it arrives. This isn't the slow, methodical culture of traditional enterprise software. It's a movement, and that movement's obsession with pushing the frontier of capability is what fuels both the incredible breakthroughs that lead to massive growth and the overlooking of the mundane-but-critical details of enterprise readiness.
So here is the paradox we all live in now as GTM professionals: we are selling products built by AGI visionaries, funded by VCs who expect light-speed growth, to customers who need enterprise-grade reliability that the technology, in its current state, often can't provide.
How do we bridge that gap? It requires a completely new playbook.
The New Playbook - Your Guide to Winning in the AI Era
So I already laid out the central paradox of AI today: we're seeing revenue growth that defies gravity right alongside performance reports that show the technology is far from ready for enterprise prime time. This isn't a problem to be solved; it's a new reality to be managed. The companies that are winning aren't waiting for the tech to be perfect. Instead, they are pioneering a new GTM playbook—one that embraces the human-in-the-loop, sells velocity over features, and builds a completely new kind of operational model. Let's break it down.
Your Customer's New Superpower (And Your New Responsibility)
For years, we've sold software with the implicit promise of making the user's job easier. Now, we must sell software that gives the user a superpower—but only if they learn how to use it. The most important shift in the AI era is that the user's skill in prompting and collaborating with the AI is now a core component of the product's value. A fascinating meta-analysis from researchers at NUS and Salesforce looked at over 150 papers on prompt engineering and found that specific, nuanced properties like being polite, asking the model to "self-verify," or providing clear context are what truly unlock performance. And crucially, they found that trying to do too much at once—stuffing a prompt with multiple commands—was often less effective than enhancing a single property.
This is a GTM leader's dream: a scientific basis for customer enablement. The absolute best proof of this comes from a report on how Anthropic's own teams use their AI coding assistant, Claude Code.
The Product Design team, with minimal coding knowledge, is now directly implementing front-end design polish and state management changes—tasks that would normally require extensive back-and-forth with engineers. They are achieving the exact visual quality they envision because they can "drive" the AI themselves.
The one-person Growth Marketing team built an entire agentic workflow to automate Google Ads creative generation, a task that would traditionally require significant engineering resources. The result was a 10x increase in creative output.
The Legal team, with no developers, built custom accessibility tools for family members and workflow apps for their department. They've gone from being consumers of technology to creators of it. This isn't automation; it's empowerment. Your GTM strategy must be to sell this empowerment, which means investing heavily in education, prompt libraries, and best practices.
From Incremental Gains to Parallel Universes
The core ROI of AI isn't just about making one person faster; it's about making the entire organization work in parallel. The most forward-looking piece I read was a deep dive into how Anthropic built its multi-agent research system. Instead of having one AI model tackle a complex research question sequentially, a lead "orchestrator" agent breaks the problem down and assigns pieces to multiple "sub-agents" that work simultaneously. This is the new architecture for work.
The results are astounding. On a complex research task, the multi-agent system outperformed their most powerful single model by 90.2%. It could find answers the single model simply couldn't because it could explore multiple paths at once.
This performance comes at a cost. Multi-agent systems use about 15 times more tokens (i.e., compute power) than a simple chat interaction. This is a critical detail for pricing and positioning.
The GTM takeaway is clear: the business case for advanced AI is not "we can save you 10% on costs." It is "we can cut your 6-month research project down to 2 weeks." The value conversation must be about the immense opportunity cost of sequential work and the game-changing advantage of parallel processing.
A Glimpse of the New Marketing Funnel
Finally, this new paradigm is even changing how we attract customers in the first place. A new study from Semrush on AI and SEO shows a tectonic shift. As users turn to AI for answers, traditional organic traffic is set to be surpassed by AI-driven traffic by 2028. You might think this is bad news, but the data says otherwise. A visitor who comes to your site after being referred by an AI is 4.4 times more likely to convert. They arrive pre-vetted, highly informed, and with a specific intent to act. The game is no longer just about ranking #1. It's about having content so clear, specific, and authoritative that the AI models choose to cite you as the source of truth.
