Level 2 – Basic AI Automation(Pilot Projects Begin)

What This Level Means

At Level 2, organizations have started experimenting with AI, but adoption is fragmented and inconsistent. AI is often deployed in isolated use cases, such as rule-based automation for sales and marketing, without strategic alignment or cross-functional coordination.

Without clear governance or data centralization, AI adoption is typically driven by individual champions rather than leadership mandates. As a result, data silos persist, limiting AI’s full impact and making it difficult to scale beyond pilot projects.

While Level 2 organizations have taken the first step toward AI-driven GTM execution, they risk stagnation if AI remains an experimental initiative rather than an integrated strategy.

What It Means to Be at This Level in 2025

The AI adoption curve is steep, and organizations stuck in pilot mode risk falling behind.

• 81% of sales teams are experimenting with or have fully implemented AI, leading to 1.3x higher revenue growth than non-AI teams (Salesforce).
• 75% of businesses will use generative AI to create synthetic customer data by 2026, a massive leap from less than 5% in 2023 (Gartner).
• Only 26% of companies have scaled AI beyond initial pilots, while 74% remain stuck in early experimentation (BCG).

Level 2 organizations are at a crossroads: those who fail to move beyond pilot projects will struggle to keep pace, while those who scale AI strategically will unlock faster GTM execution, increased efficiency, and stronger competitive positioning.

How to Start Moving Toward Level 3

To break out of pilot mode and transition into AI-augmented GTM workflows, leaders should focus on four key priorities:

1. Break Down Data Silos

AI thrives on high-quality, connected data. Invest in centralizing AI-relevant data across sales, marketing, and customer success to enable real-time insights and predictive automation.

2. Define AI Governance & Strategy

AI needs ownership and direction to scale effectively. Establish clear governance frameworks, align AI investments with business objectives, and develop a roadmap for systematic AI adoption.

3. Expand AI from Experiments to Operations

Move beyond basic AI automation (e.g., chatbots, lead scoring) and implement AI-driven workflows, such as:

• AI-powered sales enablement to optimize pipeline management.
• TOFU content creation with first drafts at 90% of the way done.
• Real-time sales coaching based on AI-driven call analysis.

4. Upskill Teams for AI Readiness

AI adoption must be organization-wide, not just limited to a few AI enthusiasts. Invest in:

• Training programs and workshops to improve AI literacy
• Hands-on exposure to AI-powered GTM workflows
• Cross-team collaboration to ensure AI adoption is scalable and sustainable

By expanding AI use cases, integrating automation into core workflows, and ensuring teams are AI-ready, organizations can move from ad-hoc AI pilots to systematic, revenue-driving AI adoption.

Take the Next Step Toward AI-Augmented GTM Execution

Explore our State of GTM AI Report to learn how leading companies are breaking out of AI experimentation and driving real business impact.