AI implementation & governance for South Florida businesses

If your employees use AI at all — and in most offices they already do — you need at least basic governance, because ungoverned use means company data flowing into tools nobody vetted. The question is no longer whether AI enters the business; it's whether it enters with rules or without them.

It's the right time to talk to us if any of these sound familiar:

• Staff are pasting company information into free AI chatbots nobody approved — the pattern known as shadow AI is already in the building.
• Leadership wants the productivity gains but can't yet answer the question "where does our data go when someone uses these tools?"
• You operate in a regulated industry — finance, healthcare, law — and need an AI position that stands up to a client, auditor, or insurer asking about it.
• You already pay for Microsoft 365 and want to know whether Copilot is worth turning on before buying anything else.
• One team ran ahead with AI on its own while every other team waits for permission that never comes.

If AI genuinely isn't a fit for how your business works, we'll say that in the assessment — the goal is a defensible answer, not a subscription.

• AI-use assessment — an inventory of which AI tools are already in use across the business, sanctioned or not, what data flows into them, and where the real productivity opportunities are.
• Tool selection & rollout — Microsoft Copilot and Azure OpenAI where they fit your existing licensing and identity, vendor-neutral recommendations where they don't. Configured properly, not just purchased.
• Written AI acceptable-use policy — which tools are approved, what data may go into them, how AI output gets reviewed before it ships, and who is accountable. Short enough that people actually read it.
• Data-handling guardrails — enterprise tiers and tenant settings chosen to keep prompts out of model training, with clear rules for client, financial, and personnel data.
• Staff training — practical sessions on the approved tools and the policy: what good use looks like, what's off-limits, and why the rules exist.
• Governance framework — roles, review cadence, and risk tracking aligned to NIST's AI Risk Management Framework, the voluntary federal framework for trustworthy AI — scaled to a small business instead of an enterprise.
• Documentation and handoff — you own the policies, the settings, and the training materials. We can stay on for periodic reviews, but nothing is locked to us.

AI governance is policy and security work, and that's the foundation we come from. We authored a complete 17-document information-security policy program for a financial-services client — policies, incident-response playbooks, and business-continuity and disaster-recovery plans — the kind of program that has to hold up when a regulator or auditor reads it. Writing AI rules that people follow and examiners accept is a natural extension of that work: the same discipline and the same documentation standard, applied to a new class of tools.

Practical, not hype. We recommend AI where a pilot shows it saves real time, and we say so plainly where it doesn't. No transformation decks, no tool-of-the-month — a working rollout on the workflows that matter.

Security first. The guardrails come from actual security practice — identity, access control, and data-handling discipline from work in regulated financial services — not from a template downloaded the week before.

Policies people actually read. A forty-page policy nobody opens protects nobody. We write short, specific rules, train staff on them in plain English, and make the software settings enforce what the paper says.

Cost tracks scope: how many people are involved, which tools you already license, how sensitive your data is, and how much documentation your industry demands. Rather than a fake flat rate, here's what actually moves the number — and every engagement starts with a written estimate.