Most experts know they should be paying attention to what is happening in their industry.
New research appears constantly. Articles circulate across newsletters and LinkedIn. Industry commentary spreads quickly. The volume of information has increased dramatically.
The problem is not access to information. The problem is attention.
In this article we walk through a simple system that uses AI to monitor your industry and deliver a short daily briefing directly to your inbox.
What we’re trying to solve
Most experienced professionals operate under the same constraint: time.
There may be dozens of useful sources across your sector:
- industry news sites
- research publications
- specialist newsletters
- professional commentary
- LinkedIn posts
Checking all of these manually every day is unrealistic.
The result is a familiar pattern. Experts either:
- spend too much time scanning information
- or disengage and risk missing important signals
The goal of this system is not to replace reading entirely.
The goal is to create a structured monitoring layer that highlights developments worth paying attention to.
The simple approach
The system demonstrated in the video uses Google Workspace Studio and Gemini to automate the process.
At a high level, the workflow contains three steps.
1. Research step
The first AI prompt searches the web for relevant developments within a specific industry or topic.
The prompt tells the model:
- what subject area to monitor
- which types of sources to prioritise
- what types of content to exclude
For example, you might exclude opinion pieces or generic commentary and prioritise primary reporting or announcements.
2. Curation step
The second prompt processes the research output and converts it into a structured summary.
The goal here is clarity.
Each item in the briefing contains:
- a headline
- the source
- what happened
- why it matters
- a link to the original article
This allows the reader to quickly understand the development without scanning multiple sources.
3. Delivery step
The final step sends the summary to your inbox.
In the example shown in the video, the briefing arrives each weekday morning at 8am.
The result is a short, structured update you can read in around three minutes.
Tool settings that matter
There are a few practical details that make a large difference to the quality of the output.
Prompt specificity
If the prompts are too open, the system will collect noise.
The model needs clear instructions about:
- what qualifies as relevant information
- which sources to prioritise
- which content to exclude
Structured prompts dramatically improve the signal-to-noise ratio.
Summary length
The briefing should remain short.
If the output becomes a large block of text, it defeats the purpose of the system.
The goal is something that can be skimmed quickly before starting the day.
Source links
Every summary should include a link back to the original source.
This allows you to verify the information and explore further if something appears particularly important.
Common mistakes and trade-offs
Systems like this are powerful, but they do come with limitations.
AI summaries inevitably remove some nuance. Important details can occasionally be simplified or missed.
This is why the system should be treated as a monitoring tool, not a replacement for professional judgement.
The role of the briefing is to surface developments early.
The role of the expert is still to interpret them.
The bigger shift in information management
What’s happening here is part of a larger change.
AI tools are gradually shifting how professionals manage knowledge.
Instead of manually gathering information, experts are beginning to build systems that:
- monitor developments
- summarise signals
- surface important changes earlier
The advantage is not simply efficiency.
The advantage is awareness.
Businesses that detect shifts early tend to react faster and make better decisions.
How to implement this yourself
If you want to build the same workflow shown in the video, you can access the exact prompts used in the demonstration.
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The prompts provide the structure needed to generate useful research and summaries.
From there, you can customise the system for your own industry or topic area.
Key takeaways
- Information overload is becoming a structural challenge for experts.
- AI can help filter and summarise industry developments.
- The real value comes from structured workflows rather than individual prompts.
- AI summaries should support judgement, not replace it.
If you’re exploring how to integrate AI more deeply into an expert-led business, this type of workflow is just the starting point. The real opportunity is designing systems that turn expertise into scalable infrastructure.