Companies once hired siloed 'digital experts,' a role that became obsolete as digital skills became universal. To avoid repeating this with AI, integrate technologists into current teams and upskill existing members rather than creating an isolated AI function that will fail to scale.
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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.
Instead of searching for new "AI" job titles, non-coders should focus on applying AI capabilities to traditional roles like marketing or sales. Companies are prioritizing existing positions but now require AI fluency, such as building custom GPTs or using AI assistants, as a core competency.
Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.
It's nearly impossible to hire senior product or engineering leaders who are also fluent in AI. The advice for experienced managers is to step back into an Individual Contributor (IC) role. This allows them to build hands-on AI skills, demonstrating the humility and beginner's mindset necessary to lead in this new era.
The traditional tech team structure of separate product, engineering, and design roles is becoming obsolete. AI startups favor small teams of 'polymaths'—T-shaped builders who can contribute across disciplines. This shift values broad, hands-on capability over deep specialization for most early-stage roles.
AI reverses the long-standing trend of professional hyper-specialization. By providing instant access to specialist knowledge (e.g., coding in an unfamiliar language), AI tools empower individuals to operate as effective generalists. This allows small, agile teams to achieve more without hiring a dedicated expert for every function.
Instead of viewing AI as software, solopreneurs should integrate it as a core team member—a marketing assistant, a researcher, and a copywriter. This reframes the relationship from passive tool usage to active delegation, overcoming the limitations of being a one-person team.
To get mainstream users to adopt AI, you can't ask them to learn a new workflow. The key is to integrate AI capabilities directly into the tools and processes they already use. AI should augment their current job, not feel like a separate, new task they have to perform.
To maximize AI's impact, don't just find isolated use cases for content or demand gen teams. Instead, map a core process like a campaign workflow and apply AI to augment each stage, from strategy and creation to localization and measurement. AI is workflow-native, not function-native.
Powerful AI assistants are shifting hiring calculus. Rather than building large, specialized departments, some leaders are considering hiring small teams of experienced, curious generalists. These individuals can leverage AI to solve problems across functions like sales, HR, and operations, creating a leaner, more agile organization.
