AI adoption, explained for firm leaders

Practical guidance on choosing where AI belongs, protecting client data, and building a firm that can grow without depending on one person.

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AI adoption strategy

AI agents for professional firms: what they can and can't do today

An AI agent is software that does a defined job for your firm, using your firm's own data and rules, and hands the result to a person to approve. For a professional firm, agents add capacity to the preparation work around client service. They don't replace professional judgment, and they should never have the final say.

AI adoption strategy

Build, buy or have it built: AI agents for a mid-size firm

A 20–500 person firm adopting AI agents has three real paths: build them in-house with internal engineering time, buy off-the-shelf software with AI features bolted on, or have agents built around your own data and processes by a partner. Each path trades cost, control and fit differently, and most firms this size don't have the in-house engineering capacity that makes the first path work well.

Adopting AI safely

How to evaluate an AI vendor's security: 12 questions for your IT lead

Before any AI vendor touches your clients' data, get clear written answers on six things: how your data is kept apart from other customers, how it's encrypted, what the AI models keep, who approves what, what's been independently audited, and how you get out. The 12 questions below cover all six. A good vendor answers them plainly. A vague answer is itself an answer.

AI adoption strategy

The AI data pipeline: why your agents are only as good as your data

An AI agent can only work from what it can see, and at most businesses, the knowledge an agent would need is scattered across email, shared drives, a handful of systems that don't talk to each other, and people's heads. A data pipeline is the work of connecting, cleaning and organizing that information so an agent has something reliable to work from. Skip it, and even a well-chosen agent produces answers your team can't trust.

Adopting AI safely

The AI use policy every business adopting AI needs

A written AI use policy answers three questions for your staff: which AI tools they're allowed to use, what client or company data can never go into a tool that isn't approved, and who has to sign off before AI-prepared work reaches a client. Without one, adoption happens anyway — through whatever tool an employee found on their own — and the business has no record of what data went where. A one-page policy, reviewed with the team, closes that gap.

AI adoption strategy

What a firm brain is, and why your agents need one

A firm brain is your business's own knowledge — procedures, templates, standards, client information and past decisions — kept private to your business and organized so AI agents can work from it. It's the difference between an agent that produces generic, forgettable output and one that produces work that looks like your business actually did it. Without one, every agent you add starts from zero.

AI adoption strategy

Where to start with AI adoption in a 20–500 person business

Start with one piece of work that's repeated across many clients or customers, follows rules your team could write down, and ends with a person checking the result. Not the flashiest use of AI, and not the whole business at once. A narrow, well-chosen first project is what tells you whether AI adoption actually works inside your organization, before you commit to more.

Adopting AI safely

Why data isolation matters when your AI vendor serves other firms

Most AI vendors run one system for many customers, which may include firms you compete with. Data isolation is what guarantees your firm's data, and your clients' data, can never show up in another customer's results. The strongest isolation is enforced by the database and by encryption, not just by the vendor's application code.

AI adoption strategy

Why most AI pilots stall, and how to avoid it

Most AI pilots stall because the data behind them was never organized for the job, and because no one defined what "working" would look like before starting. The research on this is now blunt: MIT's Project NANDA reported that "95% of organizations are getting zero return" from generative AI, and Gartner has predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025. Neither report blames the underlying models.

Adopting AI safely

Zero data retention, explained: what happens to your data when AI reads it

Zero data retention (ZDR) means the AI model provider doesn't store your prompts or the model's answers once the request is processed, so there's no copy of your data sitting in the provider's logs. It's the single most important term to confirm before client data goes to an AI model. But ZDR is a contract and a configuration, not a feature you get by default, and "available" is not the same as "in effect."