The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.

Instead of sending massive text blocks, feed unstructured data like user survey responses or Slack community introductions into a presentation AI. This quickly generates digestible, visual reports with synthesized personas, key takeaways, and charts, a task that would previously take a team weeks to complete.

Markdown plans from AI agents are becoming too long and unreadable. HTML allows for richer, more engaging artifacts with visuals and better formatting. This improves human oversight and collaboration with the AI, as the plans are more likely to be read and understood by the engineer.

Remote and global teams suffer from a loss of context. An "AI Buddy" can solve this by delivering personalized, timely information about what relevant colleagues are doing. This automated, customized "newsletter" keeps everyone in the loop without them having to read everything, increasing social awareness.

Move beyond generating plain text by prompting AI to build complete, individual HTML artifacts for email campaigns. By specifying brand styles, you can get production-ready code that can be directly imported into an email service provider, significantly reducing manual design and coding work for marketing teams.

Traditional file formats like PowerPoint and Word documents are difficult for LLMs to parse. The future of work involves creating artifacts, like SOPs or presentations, in formats such as HTML that are easily understood by both humans and AI, improving workflow automation and knowledge transfer.

A powerful, non-obvious use for AI assistants is proactive stakeholder management. Amol Avasare runs a scheduled task for Claude to look across his Slack channels and projects to find potential areas of misalignment. This helps him surface and resolve issues before they derail projects.

Standard file formats like .docx and .pptx are filled with complex code that LLMs struggle to parse. To build effective AI workflows, companies must create deliverables in formats that are both human-readable and AI-friendly. HTML is a prime example, as it is visually appealing for people and easily ingested by AI.

The most advanced analytics workflow moves beyond manual dashboards to scheduled AI agents. These agents analyze data, synthesize top insights and deviations, and automatically push a report into the team's Slack channel. This frees PMs from routine reporting to focus on strategic action.