Microsoft Power Apps AI Features - Build Apps by Talking to a Computer

Microsoft changed how Power Apps works by adding AI features that let you talk to the computer instead of clicking through menus. The May 2025 feature update introduced major improvements to AI Builder integration. It's pretty cool when it works, but don't expect miracles.

Plans: AI That Actually Plans Stuff

Plans is Microsoft's attempt to make an AI that can design entire solutions for you. You describe what you need, and it tries to figure out all the moving parts - Power Apps, Power BI, Power Pages, the whole ecosystem.

The process mapping agent (still in preview as of September 2025) takes your business requirements and draws process diagrams. Sometimes they're actually useful, sometimes they look like someone had a stroke while using Visio.

Here's what I learned the hard way: Plans works great when you describe your requirements clearly. Expect to iterate 3-4 times before it gets what you actually want. The first suggestion is usually "close but not quite right," like when you ask for a CRM and it suggests 47 different tables you don't need.

Plans can generate:

• Power Apps (obviously)
• Power BI reports (that may or may not show useful data)
• Power Pages websites (basic ones work fine)
• Copilot Studio agents (if you're into that)

Copilot: Talking to Your Computer Like a Crazy Person

Copilot in Power Apps lets you build apps by describing what you want instead of dragging controls around like it's 1995. You tell it "I need a form for expense reports" and it makes one. Pretty neat until you need something specific.

What works well:

• Basic forms and galleries
• Simple data connections to Dataverse
• Standard business app layouts
• Power Fx formulas for common operations

What doesn't work so well:

• Complex business logic ("if the customer is from California but it's Tuesday...")
• Custom styling that doesn't look like every other Power App
• Performance optimization (it generates functional code, not fast code)
• Error handling (you'll need to add that yourself)

Copilot is helpful for basic apps but still generates buggy formulas for complex logic. Always test the hell out of anything it creates, especially calculations involving money or dates. The official testing guidance covers what to check for.

Generative Pages: The Shiny New Thing

Generative Pages is in early access preview and it's impressive when it works. You describe an interface or upload a wireframe, and it generates actual React code. The results are often better than what I'd drag-and-drop myself.

Reality check: This is preview software. It creates beautiful interfaces that sometimes don't actually work. The generated code looks professional but might have logic errors that only show up when real users touch it. Don't expect production-ready code without cleanup.

I've had mixed results:

• Simple dashboards: Usually work great
• Complex forms with validation: Need manual fixes
• Data entry screens: Good starting point, requires tweaking
• Reports and analytics: Hit or miss on the queries

Enhanced Component Properties: Finally, Reusable Stuff

Enhanced Component Properties went GA and now you can actually build component libraries that don't suck. You can pass both data and functions to components, which should have been possible from day one.

This is genuinely useful if you're building multiple apps that need similar functionality. Create your components once, use them everywhere, and when you fix a bug, it's fixed everywhere. Revolutionary stuff for 2025.

Custom AI Models: Expensive But Powerful

Power Apps supports custom AI models from Azure AI Foundry now. You get access to thousands of models including GPT-4, Llama, and whatever else Microsoft licenses this month.

This sounds cool until you see the Azure bills. Custom AI models aren't cheap, and they burn through credits faster than you'd expect. Unless you have specific domain expertise that standard models can't handle, stick with the built-in AI features.

Agent Supervision: Babysitting Robots

The Agent Feed feature lets humans supervise AI agents in model-driven apps. It's like watching a robot do work and stepping in when it gets confused.

Useful for scenarios where AI can handle 80% of the work but humans need to catch the edge cases. Works well for document processing, customer service routing, and data validation workflows.

Real Talk About Pricing (September 2025)

Power Apps Premium: $20/user/month

• 500 AI Builder credits (burn through these fast if you're doing serious document processing)
• That $20 quickly becomes $50+ when you add up all the Azure services you actually need

AI Builder Add-ons: $500/month per million credits

• Keep an eye on your credits because they disappear fast and Microsoft will happily charge you $500/month for more

Enterprise: $12/user/month with 2,000+ seats

• Sounds good until you realize you need 12 different licenses to make everything work

What Actually Works in Production

I've been testing these features since the previews started. Here's what I'd actually use:

Use This Stuff:

• Plans for initial solution architecture (saves hours of requirements gathering)
• Copilot for basic app generation (good starting point)
• Enhanced components (should have existed years ago)
• Agent supervision for semi-automated workflows

Maybe Use This:

• Generative Pages (when it leaves preview and gets more stable)
• Custom AI models (if you have deep pockets and specific needs)

Skip For Now:

• Anything marked "preview" for production workloads
• Custom AI models unless you have a specific use case worth the cost

The learning curve isn't technical skills - it's learning how to talk to the AI without confusing it. Expect to spend 2-3 weeks figuring out how to describe requirements clearly enough for the AI to understand. Microsoft's prompt engineering documentation actually helps with this part.

Learning to Talk to Computers (It's Weirder Than You Think)

The hardest part isn't technical - it's learning how to describe what you want clearly enough that an AI won't completely misunderstand you. Think of it like explaining your requirements to a really smart intern who takes everything literally.

Prompts That Actually Work

Bad Prompt: "I need a customer app"
Good Prompt: "I need a mobile app where field technicians can view customer service tickets, update status (Open/In Progress/Resolved), add photos of completed work, and record time spent. The app needs to work offline and sync when connected."

Why the difference matters: The AI can't read your mind. Give it specific details or you'll get a generic CRUD app that does nothing useful.

