Instead of hiring a 'Chief AI Officer' or an agency, the most successful GTM AI deployments empower existing top performers. Pair your best SDR, marketer, or RevOps person with AI tools, and let them learn and innovate together. This internal expertise is more valuable than any external consultant.

A pragmatic way to fund expensive AI tools is to reallocate the budget from headcount that leaves through natural attrition. When a GTM role departs, use their budgeted salary to fund AI agents that can scale the work of the remaining team, avoiding new budget requests and the need to fire performers.

An effective AI strategy pairs a central task force for enablement—handling approvals, compliance, and awareness—with empowerment of frontline staff. The best, most elegant applications of AI will be identified by those doing the day-to-day work.

The biggest hurdle for enterprise AI adoption is uncertainty. A dedicated "lab" environment allows brands to experiment safely with partners like Microsoft. This lets them pressure-test AI applications, fine-tune models on their data, and build confidence before deploying at scale, addressing fears of losing control over data and brand voice.

The primary focus for leaders should be fostering a culture of safe, ethical, and collaborative AI use. This involves mandatory training and creating shared learning spaces, like Slack channels for prompt sharing, rather than just focusing on tool procurement.

Organizations fail when they push teams directly into using AI for business outcomes ("architect mode"). Instead, they must first provide dedicated time and resources for unstructured play ("sandbox mode"). This experimentation phase is essential for building the skills and comfort needed to apply AI effectively to strategic goals.

To avoid chaos in AI exploration, assign roles. Designate one person as the "pilot" to actively drive new tools for a set period. Others act as "passengers"—they are engaged and informed but follow the pilot's lead. This focuses team energy and prevents conflicting efforts.

Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.

Employees hesitate to use new AI tools for fear of looking foolish or getting fired for misuse. Successful adoption depends less on training courses and more on creating a safe environment with clear guardrails that encourages experimentation without penalty.