AI strategy for SMBs and brands: going concrete, no traps

Not with a spectacular demo: with a 2-3-week audit that maps existing usage (including shadow AI), identifies 3-5 high-impact use cases and delivers a costed roadmap. Rollout starts with a measured pilot on a restricted scope to prove value before extending. Without rigorous framing, the majority of AI deployments stop at the demo stage.

Claude for Work is our default recommendation for teams producing demanding written content, analysing long documents or reasoning on complex problems: writing quality, large context window, strong nuance handling. Microsoft 365 Copilot is the natural choice when your business data already lives in SharePoint, OneDrive, Teams and Outlook: integration is native and governance sits on Entra ID plus Purview. Both can coexist: Claude for intellectual production, Copilot for integrated office productivity.

Cost breaks down into three items: (1) Microsoft 365 Copilot licenses, purchased directly by the client from Microsoft (public tariff per user added to existing M365 licenses); (2) initial rollout billed as a flat fee based on depth (data governance, connectors, per-persona workshops, adoption tracking); (3) optional continuous adoption coaching. We recommend starting with a 3-month pilot on 10-20 users before global rollout. Contact us for a tailored quote.

Several layers: (1) use enterprise platforms with explicit no-training contracts (Claude for Work, Microsoft Copilot); (2) classify sensitive documents with Microsoft Purview Sensitivity Labels and block their ingestion; (3) deploy DLP to detect critical data in prompts; (4) train teams on what may or may not be shared with an AI assistant; (5) log all exchanges for audit.

RAG (Retrieval-Augmented Generation) connects an AI model to your enterprise data so it can answer from your internal documentation rather than generic knowledge. It turns a generic assistant into a copilot that knows your products, policies and clients. Modern implementations use the Model Context Protocol (MCP, an open standard driven by Anthropic), Microsoft Copilot Studio, or Azure AI Search to connect models to your sources.

Neither ban nor allow: frame. Banning pushes teams to personal accounts outside any control (shadow AI). Allowing freely exposes data leaks. The right answer: rapidly deploy an enterprise platform (Claude for Work, Microsoft Copilot), communicate on what is allowed, block consumer accounts at the network level during transition, and train teams on good usage.

Yes, moderately. Most SMB use cases fall under "minimal risk" or "limited risk" under the AI Act, with main obligations: transparency on AI usage, information of affected users and clients, internal documentation. High-risk cases (HR scoring, credit, student grading) impose stronger obligations. We help you establish an internal AI policy and qualify each use case.

Yes, via the Anthropic API (Claude), via the Model Context Protocol (MCP) which connects Claude to your business tools in a standardised way, or via Microsoft Copilot Studio. Examples: a copilot answering RFPs based on your internal references, an assistant drafting product sheets from your ERP, a level-1 support copilot sitting on your knowledge base. Budget is calibrated to pilot scope and required integrations. Case-by-case quotes.