The new GTM playbook is here. It’s about selling velocity. It’s about enabling your users to be expert AI collaborators. And it's about building a brand that is so trustworthy, the machines themselves recommend you. The companies that master this will own the next decade.
The AI GTM Paradox: Navigating Stratospheric Growth and Ground-Level Realities
1. Executive Summary
The artificial intelligence arena is defined by a fundamental paradox: while AI-native companies are achieving unprecedented revenue growth and internal teams are unlocking massive productivity gains, the underlying agentic technology still struggles with core reliability and complexity in real-world business scenarios. This report analyzes seven seminal articles and research papers from Q2 2025 to distill the critical, cross-pattern trends that Go-To-Market (GTM) leaders must understand and act upon.
Four dominant patterns have emerged:
The Performance Paradox: The market is witnessing stratospheric financial growth and ROI in contained applications, yet ground-level enterprise-grade reliability remains low. AI startups are setting new records for speed-to-revenue, while LLM agents fail in multi-turn business tasks more often than they succeed.
The Human-in-the-Loop as a Superpower: The most significant value is being unlocked not by pure automation, but by a sophisticated human-AI partnership. The user's ability to prompt, guide, and collaborate with AI is becoming a critical component of the product itself, shifting the GTM focus from selling a tool to enabling a capability.
Velocity & Parallelization as the Core Value Proposition: The primary ROI of AI is a step-change in speed, achieved through both individual acceleration and systemic parallelization. This redefines the economic argument for AI, shifting it from incremental improvement to order-of-magnitude transformations in operational velocity, but comes at a significantly higher computational cost.
The Rise of a New "AI-Native" Operating Model: A new technology and operational stack is rapidly forming, complete with its own architectures (multi-agent systems), user interfaces (advanced prompting, configuration files), evaluation frameworks (realistic benchmarks like CRMArena-Pro), and strategic vocabulary that GTM teams must adopt to maintain credibility and relevance.
This report will first brief leaders on the key findings from each source document and then delve into a cross-pattern analysis of these four trends, concluding with strategic recommendations for navigating this new landscape.
2. The Emerging Landscape: An Article-by-Article Briefing
A comprehensive understanding requires synthesizing insights from across the AI value chain—from venture capital benchmarks to academic research and in-the-trenches engineering blogs.
a16z: Revenue Benchmarks for the AI Era
Core Thesis: AI-native startups are growing revenue at a dramatically accelerated pace compared to their pre-AI SaaS counterparts.
Key Statistics: The median enterprise AI startup now reaches $2 million+ in ARR in its first year, compared to the previous "best-in-class" benchmark of $1 million. The median consumer AI company is doing even better, hitting $4.2 million in ARR in its first year.
Implications for GTM Leaders: The baseline for "good" growth has been reset. Velocity is paramount, and the ability to demonstrate rapid commercial traction is critical for fundraising and market leadership. "Speed is becoming a moat."
Salesforce AI Research: CRMArena-Pro Benchmark
Core Thesis: There is a significant performance gap between the current capabilities of even the most advanced LLM agents and the demands of realistic, multi-step enterprise workflows.
Key Statistics: Top-tier LLM agents achieve an average success rate of only 58% in single-turn business tasks, which plummets to just 35% in multi-turn interactive settings. Agents exhibit near-zero inherent confidentiality awareness. The one bright spot is "Workflow Execution," where top agents surpass 83% success.
Implications for GTM Leaders: This is the crucial reality check. GTM teams must set realistic customer expectations. The technology is not yet a reliable, autonomous enterprise agent. The value proposition should be focused on tractable workflows and human-in-the-loop augmentation, not full automation of complex roles.
Semrush: The Impact of AI Search on SEO Traffic
Core Thesis: AI-powered search is fundamentally reshaping how users find information and interact with brands, creating both a threat to traditional SEO traffic and a new, high-value opportunity.
