Building an AI Workforce: Where Strategy Meets Scale in Modern GTM
At the recent Signals Events , keynote speakers David Elkington and Gabe Larsen discussed one of today’s most pressing questions: How do we build an AI-powered workforce that actually works in go-to-market?
In their conversation, they broke down where most organizations sit on the AI maturity curve, why GTM teams are falling behind, and what it really takes to start scaling AI effectively—not just experimenting with it.
In this newsletter, we’ll cover the most important takeaways: what’s working, what’s not, and how to start using AI in a way that’s both strategic and scalable.
The Market Reality: AI Is No Longer Optional
AI isn’t hype—it’s here. Companies across industries are being asked the same question in boardrooms: What’s your AI strategy?
But most go-to-market (GTM) teams aren’t ready. Expectations to drive efficiency are rising fast, while traditional growth tactics—hiring more people and buying more tools—are no longer enough.
According to Gartner:
• Only 9% of organizations are AI mature
• Just 48% of AI projects reach production
There’s momentum—but adoption is still slow.
GTM Is Behind
McKinsey and BCG outline three phases of AI maturity:
• Experimentation – Using tools like ChatGPT for content
• Testing – Embedding AI into workflows
• Orchestration – Automating complex tasks end-to-end
While developers are moving into Stage 3, using AI tools like Cursor to write most of their code—most GTM teams are stuck between Stage 1 and Stage 2. They're dabbling with AI, but they’re not yet deploying it at scale.
Why GTM Is Struggling
AI in GTM is high stakes—customer-facing, brand-impacting, and harder to get right.
As Gabe Larsen put it:
“You can’t just throw a tool at the problem.”
The traditional playbook of more headcount and more tools is hitting diminishing returns—especially with rising costs and siloed systems.
How to Use AI the Smart Way
David Elkington recommends a phased approach:
1. Test – Explore and experiment
Recommended by LinkedIn
2. Pilot – Validate in a focused use case
3. Scale – Roll out once proven
McKinsey reports that companies who scale AI well see 2.5x higher revenue growth. But success depends on strategy and adoption—not just access.
Where AI Actually Works
Dave emphasized the importance of identifying “pressure point positions”—repetitive, high-volume tasks where AI can consistently perform.
Some effective GTM use cases:
• Event outreach
• Inbound form follow-up
• Multi-channel nurture sequences
The most important thing? Context. AI must understand your CRM, your marketing tools, and your buyer behavior. Without that, it’s just automation—not intelligence.
What Signals Is Doing
Signals introduced Cloud Employees™—autonomous, AI-powered teammates trained on over a billion conversations and embedded across your GTM stack.
They:
• Operate via voice, email, SMS, and chat
• Connect to your entire tech stack
• Run multi-step, intelligent flows
They’re not just tools—they’re scalable, intelligent coworkers.
Final Advice for GTM Leaders
Dave closed with three key takeaways for any leader looking to scale AI in GTM:
• Avoid Tool Drops: Don’t start with tech. Start with strategy.
• Focus on Adoption: Success depends on people, not just tools.
• Think Beyond the Tool: Solve a problem first, then apply the right AI.
As AI reshapes modern GTM, the winners won’t just deploy tools. They’ll rethink how their teams work—at scale.
Program Manager @Clozd
6moSuch an insightful keynote, I learned so much! 🤩
Partnership Development @ SchoolAI
6moLoved their keynote and great newsletter! Great to see the actionable insights they shared, something more companies should take a look at 👀