Most vendor comparisons obsess over model capabilities while ignoring what actually determines success: whether they will pick up the phone when your implementation breaks at 3am. With 95% of AI pilots failing and over half of executives dissatisfied with vendor support, choosing the right partner matters more than choosing the best model.
Migrated to Azure OpenAI for compliance, then back to OpenAI for innovation speed. Azure is insurance, not improvement. Here is how to choose.
Combining text, vision, and speech sounds powerful until you realize most implementations stack capabilities without enriching context. Real value comes from modalities that inform each other.
Open source AI models look free until you add infrastructure, staffing, and maintenance. For most mid-size companies, proprietary solutions cost less overall.
Most companies pick AI vendors based on impressive demos and feature lists, missing what actually matters. The real decision comes down to support quality, technical reliability you can verify, and business stability that protects your investment when systems go down at 2 AM.
The data quality problem that breaks AI is not imperfect data - it is how AI learns from your existing data problems and multiplies them until they destroy everything you built, with 70% of AI project failures linking directly to data quality issues
The choice between RAG and fine-tuning is not about which is better. It is about data freshness, team capacity, and whether your knowledge changes daily or yearly.
The scrappy approaches that make AI pilots successful become liabilities at enterprise scale. Here is how to build AI capabilities that work for 50 people, not just 5.
Most AI consultants fail at Claude Code because they treat it like ChatGPT with a different logo. Specialists understand MCP, context windows, and why tens of thousands of tokens disappear before you even start. Here is how to spot the difference between someone who read the docs yesterday and someone who can implement.