Which AI model should my firm use?
For most firms, the model matters less than the plan around it. The major assistants (ChatGPT, Claude, Microsoft Copilot, Google Gemini) all handle everyday writing, summarizing, and analysis well. The better questions are which one fits the tools you already run on, such as Microsoft 365 or Google Workspace; which business plan gives you admin controls and terms that keep your data out of model training; and which one your team will actually use. Start with one approved tool on a business plan rather than letting each person pick their own.
How much does AI cost a small or mid-sized firm?
There are three costs. Licenses: business plans for the major assistants are priced per user per month, in the same range as other business software. Usage: automations that call a model directly are billed by how much text they process, which for most small-firm workflows is modest next to the time they save. Time: training, a usage policy, and building and maintaining automations. That last one is where most of the real investment goes, and firms that budget only for licenses usually end up with seats nobody uses.
Where can my firm actually use AI?
Start with work that is repetitive, text-heavy, and checked by a person anyway: drafting and summarizing emails and documents, meeting notes, first drafts of proposals and reports, intake and triage of requests, pulling details out of PDFs and forms, and answering questions from your own policies and documents. Keep a person in the loop on anything that goes to a client, moves money, or decides something about someone. Month one of an engagement ranks these by how much time each returns, so the first work is the work that pays off.
How do I connect my firm's data to AI securely?
Through the business plan's own connectors or an integration you control, not by pasting files into personal accounts. The essentials: single sign-on, so access follows the accounts you already manage; permissions that match what each person can already see, so AI can't surface a file someone shouldn't open; a written policy on what data can go where, with privileged and regulated data called out; logging, so you can see what was accessed; and your IT provider involved from the start. This is the security review a CISO would run, and it's the step most AI projects skip.
What does an AI agent actually do?
An agent is AI that takes steps, not just answers questions. You give it a goal and access to specific tools, such as your inbox, CRM, or document store, and it decides what to do next: look something up, draft a reply, update a record, or hand off to a person. That is what makes agents useful, and what makes them riskier than a chatbot. A well-built agent gets only the access it needs, logs what it does, and asks a person before anything that's hard to undo.
What is an AI workflow, and how is it different from an agent?
A workflow follows steps you define in advance: when a form comes in, pull out the details, summarize them, and route them to the right person. AI handles one or more of the steps, but the path is fixed. An agent chooses its own steps toward a goal. Workflows are predictable and easier to secure, so they're usually the right first build; agents fit work where the path changes from case to case.
Do we need an AI policy before we start?
Yes, and it doesn't need to be long. Most firms already have AI in use, on personal accounts, with no rules about what goes in. A short usage policy names the approved tools, says what data can and can't go into them (client, privileged, and regulated data in particular), and says who to ask. Written alongside the training, it's what lets cautious partners say yes.
