Tag: enterprise-ai
How to organize SharePoint and OneDrive so Claude can actually find your documents
Your SharePoint and OneDrive setup is organized for people browsing folders. Claude reads metadata. That mismatch is why the M365 Connector finds nothing useful and Cowork struggles with your files. Restructuring for AI access takes a weekend and pays off immediately.
How to pick and run a lighthouse site for your AI rollout
Most companies deploy AI everywhere at once. CIO data shows 88 percent of AI pilots never reach production. A lighthouse site lets you prove value with one team first, build a playbook, then expand with evidence instead of chaos.
Your AI can not think straight when your data lives in four different ERPs
Mid-size companies almost always run multiple ERP systems after acquisitions and organic growth. With 47% of ERP consolidation attempts exceeding budget, most keep running parallel systems. MCP servers offer a different integration pattern: connect each system to the AI layer instead of connecting them to each other.
Azure OpenAI vs OpenAI: the enterprise decision
Azure OpenAI offers over 100 Microsoft compliance certifications but trails OpenAI on new platform features and APIs. It is insurance, not improvement. Here is how to choose.
Multimodal AI is about context, not features
Multimodal AI combining text, vision, and speech sounds powerful until you see the 10x token cost increase. With models like GPT-5.6 and Claude, real value comes from modalities that inform each other, not from stacking capabilities.
Open source vs proprietary AI models - why free costs more
Open source AI models look free until you add infrastructure, staffing, and maintenance. With RAND Corporation noting in its August 2024 report that by some estimates over 80 percent of AI projects fail, most mid-size companies find proprietary solutions cost less overall.
Support beats features every time - the real ai vendor evaluation checklist
Most AI vendor evaluation checklists obsess over model capabilities while ignoring what actually determines success: whether the vendor picks up the phone when your implementation breaks at 3am. RAND Corporation research shows more than 80% of AI projects fail, and 85% of companies miss AI cost forecasts by more than 10%.
RAG vs fine-tuning: The decision that actually matters
Research across twelve language models shows RAG vs fine-tuning is not about which is better. It is about data freshness, team capacity, and whether your knowledge changes daily or yearly.
Scaling AI to enterprise requires unlearning everything
Only 7 percent of organizations fully scale AI past the pilot stage, per MIT Sloan research. The approaches that work for 5 people become liabilities at enterprise scale for 50.
The data quality problem that breaks AI
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 a 2024 RAND Corporation study finding more than 80 percent of AI projects fail, and poor data quality among the leading culprits
How to find a Claude Code implementation specialist who delivers
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.