Implement AI effectively by allocating 10% of your time to human-led strategy (ideation), delegating 80% to AI for repetitive execution (research, list building), and reserving the final 10% for human review and integration. This framework ensures human taste and vision remain central to the process.
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Don't expect an AI agent to invent a successful sales process. First, have your human team identify and document what works—effective emails, scripts, and objection handling. Then, train the AI on this proven playbook to execute it flawlessly and at scale. The AI is a scaling tool, not a strategist from day one.
Product managers should leverage AI to get 80% of the way on tasks like competitive analysis, but must apply their own intellect for the final 20%. Fully abdicating responsibility to AI can lead to factual errors and hallucinations that, if used to build a product, result in costly rework and strategic missteps.
The best initial use for AI in marketing operations is automating high-volume, low-complexity "digital janitor" tasks. Focus AI agents on answering repetitive questions (e.g., "Why didn't this lead qualify?") and cleaning data (e.g., event lists) to free up specialist time for more strategic work.
Instead of fully automating conversations and risking sounding robotic, use AI to provide real-time suggestions and prompts to a human sales rep. This scales expertise and consistency without sacrificing the human touch needed to close deals.
Avoid using AI to create sales outreach from scratch ('black pen'). Instead, use it as an editor ('red pen'). Apply the 10-80-10 rule: 10% human-led prompting, 80% AI-driven task execution, and a final 10% human refinement. This maintains quality while boosting efficiency.
AI's power is not in creating successful strategies from scratch, but in scaling your existing best practices. An AI agent cannot make a broken process work. First, identify what messaging and campaigns are effective, then use AI to execute them at a near-infinite scale, 24/7.
Rather than fully replacing humans, the optimal AI model acts as a teammate. It handles data crunching and generates recommendations, freeing teams from analysis to focus on strategic decision-making and approving AI's proposed actions, like halting ad spend on out-of-stock items.
Instead of viewing AI collaboration as a manager delegating tasks, adopt the "surgeon" model. The human expert performs the critical, hands-on work while AI assistants handle prep (briefings, drafts) and auxiliary tasks. This keeps the expert in a state of flow and focused on their unique skills.
Adopt a 'more intelligent, more human' framework. For every process made more intelligent through AI automation, strategically reinvest the freed-up human capacity into higher-touch, more personalized customer activities. This creates a balanced system that enhances both efficiency and relationships.
AI should not be the starting point for creation, as that leads to generic, spam-like output. Instead, begin with a distinct human point of view and strategy. Then, leverage AI to scale that unique perspective, personalize it with data, and amplify its distribution.