Patterns that work well:

• Start with who will use it ("Field technicians need...")
• Describe the key actions ("View tickets, update status, add photos")
• Mention constraints ("Must work offline")
• Include data they need to see/capture

Plans: Getting AI to Architecture Your Solution

Plans works best when you give it a complete business scenario instead of a shopping list of features.

Here's what I've learned from using Plans on real projects:

What works: "We're a 200-person manufacturing company. When equipment breaks down, operators fill out paper forms, maintenance staff gets notified by phone, parts are ordered manually, and we have no visibility into repair status. We need to digitize this entire process for 50 operators and 10 maintenance staff."

What doesn't work: "We need asset management software with workflows and reporting."

The AI understands business processes better than technical requirements. Describe the problem, not the solution.

Real Production Implementation Notes

AI Builder Credit Budgeting (Learn From My Mistakes)

Each Power Apps Premium license comes with 500 AI Builder credits. Here's where they actually go according to the official credit consumption rates:

Credit Killers:

• Document processing: 10-50 credits per document (invoices, receipts, contracts)
• Custom model inference: Variable, but expensive for complex operations
• Form recognizer operations: 1-5 credits per form
• Translation services: 1 credit per 1000 characters

I learned this the hard way: Built an expense reporting app that processed receipts. Burned through 500 credits in two days during testing. Microsoft was happy to sell me more at $500 per million credits.

Credit preservation strategies:

• Batch process documents overnight using Power Automate scheduling
• Cache common AI responses using Dataverse tables
• Use standard Power Fx formulas where possible instead of AI
• Monitor usage religiously through the admin center
• Implement usage tracking patterns to identify credit-heavy operations
• Use AI Builder templates instead of custom models when possible

Custom AI Models: Only If You Have Deep Pockets

The Azure AI Foundry integration sounds amazing until you see the Azure bills. Custom models are expensive to run and require constant tuning.

When custom models make sense:

• You have specific domain knowledge (legal, medical, specialized industry) requiring custom entity extraction
• Standard models consistently fail on your use cases despite proper prompt engineering
• You have budget for $1000+/month in Azure AI services costs
• You have someone who understands ML model management and Azure ML integration

When to skip them:

• You're trying to save money (stick to prebuilt AI Builder models)
• Your use case works fine with standard AI Builder features
• You don't have ML expertise on staff

Performance Reality Check

What AI Actually Does to Your App Performance

AI-enhanced apps behave differently than traditional Power Apps:

New bottlenecks:

• AI model inference adds 2-10 seconds to operations
• Generated content creates larger datasets
• Conversation logs pile up fast
• More complex formulas = slower app loading

Storage considerations:

• Copilot conversations get logged and take up Dataverse space
• AI-generated content needs versioning support (more storage)
• Agent activity feeds create audit trails (even more storage)
• AI Builder model outputs consume additional storage capacity

Monitor your Dataverse storage consumption - AI apps use significantly more storage than traditional ones. Consider implementing data retention policies for historical AI interaction data.

Security Stuff That Actually Matters

What AI Knows About Your Data

The uncomfortable truth: Everything you type into Copilot gets logged. Every generated formula, every conversation, every requirement description goes into Microsoft's telemetry systems.

Real security considerations:

• Don't put customer SSNs or credit card numbers in AI prompts or interactions
• Assume AI conversations are visible to Microsoft per data processing agreements
• Generated content may contain sensitive information patterns requiring content filtering
• Custom models cache training data creating potential data leak risks

What I do in practice:

• Use fake data during AI-assisted development
• Replace real customer names with placeholders
• Review all AI-generated content for sensitive data
• Implement DLP policies for AI interactions

Change Management: Getting Your Team to Adopt This Stuff

Training That Actually Works

Don't train people on Power Apps features - train them on requirement articulation and prompt engineering.

Useful training scenarios:

• How to describe a business process clearly using requirement gathering templates
• Common prompt patterns that work with AI Builder models
• How to iterate with AI when first attempts fail using testing methodologies
• Budget and credit management with monitoring tools

Useless training:

• "Introduction to Copilot" presentations
• Feature overview sessions
• Technical deep dives on AI models

Success Metrics That Matter

Track these instead of vanity metrics:

Good metrics:

• Time from requirement to working prototype (should improve 40-60%)
• Number of iterations needed to get AI to understand requirements (should decrease over time)
• Credit consumption per app (should stabilize after learning curve)
• User satisfaction with AI-generated starting points

Vanity metrics to ignore:

• "AI adoption rate" (meaningless)
• "Number of AI features used" (who cares?)
• "Reduction in development time" (too variable to measure accurately)

The Bottom Line on AI-Enhanced Development

AI features are useful for getting started quickly, but they're not magic. You still need to understand business requirements, test thoroughly, and fix the weird edge cases the AI misses.

Expect a 2-3 week learning curve where productivity actually decreases while people figure out how to work with AI. After that, you'll see 30-50% improvements on simple apps and 10-20% improvements on complex ones.

The biggest win isn't development speed - it's that business users can now create functional prototypes without learning Power Fx or complex app development patterns. That's genuinely useful if you manage the expectations properly. Microsoft's adoption guidance and Center of Excellence toolkit cover change management strategies that actually work.

But here's the thing: once people see how easy it is to create these AI-powered apps, they'll want to build dozens of them. And that's when you discover why enterprise governance isn't just bureaucracy - it's what keeps your organization from drowning in a sea of unsupported, undocumented applications that nobody remembers how to maintain.