Background
AI use cases emerge constantly across technical blogs, developer communities, and open-source platforms. For a consulting organization, the challenge is not finding AI information. It is identifying which ideas are practical, relevant, and worth bringing into client conversations.
IntelliTect had already automated part of that process through a collection of third-party workflows that surfaced AI content in Microsoft Teams. While those automations increased visibility, they did not create a durable body of knowledge. Useful ideas appeared alongside lower-value content, were difficult to find later, and were not evaluated consistently.
IntelliTect saw an opportunity to build a more structured approach using Microsoft Power Platform.
In approximately four to five days, an internal team member used AI-assisted development to create an AI Use Case Intelligence system that continuously discovers, evaluates, stores, and surfaces practical AI use cases for IntelliTect consultants.
Challenges
The volume of new AI content made manual monitoring impractical. Relevant examples were distributed across developer communities, industry publications, and technical sources, with no consistent way to distinguish actionable use cases from general AI news.
IntelliTect’s existing monitoring approach relied on roughly twenty separate third-party automations that primarily pushed links into Teams. The information was visible in the moment, but it was not systematically scored, summarized, categorized, or preserved for future use.
That created a larger knowledge-management challenge. Consultants needed to be able to ask questions such as which AI use cases were most relevant to a particular client scenario and receive answers grounded in curated examples rather than searching through old messages or relying on the open web.
The goal was to turn a stream of information into reusable organizational knowledge.
Solutions
IntelliTect built the system on Microsoft Power Platform using Power Automate, Dataverse, AI Builder, Copilot Studio, and Microsoft Teams.
The solution includes:
- Automated source monitoring
Power Automate monitors eight curated RSS sources selected for their likelihood of producing practical AI use cases. - Content cleaning and deduplication
Incoming items are normalized and checked for duplicates before entering the knowledge pipeline, reducing repeated or low-value content. - AI-assisted classification and scoring
AI Builder evaluates each item and scores it based on factors such as practical buildability. This allows the system to prioritize examples that are more likely to translate into useful consulting scenarios. - Structured storage in Dataverse
Rather than allowing useful examples to disappear into a Teams feed, scored and summarized use cases are stored in Dataverse as a searchable, persistent knowledge base. - High-signal Teams notifications
Strong use cases are surfaced to the team through Microsoft Teams, preserving the immediacy of the previous system while reducing noise. - Grounded conversational access
A Copilot Studio agent gives consultants natural-language access to the curated use case library and IntelliTect’s AI Agent Playbook. Open-web responses are disabled so answers remain grounded in approved internal knowledge and methodology. - Ongoing human review
A quarterly workflow surfaces the strongest use cases for review, allowing IntelliTect to identify patterns and incorporate relevant lessons into its broader AI methodology.
The result is a closed-loop intelligence process: discover, evaluate, preserve, surface, query, and review.tions.
Outcome
The new system replaced roughly twenty fragmented third-party automations with a single Power Platform architecture and eliminated the recurring subscription cost associated with the previous automation platform.
More importantly, IntelliTect now has a structured and continuously growing library of AI use cases instead of a transient stream of links.
Consultants can access the strongest examples as they emerge and query the accumulated knowledge through a grounded conversational agent when preparing for client discussions, evaluating opportunities, or exploring potential solutions.
The system was built by one non-developer team member in approximately four to five days using AI-assisted development and low-code tooling. That speed demonstrates another important use case in itself: domain experts can increasingly build sophisticated internal applications when they understand both the business problem and the capabilities of modern AI and low-code platforms.
The architecture also provides a reusable pattern for client scenarios including competitive intelligence, market monitoring, sales enablement, research curation, and internal knowledge assistants.
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