AI prototype case study
This week’s roundup highlights practical takes on AI prototype case study—what’s working now and where teams are finding real value. Start with the main piece, “How a Prototype Helped a Government Department Save $1.2M,” then explore the other two.
- How a Prototype Helped a Government Department Save $1.2M — overview and context from our site.
- How to Govern AI Prototypes and Build Stakeholder Buy-In for Enterprise Success — a field note that riffs on the main theme. Focus: AI prototype governance and stakeholder buy-in strategies for enterprise projects.
- From Prototype to Production: Lessons Learned in Scaling Enterprise AI Systems — a complementary perspective for broader context. Focus: AI prototype lessons learned for scaling enterprise AI systems.
For a broader overview of this topic, see our in-depth resource: AI Projects Too Expensive or Risky? Affordable .NET AI Solutions.
How to start with AI
This week’s roundup highlights practical takes on How to start with AI—what’s working now and where teams are finding real value. Start with the main piece, “How to Start with AI in Business: Expert Guide for Startups to Scale Fast with AI Tools,” then explore the other two.
- How to Start with AI in Business: Expert Guide for Startups to Scale Fast with AI Tools — overview and context from our site.
- From Curiosity to Capability: Building an AI Adoption Roadmap for Startups — a field note that riffs on the main theme. Focus: AI adoption roadmap for startup founders and technical leads.
- Before You Build: The AI Readiness Framework Every Startup Should Use — a complementary perspective for broader context. Focus: AI readiness assessment framework for startups and small enterprises.
For a broader overview of this topic, see our in-depth resource: How .NET Teams Can Get Started with AI.
Low-code AI risks
This week’s roundup highlights practical takes on low-code AI risks—what’s working now and where teams are finding real value. Start with the main piece, “Why You Should Avoid Overbuilding with Low-Code AI Platforms,” then explore the other two.
- Why You Should Avoid Overbuilding with Low-Code AI Platforms — overview and context from our site.
- When Low-Code AI Stops Scaling: The Hidden Technical Debt No One Talks About — a field note that riffs on the main theme. Focus: low-code AI scalability and technical debt risks for enterprise teams.
- Escaping the Low-Code Trap: How to Migrate Your AI System Before It Breaks — a complementary perspective for broader context. Focus: low-code AI migration strategy for scaling enterprise systems.
For a broader overview of this topic, see our in-depth resource: Microsoft AI Development: Build Smarter, Scalable, Cost-Effective AI with .NET and Azure.
Feature engineering in .NET
This week’s roundup highlights practical takes on feature engineering in .NET—what’s working now and where teams are finding real value. Start with the main piece, “Feature Engineering in .NET: Real-World Tactics for Business Data,” then explore the other two.
- Feature Engineering in .NET: Real-World Tactics for Business Data — overview and context from our site.
- Scaling Smarter: Automating Feature Engineering in ML.NET for Enterprise AI — a field note that riffs on the main theme. Focus: feature engineering automation strategies in ML.NET for enterprise AI.
- From Lab to Production: Feature Engineering Best Practices for .NET AI Models — a complementary perspective for broader context. Focus: feature engineering best practices for production-ready AI models in .NET.
For a broader overview of this topic, see our in-depth resource: ML.NET for Data Prep – AI-Ready Preprocessing in .NET.