Managing AI Use in Your Organization: Practical Strategies and Quick Wins

What should you consider as you develop your organization’s AI policy?

Enterprise Versus Free

Choosing enterprise tools over free offerings is the single highest-impact risk decision. Public tools ingest and may reuse whatever you upload or paste into them. In litigation or investigations, those disclosures can undermine trade secret protection or waive attorney-client privilege. Enterprise contracts, by contrast, typically commit to data isolation and retention, ownership, and security, but they are not plug-and-play substitutes for governance.

Operational diligence matters as much as contract language. Understand how the tool is constructed and what it can access, including whether it can generate outbound communications or automatically export data. Require human review before anything leaves your environment, especially where a tool connects to core systems. For data sent to external model providers, negotiate where possible for zero data retention and no training on your content. Also confirm that your vendor cannot “peek” into your workspace and does not train on your prompts or outputs.

Records and Retention

AI note‑takers can create precise audio, video, and transcripts, presenting a productivity boon that is also a litigation risk. By default, many tools retain recordings and transcripts indefinitely unless you configure them otherwise. From a litigation perspective, a permanent record of what was said can cut both ways — does it prove you are right, or confirm poor actions? A practical path is often to keep the AI‑generated summary most teams need and delete raw recordings and transcripts on short cycles, while recognizing that some teams (like engineering and project teams) may justify longer access for operational continuity.

Retention settings vary and many vendors’ admin consoles are still maturing. Ask two questions for every AI workflow.

1. What is retained in your environment and for how long?

2. What is retained by any model provider?

Typical windows range from six months to a year for AI workspaces, but shorter periods can reduce exposure. If a use case involves highly sensitive content, consider on‑premises or fully self-contained options that keep data within your environment and under your policy.

Sensitive Workflows

Using AI to draft performance reviews or inform employment decisions can save time, but it does not change your obligations to avoid bias and discrimination. Models can hallucinate and may embed biases from training data or from how you frame a prompt.

A best practice is using AI as an assistant to accelerate drafting and synthesis, while keeping a human decision‑maker responsible for the analysis and conclusion, documenting the rationale, and verifying facts against original records. For human resources (HR) conversations and other sensitive meetings, avoid AI note takers unless your legal team approves a specific need and a retention plan.

These principles extend to other regulated or high-stakes domains, including legal strategy discussions and investigations. The more consequential the decision or the more sensitive the data, the stronger the requirement for human review and the narrower the audience for any AI-generated output. When in doubt, route the task to an approved tool that operates in your enterprise environment and mark the output clearly as a draft to be checked.

Adoption That Sticks

An effective AI policy should set your organization’s approach, name approved tools and how to access them, enumerate prohibited uses, and specify when extra approvals are required. Because the market changes quickly, treat the policy as a living document and align it with training and communications that drive adoption. Mandatory training, even brief and online, should remind personnel that they are ultimately responsible for their AI-generated outputs and decisions, even if this means reading every word, confirming accuracy, and complying with document retention limitations.

Forcing compliance can be tricky. For example, blocking public AI tools is rarely effective and can backfire by depriving teams of useful research resources. Instead, provide a capable enterprise option, reinforce it with just‑in‑time warnings if someone visits a public tool, and consider data loss prevention to flag risky exfiltration.

Companies may consider piloting two or three tools on a time‑boxed basis, measure real usage and outcomes, and then pick one to scale. Long procurement cycles struggle in a market where models and features change every few months.

Quick Wins to Improve Testing and Use

Refine your prompts.

• Do not expect the first answer to be perfect.

• Ask for multiple options in a single pass when brainstorming subject lines, summaries, or clause variants; models return several options almost as fast as one.

• Specify the output format you want, such as a table with defined columns, to clarify the task and help the model organize the answer.

Critique your own drafts.

• Assign the model a smart, good‑faith opponent and ask where they would attack your argument or terms. An adversarial pass can surface gaps that you can shore up before external review.

Separate learning from production work.

• Encourage people to learn and experiment in safe sandboxes with non‑sensitive prompts, then move real work to your enterprise tools with human‑in‑the‑loop checks and retention controls.

Implementation Checklist

Building an effective AI use policy must involve a “whole team” approach and evolve as your team and the tools evolve. Here’s a handy checklist of considerations:

For more about how ArentFox Schiff’s team is guiding clients through AI adoption, listen to our webinar and podcast.

Additional writing and participation from Douglas Schulz, innovation manager in ArentFox Schiff’s Washington, DC, office.