Instead of an abstract, top-down AI strategy, a practical starting point is to identify the most tedious, repetitive tasks your team performs. Focusing automation efforts on these "chores" provides a tangible win, builds momentum, and offers a low-risk environment for learning AI tools.

The path to adopting AI is not subscribing to a suite of tools, which leads to 'AI overwhelm' or apathy. Instead, identify a single, specific micro-problem within your business. Then, research and apply the AI solution best suited to solve only that problem before expanding, ensuring tangible ROI and preventing burnout.

To overcome employee fear of AI, don't provide a general-purpose tool. Instead, identify the tasks your team dislikes most—like writing performance reviews—and demonstrate a specific AI workflow to solve that pain point. This approach frames AI as a helpful assistant rather than a replacement.

Instead of hiring for a role like "video editor," break the job into its core tasks. Analyze which individual workflows can be automated with AI first. This shifts focus from headcount to outputs, revealing opportunities to augment or replace traditional roles with technology.

Don't assume AI can effectively perform a task that doesn't already have a well-defined standard operating procedure (SOP). The best use of AI is to infuse efficiency into individual steps of an existing, successful manual process, rather than expecting it to complete the entire process on its own.

Onboard users (or yourself) to an AI agent like a new human teammate. Start with easy, high-frequency tasks (e.g., summarizing Slack threads). Progress to harder, multi-step tasks (e.g., scheduling a meeting based on replies). Only then, attempt to automate an entire workflow (e.g., running daily growth experiments).

Instead of just augmenting existing roles, companies should deconstruct jobs into their component tasks. Analyze each task and reassign it to either a machine or a person based on what each does best. For example, remove 'prospect list building' from BDRs and centralize it with an AI-powered data team, freeing reps to focus on selling.

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.

To bridge the AI skill gap, avoid building a perfect, complex system. Instead, pick a single, core business workflow (e.g., pre-call guest research) and build a simple automation. Iterating on this small, practical application is the most effective way to learn, even if the initial output is underwhelming.