Key Statistics: AI search traffic is projected to surpass traditional organic search by 2028. Visitors acquired via AI search are 4.4 times more valuable (based on conversion rate) than traditional search visitors. Notably, nearly 90% of ChatGPT citations come from pages ranking 21 or lower in Google search results.
Implications for GTM Leaders: The marketing funnel is being compressed and reoriented. While overall traffic may decrease, the quality and intent of AI-referred traffic are significantly higher. The focus must shift from ranking #1 to being cited by the AI, which prioritizes content that directly answers highly specific questions over general authority.
Anthropic: How We Built Our Multi-Agent Research System
Core Thesis: Multi-agent AI systems, where a lead agent orchestrates multiple specialized sub-agents, dramatically outperform single-agent systems on complex, parallelizable tasks, representing the next frontier of AI capability.
Key Statistics: Anthropic's multi-agent system outperformed its most powerful single agent by 90.2% on a research evaluation. However, this performance comes at a cost: multi-agent systems use approximately 15 times more tokens than standard chat interactions.
Implications for GTM Leaders: This explains the "how" behind solving more complex problems. GTM teams can now articulate a vision for advanced AI that goes beyond simple chatbots. The ROI conversation must frame the 15x cost against the 90%+ performance gain and the ability to tackle previously intractable, open-ended business challenges.
Anthropic: How Anthropic Teams Use Claude Code
Core Thesis: Internally "dogfooding" an AI assistant reveals its true power as a force multiplier that bridges skill gaps and breaks down silos between technical and non-technical teams.
Key Statistics: The security team reduced incident resolution time from 15 minutes to 5 minutes. The growth marketing team of one achieved a 10x increase in creative output. The product design team cut a complex, cross-functional project from a week of coordination to two 30-minute calls.
Implications for GTM Leaders: This paper is a goldmine of concrete, quantifiable value propositions. It provides the "proof in the pudding" that AI assistants can fundamentally change how work gets done, empowering non-specialists to perform specialist tasks and creating massive operational leverage.
NUS/Salesforce: What Makes a Good Natural Language Prompt?
Core Thesis: Prompt quality can be defined by a framework of 21 properties, and contrary to intuition, enhancing a single property often yields better results than combining many.
Key Statistics: The meta-analysis of 150+ papers identifies specific properties like "Politeness," "Metacognition," and managing "Germane Load." A key experimental finding is that single-property enhancements often outperform multi-property ones.
Implications for GTM Leaders: This provides a scientific basis for customer enablement. The GTM strategy must include educating users on how to interact with the AI. The "less is more" finding is a powerful, non-obvious insight that can dramatically improve user success and product stickiness.
Futurism: OpenAI's AGI "Bunker"
Core Thesis: The leadership at top AI labs is driven by an intense, almost messianic belief in the imminent arrival of AGI, which shapes their culture, risk tolerance, and the ferocious pace of development.
Key Statistics: The article is anecdotal, centering on former chief scientist Ilya Sutskever's comment about an optional "bunker" for when AGI is released.
Implications for GTM Leaders: This provides essential context. The technology is not being built by typical enterprise software developers. It's being driven by true believers with a world-changing mandate. This explains the constant, disruptive pace and the "move fast and break things" ethos that GTM teams must adapt to.
3. Cross-Pattern Analysis: Four Key Trends for GTM Leadership
Trend 1: The Performance Paradox
There is a jarring disconnect between the financial success of AI companies and the functional success of their agents in enterprise settings. The a16z data shows a market rewarding potential and early traction with unprecedented valuations and revenue growth. Meanwhile, the Salesforce data provides a sobering view from the trenches: in complex, interactive scenarios—the bread and butter of enterprise software—agents fail almost two-thirds of the time (a 35% success rate).
