A Practical Guide to Copilots, Agents, and Enterprise AI
In a recent session at the Global Power Platform Bootcamp, AJ Sari, Chief Operating Officer at DSWI and Microsoft MVP shared a clear, experience-driven framework for selecting the right platform to build intelligent assistants.
As the industry moves beyond traditional chatbots toward more capable copilots and agents, the decision is no longer about whether to use AI. but where to build it. The right choice depends on data sensitivity, governance needs, customization depth, and the technical maturity of the team.
🎥 Watch the Session: Choosing the Right Platform for Your AI Assistant
This session walks through real-world scenarios and explains when to use Custom GPTs, Copilot Studio, or Azure AI Foundry.
Understanding the AI Spectrum
Before comparing platforms, it’s essential to align on terminology.
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Artificial Intelligence (AI)
The broad umbrella, including rule-based and decision systems.
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Machine Learning (ML)
Systems that generate their own logic from data instead of hardcoded rules.
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Deep Learning
Removes predefined logic entirely, relying on neural networks.
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Generative AI
Creates new content like text, images, audio, or code.
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Large Language Models (LLMs)
A subset of Generative AI focused on understanding and generating human language.
Microsoft further differentiates:
- Copilots → Personal productivity assistants
- Agents → Role-based, task-oriented assistants operating inside organizations
Understanding this spectrum helps clarify why different platforms exist and why no single tool fits every scenario.
The Three Main Contenders
AJ Sari identifies three primary platforms for building AI assistants:
- Custom GPTs (ChatGPT / OpenAI)
- Microsoft Copilot Studio
- Azure AI Foundry
Each serves a distinct audience and use case.
1️⃣ Custom GPTs (OpenAI)
Custom GPTs provide the fastest entry point into AI assistant creation, with almost no technical barrier.
Key Capabilities
- Grounding with uploaded documents (PDFs, Word files, etc.)
- Native web browsing
- Image generation (DALL·E)
- Code Interpreter for data analysis
Limitations
- No Enterprise Data Protection (EDP)
- Conversations may be used to train foundational models
- Limited control over fine-tuning and hallucination management
Best For
- Individual creators
- Personal productivity assistants
- Public or non-sensitive use cases
- Quick experimentation at low cost
2️⃣ Microsoft Copilot Studio
Often described as Microsoft’s “sweetheart tool”, Copilot Studio is a low-code/no-code platform designed for secure, business-ready AI.
Why It Stands Out
- Connects to SharePoint, Dataverse, public websites, and external systems like ServiceNow
- Strong semantic indexing when content is stored in SharePoint
- Built-in AI orchestration to dynamically select sources and actions
- Native integration with Power Automate for real business processes
Deployment Channels
- Microsoft Teams
- Mobile applications
- Company portals
- External channels like Facebook
Best For
- Organizations using Microsoft 365
- Secure, authenticated AI assistants
- Business workflows and internal support scenarios
- Teams that want power without heavy coding
3️⃣ Azure AI Foundry
Formerly Azure OpenAI Service, Azure AI Foundry represents the most advanced and flexible option.
Advanced Capabilities
Considerations
- Token-based pricing (more complex cost management)
- Requires stronger developer expertise
- Higher operational responsibility
Best For
- Enterprise-scale AI platforms
- Highly regulated or specialized use cases
- Custom architectures and open-source model adoption
- Developer-led AI strategies
Decision Matrix: How to Choose
Make your decision based on your needs clearly:
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✅ Choose Custom GPTs
When you want speed, simplicity, and low cost for personal or public use.
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✅ Choose Copilot Studio
When building secure, governed AI inside an organization using Microsoft data.
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✅ Choose Azure AI Foundry
When you need full control over models, tuning, and architecture and have the technical depth to manage it.
Final Thoughts
There is no “best” platform only the right platform for the right scenario.
The key is to align:
- Business goals
- Data sensitivity
- Governance requirements
- Team capabilities
By understanding the strengths and trade-offs of each option, organizations can avoid costly rework and build AI assistants that are scalable, secure, and future-ready.
🌍 Continuing the Journey
This bootcamp 2025 may have ended, but our community journey continues.
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