According to research we conducted with the Healthtech Marketing Network, a community of 200+ healthcare technology marketing leaders, most marketing teams in our market are using AI for a few use cases. Most are using AI in content creation and market research. A few are using it for video production. Very few have truly operationalized its use.
To help you get moving with this, here are ten best practices for using AI to level up your team’s productivity.
1. Dedicate Weekly Learning Time
Set aside specific time (e.g., Friday mornings, 9-10 AM) for team members to experiment with AI tools. Use this time to explore new features, test different prompts, and document what works. This systematic approach prevents AI learning from being pushed aside by daily tasks and creates a culture of continuous improvement.
2. Implement Quarterly Showcases
Hold quarterly meetings where team members demonstrate their AI implementations, share learnings, and discuss both successes and failures. These sessions should highlight both what worked and what didn’t, creating a learning environment where teams can build on each other’s experiences and avoid repeating mistakes. Set specific goals for the next quarter based on these learnings.
3. Start With Department-Specific AI Use Cases
Ask each department head to identify and implement one AI use case within their area annually. This approach ensures AI adoption is relevant to each team’s specific needs and creates accountability at the leadership level. It also helps prevent overwhelming teams with too many changes at once.
4. Create Robust Test Processes
When implementing new AI tools, thoroughly test outputs across multiple scenarios. For example, with voice generation, listen to entire recordings to catch potential errors or inconsistencies. Pay special attention to how AI handles industry-specific terms and establish clear quality control checkpoints.
5. Build Multi-Tool Workflows
Instead of relying on single AI tools, create workflows that combine multiple tools for better results. For instance, use Perplexity for research, NotebookLM for analysis, and ChatGPT for final content creation. This approach leverages each tool’s strengths while compensating for individual limitations.
6. Record and Leverage Sales Calls
Automatically record sales calls and use AI to analyze transcripts for creating customized proposals and improving sales enablement materials. Use these recordings to build a knowledge base of successful conversations and common customer questions that can inform future sales strategies.
7. Establish Document Libraries
Create organized collections of successful RFPs, proposals, and other key documents in NotebookLM to train AI on your organization’s best work. Regularly update these libraries with new successful examples and remove outdated materials to maintain relevance.
8. Implement Version Control
When using AI for content creation, maintain clear processes for human review and approval, especially for client-facing materials. Establish clear editing stages and approval workflows to ensure AI-generated content meets quality standards and brand guidelines.
9. Create AI-Specific Brand Guidelines
Develop clear parameters for AI tools regarding brand voice, approved terminology, and style requirements to maintain consistency. Include specific examples of acceptable and unacceptable AI outputs to help teams understand the standards they should maintain.
10. Track ROI Metrics
Measure specific efficiency gains from AI implementation, such as time saved in content creation or increased proposal win rates, to justify continued investment and expansion. Develop both quantitative metrics (like time saved) and qualitative assessments (like content quality improvements) to build a comprehensive picture of AI’s impact.
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For a deeper dive and a ton of ideas, check the AI Resource Center