Analysis: This is the central tension GTM leaders must manage. The market's enthusiasm is fueled by narrow AI applications, consumer-facing hits, and developer-led tools where an expert human is always in the loop. The Anthropic use cases confirm this; massive ROI is achieved when a skilled human partners with the AI. However, the vision often sold is one of autonomous agents replacing entire workflows, a vision the CRMArena-Pro benchmark shows is still distant.
For GTM Leaders: Your strategy must be two-pronged. The marketing and sales vision must align with the massive potential and ROI seen in the Anthropic and a16z reports. However, your solutions architects, customer success teams, and implementation partners must be deeply grounded in the reality of the Salesforce report, focusing on use cases that play to the current strengths (like single-turn tasks or templated workflow execution) and ensuring a robust human-in-the-loop process for everything else.
Trend 2: The Human-in-the-Loop as a Superpower
The narrative is decisively shifting from AI as a replacement for human workers to AI as a catalyst for human expertise. The research from NUS on prompt properties demonstrates that the quality of human input—its clarity, politeness, and cognitive framing—directly and significantly impacts model output. This is not a simple "garbage in, garbage out" dynamic; it's a sophisticated partnership.
Analysis: The Anthropic "Claude Code" paper is a living document of this trend. A designer who cannot code can now implement front-end polish. A lawyer can build a functional accessibility app. This isn't happening because the AI is fully autonomous; it's happening because the human is directing, guiding, and iterating with the AI as a powerful, tireless partner. The report states non-technical users get a “holy crap, I’m a developer workflow.” This is the superpower: AI as a skill synthesizer.
For GTM Leaders: Customer enablement is no longer a post-sales function; it is a core part of the product value. The "user manual" of the past is now a dynamic "prompting playbook." Your GTM motion must be built around teaching customers how to become expert AI directors. Invest in prompt libraries, use-case-specific recipes, and training that focuses on the art of collaboration with an LLM.
Trend 3: Velocity & Parallelization as the Core Value Proposition
The most consistent and staggering metric across all positive use cases is speed. a16z explicitly calls "speed a moat." The Anthropic multi-agent system cut research time by up to 90%. This is achieved by attacking problems in parallel rather than sequentially. This represents a fundamental shift in how work is structured and measured.
Analysis: The multi-agent architecture is the ultimate expression of this trend. By breaking a complex problem into sub-tasks and deploying multiple agents simultaneously, it transforms the workflow. This comes at a 15x token cost, a crucial detail for managing COGS and pricing. This isn't an incremental "10% faster." It's a paradigm shift that allows companies to explore more possibilities, get to market faster, and make decisions with more complete information in a fraction of the time.
For GTM Leaders: The ROI case for AI is not about efficiency; it's about velocity. You must arm your sales teams to build business cases around the value of time. For example: "What is the value of reducing your product development cycle by 70%?" or "What is the opportunity cost of your current, sequential research process?" The pricing strategy must account for the high computational cost, framing it as a premium investment for an exponential return in speed and parallel processing capability.
Trend 4: The Rise of a New "AI-Native" Operating Model
Successful adoption of AI is not about plugging an API into an old workflow. A new, native ecosystem of tools, processes, and vocabulary is emerging. This includes the Claude.md files that serve as a persistent memory and instruction manual for an AI assistant, the "orchestrator-worker" pattern for multi-agent systems, the use of LLMs-as-judges for evaluation, and the scientific framing of prompt engineering.
Analysis: The teams seeing the most success are not just using AI; they are restructuring their work around AI. The Semrush study points to this in the marketing world, where optimizing for AI citation is a different skill than traditional SEO. The Salesforce paper argues for entirely new, realistic benchmarks. The Futurism piece hints at the grand ambition driving the creators of these new paradigms.
For GTM Leaders: Your team's credibility depends on speaking this new language. Your product marketing must evolve beyond "AI-powered" to describe how your AI works. Does it use a multi-agent architecture? How does it manage state? What kind of prompt engineering frameworks do you provide? Your sales and marketing efforts must align with this new reality, positioning your product not as a feature, but as a key component of the customer's emerging AI-native operating model.