How I approach AI strategically in product
ChatGPT and other LLMs are the biggest hype and everyone in your company wants to have feature with it. Now.
But as a product leader, you approach it strategically. Here's how I do it:
- Identify Existing Problem Areas: Identify the current problem areas within the organization where generative models, particularly LLMs (Language Models), can be applied.
- Brainstorm with Leadership: Collaborate with the leadership team to brainstorm and explore how generative models can be integrated into existing product priorities to address the identified problem areas.
- Exclude Common Use Cases: Exclude use cases that are likely to be provided by other companies or existing tools. Focus on unique applications that align with the organization's core competencies.
- Filter Based on Improvement: Filter out and prioritize only those ideas that have the potential to enhance the organization's core competencies and add value to its products.
- Feasibility and Complexity Assessment: Have Data Scientists assess the feasibility and complexity of the selected ideas. Assign feasibility/complexity estimates to each idea to understand resource requirements.
- Run Experiments: Implement a couple of experiments to test the selected ideas and evaluate their effectiveness.
- Evaluate High-Value Opportunities: Measure the success and impact of the experiments. Determine how much of a low hanging fruit the high-value ideas are, based on the results obtained.
- Mitigate Hype-driven Decisions: Ensure that decisions are not solely based on hype or trend. Focus on practicality and value creation for the company.
- Consider Sales and Value Creation: Recognize that integrating hyped technologies into products can positively impact sales. Prioritize projects that align with business goals and create value for the organization.
- Report and Communicate Results: Regularly report and communicate the findings, progress, and outcomes of the experiments to stakeholders within the organization.