Claude office agents let Claude share context across Excel and PowerPoint through a single toggle. Here is what the feature actually does, what the Skills framework changes, and the security gaps you need to know about before enabling it.
AI-generated code has 2.74x more vulnerabilities than human code. Most of it lives on employee laptops with zero scanning, logging, or continuity planning. Here is a three-pillar blueprint that costs about 12000 dollars a year to implement.
When AI indexes your files, SharePoint and OneDrive expose data very differently. Here is how permission models, governance gaps, and the oversharing problem change everything about where you store documents for AI workflows.
Claude offers three distinct extension mechanisms - connectors for data access, skills for custom instructions, and plugins for team-wide bundles. Each carries different security implications and admin controls that Anthropic does not make obvious upfront.
A Seagate and IDC study of 1,500 enterprise leaders found 68% of business data goes completely unused. AI amplifies whatever state your files are in. If SharePoint is a graveyard of duplicate presentations and orphaned project sites, AI just indexes the mess faster. File organization is not a nice-to-have before AI adoption. It is a prerequisite.
Claude models now run inside GitHub Copilot at no extra cost. That does not mean Copilot replaces a direct Claude subscription. The context window shrinks, extended thinking disappears, and MCP support is missing. Most mid-size teams end up needing both. Here is what to buy and why.
Claude Projects have no character limit on instructions. You can paste an entire 80-page brand guide. But most companies have not done this, so every AI-generated document goes out with default formatting and generic voice. The five-layer brand enforcement stack fixes this systematically without slowing anyone down.
Harmonic Security analysed 22.4 million AI prompts across enterprises and found 665 distinct tools in use. ChatGPT alone caused 71.2% of data exposures. Standardizing on one vendor is not about picking favourites. It is about making the approved option so good that nobody bothers looking elsewhere.
AI4SP research across 600,000 individuals found 80% of enterprises see zero ROI from top-down AI mandates. The companies getting adoption right build three-tier champion networks where trusted peers test, validate, and spread use cases from the middle of the organization outward.
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.
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.
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.
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%.
Azure OpenAI offers over 100 Microsoft compliance certifications but lags weeks behind OpenAI on new models like GPT-5.2. It is insurance, not improvement. Here is how to choose.
Multimodal AI combining text, vision, and speech sounds powerful until you see the 10x token cost increase. With models like GPT-4o and Claude, real value comes from modalities that inform each other, not from stacking capabilities.
Open source AI models look free until you add infrastructure, staffing, and maintenance. With RAND Corporation research showing over 80 percent of AI projects fail, most mid-size companies find proprietary solutions cost less overall.
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 RAND Corporation study linking the majority of AI project failures directly to data quality issues
Stanford and UC Berkeley 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.
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.
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.