AI that works for your business.
Not against it.

Your team is already using AI — the question is whether you know about it and whether it is safe. The average 30-person organization has 14+ AI tools in use across employees, most paid for personally and unsanctioned. Without governance, you are running an unmonitored data exfiltration channel into vendors you have not vetted. Even if you decide not to deploy Copilot, an AI policy and Shadow AI scan is the minimum responsible posture in 2026.

It depends on where your data lives, what compliance frameworks you operate under, and what work your team actually does. Microsoft 365 Copilot makes sense for M365-native shops that want AI inside Word, Excel, Outlook, and Teams. ChatGPT Enterprise is strong for general-purpose work with the broadest model access. Claude for Enterprise excels at long-document analysis (legal, research, contracts). Custom Azure OpenAI or AWS Bedrock fit when you need RAG over proprietary knowledge or strict residency. We do not push one platform — we score them on a decision matrix against your specific needs.

Microsoft 365 Copilot is $30/user/month on top of M365. ChatGPT Enterprise is roughly $60/user/month at typical seat counts. Claude for Enterprise is similar. Azure OpenAI and AWS Bedrock are usage-based (per-token) which can be cheaper or more expensive depending on volume. The real cost trap regardless of platform is overprovisioning: most clients buy seats for everyone and discover 40-60% never log in. We deploy in pilot waves, measure actual usage, and rightsize the license count. Typical client saves $20K-$80K/year vs. an org-wide rollout.

When configured correctly, yes — for the enterprise tiers. Copilot, ChatGPT Enterprise, Claude for Enterprise, Gemini Enterprise, and Azure OpenAI all contractually do not train their foundation models on your data. The free consumer versions of each (chat.openai.com, claude.ai, etc.) typically do — which is why Shadow AI is a real risk. We deploy only the enterprise tiers, configure the opt-out toggles, verify the contractual terms, and audit-log every interaction. The actual security work, regardless of platform, is fixing oversharing in your file repositories before turning the AI loose.

Probably not — outright blocking pushes usage to personal devices, which is worse. The right move is sanctioning enterprise versions (ChatGPT Enterprise, Claude for Enterprise, Gemini Enterprise) that contractually do not train on your data, then blocking the free consumer tiers at the network/browser layer. Our Shadow AI Discovery scan tells us which ones your team is already using so we can sanction the right ones first.

No. AI replaces tasks, not people. The clients we work with use AI to give their existing team capacity — not to reduce headcount. A bookkeeper with Copilot processes more invoices. A sales rep with AI drafts proposals faster. A help-desk engineer with AI ticket triage closes more tickets. The output of your team grows; your team itself stays.

NIST AI RMF (Risk Management Framework) is the U.S. federal standard for governing AI systems. It defines four functions — Govern, Map, Measure, Manage — each with categories and subcategories that map to operational controls. It is voluntary but is becoming the de facto baseline that auditors, insurers, and federal contracts require. We map your AI inventory and controls to NIST AI RMF and produce evidence packages for assessments.

The technical pattern is the same regardless of platform: (1) Source-of-truth hygiene — audit oversharing in SharePoint, OneDrive, Google Drive, Box, or wherever your files live. Fix broken inheritance and "Anyone with the link" sharing. (2) Classification — apply sensitivity labels (Public, Internal, Confidential, Restricted) so the AI knows what it can surface. (3) Policy enforcement — Microsoft Purview, Google DLP, or platform-native controls prevent labeled content from reaching unauthorized users. (4) Pilot validation — deploy in waves to validate controls under real usage before broader rollout. Same playbook, different platform-specific tooling.

Microsoft 365 Copilot, ChatGPT Enterprise, Claude for Enterprise, Gemini for Workspace / Gemini Enterprise, Azure OpenAI Service, AWS Bedrock, Google Vertex AI, and custom RAG platforms built on open-source models (Llama, Mistral) when data residency or cost demands it. We also evaluate point solutions (Glean for enterprise search, Notion AI, etc.) when they fit better than a horizontal AI tool. Selection is driven by fit and security posture — not by vendor partnerships.

Shadow AI is unauthorized AI tool usage by employees — pasting client data into ChatGPT, summarizing contracts in Claude, generating images in Midjourney with company IP. The risk: data exits your tenant boundary into vendors you have not vetted, with no audit trail and frequently with usage rights you have not reviewed. Our Shadow AI Discovery uses network telemetry, browser extensions, and SaaS spend analysis to inventory AI tool usage so you can sanction or block it intentionally.

Yes. We can deliver AI advisory and Copilot rollout as a standalone engagement while another MSP handles your day-to-day IT. We need read access to your Microsoft 365 tenant and coordination with your IT team for policy enforcement. Most clients who try this arrangement eventually consolidate to RRG for both — having one accountable team simplifies governance reporting and incident response.